Statistical Assessment on Student Engagement in Asynchronous Online Learning Using the k-Means Clustering Algorithm

Kim, Sohee; Cho, Sunghee; Kim, Joo Yeun; Kim, Dae‐Jin

doi:10.3390/su15032049

Cited by 13 publications

(7 citation statements)

References 50 publications

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“…K-Means clustering algorithm has gained popularity in a variety of fields, including big data analysis (Tao et al, 2016), environmental studies (Xiaojie et al, 2015), social studies (Ding et al, 2013), and power generation (Alkilany et al, 2014). K-means have been frequently used in the learning analytics community as well to classify students into groups with similar characteristics (Chang et al, 2020;Kim, Cho, et al, 2023;Moubayed et al, 2020). Clustering may serve as the foundation for recommendations that may later be made to these students based on their cluster.…”

Section: Discussionmentioning

confidence: 99%

Unfolding self‐regulated learning profiles of students: A longitudinal study

Esnaashari

Gardner

Arthanari

et al. 2023

Computer Assisted Learning

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Background It is vital to understand students' Self‐Regulatory Learning (SRL) processes, especially in Blended Learning (BL), when students need to be more autonomous in their learning process. In studying SRL, most researchers have followed a variable‐oriented approach. Moreover, little has been known about the unfolding process of students' SRL profiles. Objectives We present the insights derived from a study that measured motivation and the learning strategies used by 198 students of a university entry‐level, business school, BL course to develop an understanding of students' SRL processes. Methods The Strategies for Learning Questionnaire (MSLQ) was used to survey 198 students three times during a semester to investigate SRL profiles and how they unfolded as the course progressed using a person‐oriented approach. Through a clustering approach, we focus on MSLQ's motivation aspects as its importance has been emphasised by different SRL theories, and extant research into motivation in learning analytics (LA) is still lacking. Results and Conclusions Through the longitudinal clustering approach, we identified minimally, average, and highly SRL profiles. We acknowledged that students might change their SRL profiles as the course progressed as a result of feedback they received. What are the 1 or 2 Major Takeaways from the Study? This study contributes to the SRL theory by examining students' SRL profiles adaptation longitudinally (addressing the challenge identified regarding the cyclical nature of SRL). This study contributes to LA by investigating motivational constructs currently lacking in the field and bringing forward theory based empirical evidence to inform theory and practice.

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Section: Discussionmentioning

confidence: 99%

Unfolding self‐regulated learning profiles of students: A longitudinal study

Esnaashari

Gardner

Arthanari

et al. 2023

Computer Assisted Learning

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“…Similarly, Current [36] emphasized the need for feedback in formative assessments to evaluate teaching and learning systems, while Geletu [37] found that teacher participation positively affects students' learning engagement in science. Ma and Luo [38] demonstrated that engaging in student and peer assessment can positively impact learning performance, and Kim et al [39] showed that clustering algorithms could effectively assess student engagement in asynchronous online learning. Ndihokubwayo et al [40] and Petričević et al [41] also found that active learning environments and contextual and individual factors can influence student engagement in physics.…”

Section: Student Engagement In Learningmentioning

confidence: 99%

FLM-based AFL improves physics engagement and conceptual understanding

Asyhari,

Irawan,

Sunyono

et al. 2024

EduLearn

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Assessment for learning (AFL) is a pedagogical approach that enhances student learning outcomes through high-quality feedback. This study investigates the effectiveness of integrating the feedback loop model (FLM) with AFL to improve students' engagement and understanding of physics, specifically in kinematics and motion dynamics. The study employs a mixed-methods research design, combining quantitative and qualitative data to assess the impact of the FLM-based AFL approach. A one-group pretest-posttest design was used, supported by research instruments that measured student engagement and their conceptual grasp of physics. The findings indicate that integrating FLM into AFL led to significant improvements, evidenced by Cohen’s effect size of 1.91, highlighting a substantial impact on student learning. These results affirm that FLM-based AFL positively affects student engagement and understanding of physics. The study contributes to the existing research on effective assessment methods, providing valuable insights for educators and policymakers in developing enhanced assessment and teaching strategies. This study emphasizes the potential benefits of incorporating FLM-based AFL in diverse educational settings to elevate student learning experiences and outcomes.

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“…Efforts to build students' autonomy are often reciprocated by greater enthusiasm from learners, such as asking questions out of sheer interest [7] rather than gaining bonus grades from the instructor. Moreover, pedagogical strategies such as peer feedback, group discussions, and ice-breaking often help to facilitate peer interaction [67] .…”

Section: Relation Between Teacher Support and Students' Behavioral En...mentioning

confidence: 99%

Effects of Perceived Teacher Support on Student Behavioral Engagement in the Blended Learning Environment: Learning Experience as a Mediator

Zhang,

Chen,

Jiang

et al. 2024

JCER

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Blended learning (BL) has been widely adopted to improve students’ academic achievements in higher education. However, its success relies mainly on student engagement, which plays an essential role in active learning and provides a rich understanding of students’ experiences. The study utilized three self-designed scales—the Teacher Support Scale, Student Engagement Scale, and Student Learning Experience Scale—to gauge and examine the impact and relationship between perceived teacher support, student behavioral engagement, and the intermediary role of learning experiences. A cohort of 899 college students undertaking the obligatory College English course through BL modes across five Chinese universities actively participated by completing a comprehensive questionnaire. The results showed significant correlations between perceived teacher support, learning experience, and behavioral engagement. Perceived teacher support significantly predicted students’ behavioral engagement, with socio-affective support exerting the most substantial predictive effects. All predictive effects were partially mediated by learning experience (learning mode, online resources, overall LMS-based learning, interaction with their instructor and peers, and learning outcome). The influence of perceived teacher support on behavioral engagement differed between students who reported the most positive (vs. negative) learning experiences. Suggestions for further research are offered for consideration.

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Statistical Assessment on Student Engagement in Asynchronous Online Learning Using the k-Means Clustering Algorithm

Cited by 13 publications

References 50 publications

Unfolding self‐regulated learning profiles of students: A longitudinal study

Unfolding self‐regulated learning profiles of students: A longitudinal study

FLM-based AFL improves physics engagement and conceptual understanding

Effects of Perceived Teacher Support on Student Behavioral Engagement in the Blended Learning Environment: Learning Experience as a Mediator

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