Student Engagement Patterns in a Blended Learning Environment: an Educational Data Mining Approach

Nkomo, Larian M.; Nat, Müesser

doi:10.1007/s11528-021-00638-0

Cited by 12 publications

(8 citation statements)

References 57 publications

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“…Through entropy measurement, educators can assess the level of student engagement by tracking the percentage of students with positive entropy values. This is in line with [ 13 , 69 , 70 ], who affirmed the importance of monitoring student engagement in virtual learning environments, suggesting the use of data mining techniques to track student behaviours. Our study extends this proposition by introducing entropy as a simplified, yet insightful, metric.…”

Section: Discussionsupporting

confidence: 87%

Students’ Learning Behaviour in Programming Education Analysis: Insights from Entropy and Community Detection

Mai,

Crane,

Bezbradica

2023

Entropy

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The high dropout rates in programming courses emphasise the need for monitoring and understanding student engagement, enabling early interventions. This activity can be supported by insights into students’ learning behaviours and their relationship with academic performance, derived from student learning log data in learning management systems. However, the high dimensionality of such data, along with their numerous features, pose challenges to their analysis and interpretability. In this study, we introduce entropy-based metrics as a novel manner to represent students’ learning behaviours. Employing these metrics, in conjunction with a proven community detection method, we undertake an analysis of learning behaviours across higher- and lower-performing student communities. Furthermore, we examine the impact of the COVID-19 pandemic on these behaviours. The study is grounded in the analysis of empirical data from 391 Software Engineering students over three academic years. Our findings reveal that students in higher-performing communities typically tend to have lower volatility in entropy values and reach stable learning states earlier than their lower-performing counterparts. Importantly, this study provides evidence of the use of entropy as a simple yet insightful metric for educators to monitor study progress, enhance understanding of student engagement, and enable timely interventions.

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Section: Discussionsupporting

confidence: 87%

Students’ Learning Behaviour in Programming Education Analysis: Insights from Entropy and Community Detection

Mai,

Crane,

Bezbradica

2023

Entropy

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“…There are three common dimensions of student engagement ; behavioral, cognitive and emotional (de Milliano et al, 2017;Fredricks et al, 2016;Pilotti et al, 2017;Reading, 2008;Wang, 2017;Yuan & Kim, 2018) cited in Nkomo & Nat , (2021).…”

Section: Students' Engagementmentioning

confidence: 99%

“…According to Nkomo & Nat (2021), there are 3 common types of students' engagement; Behavioral, Cognitive and Emotional. Therefore, here the researcher discusses one by one the types of students' engagement in blended learning in EFL reading classroom according to the teachers' perception based on the theories used.…”

Section: Students' Emotional Engagementmentioning

confidence: 99%

“…Furthermore, there have been previous studies related to students' engagement in blended learning. Studies from Nkomo & Nat (2021), showed the result that students engaged differently with learning resources as students had different engagement patterns based on low, moderate and high engagement levels. In this study used SimpleKmeans clustering technique to known the data result.…”

Section: Introductionmentioning

confidence: 99%

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Teachers’ Perception on Students’ Engagement in Blended Learning in Efl Reading Classroom

Pramadita

2022

EEAL

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In this pandemic era, blended learning model applied in school one of them in teaching EFL. Blended learning is a relatively new field that combines traditional teaching approaches with online learning. Other than that, students’ engagement is a key aspect in the improvement of getting to know in classroom. The purpose of this research is to find out students’ engagement in blended learning in EFL reading classroom from the teacher's point of view. This research guided by qualitative research approach with case study design. This research was conducted at one of junior high school in Garut. The participants in this research are three English teachers from that school. The data was collected through face-to-face and online interviews. To analyze the data of this research, researcher used inductive analysis data. The findings of this research based on teachers’ perception from participants are behavioral engagement of students in blended learning in EFL reading classroom have the same position between offline and online learning in classroom. Cognitive engagement of students in blended learning in EFL reading classroom more positive when offline learning than when online learning. Emotional engagement of students in blended learning in EFL reading classroom more positive when offline learning than when online learning. It means that in blended learning, student involvement is more likely to look good or positive during offline learning compared to online learning.

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“…Traditionally, student engagement has been assessed using self-report via surveys, with some students over-reporting their engagement (Henrie et al, 2015 ). LMSs represent an opportunity to overcome the drawbacks of self-reports by data mining to assess student engagement objectively (Nkomo & Nat, 2021 ) using learning analytics. Learning analytics has been defined as “the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising [sic] learning and the environments in which it occurs” (Long & Siemens, 2011 , p. 33).…”

Section: Introductionmentioning

confidence: 99%

Increasing student engagement with course content in graduate public health education: A pilot randomized trial of behavioral nudges

et al. 2023

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Digital advances in the learning space have changed the contours of student engagement as well as how it is measured. Learning management systems and other learning technologies now provide information about student behaviors with course materials in the form of learning analytics. In the context of a large, integrated and interdisciplinary Core curriculum course in a graduate school of public health, this study undertook a pilot randomized controlled trial testing the effect of providing a “behavioral nudge” in the form of digital images containing specific information derived from learning analytics about past student behaviors and performance. The study found that student engagement varied significantly from week to week, but nudges linking coursework completion to assessment grade performance did not significantly change student engagement. While the a priori hypotheses of this pilot trial were not upheld, this study yielded significant findings that can guide future efforts to increase student engagement. Future work should include a robust qualitative assessment of student motivations, testing of nudges that tap into these motivations and a richer examination of student learning behaviors over time using stochastic analyses of data from the learning management system.

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Student Engagement Patterns in a Blended Learning Environment: an Educational Data Mining Approach

Cited by 12 publications

References 57 publications

Students’ Learning Behaviour in Programming Education Analysis: Insights from Entropy and Community Detection

Students’ Learning Behaviour in Programming Education Analysis: Insights from Entropy and Community Detection

Teachers’ Perception on Students’ Engagement in Blended Learning in Efl Reading Classroom

Increasing student engagement with course content in graduate public health education: A pilot randomized trial of behavioral nudges

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