Relationship between students’ online learning behavior and course performance: What contextual information matters?

Chen, Zhongzhou; Xu, Mengyu; Garrido, Geoffrey; Guthrie, Matthew W.

doi:10.1103/physrevphyseducres.16.010138

Cited by 17 publications

(23 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The symptoms varied in terms of duration from less than a week to more than three months. The online learning is quite engaging in terms of mental efforts and reading from screen of either mobile or laptop (Chen et al, 2020) and therefore requires the engagement of the head, eyes and neck. It was found that there is considerable stress on the neck muscles during prolonged use of a smartphone (Kim, 2015).…”

Section: Discussionmentioning

confidence: 99%

Demonstration of the relationship between MSD and online learning during the COVID-19 pandemic

Karingada¹,

Sony

2021

JARHE

View full text Add to dashboard Cite

PurposeThe COVID-19 pandemic lockdown has caught many educational institutions by surprise and warranted an abrupt migration from offline to online learning. This has resulted in an education change, without any time for due consideration, as regards its impact on musculoskeletal disorders (MSD) on students. The purpose of this study is to investigate MSD related to online learning during the COVID-19 pandemic lockdown.Design/methodology/approachA cross-sectional study was conducted on undergraduate students in India. In total, 261 students participated in this online survey.FindingsThe study finds that around 80% of students have reported some symptom in the head, neck and eyes since they started online learning. In total, 58% have reported MSD symptom in the right shoulder and 56% in the right hand fingers. Besides, more than 40 % of students experienced some MSD symptoms, in almost all the body parts studied, due to online learning. Correlation analysis is conducted between time spent on online learning per day and MSD symptoms.Originality/valueThis is the first study conducted on MSD and online learning during COVID-19 pandemic.

show abstract

Section: Discussionmentioning

confidence: 99%

Demonstration of the relationship between MSD and online learning during the COVID-19 pandemic

Karingada¹,

Sony

2021

JARHE

View full text Add to dashboard Cite

show abstract

“…1.2.1 Level I: Clustering of Individual Events. Prior research on OLMs has shown that students' use of learning strategies may not be reflected by the order of events, but rather by the quality of events [4,9]. More specifically, an abnormally short assessment attempt is likely the result of random guessing or answer copying and may be indicative of the student adopting a performance-avoidance goal.…”

Section: Analysis Of Olm Trace Data Via Multi-level Clusteringmentioning

confidence: 99%

A Multi-Level Trace Clustering Analysis Scheme for Measuring Students' Self-Regulated Learning Behavior in a Master-Based Online Learning Environment

Zhang¹,

Taub²,

Chen³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

The study introduces a new analysis scheme to analyze trace data and visualize students' self-regulated learning strategies in a mastery-based online learning modules platform. The pedagogical design of the platform resulted in fewer event types and less variability in student trace data. The current analysis scheme overcomes those challenges by conducting three levels of clustering analysis. On the event level, mixture-model fitting is employed to distinguish between abnormally short and normal assessment attempts and study events. On the module level, trace level clustering is performed with three different methods for generating distance metrics between traces, with the best performing output used in the next step. On the sequence level, trace level clustering is performed on top of module-level clusters to reveal students' change of learning strategy over time. We demonstrated that distance metrics generated based on learning theory produced better clustering results than pure data-driven or hybrid methods. The analysis showed that most students started the semester with productive learning strategies, but a significant fraction shifted to a multitude of less productive strategies in response to increasing content difficulty and stress. The observations could prompt instructors to rethink conventional course structure and implement interventions to improve self-regulation at optimal times.

show abstract

“…We measure these breaks using the time elapsed between a "viewer-exit" event, which triggers when either the module is closed or the browser window is minimized or remain inactive for more than 10 minutes, and the following "viewer-enter" event, triggered when the student returns. Therefore, the distribution of time elapsed between consecutive exit and enter events should contain two or more separate distributions, which can be separated by fitting the data using multi-component mixture model, similar to the analysis in several earlier papers [14,[24][25][26].…”

Section: B Identifying Valid Study Sessions From Event Logs (Rq1)mentioning

confidence: 99%

The impact of extra credit incentives on students’ work habits when completing online homework assignments

Felker¹,

Chen²

2020

2020 Physics Education Research Conference Proceedings

Self Cite

View full text Add to dashboard Cite

The current paper examines the effectiveness of offering a small amount of extra credit as an incentive to encourage proper work distribution and reduce procrastination and cramming among college students completing introductory physics homework assignments in the form of online learning modules. Students' distribution of work over time is systematically measured by clustering clickstream log events into study sessions according to a cutoff determined empirically using mixture model analysis. Significantly more study sessions are initiated well before the assignment due date when extra credit is offered compared to data from a previous semester. Using two proxy variables designed to capture the distribution and duration of work, we found that in addition to starting the assignments earlier, students also spent a longer time on the assignments. Finally, the benefit of extra credit in encouraging work distribution is not limited to high-performing students, as shown by a reduction in score gap between early and late starters on a midterm exam administered prior to the release of the homework assignment.

show abstract

Relationship between students’ online learning behavior and course performance: What contextual information matters?

Cited by 17 publications

References 32 publications

Demonstration of the relationship between MSD and online learning during the COVID-19 pandemic

Demonstration of the relationship between MSD and online learning during the COVID-19 pandemic

A Multi-Level Trace Clustering Analysis Scheme for Measuring Students' Self-Regulated Learning Behavior in a Master-Based Online Learning Environment

The impact of extra credit incentives on students’ work habits when completing online homework assignments

Contact Info

Product

Resources

About