Analysis of Students’ Personalized Learning and Engagement within a Cyberlearning System

Basu, Debarati; Lohani, Vinod K.; Kang, Xia

doi:10.18260/1-2--32088

Cited by 2 publications

(2 citation statements)

References 41 publications

(51 reference statements)

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“…The obtained results show that the coefficient of hours indicates that the level of engagement in e-learning has a positive, and statistically significant, impact on the module mark. Basu et al [43] developed an Online Watershed Learning System (OWLS) over a Learning Enhanced Watershed Assessment System (LEWAS), which is a unique real-time high-frequency environmental monitoring system established to promote environmental monitoring education and research. It was inspired by Google Analytics to track users and their actions (i.e., mouse clicks, typed keys, and navigation through webpages) across devices in a cyberlearning system.…”

Section: Tools For Assessing the Level Of Student Engagement In Virtu...mentioning

confidence: 99%

A Data-Driven Approach to Quantify and Measure Students’ Engagement in Synchronous Virtual Learning Environments

Solé-Beteta

Navarro

Gajšek

et al. 2022

Sensors

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In face-to-face learning environments, instructors (sub)consciously measure student engagement to obtain immediate feedback regarding the training they are leading. This constant monitoring process enables instructors to dynamically adapt the training activities according to the perceived student reactions, which aims to keep them engaged in the learning process. However, when shifting from face-to-face to synchronous virtual learning environments (VLEs), assessing to what extent students are engaged to the training process during the lecture has become a challenging and arduous task. Typical indicators such as students’ faces, gestural poses, or even hearing their voice can be easily masked by the intrinsic nature of the virtual domain (e.g., cameras and microphones can be turned off). The purpose of this paper is to propose a methodology and its associated model to measure student engagement in VLEs that can be obtained from the systematic analysis of more than 30 types of digital interactions and events during a synchronous lesson. To validate the feasibility of this approach, a software prototype has been implemented to measure student engagement in two different learning activities in a synchronous learning session: a masterclass and a hands-on session. The obtained results aim to help those instructors who feel that the connection with their students has weakened due to the virtuality of the learning environment.

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Section: Tools For Assessing the Level Of Student Engagement In Virtu...mentioning

confidence: 99%

A Data-Driven Approach to Quantify and Measure Students’ Engagement in Synchronous Virtual Learning Environments

Solé-Beteta

Navarro

Gajšek

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Moreover, analytics in student engagement has shifted the focus from predicting outcomes to displaying information about student engagement (Verbert;Duval;Klerkx;Govaerts;& Santos1, 2013). This type of tracking systems uses LMS log data, utilisation of course resources, student attendance, assessment scores, behaviour monitoring, and classroom participation, to facilitate visualisation of student engagement (Hussain;Zhu;Zhang;& Abidi, 2018;Burnik;Zaletelj;& Košir, 2017;Basu;Lohani;& Xia, 2019). Most of these tracking systems use machine learning predictive modelling techniques.…”

Section: Student Engagement Tracking Systemsmentioning

confidence: 99%

Formative assessment tasks as indicators of student engagement for predicting at-risk students in programming courses

Veerasamy¹,

Laakso²,

D'Souza³

2021

Informatics in Education

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Previous studies have proposed many indicators to assess the effect of student engagement in learning and academic achievement but have not yet been clearly articulated. In addition, while student engagement tracking systems have been designed, they rely on the log data but not on performance data. This paper presents results of a non-machine learning model developed using ongoing formative assessment scores as indicators of student engagement. Visualisation of the classification tree results is employed as student engagement indicators for instructors to observe and intervene with students. The results of this study showed that ongoing assessment is related to student engagement and subsequent final programming exam performance and possible to identify students at-risk of failing the final exam. Finally, our study identified students impacted by the Covid-19 pandemic. These were students who attended the final programming exam in the semester 2019-2020 and who scored well in formative assessments. Based on these results we present a simple student engagement indicator and its potential application as a student progress monitor for early identification of students at risk.

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Analysis of Students’ Personalized Learning and Engagement within a Cyberlearning System

Cited by 2 publications

References 41 publications

A Data-Driven Approach to Quantify and Measure Students’ Engagement in Synchronous Virtual Learning Environments

A Data-Driven Approach to Quantify and Measure Students’ Engagement in Synchronous Virtual Learning Environments

Formative assessment tasks as indicators of student engagement for predicting at-risk students in programming courses

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