Peer and self assessment in massive online classes
Open accessAbstract
Peer and self-assessment offer an opportunity to scale both assessment and learning to global classrooms. This article reports our experiences with two iterations of the first large online class to use peer and self-assessment. In this class, peer grades correlated highly with staff-assigned grades. The second iteration had 42.9% of students’ grades within 5% of the staff grade, and 65.5% within 10%. On average, students assessed their work 7% higher than staff did. Students also rated peers’ work from their own country 3.6% higher than those from elsewhere. We performed three experiments to improve grading accuracy. We found that giving students feedback about their grading bias increased subsequent accuracy. We introduce short, customizable feedback snippets that cover common issues with assignments, providing students more qualitative peer feedback. Finally, we introduce a data-driven approach that highlights high-variance items for improvement. We find that rubrics that use a parallel sentence structure, unambiguous wording, and well-specified dimensions have lower variance. After revising rubrics, median grading error decreased from 12.4% to 9.9%.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
2 of 52 cited sources checked · 1 supported · 0 flagged · accuracy not yet measured
- Premisecitation
Accurate, actionable feedback helps students improve their work.
“Accurate, actionable feedback helps students improve their work [Nicol and Macfarlane-Dick 2006;Boud 2000].”
Quote in the paperSubstantial claim1 of 2 sources checked - Premisecitation
Crowdworkers perform better when they are intrinsically motivated by the task's importance.
“First, crowdworkers perform better when they are intrinsically motivated by the task's importance [Cheshire and Antin 2008].”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Cheshire and Antin 2008 source
The Discussion (page unavailable) says that high intrinsic motivation can sustain contributions and that added incentives may reduce them. But the paper discusses contributors to online information pools, not crowdworkers, and does not establish that motivation arising from a task’s importance improves their performance. (LLM, conf=0.84)
Experimental — accuracy not yet measured
- Findingown data
Over both iterations, the average absolute difference between peer and staff grades was 3%.
“Overall, the correlation between peer grades and staf-assigned grades was r = 0.73, and the average absolute difference between peer and staff grades was 3% (positive and negative errors were approximately balanced).”
Quote in the paperSubstantial claim- Rests on an assumption: Staff-assigned grades are a valid ground truth for assessing peer and self grading accuracy.
- Findingown data
On a 7-point Likert scale, the median rating of peer feedback and peer assessment as learning experiences was 6 (7 = very valuable).
“On a 7-point Likert scale, the median rating was 6 (7 = very valuable).”
Quote in the paperSubstantial claim- Rests on an assumption: Survey respondents are representative of the class.
- Findingown data
In a between-subjects experiment, giving students feedback about whether they scored peers high or low decreased mean error by 0.97% (6.77% experimental vs 7.74% control).
“A between-subjects experiment found a 0.97% decrease in mean error (6.77% in the experimental group vs. 7.74% in the control group).”
Quote in the paperSubstantial claim
- Findingown data
Students reported that they felt their peers put less effort into peer assessment than they did: mean ratings 4.57 for peer effort and 5.46 for own effort, p < 0.001.
“However, students also reported that they felt their peers put less effort into peer assessment than they did (Table III).On a Mann-Whitney U-test, mean ratings were 4.57 for peer effort and 5.46 for their own effort (6-point Likert scale, 6:”
Quote in the paperNot a substantial claim- Rests on an assumption: Survey respondents are representative of the class.
- Findingown data
Giving students feedback about their grading bias increased subsequent accuracy.
“We found that giving students feedback about their grading bias increased subsequent accuracy.”
- Also stated in: § 1.4. Contributions · plain
“First, we found that giving students feedback about whether they scored peers high or low increased their subsequent accuracy.”
Quote in the paperSubstantial claim - Also stated in: § 1.4. Contributions · plain
- Findingown data
Over both iterations, 5,876 students submitted at least one assignment and participated in peer assessment.
“Over both iterations of the class, 5,876 students submitted at least one assignment and participated in peer assessment.”
Quote in the paperSubstantial claim - Findingown data
On average, students assessed their work 7% higher than staff did.
“On average, students assessed their work 7% higher than staff did.”
Quote in the paperNot a substantial claim- Rests on an assumption: Staff-assigned grades are a valid ground truth for assessing peer and self grading accuracy.
- Findingown data
67% of students obtained open-ended peer feedback using short, customizable feedback snippets.
“Second, to help students provide peers with high-quality personalized feedback, we introduce short, customizable feedback snippets that address common issues with assignments; 67% of students obtained open-ended peer feedback using this method.”
Quote in the paperSubstantial claim - Findingown data
In the second iteration, 65.5% of students' grades were within 10% of the staff grade.
“The second iteration had 42.9% of students' grades within 5% of the staff grade, and 65.5% within 10%.”
- Also stated in: § 3.2. Results: Grading Agreement · plain
“The second iteration improved to 42.9% within 5% of the staff grade, and 65.5% within 10%.”
Quote in the paperSubstantial claim- Rests on an assumption: Staff-assigned grades are a valid ground truth for assessing peer and self grading accuracy.
- Also stated in: § 3.2. Results: Grading Agreement · plain
- Findingown data
In the first iteration, 34.0% of submissions had a median peer grade within 5% of the staff grade.
“For the first iteration, 34.0% of submissions had a median peer grade within 5% of the staff grade, and 56.9% within 10% (Figure 4).”
Quote in the paperNot a substantial claim- Rests on an assumption: Staff-assigned grades are a valid ground truth for assessing peer and self grading accuracy.
- Findingown data
Rubrics that use a parallel sentence structure, unambiguous wording, and well-specified dimensions have lower variance.
“We find that rubrics that use a parallel sentence structure, unambiguous wording, and well-specified dimensions have lower variance.”
Quote in the paperSubstantial claim - Findingown data
After revising rubrics, median grading error decreased from 12.4% to 9.9%.
“After revising rubrics, median grading error decreased from 12.4% to 9.9%.”
- Also stated in: § 1.4. Contributions · plain
“After making these changes, the mean error on grades decreased from 12.4% to 9.9%.”
Quote in the paperSubstantial claim - Also stated in: § 1.4. Contributions · plain
- Findingown data
In the second iteration, 42.9% of students' grades were within 5% of the staff grade.
“The second iteration had 42.9% of students' grades within 5% of the staff grade, and 65.5% within 10%.”
- Also stated in: § 3.2. Results: Grading Agreement · plain
“The second iteration improved to 42.9% within 5% of the staff grade, and 65.5% within 10%.”
Quote in the paperSubstantial claim- Rests on an assumption: Staff-assigned grades are a valid ground truth for assessing peer and self grading accuracy.
- Also stated in: § 3.2. Results: Grading Agreement · plain
- Findingown data
20% of students voluntarily assessed more submissions than required.
“Surprisingly, 20% of students voluntarily assessed more submissions than required.”
- Also stated in: § 3.5. Results: Student Reactions · plain
“Student reactions to the peer assessment system were generally positive, and 20% of students completed more peer assessments than the class required them to (Figure 7).”
Quote in the paperNot a substantial claim- Rests on an assumption: Survey respondents are representative of the class.
- Also stated in: § 3.5. Results: Student Reactions · plain
- Findingown data
Students reported learning more by assessing their peers than by assessing themselves: mean ratings 4.97 vs 4.51, p < 0.001.
“Overall, students reported learning more by assessing their peers than by assessing themselves: mean ratings were 4.97 and 4.51, respectively, for peer and self-assessment (6-point Likert scale, 6:”
Quote in the paperSubstantial claim- Rests on an assumption: Survey respondents are representative of the class.
- Findingown data
Over both iterations, the correlation between peer grades and staff-assigned grades was r = 0.73.
“Overall, the correlation between peer grades and staf-assigned grades was r = 0.73, and the average absolute difference between peer and staff grades was 3% (positive and negative errors were approximately balanced).”
- Also stated in: § Abstract · plain
“In this class, peer grades correlated highly with staff-assigned grades.”
Quote in the paperSubstantial claim- Rests on an assumption: Staff-assigned grades are a valid ground truth for assessing peer and self grading accuracy.
- Also stated in: § Abstract · plain
- Findingown data
Over the second iteration, the average disagreement between peer raters was 25.0%.
“In contrast, over the second iteration of the class, the average disagreement between peer raters was 25.0%.”
Quote in the paperSubstantial claim- Rests on an assumption: Staff-assigned grades are a valid ground truth for assessing peer and self grading accuracy.
- Findingown data
31% of students reported learning how to communicate their ideas as a benefit of peer assessment.
“Forty-two percent of students cited seeing other students' work as the biggest benefit of peer assessment; 31% reported learning how to communicate their ideas as a benefit.”
Quote in the paperNot a substantial claim- Rests on an assumption: Survey respondents are representative of the class.
- Findingown data
42% of students cited seeing other students' work as the biggest benefit of peer assessment.
“Forty-two percent of students cited seeing other students' work as the biggest benefit of peer assessment; 31% reported learning how to communicate their ideas as a benefit.”
Quote in the paperNot a substantial claim- Rests on an assumption: Survey respondents are representative of the class.
- Findingown data
In the first iteration, 56.9% of submissions had a median peer grade within 10% of the staff grade.
“For the first iteration, 34.0% of submissions had a median peer grade within 5% of the staff grade, and 56.9% within 10% (Figure 4).”
Quote in the paperSubstantial claim- Rests on an assumption: Staff-assigned grades are a valid ground truth for assessing peer and self grading accuracy.
- Findingown data
Students rated peers' work from their own country 3.6% higher than those from elsewhere.
“Students also rated peers' work from their own country 3.6% higher than those from elsewhere.”
Quote in the paperNot a substantial claim- Rests on an assumption: Staff-assigned grades are a valid ground truth for assessing peer and self grading accuracy.
- Findingown data
In the third iteration, the average disagreement between staff raters was 6.7%.
“For these submissions, the average disagreement between staff raters (defined as the median difference between a staff grade and the mean staff grade) was 6.7%; 28% of submissions had all staff grades within 5% of the assignment grade, and 42% within 10%.”
Quote in the paperSubstantial claim- Rests on an assumption: Staff-assigned grades are a valid ground truth for assessing peer and self grading accuracy.
- Findingown data
Raters typed the same amount of feedback whether or not an assignment contained fortune cookies.
“Raters typed the same amount of feedback whether or not an assignment contained fortune cookies.”
Quote in the paperSubstantial claim - Findingown data
Five assignments had 26.4%, 36.2%, 36.9%, 43.9%, and 36.8% of submissions estimated within 5% of the staff grade.
“Using the repeated sampling scheme described in Section 3.1, five assignments had 26.4%, 36.2%,36.9%,43.9%, and 36.8% of submissions estimated within 5% of the staff grade.”
Quote in the paperSubstantial claim- Rests on an assumption: Staff grades are a valid ground truth for assessing peer grading accuracy.
- Findingown data
A chi-square test suggests that fortune cookies do not encourage more students to leave feedback (χ2=0.1, p=0.75).
“A chi-square test on the number of assessments that contained feedback suggests that fortune cookies do not encourage more students to leave feedback (χ 2 = 0.1, p = 0.75).”
Quote in the paperSubstantial claim - Findingown data
Agreement of peer grades with staff grades generally increases across the class.
“Agreement of peer grades with staff grades generally increases across the class.”
Quote in the paperSubstantial claim- Rests on an assumption: Staff grades are a valid ground truth for assessing peer grading accuracy.
- Findingreasoning
Using repeated sampling, more than 75% of students got a course grade within 5% of staff grade, assuming grades in different assignments are uncorrelated.
“Using repeated sampling, we estimate more than 75% of students got a course grade within 5% of staff grade (assuming grades in different assignments are uncorrelated).”
Quote in the paperSubstantial claim- The authors state this as an assumption: Grades in different assignments are uncorrelated.
- Rests on an assumption: Staff grades are a valid ground truth for assessing peer grading accuracy.
- Findingown data
If the text of the cookie itself is subtracted, there was no significant difference in comment lengths whether or not cookies were used (t(10,673)=0.44, p>0.6).
“If we subtract the text of the cookie itself, there was no significant difference in comment lengths whether or not cookies were used (t(10, 673) = 0.44, p > 0.6).”
Quote in the paperSubstantial claim - Findingown data
The linear model weighted the five peer grades from lowest to highest at 15.6%, 13.6%, 21.3%, 27.6%, and 18.3%.
“The linear model weighted the five peer grades from lowest to highest at 15.6%, 13.6%, 21.3%, 27.6%, and 18.3%.”
Quote in the paperNot a substantial claim - Findingown data
About 40% of assignments are a full letter grade away from the staff grade.
“because currently about 40% of assignments are a full letter grade away”
Quote in the paperSubstantial claim- Rests on an assumption: Staff grades are a valid ground truth for assessing peer grading accuracy.
- Findingown data
The best linear regression does not materially improve accuracy over simpler aggregation methods.
“The best linear regression doesn't materially improve accuracy.”
Quote in the paperSubstantial claim - Findingown data
If the text is included, comments that used fortune cookies were longer (t(10,673)=3.61, p<0.05).
“If the text is included, comments that used fortune cookies were longer (t(10, 673) = 3.61, p < 0.05).”
Quote in the paperSubstantial claim - Findingown data
Holding out 10% of ground truth grades and testing on samples drawn from them, the regression model yields an accuracy of 35.8% of samples within 5% and 58.8% within 10%.
“Holding out 10% of ground truth grades, and testing on samples drawn from them, the regression model yields an accuracy of 35.8% of samples within 5%, and 58.8% within 10%.”
Quote in the paperSubstantial claim- Rests on an assumption: Staff grades are a valid ground truth for assessing peer grading accuracy.
- Findingown data
Increasing the number of assessments to 101 per submission increases the number of submissions graded within 10% of the staff grade by 8.1%.
“Increasing the number of assessments to an (unreasonable) 101 per submission increases the number of submissions graded within 10% of the staff grade by 8.1%.”
Quote in the paperNot a substantial claim- Rests on an assumption: Staff grades are a valid ground truth for assessing peer grading accuracy.
- Findingown data
Staff differences in grading were usually due to differing judgments or interpretations.
“Staff differences in grading were usually due to differing judgments or interpretations.”
Quote in the paperSubstantial claim - Findingown data
Providing students feedback about their rating bias improves subsequent grading accuracy.
“The effect of the presence of feedback is significant: t(4, 998) = -3.38,p < 0.01.”
- Also stated in: § 7. CONCLUSIONS AND FUTURE WORK · boosted
“We demonstrated that providing students feedback about their rating bias improves subsequent accuracy.”
Quote in the paperSubstantial claim- Rests on an assumption: Staff grades are a valid ground truth for assessing peer grading accuracy.
- Also stated in: § 7. CONCLUSIONS AND FUTURE WORK · boosted
- Findingown data
The five assignments had 49.1%, 53.6%, 60.9%, 68.5%, and 64.3% within 10% of the staff grade.
“Within a 10% range, the assignments had, respectively, 49.1%, 53.6%, 60.9%, 68.5%, and 64.3% within 10% (Figure 10(a)).”
Quote in the paperSubstantial claim- Rests on an assumption: Staff grades are a valid ground truth for assessing peer grading accuracy.
- Findingown data
Overall, 36.2% of assessments included feedback, compared to 36.4% in the previous iteration without cookies.
“Overall, 36.2% of assessments included feedback (compared to 36.4% in the previous iteration without cookies).”
Quote in the paperSubstantial claim - Findingown data
Grading agreement between staff members and between staff and students in an in-person class are similar.
“Sections 3.3 and 3.4 show that the grading agreement between staff members and between staff and students in an in-person class are similar.”
Quote in the paperSubstantial claim - Findingown data
When only raters who finished the class are considered, staff agreement increases as well.
“If we only consider raters who finished the class (and exclude those who dropped out), we see that staff agreement increases as well.”
Quote in the paperNot a substantial claim- Rests on an assumption: Staff grades are a valid ground truth for assessing peer grading accuracy.
- Findingown data
On average, raters grade students from their own country 3.6% higher than those from other countries (t(27,067)=3.98, p<0.001).
“On average, raters grade students from their own country 3.6% higher than those from other countries: t(27, 067) = 3.98, p < 0.001.”
Quote in the paperSubstantial claim - Findingown data
In the feedback condition, 4.4% more samples obtained a grade within 5% of the staff grade than those without feedback.
“In the feedback condition, 4.4% more samples obtained a grade within 5% of the staff grade than those without feedback.”
Quote in the paperSubstantial claim- Rests on an assumption: Staff grades are a valid ground truth for assessing peer grading accuracy.
- Findingreasoning
Fewer than 45 students (approximately 6%) were affected by grading errors in each iteration of the class.
“We estimate that fewer than 45 students (approximatel 6%) were affected by grading errors in each iteration of the class.”
Quote in the paperSubstantial claim- Rests on an assumption: Staff grades are a valid ground truth for assessing peer grading accuracy.
- Findingown data
Questions with low staff agreement also had low peer agreement (r=0.97, t(24)=19.9, p<0.05).
“and that questions with low staff agreement also had low peer agreement (r = 0.97, t(24) = 19.9,p < 0.05).”
Quote in the paperSubstantial claim - Findingown data
Students reported that assessing others' work was an extremely valuable learning activity.
“One of the most remarkable results from our experience was that students reported that assessing others' work was an extremely valuable learning activity.”
Quote in the paperSubstantial claim - Findingown data
Peer and staff raters agreed far more on some questions than others.
“We found that peer and staff raters agreed far more on some questions than others (Figure 16)”
Quote in the paperSubstantial claim - Findingown data
Most rubric revisions centered around making rubrics more easily readable.
“Most rubric revisions centered around making rubrics more easily readable.”
Quote in the paperNot a substantial claim - Findingown data
When the first assignment of the Spring 2012 class had fewer peer assessments than needed, one student rallied her peers to finish a large number of assessments over a single day, with the top 10 students assessing an average of 48 submissions, nearly 10 times their required number.
“For example, when the first assignment of the Spring 2012 class had fewer peer assessments than needed, one student rallied her peers to finish a large number of assessments over a single day (the top 10 students assessed an average of 48 submissions: nearly 10 times their required number) so that students could get feedback in time.”
Quote in the paperNot a substantial claim - Findingown data
Overall, the peer-staff agreement was 2.5% higher in the second iteration than the previous iteration.
“Overall, the peerstaff agreement was 2.5% higher than the previous iteration.”
Quote in the paperSubstantial claim - Findingnone given
In our class, several students complained in class forums about being unable to complete peer assessments on time.
“In our class, several students complained in class forums about being unable to complete peer assessments on time.”
Quote in the paperNot a substantial claim - Findingown data
Many forum posts asked for clarifications of ambiguous words, even though the rubrics had been revised for 3 years in the in-person class.
“Although the rubrics had been revised for 3 years in the in-person class, many forum posts asked for clarifications of ambiguous words.”
Quote in the paperSubstantial claim - Findingreasoning
In addition to providing factual answers and assignment clarifications, community TAs leveraged their personal experience to offer advice and cheerleading.
“In addition to providing factual answers and assignment clarifications, community TAs also leveraged their personal experience to offer advice and cheerleading.”
Quote in the paperNot a substantial claim - Findingown data
Rubric items with parallel sentence structure in the first iteration had lower disagreement scores (F(1,39)=2.07, p<0.05).
“We found that rubric items with parallel sentence structure in the first iteration had lower disagreement scores (F(1, 39) = 2.07, p < 0.05) (Figure 17).”
Quote in the paperSubstantial claim - Findingown data
Agreement of unaggregated peer grades and staff grades is much lower than between median peer grades and staff grades.
“Agreement is much lower than between median peer grades and staff grades.”
Quote in the paperNot a substantial claim - Findingown data
Students improved grading when provided feedback on accuracy.
“Students improved grading when provided feedback on accuracy.”
Quote in the paperSubstantial claim - Findingnone given
Most students received at least one piece of textual feedback.
“Most students received at least one piece of textual feedback.”
Quote in the paperNot a substantial claim - Findingnone given
Increasing the number of raters quickly yields diminishing returns.
“Increasing the number of raters quickly yields diminishing returns.”
Quote in the paperNot a substantial claim - Findingown data
In the authors' experience, only a quarter of approximately 3,000 students who completed a time-intensive first assignment did all five assignments.
“In our experience, only a quarter of approximately 3,000 students who completed a time-intensive first assignment did all five assignments.”
Quote in the paperSubstantial claim - Findingown data
Students reported both peer and self-assessment to be valuable for different reasons.
“Students reported both peer and self-assessment to be valuable for different reasons.”
Quote in the paperNot a substantial claim - Findingown data
Assignment 4 has generally higher agreement between staff grade and median peer grade.
“For example, Assignment 4 (blue) has generally higher agreement.”
Quote in the paperNot a substantial claim - Premisecitation
Viewing and critiquing others' work plays a key pedagogical role in creative domains like design.
“Furthermore, viewing and critiquing others' work plays a key pedagogical role in these domains [Schön 1985].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Schön 1985
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationown data
Aggregating peer grades leads to a remarkable increase in agreement with staff grades.
“They also suggest that aggregating peer grades leads to a remarkable increase in agreement with staff grades (Section 3.2).”
Quote in the paperSubstantial claim- Rests on an assumption: Staff-assigned grades are a valid ground truth for assessing peer and self grading accuracy.
- Premisecitation
In-class peers can assess each other well.
“In-class peers can assess each other well [Falchikov and Goldfinch 2000;Carlson and Berry 2003;Gerdeman et al. 2007].”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites Falchikov and Goldfinch 2000 source
- cites Carlson and Berry 2003
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Gerdeman et al. 2007
- Interpretationown data
Correlation among staff grades is many times higher than agreement among peer raters.
“These results suggest that correlation among staff grades is many times higher than agreement among peer raters.”
Quote in the paperSubstantial claim- Rests on an assumption: Staff-assigned grades are a valid ground truth for assessing peer and self grading accuracy.
- Premisecitation
Fields like design have traditionally relied on intimate colocation to enable these activities and to confer values and norms.
“Fields like design have also traditionally relied on intimate colocation to enable these activities and to confer values and norms [Schön 1985].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Schön 1985
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Automated assessment precludes the open-ended work that is a hallmark of education in creative fields like design.
“Many such classes use automated assessment (e.g., Widom [2012]), which precludes the open-ended work that is a hallmark of education in creative fields like design [Buxton 2007].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Widom [2012]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Buxton 2007
- Interpretationreasoning
Using rubric item scores as feedback requires students to reflect on why they did poorly on topics they often understood poorly in the first place.
“First, students must reflect on why they did poorly on some topic.Unfortunately, these are often topics the student understood poorly in the first place.”
Quote in the paperSubstantial claim - Interpretationreasoning
These differences may approximately represent the smallest discernible differences in quality.
“These differences may approximately represent the smallest discernible differences in quality.”
Quote in the paperSubstantial claim - Premisecitation
Actionable feedback is most useful if it is personalized and targets the student's recent work.
“Actionable feedback is most useful if it is personalized and targets the student's recent work [Gallien and Oomen-Early 2008].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Gallien and Oomen-Early 2008
- Study design and conductown data
Each student graded at least one ground-truth submission per assignment.
“Each student graded at least one ground-truth submission per assignment; a groundtruth assignment had a median of 160 assessments.”
Quote in the paperSubstantial claim- Rests on an assumption: Staff-assigned grades are a valid ground truth for assessing peer and self grading accuracy.
- Interpretationreasoning
Reusable precanned prompts encourage students to direct their effort to providing feedback beyond the cookie text.
“Reusable precanned prompts encourage students to direct their effort to providing feedback beyond the cookie text.”
Quote in the paperNot a substantial claim - Interpretationown data
The simple median strategy is surprisingly effective at identifying the most plausible grade.
“In summary, the simple median strategy seems to be surprisingly effective at identifying the most plausible grade.”
Quote in the paperSubstantial claim - Interpretationown data
The general similarity in accuracy across both iterations (with improvements in the second) suggests that the peer assessment process produces robust results.
“The general similarity in accuracy across both iterations (with improvements in the second) suggests that the peer assessment process produces robust results.”
Quote in the paperSubstantial claim- Rests on an assumption: Staff-assigned grades are a valid ground truth for assessing peer and self grading accuracy.
- Premisecitation
Evaluating peers' work exposes students to solutions, strategies, and insights that they otherwise would likely not see.
“Evaluating peers' work also exposes students to solutions, strategies, and insights that they otherwise would likely not see [Chinn 2005;Tinapple et al. 2013].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Chinn 2005
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Tinapple et al. 2013
- Interpretationreasoning
Such differences suggest the inherent limitations of independent assessment via rubrics due to differences in judgment.
“Such differences suggest the inherent limitations of independent assessment via rubrics due to differences in judgment.”
Quote in the paperSubstantial claim - Interpretationreasoning
A student's grade for the entire course is generally more accurate than individual assignment grades because positive and negative errors cancel out.
“However, a student's grade for the entire course is generally more accurate due to positive and negative errors canceling out.”
Quote in the paperSubstantial claim - Study design and conductreasoning
At least four randomly selected raters saw each student's submission.
“By symmetry, this means that at least four randomly selected raters saw each student's submission, and that each student saw one staff-assessed submission per assignment.”
Quote in the paperNot a substantial claim- Rests on an assumption: Staff-assigned grades are a valid ground truth for assessing peer and self grading accuracy.
- Interpretationown data
For a class with letter grades, greater accuracy is needed than for a pass/fail class.
“For a class with letter grades, greater accuracy is needed”
Quote in the paperSubstantial claim - Premisecitation
Consensus among raters serves as a useful indicator of quality.
“Second, consensus among raters serves as a useful indicator of quality [Huang and Fu 2013].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Huang and Fu 2013 source
- Premisecitation
Self-assessment provides learning gains not seen with external evaluation.
“Similarly, self-assessment helps students reflect on gaps in their understanding, making them more resourceful, confident, and higher achievers [Zimmerman and Schunk 2001;Pintrich 1995;Pintrich and Zusho 2007], and provides learning gains not seen with external evaluation [Dow et al. 2012].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Dow et al. 2012 source
- Interpretationreasoning
Familiarity and practice with peer assessment leads to more accurate assessments, regardless of individual differences in perseverance and motivation.
“This suggests that, regardless of individual differences in perseverance and motivation, familiarity and practice with peer assessment leads to more accurate assessments.”
Quote in the paperSubstantial claim - Interpretationreasoning
Rubrics only point out areas for improvement, not how to improve.
“Second, rubrics only point out areas for improvement, not how to improve.”
Quote in the paperSubstantial claim - Interpretationreasoning
For a pass/fail class such as many current MOOCs, including ours, this accuracy is sufficient for the vast majority of students.
“Consequently, for a pass/fail class (such as many current MOOCs, including ours), this accuracy is sufficient for the vast majority of students.”
Quote in the paperSubstantial claim - Premisecitation
Short, well-crafted training exercises can enable legions of motivated amateurs to perform work previously thought to require years of training.
“Third, interfaces like FoldIt [Khatib et al. 2011] and NASA Clickworkers [Szpir 2002] demonstrate that short, well-crafted training exercises can enable legions of motivated amateurs to perform work previously thought to require years of training.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Khatib et al. 2011
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Szpir 2002
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Peer feedback in design classes creates an audience that provides honest feedback and multiple perspectives.
“Peer feedback in design classes also creates an audience that provides honest feedback and multiple perspectives [Tinapple et al. 2013].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Tinapple et al. 2013
- Interpretationown data
Massive online classes offer individual students an opportunity to have large-scale positive impact.
“Massive online classes also offer individual students an opportunity to have largescale positive impact.”
Quote in the paperNot a substantial claim - Interpretationcitation
The online classroom is distinctly different from its in-person counterpart.
“The online classroom is distinctly different from its in-person counterpart.”
Quote in the paperNot a substantial claim - Premisecitation
To date, large-scale online classes, including our own, have primarily emphasized learning, rather than certification.
“To date, large-scale online classes, including our own, have primarily emphasized learning, rather than certification [Widom 2012].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Widom 2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
Peer assessment changes how instructors spend their time.
“Peer assessment also changes how instructors spend their time.”
Quote in the paperNot a substantial claim - Interpretationcitation
Cheating may also arise if the value of officially recorded performance in these classes increases.
“Cheating may also arise if the value of officially recorded performance in these classes increases (e.g., Kurhila [2012] and Lewin [2013b]).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Kurhila [2012]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Lewin [2013b]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
With peer assessment, the instructor's main task is articulating assessment criteria for others to use, rather than doing the grading.
“When staff assess student work, their effort is focused on doing the grading.By contrast, with peer assessment, the instructor's main task is articulating assessment criteria for others to use.”
Quote in the paperSubstantial claim - Interpretationreasoning
Peer assessment as described in this article works reasonably well for a pass/fail class.
“Peer assessment as described in this article works reasonably well for a pass/fail class.”
Quote in the paperSubstantial claim - Interpretationcitation
Peer assessment fundamentally changes the role of staff.
“Peer assessment fundamentally changes the role of staff.”
Quote in the paperNot a substantial claim - Premisecitation
When peer assessment provides the primary evaluative function, the staff role shifts to emphasize coaching.
“When peer assessment provides the primary evaluative function, the staff role shifts to emphasize coaching [Kuebli et al. 2008].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Kuebli et al. 2008 source
- Premisecitation
To the extent that participation in the online classroom is based on intrinsic motivations, students rarely blatantly cheat.
“To the extent that participation in the online classroom is based on intrinsic motivations (such as a desire to learn), students rarely blatantly cheat [Mazar et al. 2008].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Mazar et al. 2008 source
- Premisecitation
When teachers coach but do not grade, students focus more on conceptual understanding.
“By contrast, when teachers coach but do not grade, students focus more on conceptual understanding [Perry 1970].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Perry 1970
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationcitation
Peers and experts (e.g., staff) may interpret work differently.
“First, peers and experts (e.g., staff) may interpret work differently (see Appendix A.2).”
Quote in the paperSubstantial claim - Interpretationown data
Online learning can be blended with colocated learning.
“First, online learning can be blended with colocated learning.”
Quote in the paperNot a substantial claim - Study design and conductreasoning
Agreement distributions across different assignments are more susceptible to variations in staff grades for a particular submission because they are based on repeated sampling from a smaller number of staff-graded assignments.
“(These agreement distributions are more susceptible to variations in staff grades for a particular submission because they are based on repeated sampling from a smaller number of staff-graded assignments.)”
Quote in the paperNot a substantial claim - Interpretationown data
Comparing the peer grades (not their medians) with staff grades demonstrates the value of aggregating peer grades.
“Comparing the peer grades (not their medians) with staff grades demonstrates the value of aggregating peer grades (Figure 19):”
Quote in the paperNot a substantial claim
- May combine two claims. 56
- Classified as probably not a substantial claim. 56
- The quote only partly states this claim. 1
May combine two claims.
Findingown dataStudents reported both self-assessment and peer assessment to be valuable, and that they played different roles.
“Students reported both self-assessment and peer assessment to be valuable, and that they played different roles.”
Quote in the paperSubstantial claim- Rests on an assumption: Survey respondents are representative of the class.
May combine two claims.
Findingown dataThe Fall 2012 in-person class had 32.8% of submissions with a self-grade within 5% of the staff grade, and 60.8% within 10%.
“The in-person class had 32.8% of submissions with a self-grade within 5% of the staff grade, and 60.8% of submissions within 10% (Figure 6).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataOver the second iteration, 4.0% of submissions had all peer grades agreeing within 5%, and 16.9% within 10%.
“Only 4.0% of submissions had all peer grades agreeing within 5%, and 16.9% within 10%.”
Quote in the paperSubstantial claim- Rests on an assumption: Staff-assigned grades are a valid ground truth for assessing peer and self grading accuracy.
May combine two claims.
Findingown dataSelf-assessment was useful for identifying mistakes and reflection.
“It was also useful for identifying mistakes and reflection (Figure 8).”
Quote in the paperNot a substantial claim- Rests on an assumption: Survey respondents are representative of the class.
May combine two claims.
Findingown dataIn end-of-course surveys, students reported both receiving peer feedback and performing peer assessment to be valuable learning experiences.
“In end-of-course surveys, students reported both receiving peer feedback and performing peer assessment to be valuable learning experiences.”
Quote in the paperSubstantial claim- Rests on an assumption: Survey respondents are representative of the class.
May combine two claims.
Findingown dataEvaluating peers was useful for inspiration and to see other perspectives.
“Evaluating peers was useful for inspiration and to see other perspectives.”
Quote in the paperNot a substantial claim- Rests on an assumption: Survey respondents are representative of the class.
May combine two claims.
Findingown dataSelf-assessment provided students an opportunity to look at their own work again and encouraged comparing it with others' work they had assessed.
“Self-assessment provided students an opportunity to look at their own work again and encouraged comparing it with others' work they had assessed.”
Quote in the paperNot a substantial claim- Rests on an assumption: Survey respondents are representative of the class.
May combine two claims.
Findingown dataIn the third iteration, 28% of submissions had all staff grades within 5% of the assignment grade, and 42% within 10%.
“For these submissions, the average disagreement between staff raters (defined as the median difference between a staff grade and the mean staff grade) was 6.7%; 28% of submissions had all staff grades within 5% of the assignment grade, and 42% within 10%.”
Quote in the paperSubstantial claim- Rests on an assumption: Staff-assigned grades are a valid ground truth for assessing peer and self grading accuracy.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown data55 students left comments expressing appreciation or receptiveness to the feedback; none expressed resentment.
“Notably, 55 students left comments expressing their appreciation or receptiveness to this feedback; none expressed resentment.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe five assignments in order had 23.7%, 29.4%, 38.4%, 39.5%, and 37.1% within 5% of staff, and 47.4%, 63.8%, 61.8%, 63.3%, and 64.2% within 10%.
“The five assignments in order had 23.7%, 29.4%, 38.4%, 39.5%, and 37.1% within 5% of staff, and 47.4%, 63.8%, 61.8%, 63.3%, and 64.2% within 10% (Figure 10b).”
Quote in the paperSubstantial claim- Rests on an assumption: Staff grades are a valid ground truth for assessing peer grading accuracy.
May combine two claims.
Findingown dataIncreasing the number of raters per submission helps accuracy but quickly yields diminishing returns.
“Increasing the number of raters per submission helps accuracy but quickly yields diminishing returns (Figure 9).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe arithmetic mean, geometric mean, and a clipped arithmetic mean that only considers the middle three grades all do worse than the median.
“Similarly, the arithmetic mean, the geometric mean, and a clipped arithmetic mean (that only considers the middle three grades) all do worse than the median.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenCreating fortune cookies took a teaching assistant 3 to 4 hours per assignment.
“Creating fortune cookies took a teaching assistant 3 to 4 hours per assignment.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataErrors are approximately evenly spread across the median, so adding a constant correction term to the median grade does not significantly improve accuracy.
“In addition, errors are approximately evenly spread across the median, so adding a constant correction term to the median grade does not significantly improve accuracy either.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data94.9% of submissions received at least one piece of written feedback, compared to 83% without cookies.
“Because submissions were assessed by multiple students, 94.9% of submissions received at least one piece of written feedback (compared to 83% without cookies);”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThe difference between the median peer grade from the feedback condition and the staff grade was 6.77%, compared to 7.74% in the no-feedback condition.
“The difference between the median peer grade obtained by sampling from the feedback condition and the staff grade was 6.77%, compared to 7.74% in the no-feedback condition (Figure 13).”
Quote in the paperSubstantial claim- Rests on an assumption: Staff grades are a valid ground truth for assessing peer grading accuracy.
May combine two claims.
Findingown dataThe own-country grading effect is consistent when raters and submitters from the United States are removed, but is smaller (mean difference 1.98%, t(12,863)=2.0, p<0.05).
“This effect is consistent when the raters and submitters from the largest student enrollment (United States) are removed, but is smaller (the mean difference drops to 1.98%, t(12, 863) = 2.0, p < 0.05).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataIncreasing the number of assessments per submission from 5 to 11 increases the number of assignments graded within 5% of the staff grade by 3.8% and those graded within 10% by 3.6%.
“Increasing the number of assessments per submission from 5 to 11 increases the number of assignments that were graded within 5% of the staff grade by 3.8%, and those graded within 10% by 3.6%.”
Quote in the paperSubstantial claim- Rests on an assumption: Staff grades are a valid ground truth for assessing peer grading accuracy.
May combine two claims.
Findingown dataUsing the median yields an accuracy of 35% of samples within 5% and 58.7% within 10%.
“In contrast, using the median yields an accuracy of 35% of samples within 5%, and 58.7% within 10%.”
Quote in the paperSubstantial claim- Rests on an assumption: Staff grades are a valid ground truth for assessing peer grading accuracy.
May combine two claims.
Findingown dataThe increase in peer-staff agreement is seen both for the class as a whole and for students who submit all assignments.
“This increase is seen both for the class as a whole and for students who submit all assignments (i.e., excluding students who drop out).”
Quote in the paperSubstantial claim- Rests on an assumption: Staff grades are a valid ground truth for assessing peer grading accuracy.
Classified as probably not a substantial claim.
Findingnone givenSome rubrics sometimes used a nonparallel grammatical structure across sentences.
“Some rubrics sometimes used a nonparallel grammatical structure across sentences.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Findingnone givenSome students probably attempted to game their score by strategically overreporting their grade.
“Still, some students probably attempted to game their score by strategically overreporting their grade (Figure 18).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataOur early experiments suggest that greater accuracy is possible by weighting different raters' grades differently.
“In addition, our early experiments suggest that greater accuracy is possible by weighting different raters' grades differently, an important topic for future work.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataAlthough university-like deadlines helped generate interest in online classes, campus-paced deadlines are too rigid online.
“Although universitylike deadlines helped generate interest in online classes [Lewin 2013c], we found that campus-paced deadlines are too rigid online.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Lewin 2013c
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataFive students from across the world participated as community TAs.
“Five students from across the world participated.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataTogether, community TAs answered 547 questions on the forum, while staff answered 582 questions.
“Together, community TAs answered 547 questions on the forum, while staff (three local TAs and the instructor) answered 582 questions.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenAnecdotally, students are generally satisfied with their overall grade but are frustrated by inaccurate qualitative feedback from some peers.
“Anecdotally, we have noticed that students are generally satisfied with their overall grade but are frustrated by inaccurate qualitative feedback from some peers.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenShe also participated heavily in the forums and gathered staff-like respect from her peers.
“She also participated heavily in the forums and gathered staff-like respect from her peers.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataIn iteration 1, questions with parallel structure had lesser disagreement, both among peer graders and between the median grade and the self-assessed grade.
“In iteration 1, questions with parallel structure had lesser disagreement, both among peer graders and between the median grade and the self-assessed grade.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe age distribution of the class is remarkably similar across both iterations.
“The age distribution of the class is remarkably similar across both iterations.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenThe median agreement was 42.% and 65.5%, respectively.
“(Recall that the median agreement was 42.% and 65.5%, respectively).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenMost fortune cookie feedback was personalized.
“Most fortune cookie feedback was personalized.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataStaff rated the prototype low because while fully functional, the design did not seem appropriate to the goal, and the diary aspect seemed to be the main aspect of the app yet was hidden behind a search bar.
“Staff rated it low, because”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataPeers rated the prototype highly for being interactive.
“Peers rated the prototype highly for being”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenStudents copied snippets of feedback (fortune cookies), pasted them in a textbox, and optionally added an explanation.
“Students copied snippets of feedback (fortune cookies), pasted them in a textbox, and optionally added an explanation.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataStaff praised the application as fully interactive, with complete page flow.
“Staff praised it as”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataIn the first iteration, 28.7% of such samples were within 5% of the staff grade, and 44.9% within 10%.
“In the first iteration, 28.7% of such samples were within 5% of the staff grade, and 44.9% within 10%.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe Fall 2012 iteration of the class had 17,915 participants.
“(b) Fall 2012 (iteration 2): 17,915 participants”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown data20% of students assessed more than the required five submissions.
“Students were required to assess five submissions, and 20% of students assessed more than required.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenEven though this was a completely online class, students self-organized to meet up in 10 locations around the world including London, San Francisco, New York City, Buenos Aires, Aachen (Germany), and Bangladesh.
“Even though this was a completely online class, students self-organized to meet up in 10 locations around the world including London, San Francisco, New York City, Buenos Aires, Aachen (Germany), and Bangladesh.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingnone givenStudents in the second (Fall 2012) iteration of the class reported a self-grade >5% higher than peer grade more frequently, and so got their self-grade less frequently.
“Students in the second (Fall 2012) iteration of the class reported a self-grade >5% higher than peer grade more frequently, and so got their self-grade less frequently.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe Spring 2012 iteration of the class had 10,190 participants.
“(a) Spring 2012 (iteration 1): 10,190 participants”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataFeedback on grading accuracy reduced the overall error in assessment and made the range of errors smaller.
“Feedback on grading accuracy reduced the overall error in assessment and made the range of errors smaller.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data26.3% of peer grades were within 5% of staff grades, and 46.7% within 10%.
“26.3% of grades were within 5% of staff grades, and 46.7% within 10%.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataIn the second iteration, 24.0% of such samples were within 5% of the staff grade, and 40.63% within 10%.
“(b) Same graph for second iteration of the class: 24.0% of such samples were within 5% of the staff grade, and 40.63% within 10%.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims. The quote only partly states this claim.
Findingown dataSome peers rated the application unpolished and asked the student to try to make the UI less coloured.
“while some peers rated it”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationSelf-assessment helps students reflect on gaps in their understanding, making them more resourceful, confident, and higher achievers.
“Similarly, self-assessment helps students reflect on gaps in their understanding, making them more resourceful, confident, and higher achievers [Zimmerman and Schunk 2001;Pintrich 1995;Pintrich and Zusho 2007], and provides learning gains not seen with external evaluation [Dow et al. 2012].”
Quote in the paperSubstantial claim0 of 3 sources checkedMay combine two claims.
PremisecitationHuman grading more easily provides context-appropriate responses and better handles ill-specified constraints.
“Furthermore, human grading more easily provides context-appropriate responses and better handles ill-specified constraints [Hearst 2000].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Hearst 2000 source
May combine two claims.
PremisecitationPeer assessment can increase student involvement and maturity, lower the grading burden on staff, and enhance classroom discussion.
“Peer assessment can increase student involvement and maturity, lower the grading burden on staff, and enhance classroom discussion [Boud 1995].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Boud 1995
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S2 (checkable).
PremisecitationMassive online classes provide a valuable living lab for exploring peer-sourcing approaches.
“Massive online classes provide a valuable living lab [Chi 2009;Carter et al. 2008] for exploring peer-sourcing approaches, and our hope is that peer-sourcing insights from massive classes will contribute techniques that apply more broadly.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Chi 2009
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Carter et al. 2008 source
May combine two claims.
PremisecitationShowing a list of common, assignment-specific problems that the submission could have potentially reduces inhibition and encourages peers to think critically.
“Second, showing a list of common, assignment-specific problems that the submission could have potentially reduces inhibition and encourages peers to think critically [Galinsky and Moskowitz 2000].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Galinsky and Moskowitz 2000 source
May combine two claims.
PremisecitationProviding raters a list of potential feedback items changes a recall/identification task into a recognition task, reducing the cost of giving feedback.
“First, providing raters a list of potential feedback items changes a recall/identification task into a recognition task.This reduces the cost of giving feedback [Anderson and Bower 1972;Nielsen 1994].”
Quote in the paperSubstantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationConsensus-based mechanisms that encourage sharing perspectives may improve agreement.
“Consensus-based mechanisms that encourage sharing perspectives may improve agreement [Amabile 1982].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Amabile 1982 source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationreasoningStudents found rating their peers valuable or enjoyable and/or believed it would help their peers.
“We infer from this that students found rating their peers valuable or enjoyable and/or they believed it would help their peers.”
Quote in the paperNot a substantial claim- Rests on an assumption: Survey respondents are representative of the class.
Classified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenGrading was double-blind, so raters did not see the names of submitters.
“We remind the reader that grading was double-blind, so raters did not see the names of submitters.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationPrior work has demonstrated that feedback improves the quality of crowd work.
“Prior work has demonstrated that feedback improves the quality of crowd work [Dow et al. 2012]”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Dow et al. 2012 source
May combine two claims.
Interpretationown dataStudents expend the same amount of effort writing feedback, and using fortune cookies allows this effort to be used to add to the fortune cookie text.
“This suggests that students expend the same amount of effort writing feedback, and using fortune cookies allows this effort to be used to add to the fortune cookie text.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductreasoningThe repeated-sampling numbers for the five assignments are based on a smaller number of staff-graded assignments and are more susceptible to variations in staff grades for a particular submission.
“Note that both these numbers are based on repeated sampling from a smaller number of staff-graded assignments.As such, they are more susceptible to variations in staff grades for a particular submission.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenStudents sometimes believe that teachers grade on personal taste and focus on currying favor.
“Students sometimes believe that teachers grade on personal taste and focus on currying favor.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationStudents in online classrooms are much more diverse both demographically and in their objectives in taking the class.
“students in online classrooms are much more diverse both demographically and in their objectives in taking the class”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Breslow et al. 2013
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Interpretationnone givenWhile peer assessment works well for the large majority of students, students who receive an unfair assessment may lose motivation.
“Lastly, while peer assessment works well for the large majority of students, students who receive an unfair assessment may lose motivation.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenMany types of cheating are currently possible and unchecked in online classes.
“Many types of cheating are currently possible and unchecked in online classes.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.
PremisecitationIterative design often pays big dividends, and assessment systems are no exception.
“Iterative design often pays big dividends [Nielsen 1993], and assessment systems are no exception.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Nielsen 1993 source
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningThese differences require rethinking the design of the classroom.
“These differences require rethinking the design of the classroom.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenProviding explicit grading criteria, especially in advance, helps convey to students that grading is fair, consistent, and based on the quality of their work.
“Also, providing explicit grading criteria (especially in advance) helps convey to students that grading is fair, consistent, and based on the quality of their work.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenThe large scale of online classes allows data-driven iterative improvements of classroom materials in ways that small classes may not.
“The large scale of online classes allows data-driven iterative improvements of classroom materials in ways that small classes may not.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationA valuable skill for success is the theory of mind to intuit how others perceive one's performance.
“A valuable skill for success is the theory of mind to intuit how others perceive one's performance [Boud 1995]”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Boud 1995
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationIn key areas, online education encodes pedagogy into software, which increases consistency and supports reuse, and defaults have a powerful impact on behavior.
“In key areas, online education encodes pedagogy into software, which increases consistency and supports reuse-and defaults have a powerful impact on behavior [Palen 1999].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Palen 1999 source
Classified as probably not a substantial claim.
InterpretationreasoningCommunity TAs possessed enough knowledge to effectively offer information and guidance because they had done well in the class.
“First, because community TAs had done well in the class, they possessed enough knowledge to effectively offer information and guidance.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningPeer assessment addresses the problem of differing interpretations by providing raters with expert-made rubrics, but some differences may persist.
“Peer assessment addresses this problem by providing raters with expert-made rubrics, but some differences may persist.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S1 (asserted). May combine two claims.
InterpretationreasoningCommunity TAs could easily empathize with issues students faced and effectively offer social support because they had taken the class recently.
“Second, because they had taken the class recently, they could easily empathize with issues students faced and also could effectively offer social support.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationPlatforms make some kinds of data, such as engagement with course material, more plentiful and finer grained, while making other information, such as facial expressions of confusion, completely inaccessible.
“platforms make some kinds of data, such as engagement with course material, more plentiful and finer grained, while making other information, such as facial expressions of confusion, completely inaccessible [Breslow et al. 2013].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Breslow et al. 2013
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
InterpretationreasoningThe role of teaching staff (TAs) changes: instead of spending a majority of their time grading, they spend a large fraction of their time fielding student questions, mentoring students, boosting student morale and autonomous perspective, and making data-driven revisions to class materials.
“The role of teaching staff (TAs) changes too.Instead of spending a majority of their time grading, they spend a large fraction of their time fielding student questions, mentoring students, boosting student morale and autonomous perspective, and making data-driven revisions to class materials.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationBecause fortune cookies sometimes used terminology learned in class, they may have triggered cued recall of these concepts, leading to more conceptual comments.
“Third, because fortune cookies sometimes used terminology learned in class, they may have triggered cued recall of these concepts [Little and Bjork 2012], leading to more conceptual comments.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Little and Bjork 2012
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Classified as probably not a substantial claim.
InterpretationreasoningBecause of the diversity of submissions, articulating assessment criteria a priori can be extremely difficult.
“Because of the diversity of submissions, this can be extremely difficult to do a priori.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
PremisecitationThe design studio offers inspiration for supporting student leadership and community learning in the online classroom.
“Again, the design studio offers inspiration [Schön 1985;Pendleton-Jullian 2010].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites Schön 1985
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Pendleton-Jullian 2010
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Classified as probably not a substantial claim.
InterpretationreasoningAfter revision, rubrics can scale well for both students and other teachers to use.
“After revision, these rubrics can scale well for both students and other teachers to use.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenStudents often have work commitments, and holidays are at different times around the world.
“For instance, students often have work commitments, and holidays are at different times around the world.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationA studio facilitates dialogue between students, instructors, and artifacts that helps students collaboratively learn difficult concepts and solve problems.
“In addition, a studio facilitates dialogue between students, instructors, and artifacts that helps students collaboratively learn difficult concepts and solve problems [Schön 1985].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Schön 1985
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationNovices and experts differ in their tacit understanding of value.
“This may be because novices and experts differ in their tacit understanding of value [Kaufman et al. 2008].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Kaufman et al. 2008 source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationBy making not only the results of work but also the process of creation highly visible, the design studio helps students learn and build awareness through observation.
“By making not only the results of work but also the process of creation highly visible, it helps students learn and build awareness through observation [Klemmer et al. 2006].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Klemmer et al. 2006 source
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
InterpretationreasoningAs online classes count for more benefits, such gaming may increase.
“As online classes count for more benefits, such gaming may increase.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataPeer assessment imposes a particular schedule on class and limits student flexibility.
“Second, peer assessment imposes a particular schedule on class and limits student flexibility.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationEven examples in prior work on using rubrics suffer from nonparallel grammatical structure.
“This is not uncommon: even examples in prior work on using rubrics suffer from this problem (e.g., Andrade [2005]).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Andrade [2005] source
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenGaming may help students develop the theory of mind skill.
“and gaming may help students develop this skill.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationExperts and novices both robustly reach consensus about creativity, but their consensual judgments differ from each other.
“experts and novices both robustly reach consensus about creativity, but their consensual judgments differ from each other [Conti et al. 1996].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Conti et al. 1996 source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationSeveral universities are teaching physical classes augmented with online materials.
“Already, several universities are teaching physical classes augmented with online materials [Martin 2012].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Martin 2012 source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationOne aspect of reconceptualizing the student has to do with enrollment and retention.
“One has to do with enrollment and retention [Kizilcec et al. 2013].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Kizilcec et al. 2013 source
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenOnline classes attract students who cannot use traditional universities, such as those working full time.
“Online classes attract students who cannot use traditional universities, such as those working full time.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenHuman subjects research was reviewed by the Stanford Institutional Review Board through protocol 25001.
“Human subjects research was reviewed by the Stanford Institutional Review Board through protocol 25001.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone givenOnline education requires us to reconceptualize what it means to be a student in many ways.
“More generally, online education requires us to reconceptualize what it means to be a student in many ways.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenBy removing the constraints of the physical classroom, online classes have made education accessible to many new kinds of students-the new mother, the full-time professional, and the retiree.
“By removing the constraints of the physical classroom, online classes have made education accessible to many new kinds of students-the new mother, the full-time professional, and the retiree.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.
Interpretationnone givenTyping one's email address into a webpage is not the same as showing up for the first day of a registrar-enrolled class; it is more like peeking through the window, and the large number of signups indicates that lots of people are curious.
“Typing one's email address into a webpage is not the same as showing up for the first day of a registrar-enrolled class.It's more like peeking through the window, and what the large number of signups tells us is that lots of people are curious.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
Staff-assigned grades are a valid ground truth for assessing peer and self grading accuracy.
“To establish a ground-truth comparison of self-and staff grades, each assignment included four to 10 staff-graded submissions in the peer assessment pool (these were randomly selected).”
“We evaluated the accuracy of this grading process using the 99 assignments with a staff grade.”
The paper relies on this without support.
Survey respondents are representative of the class.
“At the end of the class, students were invited to participate in a survey; 3,550 students participated in all.Participation was voluntary, students were not compensated, and the survey did not count toward course credit.”
The authors state this as an assumption.
Grades in different assignments are uncorrelated.
“(assuming grades in different assignments are uncorrelated)”
The paper relies on this without support.
Staff grades are a valid ground truth for assessing peer grading accuracy.
“Holding out 10% of ground truth grades”
“compared to 7.74% in the no-feedback condition”
The paper relies on this without support.
At a physical university, the completion rate for an equivalent class is much higher than in the online class.
“Needless to say, at a physical university, the completion rate for an equivalent class is much higher.”
The paper relies on this without support.
Peers can help instruction itself.
“Fourth, peers can help instruction itself.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Techniques such as community TAs, peer assessment, rubrics, video feedback are available to help MOOC instructors scale up provision of perceived personal attention.
“Techniques such as involvement of community teaching assistants (TAs),21 peer assessment tools,22 well-designed elaborated rubrics with specific content feedback23 and video feedbacks24 are available to help MOOC instructors scale up the provision of perceived personal attention to students.”
From Towards AI-Powered Personalization in MOOC Learning (2017) · cites this paper as [22]
Statistics that don’t add up
Each reported p-value recomputed from its test statistic and degrees of freedom.
Inconsistent p-value across alpha = .05: F(1, 39) = 2.07, p < .05
MajorThe paper reports p < .05 here; recomputing from the test statistic and its degrees of freedom gives p = .158. The two fall on opposite sides of alpha = .05. The check cannot tell whether the statistic or the p-value is the misprint.
Reported p < .05 · recomputed p = .158
“F(1, 39) = 2.07, p < 0.05”
Statistics recomputed
Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.
- Doesn’t add upF(1, 39) = 2.07reported p < .05 · recomputed p = .158The two fall on opposite sides of .05
“F(1, 39) = 2.07, p < .05”
- Consistentt(24) = 19.9reported p < .05 · recomputed p < .001
“t(24) = 19.9,p < .05”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Figure 5 caption names the wrong comparison grade
MinorThe results describe Figure 5 as comparing median peer grades with self-assessed grades, but its caption says the percentages measure agreement with staff grades.
“In the first iteration of the class, 28.7% of submissions had their median peer grade within 5% of the self-assessed grade, and 44.9% within 10% (Figure 5).”
“Figure: 5 Fig. 5 . Fig. 5. (a) Comparison of median peer grades against self-grades.In the first iteration, 28.7% of such samples were within 5% of the staff grade, and 44.9% within 10%.”
Cites
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500 · see citing works
An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1993) · doi:10.1007/978-1-4899-4541-9 · Cited by 21,230 · see citing works
Nicol, D. J., & Macfarlane‐Dick, D. (2006) · Studies in Higher Education · doi:10.1080/03075070600572090 · Cited by 5,846 · see citing works
The Dishonesty of Honest People: A Theory of Self-Concept Maintenance
Mazar, N., Amir, O., & Ariely, D. (2008) · Journal of Marketing Research · doi:10.1509/jmkr.45.6.633 · Cited by 2,875 · see citing works
Social psychology of creativity: A consensual assessment technique.
Amabile, T. M. (1982) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.43.5.997 · Cited by 2,278 · see citing works
Peer Assessment between Students in Colleges and Universities
Topping, K. (1998) · Review of Educational Research · doi:10.2307/1170598 · Cited by 2,173 · see citing works
How Designers Think: The Design Process Demystified
Lawson, B. (1990) · Cited by 1,900 · see citing works
Self-Regulated Learning and Academic Achievement
Zimmerman, B. J., & Schunk, D. H. (1989) · Springer series in cognitive development · doi:10.1007/978-1-4612-3618-4 · Cited by 1,885 · see citing works
Proceedings for the Annual Meeting of the Cognitive Science Society
Dziemianko, M., Clarke, A. D. F., & Keller, F. (2013) · Cited by 1,726 · see citing works
Lawson, B. (2006) · Routledge eBooks · doi:10.4324/9780080454979 · Cited by 1,601 · see citing works
Sustainable Assessment: Rethinking assessment for the learning society
Boud, D. (2000) · Studies in Continuing Education · doi:10.1080/713695728 · Cited by 1,545 · see citing works
Enhancing the explanatory power of usability heuristics
Nielsen, J. (1994) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/191666.191729 · Cited by 1,439 · see citing works
(2007) · International Linear Collider · Cited by 1,382 · see citing works
Self-Regulated Learning and Academic Achievement
(2013) · doi:10.4324/9781410601032 · Cited by 1,325 · see citing works
Semantic Compositionality through Recursive Matrix-Vector Spaces
Socher, R., Huval, B., Manning, C. D., & Ng, A. Y. (2012) · Cited by 1,262 · see citing works
Student Peer Assessment in Higher Education: A Meta-Analysis Comparing Peer and Teacher Marks
Falchikov, N., & Goldfinch, J. (2000) · Review of Educational Research · doi:10.3102/00346543070003287 · Cited by 1,170 · see citing works
Kittur, A., Nickerson, J. V., Bernstein, M., Gerber, E., Shaw, A., Zimmerman, J., Lease, M., & Horton, J. (2013) · Proceedings of the 2013 conference on Computer supported cooperative work · doi:10.1145/2441776.2441923 · Cited by 1,034 · see citing works
Quality management on Amazon Mechanical Turk
Ipeirotis, P. G., Provost, F. J., & Wang, J. (2010) · doi:10.1145/1837885.1837906 · Cited by 991 · see citing works
Deconstructing disengagement: analyzing learner subpopulations in massive open online courses
Kizilcec, R. F., Piech, C., & Schneider, E. (2013) · Proceedings of the Third International Conference on Learning Analytics and Knowledge · doi:10.1145/2460296.2460330 · Cited by 984 · see citing works
Understanding experience in interactive systems
Forlizzi, J., & Battarbee, K. (2004) · Designing Interactive Systems Conference · doi:10.1145/1013115.1013152 · Cited by 930 · see citing works
(2013) · Research & Practice in Assessment · Cited by 920 · see citing works
Enhancing Learning Through Self-assessment
Boud, D. J. (2013) · Routledge eBooks · doi:10.4324/9781315041520 · Cited by 914 · see citing works
Recognition and retrieval processes in free recall.
Anderson, J. R., & Bower, G. H. (1972) · Psychological Review · doi:10.1037/h0033773 · Cited by 820 · see citing works
Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI'12)
Hoffmann, J., & Selman, B. (2012) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 718 · see citing works
Why the unskilled are unaware: Further explorations of (absent) self-insight among the incompetent
Ehrlinger, J., Johnson, K., Banner, M., Dunning, D., & Kruger, J. (2008) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2007.05.002 · Cited by 707 · see citing works
Enhancing learning through self-assessment
Vella, F. (1996) · Biochemical Education · doi:10.1016/0307-4412(96)82523-4 · Cited by 671 · see citing works
How bodies matter: five themes for interaction design
Klemmer, S. R., Hartmann, B., & Takayama, L. (2006) · Proceedings of the 6th conference on Designing Interactive systems · doi:10.1145/1142405.1142429 · Cited by 645 · see citing works
Self-citationUnderstanding self‐regulated learning
Pintrich, P. R. (1995) · New Directions for Teaching and Learning · doi:10.1002/tl.37219956304 · Cited by 633 · see citing works
Crystal structure of a monomeric retroviral protease solved by protein folding game players
Foldit Contenders Group, Khatib, F., Foldit Void Crushers Group, DiMaio, F., Cooper, S., Kazmierczyk, M., Gilski, M., Krzywda, S., Zabranska, H., Pichova, I., et al. (2011) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2119 · Cited by 540 · see citing works
PREDICTING THE RETENTION OF UNIVERSITY STUDENTS
Murtaugh, P. A., Burns, L. D., & Schuster, J. (1999) · Research in Higher Education · doi:10.1023/a:1018755201899 · Cited by 489 · see citing works
Design-oriented human-computer interaction
Fällman, D. (2003) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/642611.642652 · Cited by 486 · see citing works
Iterative user-interface design
Nielsen, J. (1993) · Computer · doi:10.1109/2.241424 · Cited by 444 · see citing works
Teaching With Rubrics: The Good, the Bad, and the Ugly
Andrade, H. G. (2005) · College Teaching · doi:10.3200/ctch.53.1.27-31 · Cited by 436 · see citing works
(1986) · Medical Entomology and Zoology · Cited by 430 · see citing works
Alben, L. (1996) · interactions · doi:10.1145/235008.235010 · Cited by 414 · see citing works
The Scholarship of Teaching and Learning in Higher Education: An Evidence-Based Perspective
Perry, R. P., & Smart, J. C. (2007) · Springer eBooks · doi:10.1007/1-4020-5742-3 · Cited by 389 · see citing works
Student Motivation and Self-Regulated Learning in the College Classroom
Pintrich, P. R., & Zusho, A. (2002) · Higher education · doi:10.1007/978-94-010-0245-5_2 · Cited by 389 · see citing works
A Comparison of Expert and Nonexpert Raters Using the Consensual Assessment Technique
Kaufman, J. C., Baer, J., Cole, J. C., & Sexton∗, J. D. (2008) · Creativity Research Journal · doi:10.1080/10400410802059929 · Cited by 374 · see citing works
Parallel prototyping leads to better design results, more divergence, and increased self-efficacy
Dow, S. P., Glassco, A., Kass, J., Schwarz, M., Schwartz, D. L., & Klemmer, S. R. (2010) · ACM Transactions on Computer-Human Interaction · doi:10.1145/1879831.1879836 · Cited by 367 · see citing works
Self-citationSplitting the Differences: A Structural Alignment View of Similarity
Markman, A. B., & Gentner, D. (1993) · Journal of Memory and Language · doi:10.1006/jmla.1993.1027 · Cited by 334 · see citing works
Shepherding the crowd yields better work
Dow, S., Kulkarni, A., Klemmer, S., & Hartmann, B. (2012) · Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work · doi:10.1145/2145204.2145355 · Cited by 334 · see citing works
Self-citationEnhancing the explanatory power of usability heuristics
Nielsen, J. (1994) · doi:10.1145/259963.260333 · Cited by 334 · see citing works
Building Successful Online Communities: Evidence-Based Social Design
Kraut, R. E., Resnick, P., Kiesler, S., Burke, M., Chen, Y., Kittur, N., Konstan, J., Ren, Y., & Riedl, J. (2012) · doi:10.7551/mitpress/8472.001.0001 · Cited by 327 · see citing works
Will massive open online courses change how we teach?
Martin, F. G. (2012) · Communications of the ACM · doi:10.1145/2240236.2240246 · Cited by 324 · see citing works
Student Motivation and Self-Regulated Learning in the College Classroom
Pintrich, P. R., & Zusho, A. (2007) · The Scholarship of Teaching and Learning in Higher Education: An Evidence-Based Perspective · doi:10.1007/1-4020-5742-3_16 · Cited by 254 · see citing works
Impression formation in online peer production
Marlow, J., Dabbish, L., & Herbsleb, J. (2013) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2441776.2441792 · Cited by 225 · see citing works
Evidence to Support the Componential Model of Creativity: Secondary Analyses of Three Studies
Conti, R., Coon, H., & Amabile, T. M. (1996) · Creativity Research Journal · doi:10.1207/s15326934crj0904_9 · Cited by 222 · see citing works
Social, individual and technological issues for groupware calendar systems
Palen, L. (1999) · Proceedings of the SIGCHI conference on Human factors in computing systems the CHI is the limit - CHI '99 · doi:10.1145/302979.302982 · Cited by 222 · see citing works
Getting the right design and the design right
Tohidi, M., Buxton, W., Baecker, R., & Sellen, A. (2006) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1124772.1124960 · Cited by 211 · see citing works
Galinsky, A. D., & Moskowitz, G. B. (2000) · Journal of Experimental Social Psychology · doi:10.1006/jesp.1999.1409 · Cited by 203 · see citing works
Design knowledge communicated in studio critiques
Uluoğlu, B. (2000) · Design Studies · doi:10.1016/s0142-694x(99)00002-2 · Cited by 185 · see citing works
Guo, S. D., Parameswaran, A., & García-Molina, H. (2012) · Proceedings - ACM-SIGMOD International Conference on Management of Data · doi:10.1145/2213836.2213880 · Cited by 183 · see citing works
The debate on automated essay grading
Hearst, M. A. (2000) · IEEE Intelligent Systems and their Applications · doi:10.1109/5254.889104 · Cited by 176 · see citing works
Teaching HCI Design With the Studio Approach
Reimer, Y. J., & Douglas, S. A. (2003) · Computer Science Education · doi:10.1076/csed.13.3.191.14945 · Cited by 158 · see citing works
Reflections on Theories of Self-Regulated Learning and Academic Achievement
Zimmerman, B. J., & Schunk, D. H. (2013) · doi:10.4324/9781410601032-13 · Cited by 155 · see citing works
(2008) · International journal on e-learning · Cited by 148 · see citing works
Validity and Automad Scoring: It's Not Only the Scoring
Bennett, R. E., & Bejar, I. I. (1998) · Educational Measurement Issues and Practice · doi:10.1111/j.1745-3992.1998.tb00631.x · Cited by 145 · see citing works
Decision-Theoretic Control of Crowd-Sourced Workflows
Dai, P., Mausam, M., & Weld, D. S. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7760 · Cited by 126 · see citing works
Dannels, D. P., & Martin, K. N. (2008) · Journal of Business and Technical Communication · doi:10.1177/1050651907311923 · Cited by 117 · see citing works
Enhancing scientific essay writing using peer assessment
Venables, A. T., & Summit, R. (2003) · Innovations in Education and Teaching International · doi:10.1080/1470329032000103816 · Cited by 111 · see citing works
Technology Enhanced Learning : Opportunities for Change
Goodman, P. S. (2001) · Medical Entomology and Zoology · Cited by 110 · see citing works
Exiting the Cleanroom: On Ecological Validity and Ubiquitous Computing
Carter, S., Mankoff, J., Klemmer, S. R., & Matthews, T. (2008) · Human–Computer Interaction · doi:10.1080/07370020701851086 · Cited by 107 · see citing works
Self-citationThe Social Psychological Effects of Feedback on the Production of Internet Information Pools
Cheshire, C., & Antin, J. (2008) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2008.00416.x · Cited by 101 · see citing works
Cognitive Tutors: From the Research Classroom to All Classrooms
Corbett, A. T., Koedinger, K. R., & Hadley, W. S. (2001) · Routledge eBooks · doi:10.4324/9781410601933-19 · Cited by 101 · see citing works
Feldman, E. B. (1994) · Medical Entomology and Zoology · Cited by 96 · see citing works
Engaging Large Classes: Strategies and Techniques for College Faculty
Stanley, C. A., & Porter, M. (2001) · Cited by 92 · see citing works
Interpretation in Architecture: Design as Way of Thinking
Snodgrass, A., & Coyne, R. (2005) · Medical Entomology and Zoology · Cited by 90 · see citing works
Intellectual Development in the College Years
Magolda, M. B. B. (2006) · Change The Magazine of Higher Learning · doi:10.3200/chng.38.3.50-54 · Cited by 84 · see citing works
Teaching critical thinking in psychology : a handbook of best practices
Dunn, D. S., Halonen, J. S., & Smith, R. A. (2008) · Cited by 81 · see citing works
Proceedings Frontiers in Education Conference
(1988) · doi:10.1109/fie.1988 · Cited by 81 · see citing works
Assessment Focus in Studio: What is Most Prominent in Architecture, Art and Design?
de la Harpe, B., Peterson, J. F., Frankham, N., Zehner, R. B., Neale, D., Musgrave, E., & McDermott, R. (2009) · International Journal of Art & Design Education · doi:10.1111/j.1476-8070.2009.01591.x · Cited by 78 · see citing works
What can we teach about human-computer interaction? (plenary address)
Winograd, T. (1990) · doi:10.1145/97243.97322 · Cited by 75 · see citing works
The Design Studio: An Exploration of Its Traditions and Potential
Dutton, T. A. G., Willenbrock, L. L., & Schön, D. A. (1989) · Journal of Architectural Education · doi:10.2307/1424986 · Cited by 75 · see citing works
Using undergraduates as teaching assistants in introductory programming courses
Roberts, E. S., Lilly, J. F., & Rollins, B. (1995) · ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/199688.199716 · Cited by 70 · see citing works
VALIDITY AND AUTOMATED SCORING: IT'S NOT ONLY THE SCORING
Bennett, R. E., & Bejar, I. I. (1997) · ETS Research Report Series · doi:10.1002/j.2333-8504.1997.tb01734.x · Cited by 69 · see citing works
The Architecture of the Ecole des Beaux-Arts
Condit, C. W., & Drexler, A. (1978) · Technology and Culture · doi:10.2307/3103775 · Cited by 68 · see citing works
Bennett, R. E., Steffen, M., Singley, M. K., et al. (1997) · Journal of Educational Measurement · doi:10.1111/j.1745-3984.1997.tb00512.x · Cited by 64 · see citing works
Distance learning through distributed collaborative video viewing
Cadiz, J. J., Balachandran, A., Sanocki, E., et al. (2000) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/358916.358984 · Cited by 61 · see citing works
Teaching software development in a studio environment
Tomayko, J. E. (1991) · ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/107004.107070 · Cited by 51 · see citing works
Enhancing reliability using peer consistency evaluation in human computation
Huang, S.-W., & Fu, W.-T. (2013) · Proceedings of the 2013 conference on Computer supported cooperative work · doi:10.1145/2441776.2441847 · Cited by 50 · see citing works
Crossing the software education chasm
Fox, A., & Patterson, D. A. (2012) · Communications of the ACM · doi:10.1145/2160718.2160732 · Cited by 49 · see citing works
(2013) · Cited by 49 · see citing works
The Architecture of the Ecole Des Beaux-Arts
beaux-arts, É. N. S. D., Percier, C., Dubut, L.-A., et al. (1984) · Medical Entomology and Zoology · Cited by 46 · see citing works
Calibrated peer review/sup TM/ and assessing learning outcomes
Carlson, P. A., & Berry, F. C. (2004) · 33rd Annual Frontiers in Education, 2003. FIE 2003. · doi:10.1109/fie.2003.1264740 · Cited by 43 · see citing works
(2007) · The journal of college science teaching · Cited by 40 · see citing works
Embedding a Design Studio Course in a Conventional Computer Science Program
Greenberg, S. (2009) · IFIP International Federation for Information Processing/IFIP · doi:10.1007/978-0-387-89022-7_3 · Cited by 38 · see citing works
Parallel Prototyping Leads to Better Design Results, More Divergence, and Increased Self-efficacy
Dow, S. P., Glassco, A., Kass, J., Schwarz, M., Schwartz, D. L., & Klemmer, S. (2011) · Springer eBooks · doi:10.1007/978-3-642-21643-5_8 · Cited by 37 · see citing works
Self-citationScaffolding knowledge integration through designing multimedia case studies of engineering design
Hsi, S., & Agogino, A. M. (2002) · doi:10.1109/fie.1995.483230 · Cited by 36 · see citing works
A Consensual Technique for Creativity Assessment
Amabile, T. (1983) · doi:10.1007/978-1-4612-5533-8_3 · Cited by 36
Promoting creativity in the computer science design studio
Cennamo, K. S., Douglas, S. A., Vernon, M., et al. (2011) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/1953163.1953344 · Cited by 35 · see citing works
Using undergraduates as teaching assistants in introductory programming courses
Roberts, E. S., Lilly, J. F., & Rollins, B. (1995) · ACM SIGCSE Bulletin · doi:10.1145/199691.199716 · Cited by 35 · see citing works
Peer assessment in the algorithms course
Chinn, D. D. (2005) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/1067445.1067468 · Cited by 34 · see citing works
Teaching software development in a studio environment
Tomayko, J. E. (1991) · ACM SIGCSE Bulletin · doi:10.1145/107005.107070 · Cited by 24 · see citing works
(2011) · eScholarship (California Digital Library) · Cited by 22 · see citing works
Ed, H. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02574-7_67 · Cited by 20 · see citing works
Creativity and HCI: From Experience to Design in Education
Kotzé, P., Wong, W., Silva, P. A., et al. (2009) · IFIP International Federation for Information Processing/IFIP · doi:10.1007/978-0-387-89022-7 · Cited by 19 · see citing works
Incorporating Student Peer-Review into an Introduction to Engineering Design Course
Carlson, P. A., Berry, F., & Voltmer, D. R. (2006) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie.2005.1612043 · Cited by 17 · see citing works
Peer assessment in the algorithms course
Chinn, D. D. (2005) · ACM SIGCSE Bulletin · doi:10.1145/1151954.1067468 · Cited by 13 · see citing works
Critical Thinking in Critical Courses: Principles and Applications
Kuebli, J. E., Harvey, R. D., & Korn, J. H. (2008) · Teaching Critical Thinking in Psychology · doi:10.1002/9781444305173.ch12 · Cited by 5 · see citing works
Erratum: Crystal structure of a monomeric retroviral protease solved by protein folding game players
Khatib, F., DiMaio, F., Cooper, S., et al. (2012) · Nature Structural & Molecular Biology · doi:10.1038/nsmb0312-364b
Flagged first, then by citations
Research works printed but not linked: 11
- H. Kim and P. Hinds . 2012. Harmony vs. disruption: The effect of iterative prototyping on teams creative processes and outcomes in the West and the East . In Proceedings of the ICIC: International Conference on Intercultural Collaboration. ACM. H. Kim and P. Hinds. 2012. Harmony vs. disruption: The effect of iterative prototyping on teams creative processes and outcomes in the West and the East. In Proceedings of the ICIC: International Conference on Intercultural Collaboration. ACM.
- J. Kurhila . 2012 . Human-Computer Interaction by Coursera opened for credit for the students of the Department . Retrieved December 13, 2013 from http://www.cs.helsinki.fi/en/uutiset/72025. J. Kurhila. 2012. Human-Computer Interaction by Coursera opened for credit for the students of the Department. Retrieved December 13, 2013 from http://www.cs.helsinki.fi/en/uutiset/72025.
- T. Lewin . 2012 a. Education site expands slate of universities and courses . The New York Times . September 19 , 2012 . T. Lewin. 2012a. Education site expands slate of universities and courses. The New York Times. September 19, 2012.
- T. Lewin . 2012 b. One course, 150,000 students . The New York Times . July 18 , 2012 . T. Lewin. 2012b. One course, 150,000 students. The New York Times. July 18, 2012.
- T. Lewin . 2013 a. College of future could be come one, come all . The New York Times . November 19 , 2012 . T. Lewin. 2013a. College of future could be come one, come all. The New York Times. November 19, 2012.
- T. Lewin . 2013 b. Five online courses are eligible for college credit . The New York Times . February 6 , 2013 . T. Lewin. 2013b. Five online courses are eligible for college credit. The New York Times. February 6, 2013.
- T. Lewin . 2013 c. Students rush to web classes, but profits may be much later . The New York Times . January 6 , 2013 . T. Lewin. 2013c. Students rush to web classes, but profits may be much later. The New York Times. January 6, 2013.
- T. Lewin . 2013 d. Universities abroad join partnerships on the web . The New York Times . February 20 , 2013 . T. Lewin. 2013d. Universities abroad join partnerships on the web. The New York Times. February 20, 2013.
- M. Szpir . 2002. Clickworkers on Mars. American Scientist 90, 3 ( 2002 ). M. Szpir. 2002. Clickworkers on Mars. American Scientist 90, 3 (2002).
- J. Widom . 2012 . From 100 Students to 100,000 . ACM SIGMOD Blog. Retreived December 13, 2013 from http://wp.sigmod.org/?p=165. J. Widom. 2012. From 100 Students to 100,000. ACM SIGMOD Blog. Retreived December 13, 2013 from http://wp.sigmod.org/?p=165.
- O. F. Zaidan and C. Callison-Burch . 2011 . Crowdsourcing translation: Professional quality from non-professionals. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics : Human Language Technologies , Vol. 1. 1220 -- 1229 . O. F. Zaidan and C. Callison-Burch. 2011. Crowdsourcing translation: Professional quality from non-professionals. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Vol. 1. 1220--1229.
Cited by
Sorted by citations
How Much Information?: Effects of Transparency on Trust in an Algorithmic Interface
Kizilcec, R. F. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858402 · Cited by 615 · see citing works
“Peer assessment is a proven method for scaling the grading of a large number of assessments, such as is required in massive open online courses (MOOCs) [14, 19].”
BackgroundMethodMassive open online course completion rates revisited: Assessment, length and attrition
Jordan, K. (2015) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v16i3.2112 · Cited by 510 · see citing works
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518 · see citing works
“Datasets from massive open online courses are a potential source of large-scale data (Kulkarni et al., 2015).”
MOOCs: Expectations and Reality
Tirthali, F. M. H. A. D. (2014) · Scholarly Commons (University of Pennsylvania) · Cited by 303 · see citing works
Re-designed flipped learning model in an academic course: The role of co-creation and co-regulation
Blau, I., & Shamir‐Inbal, T. (2017) · Computers & Education · doi:10.1016/j.compedu.2017.07.014 · Cited by 227 · see citing works
“…self-assessment that offer new opportunities for fl ipped learning in academia, has shown inaccurate grading e on average 7% higher than staff assessment and prone to be biased e students from the same country received higher grades compared to peers from different countries (Kulkarni et al., 2015).”
ResultStudents’ patterns of engagement and course performance in a Massive Open Online Course
Phan, T., McNeil, S., & Robin, B. (2015) · Computers & Education · doi:10.1016/j.compedu.2015.11.015 · Cited by 199 · see citing works
PeerStudio: Rapid Peer Feedback Emphasizes Revision and Improves Performance
Kulkarni, C. E., Bernstein, M. S., & Klemmer, S. R. (2015) · Proceedings of the Second (2015) ACM Conference on Learning @ Scale · doi:10.1145/2724660.2724670 · Cited by 192 · see citing works
“Prior work shows peer-based critique is effective both for in-person [8,29] and online classes [25], and can provide students accurate numeric grades and comments [13,25].”
BackgroundSelf-citationAutonomously Generating Hints by Inferring Problem Solving Policies
Piech, C., Sahami, M., Huang, J., & Guibas, L. J. (2015) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/2724660.2724668 · Cited by 139 · see citing works
“At what point is a student ready to consciously reflect on the challenges that they faced? Peer grading is one of the most common solutions for giving feedback to complex assignments at scale in massive open online courses [8]; however, it can only be used to give feedback on a final submissions, not hints to a stuck…”
BackgroundHow technology shapes assessment design: Findings from a study of university teachers
Bennett, S., Dawson, P., Bearman, M., Molloy, E., & Boud, D. (2017) · British Journal of Educational Technology · doi:10.1111/bjet.12439 · Cited by 130 · see citing works
“Technologies to support assessment have a long history in higher education – from the early days of programmed instruction and computerbased quizzes, to richer forms of interaction and content creation underpinned by constructivist approaches, and more recent tools that support online assignment submission, peer- and…”
BackgroundMethodXiong, Y., & Suen, H. K. (2018) · International Review of Education · doi:10.1007/s11159-018-9710-5 · Cited by 101 · see citing works
“Among all assessment methods within MOOCs, peer assessment has received considerable attention (e.g. Admiraal et al. 2014; Goldin 2012; Kulkarni et al. 2015; Luo et al. 2014; Piech et al. 2013; Raman and Joachims 2014; Suen 2014; Xiong et al. 2014).”
BackgroundMethodInfluentialTowards AI-Powered Personalization in MOOC Learning
Yu, H., Miao, C., Leung, C., & White, T. J. (2017) · npj Science of Learning · doi:10.1038/s41539-017-0016-3 · Cited by 131 · see citing works
“Techniques such as involvement of community teaching assistants (TAs), 21 peer assessment tools, 22 well-designed elaborated rubrics with speci fi c content feedback 23 and video feedbacks 24 are available to help MOOC instructors scale up the provision of perceived personal attention to students.”
MethodBuilding engagement for MOOC students
Nawrot, I., & Doucet, A. (2014) · International World Wide Web Conference (WWW) · doi:10.1145/2567948.2580054 · Cited by 124 · see citing works
“Furthermore, MOOC platforms take advantage of the newest research findings to provide their students with as engaging an experience as possible (eg. retrieval questions in videos [19, 18] or peer assessment [25, 36, 34]).”
BackgroundInformed Truthfulness in Multi-Task Peer Prediction
Shnayder, V., Agarwal, A., Frongillo, R., et al. (2016) · ACM Conference on Economics and Computation (EC) · doi:10.1145/2940716.2940790 · Cited by 114 · see citing works
“2013], as well as helping students self-adjust for bias and provide better feedback [Kulkarni et al. 2013].”
BackgroundMethodNawrot, I., & Doucet, A. (2014) · HAL · Cited by 115 · see citing works
Methods for ordinal peer grading
Raman, K., & Joachims, T. (2014) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623654 · Cited by 111 · see citing works
“In peer grading, students — not instructors or TAs — provide feedback on the work of other students in their class [14, 22], meaning that the number of “graders” naturally grows with the number of students.”
BackgroundLearner Behaviour in a MOOC Practice-oriented Course: In Empirical Study Integrating TAM and TPB
Yang, H.-H., & Su, C.-H. (2017) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v18i5.2991 · Cited by 111 · see citing works
“One common approach to overcome these issues is the mechanism of peer assessment (Kulkarni et al., 2013; Suen, 2014).”
BackgroundUnderstanding online interaction in language MOOCs through learning analytics
Martín-Monje, E., Castrillo, M. D., & Mañana-Rodríguez, J. (2018) · Computer Assisted Language Learning · doi:10.1080/09588221.2017.1378237 · Cited by 98 · see citing works
“According to several researchers, structured self-assessment provides a suitable path in an LMOOC (Sokolik, 2014; Kulkarni et al., 2013).”
BackgroundTalkabout: Making Distance Matter with Small Groups in Massive Classes
Kulkarni, C., Cambre, J., Kotturi, Y., Bernstein, M. S., & Klemmer, S. R. (2015) · Conference on Computer Supported Cooperative Work · doi:10.1145/2675133.2675166 · Cited by 120 · see citing works
“One example is the use of rater redundancy and short exercises that create microexpertise in peer review: with this structure, peers can provide expert-quality assessment and feedback [32], and act as mentors [45].”
BackgroundSelf-citationWhy do Institutions Offer MOOCs?
Hollands, F. M., & Tirthali, D. (2014) · Online Learning · doi:10.24059/olj.v18i3.464 · Cited by 101 · see citing works
“…al. (2013); Halawa, Greene, and Mitchell (2014); and Nguyen, Piech, Huang, and Guibas (2014) • natural language processing • human–computer interaction—for example, Kulkarni et al. (2013) and Cambre, Kulkarni, Bernstein, and Klemmer (2014) • personalized and adaptive learning—for example,…”
BackgroundYuan, A., Luther, K., Krause, M., Vennix, S. I., Dow, S. P., & Hartmann, B. (2016) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2818048.2819953 · Cited by 98 · see citing works
Scaling short-answer grading by combining peer assessment with algorithmic scoring
Kulkarni, C. E., Socher, R., Bernstein, M. S., & Klemmer, S. (2014) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/2556325.2566238 · Cited by 95 · see citing works
“Repeating this process multiple times estimates peer agreement with staff [17].”
BackgroundResultInfluentialSelf-citationDoes peer assessment in on-line learning environments work? A systematic review of the literature
Tenório, T., Bittencourt, I., Isotani, S., & Silva, A. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.06.020 · Cited by 82 · see citing works
Qushem, U. B., Christopoulos, A., Oyelere, S. S., Ogata, H., & Laakso, M. (2021) · Education Sciences · doi:10.3390/educsci11070338 · Cited by 114 · see citing works
“In addition to these functionalities, recent implementations discuss the integration of features that involve the engagement of educators or community assistants [62] who aim at providing participants with either video feedback [63] or peer assessment [64].”
BackgroundStructuring Interactions for Large-Scale Synchronous Peer Learning
Coetzee, D., Lim, S., Fox, A., Hartmann, B., & Hearst, M. A. (2015) · Conference on Computer Supported Cooperative Work · doi:10.1145/2675133.2675251 · Cited by 90 · see citing works
“Great strides have been made in peer grading in MOOCs, where students improve their own understanding by assessing the work of others [29].”
BackgroundMethodological approaches in MOOC research: Retracing the myth of P roteus
Raffaghelli, J. E., Cucchiara, S., & Persico, D. (2015) · British Journal of Educational Technology · doi:10.1111/bjet.12279 · Cited by 89 · see citing works
Improving Crowd Innovation with Expert Facilitation
Chan, J., Dang, S., & Dow, S. P. (2016) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2818048.2820023 · Cited by 86 · see citing works
“For example, peer-review systems have successfully employed scaffolding techniques, such as preauthored templates to improve feedback at scale [27,34].”
MethodNeuro-symbolic program corrector for introductory programming assignments
Bhatia, S., Kohli, P., & Singh, R. K. (2018) · Proceedings/Proceedings - International Conference on Software Engineering · doi:10.1145/3180155.3180219 · Cited by 84 · see citing works
“These approaches can be categorized into two broad categories – peer-grading [14, 15] and automated grading techniques [8, 17, 26, 27, 29].”
BackgroundMassive open online courses (MOOCs) as an enabler for competent employees and innovation in industry
Karnouskos, S. (2017) · Computers in Industry · doi:10.1016/j.compind.2017.05.001 · Cited by 83 · see citing works
“mediately followed by short quizzes and potentially larger assignments/homework, all of which are graded either by increasingly sophisticated software [18], engaged alumni, tutors or even peer-students [19].”
BackgroundArtificial Intelligence in Education: Exploring the Potential Benefits and Risks
Ayala-Pazmiño, M. (2023) · 593 Digital Publisher CEIT · doi:10.33386/593dp.2023.3.1827 · Cited by 122 · see citing works
2 What Constitutes an Effective Language MOOC?
Sokolik, M. (2014) · doi:10.2478/9783110420067.2 · Cited by 74 · see citing works
Reinforcement Learning for the Adaptive Scheduling of Educational Activities
Bassen, J., Balaji, B., Schaarschmidt, M., Thille, C., Painter, J., Zimmaro, D. M., Games, A., Fast, E., & Mitchell, J. C. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376518 · Cited by 79 · see citing works
“As a very different form of automation, learnersourcing has been used to manage hard-to-scale instructor tasks such as grading [19], providing feedback [20], giving explanations [40] and hints [10], and even creating new educational materials [25].”
BackgroundEnhancing peer assessment with artificial intelligence
Topping, K. J., Gehringer, E. F., Khosravi, H., Gudipati, S., Jadhav, K., & Susarla, S. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00501-1 · Cited by 68 · see citing works
“Kulkarni et al. (2013) reported on large online classes where peer assessment showed high correspondence with staff assessments.”
Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural NetworksPreprint
Bhatia, S., & Singh, R. K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.06129 · Cited by 69 · see citing works
“These approaches can be categorized into two broad categories – peer-grading [11] and automated grading techniques [18, 13].”
BackgroundInsights about large-scale online peer assessment from an analysis of an astronomy MOOC
Formánek, M., Wenger, M., Buxner, S., Impey, C., & Sonam, T. (2017) · Comput. Educ. · doi:10.1016/j.compedu.2017.05.019 · Cited by 71 · see citing works
“Unlike in the study by Kulkarni et al. (2015) our peer graders turned out to be more harsh graders than instructors.”
ResultWhiting, M. E., Gamage, D., Gaikwad, S. S., Gilbee, A., Goyal, S., Ballav, A., Majeti, D., Chhibber, N., Richmond-Fuller, A., Vargus, F., et al. (2017) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2998181.2998234 · Cited by 66 · see citing works
“Our infrastructure for crowd guilds enables workers to engage in continuous doubleblind peer assessment [32] of a random sample of members’ task submissions on the crowdsourcing platform, rating the quality of the submission and providing critiques for further improvement.”
BackgroundInfluentialImproving the Peer Assessment Experience on MOOC Platforms
Staubitz, T., Petrick, D., Bauer, M., Renz, J., & Meinel, C. (2016) · ACM Conference on Learning @ Scale · doi:10.1145/2876034.2876043 · Cited by 66 · see citing works
“Comparing the survey results of OSAP1 and OSAP2 (in terms of perceived learning impact of assessing peers or one’s own) with Kulkarni’s results [16] (see Figure 5 and 6) shows at least very similar tendencies.”
BackgroundMethodResultInfluentialA framework for designing, implementing, communicating and researching peer assessment
Adachi, C., Tai, J., & Dawson, P. (2017) · Higher Education Research & Development · doi:10.1080/07294360.2017.1405913 · Cited by 65 · see citing works
“Cluster VI: contextual elements (17) Technology use (Kulkarni et al., 2015; Li et al., 2015) What technology facilitates it? What support is required to use technology? Nil – face to face Assignment exchange done online through Learning Management System; manual group creation Simple online tool created”
BackgroundMethodDesigning moocs to facilitate participant self-monitoring for self-directed learning
Zhu, M., & Bonk, C. J. (2019) · Online Learning · doi:10.24059/olj.v23i4.2037 · Cited by 64 · see citing works
“Such Online Learning Journal – Volume 23 Issue 4 – December 2019 5 126 results align with the findings of Kulkarni et al. (2013).”
ResultZedelius, C. M., Mills, C., & Schooler, J. W. (2018) · Behavior Research Methods · doi:10.3758/s13428-018-1137-1 · Cited by 59 · see citing works
Krause, M., Garncarz, T., Song, J., Gerber, E. M., Bailey, B. P., & Dow, S. P. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025883 · Cited by 60 · see citing works
“Luther et al. compares differences between design iterations [33, 53], while others contrast critiques with feedback produced by experts [33, 31].”
BackgroundMethodResultDesign Exposition with Literate Visualization
Wood, J., Kachkaev, A., & Dykes, J. A. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2018.2864836 · Cited by 61 · see citing works
“Primarily it is a form of scaffolding [39] to direct design exposition, analogous to the use of rubrics and ‘fortune cookies’ [16] in pedagogy.”
Uncovering Trajectories of Informal Learning in Large Online Communities of Creators
Yang, S., Domeniconi, C., Revelle, M., Sweeney, M., Gelman, B. U., Beckley, C., & Johri, A. (2015) · Proceedings of the Second (2015) ACM Conference on Learning @ Scale · doi:10.1145/2724660.2724674 · Cited by 60 · see citing works
“investigated ways to scale up peer assessment [12].”
Background"I was afraid, but now I enjoy being a streamer!"
Chen, X., Chen, S., Wang, X., & Huang, Y. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3432936 · Cited by 85 · see citing works
“, MOOCs [49, 105, 106], and online peer learning systems [54, 55].”
A Systematic Review of the Socio-Ethical Aspects of Massive Online Open Courses
Rolfe, V. E. (2015) · European Journal of Open Distance and E-Learning · doi:10.1515/eurodl-2015-0004 · Cited by 59 · see citing works
“Six articles carried out a comparative analysis across two or more groups or interventions (Li et al., 2014; Kop & Carroll, 2012; Stewart, 2010; DeBoer, Stump, Seaton & Breslow, 2013; Kulkarni et al., 2013; Root, 2014).”
BackgroundUniversity of Toronto instructors’ experiences with developing MOOCs
Najafi, H., Rolheiser, C., Harrison, L., et al. (2015) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v16i3.2073 · Cited by 58 · see citing works
Mapping the Factors Influencing Success of Massive Open Online Courses (MOOC) in Higher Education
Albelbisi, N. A., Yusop, F. D., & Salleh, U. K. M. (2018) · Eurasia Journal of Mathematics Science and Technology Education · doi:10.29333/ejmste/91486 · Cited by 58 · see citing works
E-assessment process: giving a voice to online learners
Fontanillas, T. R., Carbonell, M. R., & Catasús, M. G. (2016) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-016-0019-9 · Cited by 56 · see citing works
“In Higher Education (HE), the construction of an integrated European space has been an adaptation challenge for universities (González & Wagenaar, 2003).”
BackgroundInstructors’ Experience of Designing MOOCs in Higher Education: Considerations and Challenges
Zhu, M., Bonk, C., & Sari, A. R. (2018) · Online Learning · doi:10.24059/olj.v22i4.1495 · Cited by 34 · see citing works
“participation (Anders, 2015), peer assessment (Kulkarni et al., 2015; Phan et al., 2016), interaction, and the heavy demands of time and money (Hew & Cheung, 2014; Milligan et al.”
BackgroundGlassman, E. L., Kim, J., Monroy-Hernández, A., et al. (2015) · doi:10.1145/2702123.2702304 · Cited by 56 · see citing works
“This scenario might be particularly relevant for MOOCs, where peer-based interactions sometimes supplant teacher interactions for reasons of scalability [15].”
BackgroundCambre, J., Klemmer, S., & Kulkarni, C. E. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173868 · Cited by 56 · see citing works
Self-citationUsing Worker Self-Assessments for Competence-Based Pre-Selection in Crowdsourcing Microtasks
Gadiraju, U., Fetahu, B., Kawase, R., Siehndel, P., & Dietze, S. (2017) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3119930 · Cited by 54 · see citing works
“Kulkarni et al. showed that in an online course addressing a large number of students (MOOC), the students graded their work 7% higher than those assigned by the staff on average [27].”
BackgroundPeer assessment in MOOCs: Systematic literature review
Gamage, D., Staubitz, T., & Whiting, M. E. (2021) · Distance Education · doi:10.1080/01587919.2021.1911626 · Cited by 59 · see citing works
“We highlight the studio model of human-computer interaction education, which provides an open, shared environment in which students can work (Reimer & Douglas, 2003) and an open feedback process that enhances reflective learning (Kulkarni et al., 2013).”
BackgroundInteractive Guidance Techniques for Improving Creative Feedback
Ngoon, T. J., Fraser, C. A., Weingarten, A. S., Dontcheva, M., & Klemmer, S. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173629 · Cited by 55 · see citing works
“Viewing examples of past designs can lead to greater creativity and insights [21,26]; thus, showing examples of good feedback may spark ideas reviewers would not have otherwise considered [12,22,25].”
BackgroundInfluentialSelf-citationGreenberg, M. D., Easterday, M. W., & Gerber, E. M. (2015) · Creativity and Cognition · doi:10.1145/2757226.2757249 · Cited by 51 · see citing works
“In this work they describe how structured peer training and grading mechanisms can perform at the level of expert graders and achieve high levels of agreement with previously established answers [24].”
BackgroundThe Future of Adaptive Learning: Does the Crowd Hold the Key?
Heffernan, N. T., Ostrow, K., Kim, K. N., Selent, D., Inwegen, E. G. V., Xiong, X., & Williams, J. J. (2016) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-016-0094-z · Cited by 51 · see citing works
“^ Not only might the process of explaining their actions help to solidify their understanding of the content, but the feedback they provide can in turn be connected to the problem for the benefit of future students (Kulkarni et al. 2015).”
BackgroundChiang, C., Kasunic, A., & Savage, S. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274306 · Cited by 57 · see citing works
“Yet most related work has studied peer learning within formal educational settings [17, 52, 78].”
BackgroundHicks, C. M., Pandey, V., Fraser, C. A., & Klemmer, S. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858195 · Cited by 50 · see citing works
“g: learners may tend to inflate assessments for peers from their own country [32].”
BackgroundSelf-citationThe Pandemic Shift to Remote Learning under Resource Constraints
Ravi, P., Ismail, A., & Kumar, N. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3476055 · Cited by 48 · see citing works
Vaish, R., Gaikwad, S. S., Kovacs, G., Veit, A., Krishna, R., Arrieta-Ibarra, I., Simoiu, C., Wilber, M. J., Belongie, S., Goel, S., et al. (2017) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3126594.3126648 · Cited by 48 · see citing works
Rico-Juan, J. R., Gallego, A. J., & Calvo-Zaragoza, J. (2019) · Comput. Educ. · doi:10.1016/j.compedu.2019.103609 · Cited by 48 · see citing works
An Analysis of the use of Good Feedback Practices in Online Learning Courses
Cavalcanti, A. P., de Mello, R. F. L., Rolim, V., André, M., Freitas, F., & Gašević, D. (2019) · International Conference on Advanced Learning Technologies · doi:10.1109/icalt.2019.00061 · Cited by 32 · see citing works
“of providing sufficiently detailed and personalized feedback [12].”
BackgroundDesigning planning and control interfaces to support user collaboration with flying robots
Szafir, D. J., Mutlu, B., & Fong, T. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364916688256 · Cited by 46 · see citing works
“This process was inspired by grading systems for courses (Kulkarni et al., 2015), which may create aggregate scores by assigning weights across a variety of different tasks that may have unknown difficulties.”
MethodStrategyproof peer selection using randomization, partitioning, and apportionment
Aziz, H., Lev, O., Mattei, N., Rosenschein, J. S., & Walsh, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2019.06.004 · Cited by 46 · see citing works
“The peer selection problem is closely related to peer-based grading/marking [1, 28, 32, 47, 51, 58, 60] especially when students are graded based on percentile scores.”
BackgroundStudents’ perceptions of the peer-feedback experience in MOOCs
Kasch, J., van Rosmalen, P., Löhr, A., Klemke, R., Antonaci, A., & Kalz, M. (2021) · Distance Education · doi:10.1080/01587919.2020.1869522 · Cited by 47 · see citing works
Sun, D. L., Harris, N., Walther, G., & Baiocchi, M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0143177 · Cited by 38 · see citing works
“It has already made personalized feedback feasible in a number of settings—most notably MOOCs—where it otherwise would be impossible [9].”
Example-based learning in computer-aided STEM education
Gulwani, S. (2014) · Communications of the ACM · doi:10.1145/2634273 · Cited by 42 · see citing works
“We can leverage large student populations to crowdsource tasks that are difficult to automate [31] as is done in peer grading [21].”
BackgroundStructure and messaging techniques for online peer learning systems that increase stickiness
Kotturi, Y., Kulkarni, C. E., Bernstein, M. S., & Klemmer, S. (2015) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/2724660.2724676 · Cited by 42 · see citing works
“The second platform, PeerStudio (Figure 3), provides fast feedback on in-progress open-ended work, such as essays [23].”
BackgroundSelf-citationWang, X., Talluri, S. T., Rosé, C. P., & Koedinger, K. R. (2019) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3330430.3333614 · Cited by 42 · see citing works
Reviewing and analyzing peer review Inter-Rater Reliability in a MOOC platform
García-Loro, F., Martín, S., Ruipérez‐Valiente, J. A., Sancristobal, E., & Castro, M. A. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.103894 · Cited by 42 · see citing works
Using Truth Detection to Incentivize Workers in Mobile Crowdsourcing
Huang, C., Yu, H., Berry, R., et al. (2020) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2020.3034590 · Cited by 41 · see citing works
Strategyproof Peer Selection: Mechanisms, Analyses, and Experiments
Aziz, H., Lev, O., Mattei, N., et al. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10038 · Cited by 37 · see citing works
“The peer selection problem is also related to peer-based grading/marking (Alfaro and Shavlovsky 2014; Joachims and Raman 2015; Kulkarni et al. 2013; Piech et al. 2013; Robinson 2001; Walsh 2014; Wright, Thornton, and Leyton-Brown 2015) especially when students are graded based on percentile scores.”
BackgroundBernecker, S. L., Williams, J. J., Caporale-Berkowitz, N. A., Wasil, A. R., & Constantino, M. J. (2020) · Journal of Medical Internet Research · doi:10.2196/17164 · Cited by 36 · see citing works
“Feedback took the form of self-assessments because of the challenges of providing nuanced human-delivered or machine-coded feedback at scale [79,80] and the risk associated with an untrained peer providing inaccurate or anxiety-provoking feedback [81].”
Non-native text analysis: A survey
Massung, S., & Zhai, C. (2015) · Natural Language Engineering · doi:10.1017/s1351324915000303 · Cited by 36 · see citing works
Luaces, O., Díez, J., & Bahamonde, A. (2018) · Comput. Educ. · doi:10.1016/j.compedu.2018.07.016 · Cited by 35 · see citing works
“Peer assessment has been explored as an efficient procedure to deal with this problem; see for instance (Kulkarni et al., 2015; Piech et al., 2013; Raman and Joachims, 2014, 2015; Sadler and Good, 2006; Shah et al., 2013; Labutov and Studer, 2016; D´ıez et al., 2013; Luaces et al., 2015a,b, 2017;…”
MethodThe student self-assessment paradigm in MOOC: An example in Chinese higher education
Duan, T.-T., & Wu, B.-H. (2023) · Comunicar · doi:10.3916/c75-2023-09 · Cited by 3 · see citing works
An Exploration of Automated Grading of Complex Assignments
Geigle, C., Zhai, C., & Ferguson, D. C. (2016) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/2876034.2876049 · Cited by 35 · see citing works
“While there are encouraging findings about peer assessment and methods proposed to improve it [14, 24], there are still systematic problems with this approach: discrepancy between peer and instructor ratings, variation in ratings over time by the same peer rater, inconsistency across exercises for…”
BackgroundCritique Me: Exploring How Creators Publicly Request Feedback in an Online Critique Community
Cheng, R., Zeng, Z., Liu, M., & Dow, S. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415232 · Cited by 33 · see citing works
“Many instructors have adopted peer feedback methods [39] but have noted that peers often lack the experience to provide meaningful feedback like experts [23, 31, 56].”
BackgroundRaman, K., & Joachims, T. (2015) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/2724660.2724678 · Cited by 33 · see citing works
Analyzing MOOC discussion forum messages to identify cognitive learning information exchanges
Wong, J. A., Pursel, B., Divinsky, A., & Jansen, B. J. J. (2015) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.2015.145052010023 · Cited by 33 · see citing works
“As MOOCs continue to rise in popularity (Russell & Klemmer 2013), researchers are examining specific aspects of MOOCs beyond simply exploring descriptive data and completion rates, including innovative research facets (Kulkarni et al. 2013; Coetzee, Fox, Hearst, & Hartmann, 2014).”
BackgroundLuaces, O., Díez, J., Alonso-Betanzos, A., Troncoso, A., & Bahamonde, A. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.05.019 · Cited by 35 · see citing works
“Since it is unpractical that an instructor evaluates all the assignments, or even a set of Teaching Assistants (TA), researchers have been exploring the possibilities of using methods previously employed in Journals or Conferences: peer grading or peer assessment [2, 3, 4, 5, 6, 7].”
BackgroundMethodDistributed Apprenticeship in Online Communities
Hui, J. S., Easterday, M. W., & Gerber, E. M. (2018) · Human-Computer Interaction · doi:10.1080/07370024.2018.1469409 · Cited by 33 · see citing works
“…& Ackerman, 2008), expert recommender systems (McDonald & Ackerman, 2000), social search tools (Morris & Horvitz, 2007), massive open online courses (MOOCS) (Brooks, Thompson, & Teasley, 2015; Kulkarni et al., 2013), and social media sites, like Facebook (Lampe, Wohn, Vitak, Ellison, & Wash, 2011).”
BackgroundInfluentialHearst, M. A., Laskowski, P., & Silva, L. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858280 · Cited by 33 · see citing works
“…novice critiques with that of experts; in particular, the use of rubrics has been shown in massive open online courses to bring student ratings within one grade of instructors [18] and novice guideline-based feedback in aggregate can be as acceptable to the receivers as expert feedback [30, 21].”
BackgroundAggregating Partial Rankings with Applications to Peer Grading in Massive Online Open Courses
Caragiannis, I., Krimpas, G. A., & Voudouris, A. A. (2015) · Adaptive Agents and Multi-Agents Systems · doi:10.5555/2772879.2773241 · Cited by 28 · see citing works
Peer Grading in a Course on Algorithms and Data Structures
Sajjadi, M. S. M., Alamgir, M., & von Luxburg, U. (2016) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/2876034.2876036 · Cited by 28 · see citing works
“This problem is bound to come up when using peer grading in a course such as algorithms and data structures and might be much less of an issue when grading is used to evaluate project reports (Raman & Joachims, 2014) or design questions (Kulkarni et al., 2015).”
BackgroundMethodProcko, K., Bell, J. K., Benore, M. A., et al. (2020) · Biochemistry and Molecular Biology Education · doi:10.1002/bmb.21354 · Cited by 27 · see citing works
Curiosity Notebook: A Platform for Learning by Teaching Conversational Agents
Law, E., Baghaei Ravari, P., Chhibber, N., Kulic, D., Lin, S., Pantasdo, K. D., Ceha, J., Suh, S., & Dillen, N. (2020) · Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3334480.3382783 · Cited by 28 · see citing works
“, explaining [23], questioning [9], assessment and feedback [15]—require reflective knowledge building, where students synthesize, structure and reflect [19].”
BackgroundYen, Y.-C., Kim, J. O., & Bailey, B. P. (2020) · International Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376380 · Cited by 36 · see citing works
Incorporating Training, Self-monitoring and AI-Assistance to Improve Peer Feedback Quality
Darvishi, A., Khosravi, H., Abdi, S., Sadiq, S., & Gašević, D. (2022) · Proceedings of the Ninth ACM Conference on Learning @ Scale · doi:10.1145/3491140.3528265 · Cited by 31 · see citing works
“Kulkarni et al. [47] provided training for students during the peer review process by offering them feedback about their bias, standard adaptable feedback texts, and indicating crucial items.”
BackgroundDomínguez, C., Jaime, A., Sánchez, A., Blanco, J. M., & Heras, J. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.02.061 · Cited by 28 · see citing works
Vorobyeva, A. A. (2018) · XLinguae · doi:10.18355/xl.2018.11.02.11 · Cited by 26 · see citing works
Peer Feedback in Learner‑Learner Interaction Practices. Mixed Methods Study on an xMOOC
Elizondo-García, J., & Córdova, K. E. G. (2020) · The Electronic Journal of e-Learning · doi:10.34190/ejel.20.18.2.002 · Cited by 25 · see citing works
iMOOC on Climate Change: Evaluation of a Massive Open Online Learning Pilot Experience
Rocio, V. J. R., Coelho, J., Caeiro, S., et al. (2015) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v16i6.2160 · Cited by 25 · see citing works
“Also iMOOC participants frequented a five-day “boot camp” module prior to the course for familiarization with the online environment and for initiating social bonding, following the best practices specified on Pereira et al. (2008). Furthermore, on the pilot iMOOC, the use of introductory videos in each topic and a we…”
Crowdsourcing programming assignments with CrowdSorcerer
Pirttinen, N., Kangas, V., Nikkarinen, I., et al. (2018) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3197091.3197117 · Cited by 25 · see citing works
“Course participants in various institutions and MOOCs evaluate assignment responses that the participants themselves have submitted as a part of their coursework – research suggests that peers can give actionable and accurate feedback [13].”
BackgroundFrom in the Class or in the Wild?
Wauck, H. C., Yen, Y.-C. G., Fu, W., Gerber, E. M., Dow, S. P., & Bailey, B. P. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025477 · Cited by 24 · see citing works
“Researchers continue to improve the quality of peer review by studying interface features of the review platforms [15], rubrics [23], and characteristics of the review content [31].”
BackgroundInfluentialLuaces, O., Díez, J., Alonso-Betanzos, A., Troncoso, A., & Bahamonde, A. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.06.024 · Cited by 21 · see citing works
A peer assessment approach to project based blended learning course in a Vietnamese higher education
Nguyen, V. A. (2016) · Education and Information Technologies · doi:10.1007/s10639-016-9539-0 · Cited by 26 · see citing works
“Although the implementation of evaluation process faced many obstacles especially in classes with a big number of students (Kulkarni et al. 2013; Suen 2014).”
BackgroundPandey, V., Amir, A., Debelius, J. W., Hyde, E. R., Kościółek, T., Knight, R. D., & Klemmer, S. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025769 · Cited by 23 · see citing works
“Similarly, global-scale distributed contributions like peer review have enabled massive online courses to offer creative, openended assignments through peer review [41].”
BackgroundSelf-citationEFFECTIVE BLENDED LEARNING IN HIGHER EDUCATION DURING COVID-19
N.A., J. P. S., Kalaichelvi, R., Elumalai, K. V., & Alqahtani, M. S. M. (2022) · Information Technologies and Learning Tools · doi:10.33407/itlt.v88i2.4438 · Cited by 23 · see citing works
“Further, the data-driven approach of peer interaction will increase blended learning [6].”
BackgroundInfluentialOn the validity of peer grading and a cloud teaching assistant system
Vogelsang, T., & Ruppertz, L. (2015) · International Conference on Learning Analytics and Knowledge · doi:10.1145/2723576.2723633 · Cited by 31 · see citing works
“CPRwasused for two consecutive Coursera offerings of the course Human Computer Interaction, taught by Scott Klem-mer[13] and analyzed afterwards[20] with statistical models close to the Bayesian approach used by Goldin et. al in smaller classrooms[1, 10].”
BackgroundMethodYousef, A. M. F., Wahid, U., Chatti, M. A., Schroeder, U., & Wosnitza, M. (2015) · International Conference on Computer Supported Education · doi:10.5220/0005495501480159 · Cited by 23 · see citing works
“This study included the design of a questionnaire adapted from (Brindley & Scoffield, 1998; Wolf & Stevens, 2007; Kulkarni et al., 2013).”
BackgroundMethod
- Authors
- Kulkarni, C., Wei, K. P., Le, H., Chia, D., Papadopoulos, K., Cheng, J., Koller, D., & Klemmer, S. R.
- Affiliations
- Stanford University (US)
- Stanford University, and Coursera, Inc., Stanford, CA
- Coursera, Inc., Mountain View, CA
- Venue
- ACM Transactions on Computer-Human Interaction
- Published
- 2013-12
- Volume
- 20
- Issue
- 6
- Pages
- 1-31
- ISSN
- 1073-0516 (ISSN-L)
- 1557-7325
- OpenAlex
- W2088700588
- Cited by
- 390
- References
- 97
- Citations by year
- 2026: 7 · 2025: 15 · 2024: 27 · 2023: 21 · 2022: 22 · 2021: 30 · 2020: 34 · 2019: 27 · 2018: 46 · 2017: 37 · 2016: 43 · 2015: 56 · 2014: 24 · 2013: 1
- FWCI
- 199.15
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Transactions on Computer-Human Interaction (publishedVersion)
- Funders
- Division of Information and Intelligent Systems
- Hasso Plattner Design Thinking Program
- Topics
- Social Sciences › Education › Student Assessment and Feedback
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Online and Blended Learning
- Keywords
- peer assessment, self-assessment, online learning, grading accuracy, rubric development, peer feedback, student self-assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.
Cited sources are checked by gpt-6-sol.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review