When Do Learners Study?

Probably not a substantial claim: may not pass S3 (load-bearing).

For adult e-learners, evening time-slot is closely related to better academic performance in collaborative activities (r = 0.6).

“Romero and Barber` a (2011) observed the time spent by adult e-learners on learning activities and reported a close relationship between evening time-slot and better academic performance ( r = 0 . 6) in collaborative activities, whereas both morning ( r = 0 . 9) and evening ( r = 0 . 8) were closely related to academic performance for individual activities.”

• cites Romero and Barber` a (2011) sourceSupported

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

For adult e-learners, both morning (r = 0.9) and evening (r = 0.8) are closely related to academic performance for individual activities.

“Romero and Barber` a (2011) observed the time spent by adult e-learners on learning activities and reported a close relationship between evening time-slot and better academic performance ( r = 0 . 6) in collaborative activities, whereas both morning ( r = 0 . 9) and evening ( r = 0 . 8) were closely related to academic performance for individual activities.”

• cites Romero and Barber` a (2011) sourceSupported

Classified as probably not a substantial claim.

Romero and Barberà (2011) found adult e-learners' academic performance more strongly related to morning and evening.

“This is in contrast to the observation by Romero and Barber` a (2011), where adult e-learners' academic performance was more strongly related to morning and evening.”

• cites Romero & Barberà, 2011 sourceSupported

May combine two claims.

Notifications are typically sent at random times or hard-coded times in the LMS app, which might not be based on theoretical/empirical reasoning and might result in undesirable learning impacts.

“Typically, notifications are sent at a random time of day or whatever time has been hard-coded in the LMS app-which might not be based on theoretical/empirical reasoning, both of which might result in undesirable learning impacts (Tabuenca et al., 2015; Stockwell, 2010).”

• cites Tabuenca et al., 2015 sourceNot checked — no full text of the source
• cites Stockwell, 2010 sourcePartly supported

  The 'Mobile Phones and Vocabulary Learning' section says teachers controlled the timing and frequency of messages in two studies, and some learners found the messages too frequent. It does not say that LMS notifications are typically random or hard-coded, lack a theoretical or empirical basis, or cause undesirable learning outcomes. The supplied passages give no page number. (LLM, conf=0.78)

  Experimental — accuracy not yet measured

May combine two claims.

For intensive learners, the proportion of mobile-oriented sessions in the afternoon and evening was significantly greater than expected by chance and significantly lower than expected in the morning and at night.

“For intensive learners, the proportion of mobile-oriented sessions in the afternoon and evening was significantly greater than expected by chance and significantly lower than expected in the morning and at night.”

May combine two claims.

For computer-dominant weekday, limited-computer weekday, limited-computer weekend, and intensive weekday, there were highly significant associations between TMPs and time of day.

“The tests revealed high statistical significance for four of these tests-computer-dominant weekday ( c 2 ( 9 ) = 117 . 99, p < 0 . 001), limited-computer weekday ( c 2 ( 9 ) = 120 . 30, p < 0 . 001), limited-computer weekend ( c 2 ( 9 ) = 42 . 08, p < 0 . 001), and intensive weekday ( c 2 ( 9 ) = 135 . 26, p < 0 . 001)”

May combine two claims.

For computer-dominant students, a higher than expected number of diverse sessions occurred in the afternoon and evening, and a significantly lower than expected number occurred at night.

“As can be seen from the highlighted cells in Table 2, a higher than expected number of diverse sessions from the computer-dominant students were carried out in the afternoon and evening, while a significantly lower than expected number of diverse sessions took place at night.”

Probably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing), S4 (based).

For computer-dominant learners, mobile-oriented sessions afternoon > morning/evening/night on both weekdays and weekends.

“For mobile-oriented sessions on both weekdays and weekends, the proportion in the afternoon was higher than that in the morning, in the evening, and at night, all three of which were indistinguishable from each other.”

May combine two claims.

For computer-dominant learners, diverse sessions on weekdays afternoon/evening > morning/night; not on weekend.

“For diverse sessions on weekdays, the proportion of afternoon and evening sessions was greater than that of morning and night sessions, but these results did not hold on the weekend.”

May combine two claims.

For intensive learners, mobile-oriented sessions afternoon > morning/night on weekdays but not weekends.

“An interesting observation for the mobile-oriented learning sessions was that the proportion of these types of sessions taking place in the afternoon was significantly greater than those occurring in the morning and at night only on weekdays but not on weekends.”

May combine two claims.

For computer-dominant students, a lower than expected number of short-computer sessions occurred in the afternoon and evening, and a significantly higher than expected number occurred at night.

“These patterns were entirely reversed for short-computer sessions in that a lower than expected number of short-computer sessions from the computer-dominant students were carried out in the afternoon and evening, while a significantly higher than expected number of short-computer sessions took place at night.”

May combine two claims.

For limited-computer learners, short-computer sessions night comparable to morning but greater than afternoon and evening on weekdays and weekends.

“The proportions of short-computer sessions at night on weekdays and weekends were comparable to that of the morning sessions but greater than the proportions of sessions in the afternoon and evening.”

May combine two claims.

For all strategies, the distribution of learning sessions across times of day on weekends was nearly the same as on weekdays, with maximum activity at night and minimum in the afternoon.

“Visual inspection reveals that the distribution of sessions across different times during the day on weekends was nearly the same as on weekdays for all the strategies, with the maximum activity taking place at night and the minimum in the afternoon.”

May combine two claims.

For limited-computer learners, diverse sessions: on weekends morning and evening similar; on weekdays evening > morning.

“Further, the proportions of diverse sessions in the morning and evening on weekends were similar (subscript b common in both), whereas on weekdays, the latter was significantly greater than the former.”

May combine two claims.

For the majority of TMPs in each of the three modality choices, night was the dominant time of day for learning activities, while afternoon registered the lowest traffic.

“The figure indicates that for the majority of the TMPs in each of the three modality choices, night was the dominant time of day for learning activities to take place, while afternoon registered the lowest traffic.”

May combine two claims.

For computer-dominant learners, computer session proportions across morning, afternoon, evening, and night were indistinguishable on weekends; constant throughout the day.

“The proportions of computer-dominant learners' computer sessions across morning, afternoon, evening, and night were indistinguishable on weekends (all subscripts are a ). In other words, on weekends, the proportion of computer sessions remained constant throughout the day.”

May combine two claims.

The preferences for time of day were consistent across all three modality choices, and the ratio of morning:afternoon:evening:night sessions was maintained.

“The figure shows that not only were the preferences consistent across all three modality choices, but the ratio of morning:afternoon:evening:night sessions was also maintained.”

May combine two claims.

For limited-computer students, mobile-oriented sessions on weekends numbered 29 in the morning and 51 at night, while on weekdays they numbered 94 in the morning and 216 at night.

“Similarly, for limited-computer students on weekends, the numbers of mobile-oriented sessions in the morning and at night were 29 and 51, respectively, whereas a noticeably larger difference between the two times of day was observed on weekdays (numbers of sessions = 94 and 216, respectively).”

May combine two claims.

Across all learners, there was a preference for night for learning activities, with morning and evening proportions almost comparable, and the fewest sessions in the afternoon.

“The figure reveals a preference for night for carrying out learning-related activities, while the proportions of sessions during the morning and evening hours were almost comparable. The fewest learning sessions were observed in the afternoon.”

May combine two claims.

For computer-dominant and limited-computer learners, computer sessions were the next most common TMP after short-computer; for intensive learners, mobile-oriented sessions were next.

“followed by computer for computer-dominant and limited-computer users, or mobile-oriented sessions for intensive learners.”

May combine two claims.

Preferences on weekdays were found to be different from those on weekends for all students and depended on the session's modality profiles and learners' modality choice, among other contextual factors.

“preferences on weekdays were found to be different from those on weekends for all students and instead depended on the session's modality profiles and learners' modality choice, among other contextual factors.”

Probably not a substantial claim: may not pass S4 (based).

Longer study time happens in the evening on weekdays and weekends.

“when the learner is likely to engage in longer study time, which happens in the evening on weekdays and weekends.”

May combine two claims.

TMP4 (Computer Oriented) cluster contained 5,142 sequences, representing 27.2% of all sequences.

“TMP4 ClusterComputer Oriented ( N = 5 , 142, 27.2%): This cluster was also predominantly focused on actions performed using the computer modality.”

• cites Sher and colleagues (2019)Not matched

  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).

Individual differences exist with respect to the time of day learners prefer to work.

“There also exists a considerable body of literature that suggests that individual differences exist with respect to the time of day learners prefer to work.”

• cites Loureiro & Garcia-Marques, 2015 sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

Associations between modality use and time of day need to be investigated further in blended learning environments.

“While much has been revealed from the aforementioned studies, associations between modality use and time of day need to be investigated further in blended learning environments.”

Probably not a substantial claim: may not pass S3 (load-bearing).

On comparison of the features and functionalities offered by the web-browser and mobile app versions of the LMS, no apparent differences were revealed.

“On comparison of the features and functionalities offered by the two versions, no apparent differences were revealed.”

Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

A crucial precursor to creating contextual profiling systems is understanding how learning in the presence of various modalities is apportioned across a day.

“A crucial precursor to creating such systems is understanding how learning in the presence of various modalities is apportioned across a day.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Bonferroni corrections were used to account for possible inflation of Type 1 error from multiple comparisons.

“To further highlight how these associations changed depending on the technological-modality choices adopted by the student (i.e., student clusters), three separate chi-square tests of independence were carried out for each of the three student clusters identified, after using Bonferroni corrections to account for possible inflation of Type 1 error from multiple comparisons.”

May combine two claims.

TMP1 (Diverse) cluster contained 1,498 sequences, representing 7.9% of all sequences.

“TMP1 ClusterDiverse ( N = 1 , 498, 7.9%): This cluster constituted the smallest number of sequences.”

• cites Sher and colleagues (2019)Not matched

  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).

The study sessions were filtered to remove outliers, resulting in 18,895 study sessions across 120 students.

“Once the study sessions were extracted from the events data (see Sher and colleagues (2019) for details on the extraction process), they were filtered to remove any outliers, which resulted in 18,895 study sessions across 120 students (one student was deemed as an outlier; this student registered an excessively high number of study sessions (506 sessions, compared to a median of 206)).”

• cites Sher and colleagues (2019)Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

May combine two claims.

Only a limited number of papers have looked at the associations between time flexibility and modality usage from the learner's perspective, and only in very specific application domains.

“While many researchers have examined the concept of time flexibility from instructional and organization viewpoints (Arneberg et al., 2007; McGorry, 2003), only a limited number of papers have looked at the associations between time flexibility and modality usage from the learner's perspective, and only in very specific application domains.”

• cites Arneberg et al., 2007Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites McGorry, 2003 sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

Temporal patterns of student activity have helped in the multifaceted analysis of student learning behaviour.

“Temporal patterns of student activity, e.g., the sequence and timing of student activity (Barbera et al., 2015), have helped in the multifaceted analysis of student learning behaviour.”

• cites Barbera et al., 2015 sourceNot checked — no full text of the source

May combine two claims.

Student Cluster 1 predominantly used the computer modality, with high attachment to TMP3 (short-computer) and TMP4 (computer).

“This group of students predominantly used the computer modality, which could be demonstrated by a high attachment to profiles TMP3 (short-computer) and TMP4 (computer).”

• cites Sher and colleagues (2019)Not matched

  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).

Associations between modality use and time of day need to be investigated at the level of sessions (sequence of actions) as opposed to individual actions.

“Moreover, the associations need to be investigated at the level of sessions (sequence of actions) as opposed to individual actions.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Individual differences exist with respect to the technological modality learners prefer to use for their learning.

“Existing research suggests that individual differences exist with respect to the technological modality learners prefer to use for their learning.”

• cites Sher, Hatala, & Gaˇ sevi´ c, 2019Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Stockwell, 2010, 2013Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

May combine two claims.

Student Cluster 2 predominantly used TMP3, then TMP4, and sparingly TMP1 and TMP2.

“This group of students predominantly used technology in a way consistent with TMP3 (short-computer), then TMP4 (computer), and sparingly the other two profiles (TMP1 and 2).”

• cites Sher and colleagues (2019)Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

May combine two claims.

Student Cluster 3 generated the most sequences, whose sequences fell into all modality profiles, with TMP2 (mobile) and TMP3 (short-computer) most prominent and used almost equally.

“It represents those who generated the most sequences, whose sequences fell into all modality profiles, among which TMP2 (mobile) and TMP3 (short-computer) were the most prominent and used almost equally.”

• cites Sher and colleagues (2019)Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

May combine two claims.

TMP3 (Short-Computer Oriented) cluster contained 9,571 sequences, representing 50.6% of all sequences.

“TMP3 ClusterShort-Computer Oriented ( N = 9 , 571, 50.6%): This cluster was predominantly focused on actions from the computer modality. It was the biggest of the four TMP clusters, containing more than half of all learning sequences.”

• cites Sher and colleagues (2019)Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

May combine two claims.

TMP3 sessions tended to be short, with a median of 3 actions, and the longest session had 22 actions.

“The learning sessions (and, thus, sequences) in this cluster tended to be short (median = 3 actions in one learning session), with the longest session composed of only 22 actions.”

• cites Sher and colleagues (2019)Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Classified as probably not a substantial claim.

The columns proportion test uses z-tests to compare column proportions for each TMP after adjusting for multiple comparisons using Bonferroni corrections.

“The test uses z-tests to compare column (i.e., TOD) proportions for each TMP after adjusting for multiple comparisons using Bonferroni corrections.”

Probably not a substantial claim: may not pass S1 (asserted).

Mobile phone sessions are assumed to be more spontaneous and for small actions only.

“which one would assume to be more spontaneous and for small actions only (Casany Guerrero et al., 2012)”

• cites Casany Guerrero et al., 2012Not matched

  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).

The observed weekday/weekend time-of-day preferences are explained by participants being full-time students in daytime classroom learning during the five-day work week, which leaves little room for morning or night learning.

“These preferences are not surprising since the participants were full-time students engaging in classroom learning during the day throughout the five-day work week, leaving little room for morning learning sessions when getting ready for classes or at night when they are tired.”

May combine two claims.

The clusters differentiated final grade at e2=0.12 power and discussion participation at h2=0.68.

“These clusters were able to differentiate between students' final grade at e 2 = 0 . 12 power and discussion participation at h 2 = 0 . 68 (Sher et al., 2019).”

• cites Sher et al., 2019 sourceNot checked — no full text of the source

Classified as probably not a substantial claim. May combine two claims.

Intensive learners do not rely entirely on computers for all their learning needs and therefore were successful in reserving a specific modality for a specific time-frame during the day.

“A possible rationale for this finding could be that unlike computer-dominant and limited-computer learners, intensive learners do not rely entirely on computers for all their learning needs and therefore were successful in reserving the use of a specific modality for a specific time-frame during the day.”

May combine two claims.

Student Cluster 3 had a lower median grade than the computer-dominant group, but the difference was non-significant.

“In terms of overall course grade, even though a lower median percentage than the high-performing computer-dominant group was recorded, the differences were non-significant.”

• cites Sher and colleagues (2019)Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Classified as probably not a substantial claim.

Previous studies found mobile phone usage restricted to late-night hours between 8 p.m. and midnight.

“much like the results reported by Song and colleagues (2013) and Casany Guerrero and colleagues (2012), who found that mobile phone usage was restricted to late-night hours between 8 p.m. and midnight”

• cites Song and colleagues (2013)Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Casany Guerrero and colleagues (2012)Not matched

  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.

Learning behaviour may depend not only on time of day but also on day of the week, and more specifically on type of day (weekday or weekend).

“Since the literature supports claims that learning behaviour may depend not only on time of day but also on day of the week, and more specifically type of day (weekday or weekend), we studied the hypothesis separately for weekdays and weekends.”

• cites Duck, Rutt, Hoy, & Strejc, 1991 sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

Tabuenca et al. (2015) found students most active on mobile phones from 9 a.m. to 3 p.m.

“This finding is also partially supported by Tabuenca, Kalz, Drachsler, and Specht (2015), who found that students were most active on their mobile phones from 9 a.m. to 3 p.m.”

• cites Tabuenca, Kalz, Drachsler, and Specht (2015) sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

Computer-dominant and limited-computer learners are strikingly different in academic performance.

“two groups that are strikingly different in terms of their academic performance, as reported by Sher and colleagues (2019).”

• cites Sher and colleagues (2019)Not matched

  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).

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

“The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.”

Probably not a substantial claim: may not pass S3 (load-bearing).

There can be ambiguities in the study's time periods/day slots depending on how they were defined, which usually depends on personal lifestyle and culture.

“However, there can be some ambiguities depending on how these time periods/day slots were defined, i.e., morning (5 a.m. to noon), afternoon (noon to 6 p.m.), evening (6 p.m. to 9 p.m.), and night (9 p.m. to 5 a.m.), which usually depends on personal lifestyle and culture.”

Classified as probably not a substantial claim.

Goodyear (2011) emphasized the need to understand how learning activities are distributed across different contexts.

“Goodyear (2011) emphasized the need to understand how learning activities are distributed across different contexts”

• cites Goodyear (2011) sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

The study's methodology involves tracking user interaction with the LMS within a programming course, which may raise a concern about the extent to which the results depended on the learning context.

“since our methodology involves tracking user interaction with the LMS within a programming course, this may raise a concern about the extent to which our results depended on the learning context.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

The clustering methodology may have been overly complex.

“after observing the results, we see that the methodology may have been overly complex.”

Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.

Given knowledge of high-quality learning times, students would be able to organize and regulate their activities asynchronously, leading to greater productivity.

“given the knowledge of high-quality learning times, students would be able to organize and regulate their activities asynchronously, leading to greater productivity.”

May combine two claims.

The contrast with Romero and Barberà is explained by contextual differences: their study was fully online social sciences with written assignments, while our participants were full-time programming students using specific software on computers.

“A possible explanation might be the contextual differences introduced by the course settings. Their study looked at fully online learners engaging in a social sciences course, graded based on written assignments. By contrast, the participants in our study were full-time students in programming-oriented courses that required them to spend a fair amount of time working on their programming”

Probably not a substantial claim: may not pass S4 (based).

A typical recommender system cannot aptly judge both the amount and the quality of the time a learner has to engage in a task, wasting learner time and modality as a knowledge asset.

“As of now, a typical recommender system cannot aptly judge both the amount and the quality of the time a learner has to engage in a task, thereby wasting both learner time and modality as a true knowledge asset.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Informed consent was obtained from all study participants.

“Informed consent was obtained from all study participants.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Students preferred fixed-time mobile notifications over random-time notifications because fixed times allow them to plan their day.

“Tabuenca and colleagues (2015) studied learner perceptions regarding whether mobile notifications should be sent at random or fixed times and found that students preferred the latter over the former, given that it allows them to plan their day.”

• cites Tabuenca and colleagues (2015)Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Classified as probably not a substantial claim.

Single actions originate from one modality and thus don't tell us much about whether continued use of a modality was observed for other activities too.

“Single actions originate from one modality and thus don't tell us much about whether continued use of a modality was observed for other activities too, after a student completed one activity on a particular modality.”

Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

Knowledge of students' preferred time of day can be leveraged for sending notifications, reminders on preferred modality, or feedback on academic submissions.

“knowledge of their preferred time of day can be leveraged for sending them notifications, reminders on their preferred modality type, or even feedback regarding their academic submissions.”

Classified as probably not a substantial claim.

Fixed-time notifications, unless modality is considered, entail sending notifications at pre-fixed times on all modalities without capitalizing on the unique capabilities of each modality.

“Unless modality is taken into consideration, 'fixed' time entails sending notifications, at any level of required effort or urgency, to learners at pre-fixed times on all modalities without capitalizing on the unique capabilities that each modality provides.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The clustering methodology was set upfront before the authors knew what the results would look like.

“We set the clustering methodology upfront before we knew what the results would look like”

Probably not a substantial claim: may not pass S3 (load-bearing).

All collected data was treated confidentially.

“All collected data was treated confidentially.”

Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

Personalized delivery of learning-related recommendations for each individual learner is possible and promising.

“personalized delivery of learning-related recommendations for each individual learner is possible and also promising.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

Intensive learners would benefit from such notifications if they were sent in the morning on weekdays or weekends.

“This group would benefit from such notifications if they were sent in the morning on weekdays or weekends.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

The ability to share and distribute information to learners via learning systems is important for extending learning outside the formal classroom.

“The ability to share and distribute information to learners via learning systems is important for extending learning outside the formal classroom (Cheon, Lee, Crooks, & Song, 2012).”

• cites Cheon, Lee, Crooks, & Song, 2012 sourceNot checked — no full text of the source

May combine two claims.

Evening (and late night) emerged as the preferred time of day for computer-oriented sessions because participants were in class for most of their day.

“By virtue of our participants being in class for most of their day, evening (and late night) undoubtedly emerged as the preferred time of day for computer-oriented sessions.”

Probably not a substantial claim: may not pass S3 (load-bearing).

This research has been approved by the university ethics board (2014s0127).

“This research has been approved by the university ethics board (2014s0127).”

May combine two claims.

Learners might receive instructional content at an inappropriate time, such as late evening when tired or while commuting, which results in ineffective learning.

“there is a fair possibility that they might receive it at an inappropriate time, say late evening when they are tired (Grundspenkis, Lavendels, Novitsky, & Sitikovs, 2006) or while commuting (Srinivasan, Koehler, & Jin, 2018), which in turn results in ineffective learning.”

• cites Grundspenkis, Lavendels, Novitsky, & Sitikovs, 2006Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Srinivasan, Koehler, & Jin, 2018 sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S4 (based). May combine two claims.

Judging one's performance relative to effort is difficult for many university students, mainly because they do not know how much time other students devote to the course.

“Judging one's performance, especially as it relates to the effort exerted, is difficult for many university students. The challenge mainly originates from not knowing how much time other students devote to working on the course.”