Mapping Patterns of Student Engagement Using Cluster Analysis

Wilson, Denise; Wright, Joanna; Summers, Lauren

doi:10.1007/s41979-021-00049-z

Cited by 7 publications

(4 citation statements)

References 44 publications

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“…It allows to obtain a grouping of respondents according to their level of closeness in their responses to the scale used (Hair et al ., 2014). Prior studies have used cluster analysis to identify hidden patterns of student online behaviour in flipped classroom environments (Walsh and Rísquez, 2020), map patterns of student engagement (Wilson et al ., 2021), identify categories of MOOC learners based on patterns of student engagement (Deng et al ., 2020) and so on. Hence, cluster analysis was an appropriate technique for the present study to classify students in similar groups, according to their engagement patterns in virtual classrooms.…”

Section: Data Analysis and Resultsmentioning

confidence: 99%

Exploring student engagement in virtual classrooms: a person-centred approach using cluster analysis

Gupta

2022

IJEM

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PurposeThe purpose of this paper is to identify student clusters based on patterns of behavioural, cognitive, emotional, collaborative and social engagement in virtual classrooms, within the context of higher education. The study also attempts to explore the differences amongst the student clusters with respect to various motivating and demotivating teaching behaviours.Design/methodology/approachThe study relies on the self-determination theory to describe the dynamics related to various motivating and demotivating teaching behaviours and five different dimensions of student engagement in virtual classrooms. The data were collected from a convenience sample of 831 students from higher educational institutions in the Delhi – National Capital Region of India. Cluster analysis was performed to classify students into mutually exclusive groups based on five student engagement dimensions.FindingsThe findings revealed three student clusters: ‘fully-engaged’, ‘externally-engaged’ and ‘individually-engaged’ students. The findings also provided insights into varied correlations between specific dimensions of motivating/demotivating teaching behaviours and student engagement.Originality/valueConsidering the multi-dimensionality of student engagement as well as teaching behaviours, the study adopts a person-centred approach to understand different clusters of students in virtual classrooms and investigate the relationship between teaching behaviours and student engagement dimensions.

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Section: Data Analysis and Resultsmentioning

confidence: 99%

Exploring student engagement in virtual classrooms: a person-centred approach using cluster analysis

Gupta

2022

IJEM

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“…The lack of maximum intensity of student involvement can indeed be a concern. However, if viewed positively, it could be because the level of intensity can also be caused by the homogeneity between the subjects studied by students (Wilson, Wright, & Summers, 2021). According to Piñeiro, et al (2019).…”

Section: Discussionmentioning

confidence: 99%

Abrupt Transformation in Pedagogy: How Do Students Engage Academically in the Face-to-Face Online Learning Environment?

2022

JHETP

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Use of technology is beneficial in pedagogical transformation, but student engagement is essential throughout the process. Because the number of studies investigating how students' academic involvement during the pandemic is still limited, this research is necessary to close this gap in the literature. The research goal is to determine the contribution of students' cognitive and affective engagement to their academic activities by utilizing an intrinsic motivation approach in the context of pedagogical transformation. We discuss the interplay factors between students' cognitive and affective engagement and how students become engaged in academics. This research applied cross-sectional study. The research findings demonstrate the composition of cognitive and affective engagement and the interplay factors between them, enabling students to participate in academic activities in a virtual face-to-face learning environment. One thing to highlight is that evaluating learning progress elicits pleasant emotions, whereas monitoring comprehension and setting learning goals elicits persistence. Higher education educators are recommended to create captivating face-to-face online learning experiences in order to optimize their students' engagement in academic activities.

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“…While previous SRL research primarily used self-report data to measure SRL behavior (Rovers et al, 2019), the emergence of the learning analytics field has popularized the use of interaction logs in identifying students' SRL-related behaviors (Wilson et al, 2021). Among others, unsupervised machine learning methods such as clustering and sequence mining have been the most widely used learning analytics approaches to detect students' engagement behaviors (Mirriahi et al, 2016;Walsh & Rísquez, 2020).…”

Section: Learning Strategiesmentioning

confidence: 99%

Decoding Video Logs: Unveiling Student Engagement Patterns in Lecture Capture Videos

Akçapınar,

Er,

Bayazıt

2024

IRRODL

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Lecture capture videos, a popular type of instructional content used by instructors to share course recordings online, play a significant role in educational settings. Compared to other educational videos, these recordings require minimal time and effort to produce, making them a preferred choice for disseminating course materials. Despite their numerous benefits, there exists a scarcity of data-driven evidence regarding students’ use of and engagement with lecture capture videos. Most existing studies rely on self-reported data, lacking comprehensive insights into students’ actual video engagement. This research endeavor sought to bridge this gap by investigating university students’ engagement patterns while watching lecture capture videos. To achieve this objective, we conducted an analysis of a large-scale dataset comprising over one million rows of video interaction logs. Leveraging clustering and process mining methodologies, we explored the data to reveal valuable insights into students’ video engagement behaviors. Our findings indicate that in approximately 60% of students’ video-watching sessions, only a small portion of the videos (an average of 7%) is watched. Our results also show that visiting the video page does not necessarily mean that the student watched it. This study may contribute to the existing literature by providing robust data-driven evidence on university students’ lecture capture video engagement patterns. It is also expected to contribute methodologically to capturing, preprocessing, and analyzing students’ video interactions in different contexts.

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Mapping Patterns of Student Engagement Using Cluster Analysis

Cited by 7 publications

References 44 publications

Exploring student engagement in virtual classrooms: a person-centred approach using cluster analysis

Exploring student engagement in virtual classrooms: a person-centred approach using cluster analysis

Abrupt Transformation in Pedagogy: How Do Students Engage Academically in the Face-to-Face Online Learning Environment?

Decoding Video Logs: Unveiling Student Engagement Patterns in Lecture Capture Videos

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