Exploring the Impact of Worked Examples in a Novice Programming Environment
Open accessAbstract
Research in a variety of domains has shown that viewing worked examples (WEs) can be a more efficient way to learn than solving equivalent problems. We designed a Peer Code Helper system to display WEs, along with scaffolded self-explanation prompts, in a block-based, novice programming environment called \snap. We evaluated our system during a high school summer camp with 22 students. Participants completed three programming problems with access to WEs on either the first or second problem. We found that WEs did not significantly impact students' learning, but may have impacted students' intrinsic cognitive load, suggesting that our WEs with scaffolded prompts may be an inherently different learning task. Our results show that WEs saved students time on initial tasks compared to writing code, but some of the time saved was lost in subsequent programming tasks. Overall, students with WEs completed more tasks within a fixed time period, but not significantly more. WEs may improve students' learning efficiency when programming, but these effects are nuanced and merit further study.
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Written from the paper’s full text.
TL;DR
This study evaluated a Peer Code Helper that presented incomplete worked examples with scaffolded self-explanation prompts in Snap! during a high school summer camp. Worked examples did not significantly improve learning but may have affected intrinsic cognitive load and saved time initially, with some time lost on later objectives.
Key findings
- Learning: no significant group difference or group×time interaction on pre/post tests (F(1,14)=0.20, p=.66; F(2,28)=0.13, p=.88); test scores improved over time (F(2,28)=5.26, p<.05, partial η²=.27), with significant pre-test to post-test2 and post-test1 to post-test2 improvements.
- Cognitive load: significant group×problem interaction for intrinsic load (F(2,28)=4.65, p<.05, partial η²=.25); E1 intrinsic load was lower on problem 1 than problem 2 (t(7)=-3.51, p<.01, d=0.83), and E2 intrinsic load was lower on problem 2 than problem 3 (t(7)=-4.52, p<.01, d=1.04); problem 2 group difference was significant (t(13.19)=-2.33, p<.05, d=-1.16). No significant extraneous or germane load results were found.
- Efficiency: WE groups completed WE objectives much faster: Daisy median 3.5 vs 22.5 minutes (W=2, p=.001); Spiral median 2.5 vs 30.4 minutes (W=54, p<.001), saving 19–24 minutes on 45-minute problems. Overall objectives were not significantly different: Daisy median 6 (67%) vs 5 (56%) (W=56, p=.382); Spiral median 2 (40%) vs 1 (20%) (W=31.5, p=.270).
- Post-survey: 35% said yes to future PCH use and 12% said no; the 2 students who said “no” had pre-test scores over 75%.
Methods
- Design: matched-pairs, switching replication during the first 2 days of a 5-day high school computing summer camp; 22 female students in grades 8–9 (ages 13–15), with 5 excluded leaving 17, and assessment analyses using 8 students per group after one additional exclusion.
- Materials: PCH in Snap! presented the first few steps as incomplete WEs with before/after code, a stencil, runnable but non-editable examples, scaffolded self-explanation prompts, and then provided commented example code as starter code. Three drawing problems were used: Daisy Design, Spiral Polygon, and Brick Wall, with 9, 5, and 5 objectives.
- Measures and analysis: pre-test and isomorphic post-tests adapted from the Commutative Assessments (16 multiple-choice questions), the CS CLCS cognitive load survey, logged interactions and code snapshots, and graded objectives with timestamps; analyses included mixed ANOVAs, t-tests, and Mann-Whitney U tests.
Limitations
- Stated: this was an exploratory study with a small sample size; all programming problems involved drawing patterns, so the WE design may not generalize to problems without visual output; self-selected summer campers may be more motivated to learn but may have taken assessments less seriously; learning gains may also have come from repeated exposure to tests.
- Evident: after exclusions, only 8 students per group remained for assessment and cognitive load analyses, limiting power; most students did not complete whole problems within the allotted 45 minutes, so efficiency was analyzed at the objective level; some students lost work due to Snap! errors and were removed from analyses.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
2 of 34 cited sources checked · 2 supported · 0 flagged
- Premisecitation
Ichinco et al. showed that novices struggle to use worked examples to solve block-based programming problems.
“In novice programming environment, Ichinco et al. showed that novices struggle to use WEs to solve block-based programming problems [6].”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [6] source
- Findingnone given
Worked examples may have impacted students' intrinsic cognitive load.
“but may have impacted students' intrinsic cognitive load”
- Also stated in: § 1 INTRODUCTION AND BACKGROUND · hedged
“though they may have impacted students' intrinsic cognitive load”
Quote in the paperSubstantial claim - Also stated in: § 1 INTRODUCTION AND BACKGROUND · hedged
- Findingown data
Access to worked examples did not significantly impact students' learning.
“We found that WEs did not significantly impact students' learning”
- Also stated in: § 1 INTRODUCTION AND BACKGROUND · plain
“We found that access to WEs did not significantly impact students' learning”
Quote in the paperSubstantial claim- Rests on an assumption: The pre-test and post-tests are sensitive enough to detect changes in students' learning.
- Also stated in: § 1 INTRODUCTION AND BACKGROUND · plain
- Findingown data
Worked examples saved students time on initial tasks compared to writing code.
“Our results show that WEs saved students time on initial tasks compared to writing code”
- Also stated in: § 1 INTRODUCTION AND BACKGROUND · hedged
“Our results suggest that WEs allowed students to work more efficiently on the first few problem objectives”
Quote in the paperSubstantial claim - Also stated in: § 1 INTRODUCTION AND BACKGROUND · hedged
- Findingown data
Some of the time saved by worked examples on initial tasks was lost in subsequent programming tasks.
“but some of the time saved was lost in subsequent programming tasks.”
- Also stated in: § 1 INTRODUCTION AND BACKGROUND · plain
“but they took longer to complete subsequent objectives that were not supported with WEs.”
Quote in the paperSubstantial claim - Also stated in: § 1 INTRODUCTION AND BACKGROUND · plain
- Findingown data
On Daisy Design, the non-WE group (E2) took a median 22.5 minutes to accomplish the same WE objectives by writing code.
“It took the non-WE group (E2) a median 22.5 minutes to accomplish the same WE objectives by writing code, with 20% of students never finishing the WE objectives in the 45 minutes given.”
Quote in the paperSubstantial claim - Findingown data
Both groups experienced higher intrinsic load on problems without WEs that followed problems with WEs.
“We also found both groups experienced higher intrinsic load on problems without WEs that followed problems with WEs.”
Quote in the paperSubstantial claim - Findingown data
There was a main effect of test time (F2,28=5.26, p<.05, partial η2=.27), indicating students' test scores changed significantly from pre-test to the two post-tests.
“There was a main effect of test time (F 2, 28 = 5.26, p < .05,partial η 2 = .27),indicating students' test scores changed significantly from pre-test to the two post-tests as shown in table 2.”
Quote in the paperSubstantial claim- Rests on an assumption: The pre-test and post-tests are sensitive enough to detect changes in students' learning.
- Findingown data
On Daisy Design, the difference in time to complete the WE objectives between WE and non-WE groups is significant (W = 2; p = 0.001), even ignoring students who did not finish the objectives.
“A Mann-Whitney U Test shows that, even ignoring students who did not finish the objectives, this difference in time is significant (W = 2; p = 0.001).”
Quote in the paperSubstantial claim - Findingown data
There was no statistically significant difference on any of the test score measurements for each type of problem (code tracing, code comprehension, code completion, and WE).
“We also analyzed the test score for each type of problem (code tracing, code comprehension, code completion, and WE) but found no statistically significant difference on any of the measurements above.”
Quote in the paperSubstantial claim- Rests on an assumption: The pre-test and post-tests are sensitive enough to detect changes in students' learning.
- Findingown data
Most students did not complete the whole programming problem within the allotted 45 minutes.
“However, because most students did not complete the whole problem within the allotted 45 minutes, we analyze completion time for smaller objectives within each problem.”
Quote in the paperSubstantial claim - Findingown data
On Spiral Polygon, the WE group (E2) took a median 2.5 minutes on the WE portion of the problem.
“Similarly, on Spiral Polygon, the WE group (E2) took a median 2.5 minutes on the WE portion of the problem, and the non-WE group (E1) took 30.4minutes, with 40% of students failing to achieve the objectives on 45 minutes.”
Quote in the paperSubstantial claim - Findingown data
Participants who were uncertain about the PCH indicated that they preferred the challenge of working independently.
“The participants who were uncertain about the PCH indicated that they preferred the challenge of working independently”
Quote in the paperNot a substantial claim - Findingnone given
Most students in both groups completed the core objectives.
“Since most students in both groups completed the core objectives, it is not surprising that there was no significant difference in their learning.”
Quote in the paperSubstantial claim - Findingown data
For most objectives, enough students finished that the median or first-quartile time for each group could still be calculated.
“However, for most objectives, enough students finished that we can still calculate the median time for each group (the time at which 50% of students finished and objective) or the first-quartile (Q1) time (when 25% of students finished).”
Quote in the paperSubstantial claim - Findingown data
The difference in intrinsic load between E1 and E2 is not statistically significant for problem 1 (t(13.91)=0.40, p=.69, d=0.20).
“Comparing the two groups, we find that the difference in intrinsic load between E1 and E2 is not statistically significant for problem 1 (t (13.91) = 0.40, p = .69,d = 0.20).”
Quote in the paperNot a substantial claim - Findingown data
On Spiral Polygon, 40% of non-WE students failed to achieve the WE objectives within 45 minutes.
“Similarly, on Spiral Polygon, the WE group (E2) took a median 2.5 minutes on the WE portion of the problem, and the non-WE group (E1) took 30.4minutes, with 40% of students failing to achieve the objectives on 45 minutes.”
Quote in the paperSubstantial claim - Findingown data
On Spiral Polygon, the non-WE group (E1) took a median 30.4 minutes on the WE portion of the problem.
“Similarly, on Spiral Polygon, the WE group (E2) took a median 2.5 minutes on the WE portion of the problem, and the non-WE group (E1) took 30.4minutes, with 40% of students failing to achieve the objectives on 45 minutes.”
Quote in the paperSubstantial claim - Findingown data
On Spiral Polygon, the difference in time to complete the WE objectives between WE and non-WE groups is significant (W = 54; p < 0.001).
“This difference in time is also significant (W = 54; p < 0.001).”
Quote in the paperSubstantial claim - Findingown data
We observed differences in intrinsic load rather than extraneous load.
“It is interesting that we observed differences in intrinsic load, which is assumed to be inherent in the learning task, rather than extraneous load, which is assumed to be the result of our instructional design.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Intrinsic cognitive load is inherent in the learning task.
- The authors state this as an assumption: Extraneous cognitive load is the result of the instructional design.
- Findingown data
There was no main effect of group, indicating no statistically significant difference in mean test score between the two groups (F1,14=0.20, p=.66, partial η2=.014).
“There was no main effect of group, indicating that there was not a statistically significant difference in mean test score between the two groups (F 1, 14 = 0.20, p = .66,partial η 2 = .014).”
Quote in the paperSubstantial claim- Rests on an assumption: The pre-test and post-tests are sensitive enough to detect changes in students' learning.
- Findingown data
On Daisy Design, 20% of non-WE students never finished the WE objectives in the 45 minutes given.
“It took the non-WE group (E2) a median 22.5 minutes to accomplish the same WE objectives by writing code, with 20% of students never finishing the WE objectives in the 45 minutes given.”
Quote in the paperNot a substantial claim - Findingreasoning
There was no difference in learning between groups on the WE problems.
“There was no difference in learning between groups on the WE problems.”
Quote in the paperSubstantial claim - Findingown data
The difference in intrinsic load between E1 and E2 is significant for problem 2, with a large effect size (t(13.19)=-2.33, p<.05, d=-1.16).
“However, it is significant for problem 2, with a large effect size (t (13.19) = -2.33,p < .05,d = -1.16).”
Quote in the paperSubstantial claim - Findingreasoning
We did not find significant differences in learning based on post-test scores.
“We did not find significant differences in learning based on post-test scores.”
Quote in the paperSubstantial claim - Findingown data
When asked if they would like to use the PCH on a future problem, 35% of students said yes and 12% said no, with the rest uncertain.
“When asked if they would like to use the PCH on a future problem, 35% of students said yes and 12% said no, with the rest uncertain.”
Quote in the paperNot a substantial claim - Findingown data
There was no significant difference in pretest scores between groups (t(13.96)=-0.4, p=.64, d=.24).
“We compared the pretest scores between groups and found no significant difference (t (13.96) = -0.4,p = .64,d = .24).”
Quote in the paperSubstantial claim - Findingown data
On Daisy Design, the WE group (E1) took a median 3.5 minutes to complete the WE portion of the problem.
“On Daisy Design, it took the WE group (E1) a median 3.5 minutes to complete the WE portion of the problem.”
Quote in the paperSubstantial claim - Findingown data
Looking past the WE objectives, the differences between the two groups close over time.
“However, looking past the WE objectives in Figure 2, we see that the differences between the two groups closes over time.”
Quote in the paperNot a substantial claim - Findingown data
The WE group finished the WE content 6-12 times faster than the non-WE group.
“It is not surprising that the WE group, who only had to read code and answer questions, finished this content much faster (6-12 times faster), but the magnitude of the difference is worth noting.”
Quote in the paperSubstantial claim - Premisecitation
Worked examples are an effective instructional strategy to help novices learn complex tasks.
“It is an effective instructional strategy to help novices learn complex tasks and has been studied for decades [18].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [18]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Cognitive Load Theory suggests that students' learning can be impaired if instructional materials require students to hold excessive amounts of information in working memory.
“and it suggests that students' learning can be impaired if instructional materials require students to hold excessive amounts of information in working memory.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [21]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Few studies have specifically compared WEs to traditional problem solving, especially in novice programming environments.
“Though a number of studies have explored the impact of WEs in the domain of programming [2,3,12,14,[22][23][24], few of these studies have specifically compared WEs to traditional problem solving [22], especially in novice programming environments [23].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [22]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites [23] source
- Premisecitation
Intrinsic load and extraneous load are additive.
“Since the intrinsic load and extraneous load are additive [19]”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [19]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Cognitive Load Theory posits that humans have limited working memory.
“CLT posits that humans have limited working memory”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [21]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Our prior work with worked examples in a novice programming game showed that when students are given only worked examples, they may skip through them quickly without learning.
“Our prior work with WEs in a novice programming game showed that when students are given only WEs, they may skip through them quickly without learning [26].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [26] source
- Interpretationown data
Worked examples with scaffolded prompts may be an inherently different learning task.
“suggesting that our WEs with scaffolded prompts may be an inherently different learning task.”
Quote in the paperSubstantial claim - Premisecitation
Combining self-explanation with worked examples can be especially beneficial to students' learning.
“It has been shown that combining self-explanation with WEs can be especially beneficial to students' learning [1].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [1] source
- Premisecitation
Merriënboer and Croock found that incomplete worked examples improved novice's programming performance and post-test scores, compared with those who only had the worked examples as a reference.
“Merriënboer and Croock found that incomplete WEs, in which students are given part of a WE and asked to complete the rest, improved novice's programming performance and post-test scores, compared with those who only had the WEs as a reference [23].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [23] source
- Interpretationown data
Worked examples may improve students' learning efficiency when programming.
“WEs may improve students' learning efficiency when programming, but these effects are nuanced and merit further study.”
Quote in the paperSubstantial claim - Interpretationown data
This may indicate that our design of WEs increased students' perceived difficulty of problem solving immediately following WEs.
“This may indicate that our design of WEs increased students' perceived difficulty of problem solving immediate following WEs.”
Quote in the paperNot a substantial claim - Interpretationown data
Based on these results, WEs saved students 19-24 minutes on a 45 minute problem.
“Based on these results, we can say the WEs saved students lots of time (19-24 minutes on a 45 minute problem), and allowed all students to finish the WE objectives (compared to only 80% and 60% for non-WE students).”
Quote in the paperSubstantial claim - Premisecitation
Berthold found that scaffolded self-explanation prompts following worked examples were more beneficial to students in terms of conceptual and procedural understanding than open-ended self-explanation prompts in multimedia environments.
“Berthold found that scaffolded self-explanation prompts following WEs were more beneficial to students in terms of conceptual and procedural understanding than open-ended self-explanation prompts in multimedia environments [1].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [1] source
- Interpretationown data
Both groups experienced lower intrinsic cognitive load on problems with worked examples than on the following, more difficult problem without worked examples.
“This suggests that both groups experienced lower intrinsic CL on problems with WEs than on the following, more difficult problem without WEs.”
Quote in the paperSubstantial claim - Interpretationown data
Most of students' learning occurred during the second problem.
“This suggests that most of students' learning occurred during the second problem.”
Quote in the paperNot a substantial claim - Interpretationown data
The difference in efficiency between WE and non-WE groups decreases over time.
“However, it also seems that this difference decreases over time.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Students who completed the WE objectives the slowest would have completed additional objectives no quicker than the other students in their group.
- Interpretationreasoning
Results suggest that most of students' learning occurred during problem 2.
“However, results suggest that most of students' learning occurred during problem 2.”
Quote in the paperNot a substantial claim
- Classified as probably not a substantial claim. 32
- May combine two claims. 45
- The quote only partly states this claim. 1
Classified as probably not a substantial claim. May combine two claims.
PremisecitationNovices have difficulty in understanding the examples and finding relevant blocks to finish the programming task.
“For example, they found that novices have difficulty in understanding the examples and finding relevant blocks to finish the programming task.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [6] source
May combine two claims.
Findingown dataStudents with worked examples completed more tasks within a fixed time period, but not significantly more.
“Overall, students with WEs completed more tasks within a fixed time period, but not significantly more.”
- Also stated in: § 1 INTRODUCTION AND BACKGROUND · plain
“Students with WEs on a problem completed more objectives on that problem, but this difference was not significant.”
Quote in the paperSubstantial claim- Also stated in: § 1 INTRODUCTION AND BACKGROUND · plain
May combine two claims.
Findingown dataFor Daisy Design, the median student in the WE group (E1) took 9.5 minutes to complete their next objective, while the median student in the non-WE group (E2) took 0 minutes.
“For Daisy Design, we see that the median student in the WE-group (E1) took 9.5 minutes to complete their next objective, while the median student in the non-WE group (E2) took 0 minutes (they accomplished the next objective at the same time as the WE objectives).”
Quote in the paperSubstantial claim- The authors state this as an assumption: Students who completed the WE objectives the slowest would have completed additional objectives no quicker than the other students in their group.
May combine two claims.
Findingown dataOn Spiral Polygon, it took the WE group much longer to accomplish one additional objective (Q1 = 12.0m) compared to the non-WE group (Q1 = 2.6m), but this gap quickly reduced for the second objective and reversed by the third.
“Similarly, it takes the WE group much longer to accomplish one additional objective (Q1 = 12.0m) compared to the non-WE group (Q1 = 2.6m), but this gap quickly reduces for the second objective, and even reverses by the third (where not even 25% of non-WE of students had completed the third objective by 39 minutes).”
Quote in the paperSubstantial claim- The authors state this as an assumption: Students who completed the WE objectives the slowest would have completed additional objectives no quicker than the other students in their group.
May combine two claims.
Findingown dataThere were no significant main or interaction effects on the number of help requests (F2,28=2.15, p=.14, partial η2=.13), indicating worked examples likely did not impact the number of help requests students made, nor did the problem, despite increasing levels of problem difficulty.
“We found no significant main or interaction effects (F 2, 28 = 2.15, p = .14,partial η 2 = .13),indicating that WEs likely did not impact the number of help requests students made, and nor did the problem, despite increasing levels of problem difficulty.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThere was no statistically significant interaction between group and test time (F2,28=0.13, p=.88, partial η2=.009), indicating students in both groups had similar learning across the study.
“Additionally, there was no statistically significant interaction between group and test time (F 2, 28 = 0.13, p = .88,partial η 2 = .009),indicating that students in both groups had similar learning across the study.”
Quote in the paperSubstantial claim- Rests on an assumption: The pre-test and post-tests are sensitive enough to detect changes in students' learning.
The quote only partly states this claim.
Findingown dataThe next two objectives also took the WE group longer on Daisy Design.
“The next two objectives also take the WE group longer.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Students who completed the WE objectives the slowest would have completed additional objectives no quicker than the other students in their group.
May combine two claims.
Findingown dataWe found significant differences between the groups' intrinsic cognitive load for problem 2 but not for problem 1.
“We found significant differences between the groups' intrinsic cognitive load for problem 2 but not for problem 1.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThere were statistically significant improvements for all students from pre-test to post-test2 (t(15)=3.05, p<.01, d=.30) and post-test1 to post-test2 (t(15)=2.49, p<.05, d=.24), but not from pre-test to post-test1 (t(15)=-0.86, p=.40, d=.08).
“We found statistically significant improvements for all students from both pre-test to post-test2 (t (15) = 3.05, p < .01,d = .30)and post-test1 to post-test2 (t (15) = 2.49, p < .05,d = .24),but not from pre-test to post-test1 (t (15) = -0.86,p = .40,d = .08).”
Quote in the paperSubstantial claim- Rests on an assumption: The pre-test and post-tests are sensitive enough to detect changes in students' learning.
May combine two claims.
Findingown dataThe difference in time to complete additional objectives never exceeded 15 minutes on Daisy Design and 10 minutes on Spiral Polygon, which is still far less time than the WEs saved the median student on each problem (19 and 24 minutes, respectively).
“In addition, the difference never exceeded 15 minutes on Daisy Design, and 10 minutes on Spiral Polygon, which is still far less time than the WEs saved the median student on each problem (19 and 24 minutes, respectively).”
Quote in the paperSubstantial claim- The authors state this as an assumption: Students who completed the WE objectives the slowest would have completed additional objectives no quicker than the other students in their group.
May combine two claims.
Findingnone givenSome students finished the WE objectives slowly, or not at all, and ran out of time before they could complete additional objectives.
“Some students finished the WE objectives slowly, or not at all, and ran out of time before they could complete additional objectives.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataFor Spiral Polygon, no additional objectives were completed by more than half of students, so only the Q1 time could be computed.
“For Spiral Polygon, no additional objectives were completed by more than half of students, so we can only compute the Q1 time.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Students who completed the WE objectives the slowest would have completed additional objectives no quicker than the other students in their group.
May combine two claims.
Findingown dataThe differences between groups in objectives completed are most apparent at the beginning of the curves, when students are completing the WE objectives, though the same trend holds even if the WE objectives are not included.
“These differences between groups is most apparent at the beginning of the curves, when students are completing the WE objectives, though the same trends holds, even if we do not include the WE objectives.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThere was no significant result for extraneous load or germane load.
“We did not find any significant result for extraneous load or germane load.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataOn both Daisy Design and Spiral Polygon, the group with WEs (E1 and E2, respectively) had more objectives completed at all times throughout the 45 minute work period, while on Brick Wall (with no WEs) there is little difference.
“On both Daisy Design and Spiral Polygon, the group with WEs (E1 and E2, respectively), had more objectives completed at all times throughout the 45 minute work period, while on Brick Wall (with no WEs) there is little difference.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataOn Daisy Design, the WE group (E1) ended with a median 6 objectives complete (67%), compared to 5 objectives (56%) for the non-WE group (E2), and the difference is not significant (W = 56.0; p = 0.382).
“On Daisy Design, the WE group (E1) end with a median 6 objectives complete (67%), compared to 5 objectives (56%) for the non-WE group (E2), and the difference is not significant (W = 56.0;p = 0.382).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataOn Spiral Polygon, the WE group (E2) ended with a median 2 objectives complete (40%), compared to 1 objective (20%) for the non-WE group (E1), and the difference is not significant (W = 31.5; p = 0.270).
“On Spiral Polygon, the WE group (E2) ends with a median 2 objectives complete (40%), compared to 1 objective (20%) for the non-WE group (E1), and the difference is not significant (W = 31.5;p = 0.270).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataE2 students' intrinsic load on problem 2 was significantly lower than that on problem 3 (t(7)=-4.52, p<.01, d=1.04), but no such results were found for E1.
“Similarly, we found E2 students' intrinsic load on problem 2 was significantly lower than that on problem 3 (t (7) = -4.52,p < .01,d = 1.04), but no such results were found for E1.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataFor intrinsic load, there was no significant main effect of group (F1,14=0.10, p=.76, partial η2=.007) or problem (F2,28=1.78, p=.19, partial η2=.11), but there was a statistically significant interaction between group and problem on intrinsic load scores (F2,28=4.65, p<.05, partial η2=.25).
“For the intrinsic load, there was not a significant main effect of group (F 1, 14 = 0.10, p = .76,partial η 2 = .007)or problem (F 2, 28 = 1.78, p = .19,partial η 2 = .11)factors, but there was a statistically significant interaction between group and problem on the intrinsic load scores (F 2, 28 = 4.65, p < .05,partial η 2 = .25).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataE1 students' intrinsic load on problem 1 was significantly lower than on problem 2 (t(7)=-3.51, p<.01, d=0.83), but no such results were found for E2.
“We investigated this further with post hoc t-tests and found that E1 students' intrinsic load on problem 1 was significantly lower than on problem 2 (t (7) = -3.51,p < .01,d = 0.83), but no such results were found for E2.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPatitsas et al. found that students in the comparing and contrasting condition showed greater learning gains for procedural knowledge and flexibility than students in the worked example condition.
“They found that students in the comparing and contrasting condition showed greater learning gains for procedural knowledge and flexibility than students in the WE condition [13].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [13] source
Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.
PremisereasoningExtraneous load should be minimized to maximize germane load and thus learning.
“extraneous load should be minimized to maximize germane load and thus learning.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationEmpirical studies have shown that worked examples are more beneficial to novice learners compared with traditional problem solving in learning algebra.
“Empirical studies have shown that WEs are more beneficial to novice learners compared with traditional problem solving in learning algebra [20].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [20]
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). May combine two claims.
PremisecitationLiu et al.'s work shows that worked examples are beneficial to undergraduate students in the first few problems they solve but less beneficial for later problems in the students' homework set.
“In an intelligent tutoring system for teaching and practicing propositional logic problems in discrete math, Liu et al. 's [9] work shows that WEs are beneficial to undergraduate students in the first few problems they solve but less beneficial for later problems in the students' homework set.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [9] source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationSweller and Cooper found that students who were presented with worked example problems solved similar problems quicker and with fewer errors than students who solved the same problems themselves.
“Sweller and Cooper found that students who were presented with WE problems solved similar problems quicker and with fewer errors than students who solved the same problems themselves [20].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [20]
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). May combine two claims.
PremisecitationTrafton and Reiser's study suggests interleaving worked examples with similar practice problems to maximize students learning gains, compared to blocking worked examples with problems, or solving equivalent problems by writing LISP programs.
“Trafton and Reiser's study suggests interleaving WEs with similar practice problems to maximize students learning gains, compared to blocking WEs with problems, or solving equivalent problems by writing LISP programs [22].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [22]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Classified as probably not a substantial claim.
PremisecitationCognitive Load Theory describes the relationship between mental effort and learning.
“Our work is based on Cognitive Load Theory (CLT), which describes the relationship between mental effort and learning [21].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [21]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationAlong with worked examples, self-explanation prompts help students foster greater understanding and improve students' problem solving performance.
“A review of self-explanation shows that along with WEs, self-explanation prompts help students foster greater understanding [16] and improves students' problem solving performance [12].”
Quote in the paperSubstantial claim0 of 2 sources checkedProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPirolli and Anderson found that three novices relied heavily on examples while writing new programs.
“Pirolli and Anderson investigated how three novices learned recursive functions and found that they relied heavily on examples while writing new programs [14].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [14]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationWorked examples (WEs) have been studied in a variety of domains, and results consistently show that replacing some or all practice problems with WEs results in higher learning efficiency, where students take less time to learn the same amount of content.
“WEs have been studied in a variety of domains [9,10,20], and the results consistently show that replacing some or all practice problems with WEs results in higher learning efficiency, where students take less time to learn the same amount of content.”
Quote in the paperSubstantial claim0 of 3 sources checkedMay combine two claims.
PremisecitationMcLaren et al. found that students in the worked example condition performed similarly in the post-test but were more efficient in finishing intervention problems and with less cognitive load, compared to students in other conditions including problem solving, erroneous worked examples, and tutored problem solving.
“They found that students in the WE condition performed similarly in the post-test but were more efficient in finishing intervention problems and with less cognitive load, compared to students in other conditions including problem solving, erroneous worked examples, and tutored problem solving [10].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [10] source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationEricson et al. found that interleaving worked examples with Parsons problems can improve programming learning efficiency compared to worked examples paired with writing code or fixing code.
“Recently, Ericson et al. found that interleaving WEs with Parsons problems can improve programming learning efficiency compared to WEs paired with writing code or fixing code [3].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [3] source
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationOne way WEs may increase learning efficiency is by reducing students' cognitive load, or mental effort when learning.
“One way WEs may increase learning efficiency is by reducing students' cognitive load, or mental effort when learning [18].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [18]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
PremisecitationOnly Trafton and Reiser directly evaluate the effectiveness of worked examples by comparing them to equivalent problem solving tasks, but their results may not apply to novice programming environment.
“However, to our knowledge, only Trafton and Reiser directly evaluate the effectiveness of WEs by comparing them to equivalent problem solving tasks, but their results may not apply to novice programming environment.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [22]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Classified as probably not a substantial claim. May combine two claims.
Interpretationown dataWEs may have an effect on students' intrinsic cognitive load, but further research is needed on how and why they have this effect.
“We found that WEs may have an effect on students' intrinsic cognitive load, but further research is needed how and why they have this effect.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Intrinsic cognitive load is inherent in the learning task.
- The authors state this as an assumption: Extraneous cognitive load is the result of the instructional design.
Classified as probably not a substantial claim.
Study design and conductcitationThe pre-test, and isomorphic post-test1 and post-test2, were adapted from the block-based version of the Commutative Assessments, with 16 multiple-choice questions on variables, loops, procedures, and conditionals.
“The pre-test, and isomorphic post-test1 and post-test2, were adapted from the block-based version of the Commutative Assessments [25], with 16 multiple-choice questions on variables, loops, procedures, and conditionals.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [25] source
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataSome students were removed from the analysis because they lost their work due to errors in Snap!, leaving 9, 10, and 10 students in E1 and 10, 9, and 10 students in E2 on the three problems, respectively.
“On each problem we removed some students from our analysis who lost their work due to errors in Snap!, leaving 9, 10, and 10 students in E1 and 10, 9, and 10 students in E2 on the three problems, respectively.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.
Study design and conductnone givenThe self-selected summer campers may be more motivated to learn, but may have taken the assessments less seriously than other student populations.
“The self-selected summer campers and may be more motivated to learn, but may have taken the assessments less seriously than other student populations.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationMorrison et al. showed that students who self-explained subgoal labels perform better than those who were not given subgoal labels.
“Morrison et al. showed that students who self-explained subgoal labels perform better than those who were not given subgoal labels [12].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [12] source
Probably not a substantial claim: may not pass S3 (load-bearing).
Interpretationown dataIt is not surprising that there was no significant difference in learning between groups.
“Since most students in both groups completed the core objectives, it is not surprising that there was no significant difference in their learning.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataPaired with the other results, the PCH is an overall improvement to Snap!, which students generally prefer over purely independent programming.
“Paired with the other results presented here, this indicates that the PCH is an overall improvement to Snap!, which students generally prefer over purely independent programming.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataTwo researchers reached an inter-rater reliability between 0.744 and 0.979 for the first 15 student submissions (5 in each problem).
“The two researchers reached an inter-rater reliability between 0.744 and 0.979 for first 15 student submissions (5 in each problem).”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationPrevious WE studies found that students learned more efficiently with WEs, but they did not learn more, since both groups were exposed to the same learning content.
“This is consistent with previous WE studies [10,17], where students learned more efficiently with WEs, but they did not learn more, since both groups were exposed to the same learning content.”
Quote in the paperNot a substantial claim0 of 2 sources checkedMay combine two claims.
Interpretationown dataWEs may have impacted students' overall performance, but the difference was not significant.
“This suggests that WEs may have impacted students' overall performance, but the difference was not significant.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationStudents' desire for independence is a common reason that they avoid help when programming, and overcoming this barrier can be very challenging.
“Students' desire for independence is a common reason that they avoid help when programming, and overcoming this barrier can be very challenging [15].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [15] source
Probably not a substantial claim: may not pass S3 (load-bearing).
Interpretationown dataOne interpretation of these results is that students experienced reduced intrinsic cognitive load when using worked examples.
“One interpretation of these results is that students experienced reduced intrinsic CL when using WEs.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataThe time saved by WEs is longer than the time students need to complete additional objectives, which allowed students who received WEs to complete more objectives overall on both problems 1 and 2, although this difference was not significant due to the low sample size.
“However, the time saved is longer than the time students need to complete additional objectives, which allowed students who received WEs to complete more objectives overall on both problems 1 and 2, although this difference was not significant due to the low sample size.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Study design and conductnone givenParticipants' learning gains may also have come from repeated exposure to tests.
“Participants' learning gains may also have come from repeated exposure to tests.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataFor analysis of pre- and post-tests, one additional student from Group E1 who did not fill in post-test1 was excluded, leaving 8 students in each group.
“For our analysis of pre-and post-tests, we exclude one additional student from Group E1 who did not fill in post-test1, leaving 8 students in each group.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductreasoningAfter excluding these students, there are 9 students in Group E1 and 8 students in Group E2.
“After excluding these students, there are 9 students in Group E1 and 8 students in Group E2.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningThis helps to explain why the WE group accomplished more objectives on both problems, but the difference was not significant.
“This helps to explain why the WE group accomplished more objectives on both problems, but the difference was not significant.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Students who completed the WE objectives the slowest would have completed additional objectives no quicker than the other students in their group.
May combine two claims.
Interpretationown dataThe two groups experienced different levels of intrinsic cognitive load on different problems, which may be due to the worked examples.
“This indicates that the two groups experienced different levels of intrinsic CL on different problems, which may be due to the WEs.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitationThe CS Cognitive Load Component Survey (CS CLCS) was used as the cognitive load instrument.
“We used the CS Cognitive Load Component Survey (CS CLCS) [11] as our cognitive load instrument.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [11] source
Classified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenStudents were randomly assigned to groups (E1 receiving worked examples on problem 1, E2 with worked examples on problem 2), with matched pairs according to pre-test scores.
“In Step 1, students took the pre-test and the pre-survey, and were randomly assigned to groups (E1 receiving WEs on problem 1, E2 with WEs on problem 2), with matched pairs according to pre-test scores.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenAll of the programming problems involved drawing patterns, and the design of WEs may not generalize to problems that lack visual output.
“All of the programming problems involved drawing patterns, and our design of WEs may not generalize to problems that lack visual output.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataWEs allowed all students to finish the WE objectives, compared to only 80% and 60% for non-WE students.
“Based on these results, we can say the WEs saved students lots of time (19-24 minutes on a 45 minute problem), and allowed all students to finish the WE objectives (compared to only 80% and 60% for non-WE students).”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningOur analysis of programming efficiency suggests that initially, WEs save students considerable time in completing programming objectives (up to half of the time allotted), but that students take longer to complete later objectives.
“Our analysis of programming efficiency suggests that initially, WEs save students considerable time in completing programming objectives (up to half of the time allotted), but that students take longer to complete later objectives.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductown dataFive students were excluded from survey and assessment analysis because they were not present for the whole study (2) or encountered errors in Snap! that caused them to lose significant programming progress (3).
“However, five students were excluded from survey and assessment analysis because they were not present for the whole study (2) or encountered errors in Snap! that caused them to lose significant programming progress (3) .”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
Study design and conductnone givenCamp instructors followed a tutoring protocol to ensure that each student received the same quality and quantity of help.
“Camp instructors followed a tutoring protocol to ensure that each student received the same quality and quantity of help, using an audio recorder to capture the dialogue of these help episodes, and record who they helped, the help duration (time), and the type of help (e.g.high-level planning, programming, debugging, interface related).”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenThe study used a matched pairs, switching replication design to investigate the impact of worked examples on problem 1, and whether or not it matters which problem receives worked example support.
“The study used a matched pairs, switching replication design as shown in Table 1 to investigate the impact of WEs on problem 1, and whether or not it matters which problem receives WE support.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataProgramming WEs may improve students' programming efficiency in the short term, but students do require additional time to process WEs before they can construct their own code.
“Our results also suggest that programming WEs may improve students' programming efficiency in the short term, but that students do require additional time to process WEs before they can construct their own code.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningAnother interpretation is that students experienced higher intrinsic cognitive load on problem solving that followed a worked example, which made it seem harder by contrast.
“However, another interpretation is that students experienced higher intrinsic CL on problem solving that followed a WE, which made it seem harder by contrast.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataAfter completing the WE-supported objectives, students with WEs initially worked less efficiently, especially since non-WE students could accomplish additional objectives while completing the WE objectives.
“From this, we conclude that, after completing the WE-supported objectives, students with WEs initially worked less efficiently, especially since non-WE students could accomplish additional objectives while completing the WE objectives (e.g.putting code into a procedure).”
Quote in the paperSubstantial claim- The authors state this as an assumption: Students who completed the WE objectives the slowest would have completed additional objectives no quicker than the other students in their group.
Classified as probably not a substantial claim. May combine two claims.
Study design and conductnone givenThis is an exploratory study with a small sample size, which makes it difficult to rule out possible differences between groups.
“This is an exploratory study with a small sample size, which makes it difficult to rule out possible differences between groups.”
Quote in the paperSubstantial claim
The paper relies on this without support.
The pre-test and post-tests are sensitive enough to detect changes in students' learning.
“The pre-test, and isomorphic post-test1 and post-test2, were adapted from the block-based version of the Commutative Assessments [25], with 16 multiple-choice questions on variables, loops, procedures, and conditionals.”
The paper relies on this without support.
Previous research in other domains has shown that replacing problem solving exercises with WEs can increase learning efficiency by decreasing the time students take to complete the exercises without affecting how much they learn.
“Previous research in other domains has shown that replacing problem solving exercises with worked examples (WEs) can increase learning efficiency by decreasing the time that students take to complete the exercises without affecting how much they learn.”
The authors state this as an assumption.
Students who completed the WE objectives the slowest would have completed additional objectives no quicker than the other students in their group.
“To do this, we make the simplifying assumption that the students who completed the WE objectives the slowest would have completed additional objectives no quicker than the other students in their group.”
The authors state this as an assumption.
Intrinsic cognitive load is inherent in the learning task.
“It is interesting that we observed differences in intrinsic load, which is assumed to be inherent in the learning task, rather than extraneous load, which is assumed to be the result of our instructional design.”
The authors state this as an assumption.
Extraneous cognitive load is the result of the instructional design.
“It is interesting that we observed differences in intrinsic load, which is assumed to be inherent in the learning task, rather than extraneous load, which is assumed to be the result of our instructional design.”
Statistics that don’t add up
Each reported p-value recomputed from its test statistic and degrees of freedom.
Inconsistent p-value: t (13.96) = -0.4,p = .64
MinorThe paper reports p = .64 here; recomputing from the test statistic and its degrees of freedom gives p = .695 (two-tailed). The check cannot tell whether the statistic or the p-value is the misprint.
Reported p = .64 · recomputed p = .695
“t (13.96) = -0.4,p = .64”
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 upt(13.96) = -0.4reported p = .64 · recomputed p = .695
“t (13.96) = -0.4,p = .64”
- Consistentt(15) = 3.05reported p < .01 · recomputed p = .008
“t (15) = 3.05, p < .01”
- Consistentt(15) = 2.49reported p < .05 · recomputed p = .025
“t (15) = 2.49, p < .05”
- Consistentt(15) = -0.86reported p = .4 · recomputed p = .403
“t (15) = -0.86,p = .40”
- Consistentt(7) = -3.51reported p < .01 · recomputed p = .010
“t (7) = -3.51,p < .01”
- Consistentt(7) = -4.52reported p < .01 · recomputed p = .003
“t (7) = -4.52,p < .01”
- Consistentt(13.91) = 0.4reported p = .69 · recomputed p = .695
“t (13.91) = 0.40, p = .69”
- Consistentt(13.19) = -2.33reported p < .05 · recomputed p = .037
“t (13.19) = -2.33,p < .05”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Time saved on Spiral Polygon is understated
MajorThe reported median times on Spiral Polygon are 30.4 minutes without WEs and 2.5 minutes with WEs, a difference of 27.9 minutes-not 24 minutes. This understates a result used to support the paper's programming-efficiency conclusion.
Reported 24 · recomputed 27.9
“Based on these results, we can say the WEs saved students lots of time (19-24 minutes on a 45 minute problem)”
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Self-citation
Flagged first, then by citations
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Cited by
Sorted by citations
Comparing Code Explanations Created by Students and Large Language Models
Leinonen, J., Denny, P., MacNeil, S., Sarsa, S., Bernstein, S., Kim, J., Tran, A., & Hellas, A. (2023) · Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 1 · doi:10.1145/3587102.3588785 · Cited by 210 · see citing works
“Learning by example is an effective pedagogical technique, often employed in programming education [2, 48].”
BackgroundEvaluating LLM-generated Worked Examples in an Introductory Programming Course
Jury, B., Lorusso, A., Leinonen, J., Denny, P. C., & Luxton-Reilly, A. (2024) · Australasian Computing Education Conference · doi:10.1145/3636243.3636252 · Cited by 107 · see citing works
“There is a wealth of literature on worked examples and their efficacy in the specific domain of programming [34, 37].”
BackgroundMalmi, L., Sheard, J., Kinnunen, P., Simon, & Sinclair, J. E. (2019) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3291279.3339409 · Cited by 67 · see citing works
“The new instrument has subsequently been used by other researchers for measuring cognitive load in different contexts, such as worked examples in programming [100], Parsons problems [20], and collaborative algorithms labs [91].”
MethodEricson, B. J., Denny, P. C., Prather, J. E., Duran, R., Hellas, A., Leinonen, J., Miller, C. S., Morrison, B. B., Pearce, J. L., & Rodger, S. H. (2022) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3571785.3574127 · Cited by 59 · see citing works
Improving Engagement in Program Construction Examples for Learning Python Programming
Hosseini, R., Akhüseyinoğlu, K., Brusilovsky, P. L., Malmi, L., Pollari-Malmi, K., Schunn, C. D., & Sirkiä, T. (2020) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-020-00197-0 · Cited by 48 · see citing works
“…of research has been devoted to the development of example-based learning environments to support students in learning programming in various programming languages, such as LISP [25, 36, 74, 75, 76], Prolog [7], C/Java [9, 22, 39, 66], Javascript [19], SQL [13, 14, 45], and mini-languages [8, 78].”
BackgroundUsing Adaptive Parsons Problems to Scaffold Write-Code Problems
Hou, X., Ericson, B. J., & Wang, X. (2022) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3501385.3543977 · Cited by 32 · see citing works
“found that solving blocks-based Parsons problems improved learning efficiency on lab assignments without reducing performance on subsequent homework or programming assignments [83].”
BackgroundStep Tutor: Supporting Students through Step-by-Step Example-Based Feedback
Wang, W., Rao, Y., Zhi, R., Marwan, S., Gao, G., & Price, T. (2020) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3341525.3387411 · Cited by 24 · see citing works
“Peer Code Helper offers such step-bystep WEs from the same task, during block-based programming [53].”
BackgroundSelf-citationTyping Exercises as Interactive Worked Examples for Deliberate Practice in CS Courses
Gaweda, A. M., Lynch, C. F., Seamon, N., Silva de Oliveira, G., & Deliwa, A. (2020) · Proceedings of the Twenty-Second Australasian Computing Education Conference · doi:10.1145/3373165.3373177 · Cited by 21 · see citing works
“Comparing example solutions has been shown to help students form more abstractmentalmodels in STEMdomains such asmathematics, physics, and block-based computer programming [12, 41, 57].”
BackgroundMethodNovices' Learning Barriers When Using Code Examples in Open-Ended Programming
Wang, W., Kwatra, A., Skripchuk, J., Gomes, N., Milliken, A., Martens, C., Barnes, T. M., & Price, T. (2021) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3430665.3456370 · Cited by 23 · see citing works
“Many were built for closed-ended tasks [45, 46].”
Self-citationGardner, T. Q., Leonard, H. C., Waite, J., & Sentance, S. (2022) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3501385.3543960 · Cited by 17 · see citing works
“Free text responses to surveys Short answers to questions, e.g. [102] Interviews with subset of survey respondents e.g. [20] Interviews as the primary source e.g. [ Programmatic evaluation of daily project artefacts [34] Cognitive load: Paas’s 9-point scale [117] and CS CLCS [116] Qualitative Audio/video coding, e.g.…”
BackgroundMethodExploring Novice Programmers' Hint Requests in an Intelligent Block-Based Coding Environment
Wiggins, J. B., Fahid, F. M., Emerson, A., Hinckle, M., Smith, A., Boyer, K. E., Mott, B., Wiebe, E., & Lester, J. C. (2021) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3408877.3432538 · Cited by 18 · see citing works
“To respond to hint requests at this stage, the literature would suggest using worked examples and bottom-out hints [34] since the requests likely stem”
BackgroundInfluentialMeasuring the Cognitive Load of Learning to Program: A Replication Study
Zavgorodniaia, A., Duran, R., Hellas, A., Seppälä, O., & Sorva, J. (2020) · doi:10.1145/3416465.3416468 · Cited by 16 · see citing works
“In computing education research (CER), CLT has been applied to example-based learning [40, 60, 70], course design [8, 61, 66], multimedia and visualizations [46, 48, 59], novel practice tasks [15, 20], and complexity analysis [14], among other things.”
BackgroundAdaptive Support With Working Examples in Serious Games About Programming
Toukiloglou, P., & Xinogalos, S. (2023) · Journal of Educational Computing Research · doi:10.1177/07356331231151393 · Cited by 15 · see citing works
“The WEs have been used before as a support mechanism for teaching programming both in a SG (Toukiloglou & Xinogalos, 2022) and in a programming environment (Zhi et al., 2019) with positive results.”
MethodToukiloglou, P., & Xinogalos, S. (2022) · Journal of Educational Computing Research · doi:10.1177/07356331211073655 · Cited by 14 · see citing works
“In another empirical study, Zhi et al. (2019) explored the impact of worked examples by designing a peer code helper which integrates worked examples into the programming environment Snap!”
BackgroundMethodInfluentialUnderstanding Wikipedia as a Resource for Opportunistic Learning of Computing Concepts
Robillard, M. P., & Treude, C. (2020) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3328778.3366832 · Cited by 11 · see citing works
“…the design of software documentation, so we note Trafton and Reiser’s observation of the usefulness of examples [29], Novick and Ward’s empirical confirmation that documentation users want examples [26], as well as attempts to integrate more examples in technology targeting novice programmers [35].”
MethodSelf-Explanation Effect of Cognitive Load Theory in Teaching Basic Programming
Sandoval-Medina, C., Mercado, C. A. A., Andrade, E. L. M., & Arteaga, J. M. (2024) · Journal of Information Systems Education · doi:10.62273/gmiv1698 · Cited by 10 · see citing works
“…effect (Price et al., 2020; Vihavainen et al., 2015; Yen & Wang, 2017) and the worked-example effect (Beege et al., 2021; Sands, 2019; Zhi et al., 2019) in programming education and various other fields of study, most of these studies have reported positive findings regarding their…”
BackgroundResultProgression Trajectory-Based Student Modeling for Novice Block-Based Programming
Fahid, F. M., Tian, X., Emerson, A., Wiggins, J. B., Bounajim, D., Smith, A., Wiebe, E., Mott, B., Boyer, K. E., & Lester, J. C. (2021) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3450613.3456833 · Cited by 12 · see citing works
“, found that worked examples reduce cognitive load but do not result in significant learning gains [59].”
BackgroundA Semblance of Similarity: Student Categorisation of Simple Algorithmic Problem Statements
Finnie-Ansley, J., Denny, P. C., & Luxton-Reilly, A. (2021) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3446871.3469745 · Cited by 10 · see citing works
“Worked examples present learners with step-by-step solutions to a problem, and are commonly used in computing education [83, 103].”
BackgroundExploring Design Choices to Support Novices' Example Use During Creative Open-Ended Programming
Wang, W., Meur, A. L., Bobbadi, M., Akram, B., Barnes, T. M., Martens, C., & Price, T. (2022) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3478431.3499374 · Cited by 11 · see citing works
“By using such tools during the completion of closedended tasks, novices were shown to be able to complete tasks faster [32].”
BackgroundMethodSelf-citationDong, Y., Shabrina, P., Marwan, S., & Barnes, T. M. (2021) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3446871.3469764 · Cited by 9 · see citing works
“They found that these worked examples helped students solve more problems in fixed time [39].”
BackgroundSelf-citationKharrufa, A., Alghamdi, S. S., Aziz, A., & Bull, C. (2025) · ACM Transactions on Computing Education · doi:10.1145/3779296 · Cited by 16 · see citing works
“Another research focus is the pedagogical value of learning from worked examples, a widely studied approach in education, particularly in programming education [1, 11, 57].”
Comparing the Experiences of Live Coding versus Static Code Examples for Students and Instructors
Watkins, A., Miller, C. S., & Settle, A. (2024) · doi:10.1145/3649217.3653562 · Cited by 8 · see citing works
“Zhi et al. [30] looked into the impact of worked examples in the classroom and showed students worked examples that guided them step by step through solving a problem.”
BackgroundShabrina, P., Marwan, S., Chi, M., et al. (2020) · Cited by 8 · see citing works
Self-citationAdaptive User Interface for Smart Programming Exercise
Watanobe, Y., Rahman, M. M., Vazhenin, A. P., et al. (2021) · doi:10.1109/tale52509.2021.9678757 · Cited by 7 · see citing works
“In the area of visual programming, block-based programming environments can reduce problem-solving time [2], [18].”
BackgroundA Study of Worked Examples for SQL Programming
Akhüseyinoğlu, K., Hardt, R., Barria-Pineda, J., Brusilovsky, P. L., Pollari-Malmi, K., Sirkiä, T., & Malmi, L. (2022) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3502718.3524813 · Cited by 8 · see citing works
“More recently, there have been several attempts to develop other types of worked examples that focus on code exploration rather than code animation [8, 31].”
BackgroundPinpoint: A Record, Replay, and Extract System to Support Code Comprehension and Reuse
Wang, W., Fraser, G., Bobbadi, M., Tabarsi, B. T., Barnes, T. M., Martens, C., Jiao, S., & Price, T. (2022) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vl/hcc53370.2022.9833105 · Cited by 7 · see citing works
Self-citationEffect of Feedback with Video-based Peer Modeling on Learning and Self-efficacy
Eghterafi, W., Tucker, M. C., Zhang, I., & Son, J. Y. (2022) · Online Learning · doi:10.24059/olj.v26i2.2732 · Cited by 5 · see citing works
“Prior studies have largely implemented text-based feedback, such as static worked examples (Gee, 2009; Zhi et al., 2019) and “just-in-time” prompts (Graesser et al., 1999).”
MethodMalmi, L., Sheard, J., Kinnunen, P., Simon, & Sinclair, J. E. (2020) · ACM Inroads · doi:10.1145/3381889 · Cited by 5 · see citing works
Applying Cognitive Load Theory and the Split Attention Effect to Learning Data Structures
Mercado, C. A. A., Andrade, E. L. M., Cardona-Reyes, H., et al. (2023) · IEEE Revista Iberoamericana de Tecnologias del Aprendizaje · doi:10.1109/rita.2023.3250580 · Cited by 5 · see citing works
“Of these, the ‘Worked Example Effect’ is the most studied and the one with the most reported empirical evidence of its effectiveness in knowledge transfer [19], [20].”
BackgroundGreifenstein, L., Heuer, U., & Fraser, G. (2024) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3626252.3630868 · Cited by 3 · see citing works
“The importance of, e.g., worked examples [36] for programming education can be seen from their general relation to computer science [34], to their extension with subgoal labels [18] or their adaptation in automated tools [43].”
BackgroundTabarsi, B. T., Reichert, H., Lytle, N., Cateté, V., & Barnes, T. M. (2024) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3632620.3671110 · Cited by 3 · see citing works
“The end result of reducing cognitive load is similar (or improved) performance but with less time spent on tasks [73].”
Self-citationUser-Centered Design of a Mobile Java Practice App
Ahmed, M., Ying, K. M., & Boyer, K. E. (2020) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3328778.3366942 · Cited by 2 · see citing works
“There has been considerable effort invested in creating learning tools for students in computer science such as coding environments [21, 29], virtual tutors [5], serious games [27], in-class assessment”
BackgroundGupta, N., Rajput, A., & Chimalakonda, S. (2022) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3510454.3516842 · Cited by 2 · see citing works
“[28] emphasized that the effort required and difficulty involved in accomplishing a learning task for novice programmers can be reduced if worked examples are provided to the learners.”
BackgroundInvestigating the Impact of On-Demand Code Examples on Novices' Open-Ended Programming Experience
Wang, W., Bacher, J. T., Isvik, A., Limke, A., Sthapit, S., Shi, Y., Tabarsi, B. T., Tran, K., Cateté, V., Barnes, T. M., et al. (2023) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3568813.3600141 · Cited by 2 · see citing works
“The Peer Code Helper [56] is an on-demand example system in Snap! .”
BackgroundInfluentialSelf-citationBai, G. R., Jiang, Z., Price, T., & Stolee, K. T. (2024) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3632620.3671102 · Cited by 2 · see citing works
“In computing education, cognitive load theory has been used to improve instructional design, by reducing the extraneous cognitive load imposed by the design of the assignment, freeing up students’ cognitive resources for learning (e.g., through worked examples [19, 39, 60]).”
Self-citationCOSPEX: A Program Comprehension Tool for Novice Programmers
Gupta, N., Rajput, A., & Chimalakonda, S. (2022) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse-companion55297.2022.9793787 · Cited by 2 · see citing works
Griffith, A. E., Katuka, G. A., Wiggins, J. B., Boyer, K. E., Freeman, J., Magerko, B. S., & McKlin, T. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-022-00302-5 · Cited by 2 · see citing works
“For example, providing relevant worked or partial worked examples (Zhi et al., 2019; McLaren & Isotani, 2011) and encouraging good coding and debugging habits as they move through the project are ways that can reduce the cognitive load of the technical portion of the task and allow students to spend more time on creat…”
MethodClassroom Practice Using a Code-Sharing Platform to Encourage Refinement Activities
Maeda, S., Koike, K., & Tomoto, T. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35129-7_21 · Cited by 2 · see citing works
Tonhão, S. D. F., Shigenaga, M. Y., Silva, W., et al. (2023) · doi:10.5753/sbie.2023.233544 · Cited by 1 · see citing works
“…examples pode apresentar bons resultados de aprendizagem, principalmente para estudantes iniciantes, com pouco conhecimento pr´evio, podendo resolver mais tarefas em determinado tempo em comparac¸˜ao com outras abordagens como a aprendizagem baseada na resoluc¸˜ao de problemas [Zhi et al. 2019].”
BackgroundSingh, D., Mishra, A., & Aggarwal, A. (2024) · doi:10.1145/3675888.3676024 · Cited by 1 · see citing works
“The results did, however, demonstrate that the participants’ times for solving the coding tasks improved [23].”
Ghosh, A., Malva, L., Gotovos, A., et al. (2025) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3641554.3701782 · Cited by 1 · see citing works
“However, studies have found the results to be mixed, often showing benefits during the learning phase but a limited transfer of learned skills during the post-learning (i.e., post-test) phase [14, 16, 26, 33].”
Tithi, S. D., Tian, X., Limke, A., Chi, M., & Barnes, T. M. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791631
Self-citationStarter Projects in Python Programming Classes
Weigend, M. (2022) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-97986-7_9
Rice, M. F. (2022) · Online Learning · doi:10.24059/olj.v26i2.3442
A Worked Example Model for Teaching Dynamic Programming
Novak, E. (2022) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3545947.3576232
“Two notable works are [5] and [4], which both are exemplary of the most common idea: providing code examples to novice programmers.”
BackgroundMaeda, S., Koike, K., & Tomoto, T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-60125-5_23
Code Sharing Platform for Better Code Writing Through Visual Programming and Trial and Error
Maeda, S., Takeda, K., & Tomoto, T. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93822-1_19
Tonhão, S. D. F., de Souza, J. C., Colanzi, T. E., et al. (2025) · Computer Science Education · doi:10.1080/08993408.2025.2587039
CS Teaching Assistant Perceptions on LLM-Generated Faded Worked Examples for Feedback Training
Gonzaga, J. T., Vassar, A., & Jiang, Y. (2026) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3770762.3772582
“In CS contexts, worked examples have traditionally been used for programming contexts due to their algorithmic nature[27, 30, 51].”
On Generating and Validating Erroneous Examples in CS1 Using LLMs
Chen, Y., Zhao, C., Levine, J., et al. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29760-0_14
Encouraging Higher-Quality Code Through Cost and Productivity-Based Ranking in Visual Programming
Maeda, S., Koike, K., & Tomoto, T. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29178-3_8
Münstermann, M., & Chounta, I. (2026) · Mensch und Computer (MuC) · doi:10.1145/3820253.3831394
“However, performance gains may reflect reduced cognitive load rather than deeper learning [ 30 ], so instructional designs should pair worked examples with active engagement and reflection.”
- Authors
- Zhi, R., Price, T. W., Marwan, S., Milliken, A., Barnes, T., & Chi, M.
- Affiliations
- North Carolina State University (US)
- Venue
- Proceedings of the 50th ACM Technical Symposium on Computer Science Education
- Publisher
- ACM
- Published
- 2019-02-22
- Pages
- 98-104
- OpenAlex
- W2916970057
- Semantic Scholar
- 364eb9840d80e253dc4e77bab398a2e63753494f
- Cited by
- 55
- References
- 25
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 10 · 2023: 7 · 2022: 14 · 2021: 6 · 2020: 8 · 2019: 1
- FWCI
- 8.47
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Technical Symposium on Computer Science Education (SIGCSE) (publishedVersion)
- Funders
- National Science Foundation (1623470)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- worked examples, programming for beginners, block-based programming, self-explanation prompts, scaffolding, cognitive load, high school students, programming education
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