Self-Regulated Studying Behavior, and the Social Norms that Influence It

Eyink, Julie; Motz, Benjamin; Heltzel, Gordon; Liddell, Torrin M.

doi:10.31234/osf.io/bf6kg

Cited by 4 publications

(4 citation statements)

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“…In contrast to Experiment 1, which estimated the benefit of automated deadline reminders over instructor announcements, Experiment 2 estimated the cumulative benefit of both types of notifications, comingled in the same pragmatic treatment. Considering that instructor announcements do have benefits for student motivation (e.g., [39]) and behavior (e.g., [40]), it makes sense that the estimated improvement in assignment submission rates (compared with no notification whatsoever) in Experiment 2 (+5.7%) was higher than Experiment 1 (+3.7%).…”

Section: Discussionmentioning

confidence: 99%

“…For example, even while administrators advocate against views of students as deficient (e.g., [48]), empirical research demonstrates that some paternalistic policies are effective at closing achievement gaps and increasing college preparation in inner-city schools [49,50]. Similarly, an instructor's tailored suggestion of what a student should do can be more effective at improving student engagement than purely informative interventions [40], and instituting a firm schedule of assignment deadlines leads to improved performance over flexible student-selected assignment deadlines [51]. These successful paternalistic approaches share common features of being highly personalized and contextual, and thus difficult to implement at scale.…”

Section: Discussionmentioning

confidence: 99%

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Automated Educative Nudges to Reduce Missed Assignments in College

Motz¹,

Mallon²,

Quick³

2020

Preprint

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As institutions of higher education increasingly utilize online learning management systems, college students are asked to submit more assignments online. Under this regime, when most assignments are posted and submitted online, it is possible to know if a student is missing a submission for an imminent deadline, and to intervene proactively to reduce missed assignments and improve student outcomes. Toward this goal, we designed and evaluated a scalable targeted intervention: a mobile app that would deploy push notifications when students were missing submissions for assignments with imminent deadlines. Results from two experimental pilots demonstrate that this intervention system significantly decreased missed assignments compared with control notifications about instructor announcements to the class (in Experiment 1), and improved assignment adherence and course grades compared with courses that were not using the app (in Experiment 2). We discuss the benefits and theoretical implications of this behavioral guide rail, a purely informative proactive intervention to mitigate risk in advance of a negative outcome.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Automated Educative Nudges to Reduce Missed Assignments in College

Motz¹,

Mallon²,

Quick³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Relatedly, the development of automated intervention techniques remains a fertile ground to sow. Continued research on all sorts of students’ behaviors, such as diligence and time management skills [ 36 , 37 ], and continued research on interventions targeting students’ studying behaviors [ 38 ] will likely allow systems to be created that can train students to optimize their study behaviors, not only through the provision of advice to struggling students, but through training students how they can best study (e.g., through algorithm-driven click behavior guidance).…”

Section: Discussionmentioning

confidence: 99%

How do online learners study? The psychometrics of students’ clicking patterns in online courses

2019

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College students’ study strategies were explored by tracking the ways they navigated the websites of two large (Ns of 1384 and 671) online introductory psychology courses. Students’ study patterns were measured analyzing the ways they clicked outside of the regularly scheduled class on study materials within the online Learning Management System. Three main effects emerged: studying course content materials (as opposed to course logistics materials) outside of class and higher grades are consistently correlated; studying at any time except in the late night/early morning hours was strongly correlated with grades; students with higher Scholastic Aptitude Test (SAT) scores made higher grades but accessed course materials at lower rates that those with lower SATs. Multiple regressions predicting grades using just SATs and click rates accounted for almost 43 and 36 percent of the grade variance for the Fall and Spring classes respectively. Implications for using click patterns to understand and shape student learning are discussed.

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“…As mentioned previously, we aim to predict student engagement level to be either not engaged, passively engaged or actively engaged. As engagement directly affects the students’ results [ 51 – 53 ], the goal of the proposed prediction system is to evaluate their engagement and motivate them to progress continuously and move up from one category to another; it also helps engaged students to keep the same pace of work.…”

Section: Methodsmentioning

confidence: 99%

A new ML-based approach to enhance student engagement in online environment

et al. 2021

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The educational research is increasingly emphasizing the potential of student engagement and its impact on performance, retention and persistence. This construct has emerged as an important paradigm in the higher education field for many decades. However, evaluating and predicting the student’s engagement level in an online environment remains a challenge. The purpose of this study is to suggest an intelligent predictive system that predicts the student’s engagement level and then provides the students with feedback to enhance their motivation and dedication. Three categories of students are defined depending on their engagement level (Not Engaged, Passively Engaged, and Actively Engaged). We applied three different machine-learning algorithms, namely Decision Tree, Support Vector Machine and Artificial Neural Network, to students’ activities recorded in Learning Management System reports. The results demonstrate that machine learning algorithms could predict the student’s engagement level. In addition, according to the performance metrics of the different algorithms, the Artificial Neural Network has a greater accuracy rate (85%) compared to the Support Vector Machine (80%) and Decision Tree (75%) classification techniques. Based on these results, the intelligent predictive system sends feedback to the students and alerts the instructor once a student’s engagement level decreases. The instructor can identify the students’ difficulties during the course and motivate them through e-mail reminders, course messages, or scheduling an online meeting.

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Self-Regulated Studying Behavior, and the Social Norms that Influence It

Cited by 4 publications

References 0 publications

Automated Educative Nudges to Reduce Missed Assignments in College

Automated Educative Nudges to Reduce Missed Assignments in College

How do online learners study? The psychometrics of students’ clicking patterns in online courses

A new ML-based approach to enhance student engagement in online environment

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