Analytics for Student Engagement

Vytasek, Jovita M.; Patzak, Alexandra; Winne, Philip H.

doi:10.1007/978-3-030-13743-4_3

Cited by 42 publications

(33 citation statements)

References 111 publications

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“…The authors, based on student interviews, including discussion of how blogging enables students to develop their own voices as part of the writing process suggest that a broader understanding of student engagement depends not only on the complex interaction between students and assessment practices but on understanding the role of students' investment in the learning process which can be increased through online tools such as blogs. Similarly, machine learning based student engagement analytics can help track student performance and provide individual improvement strategies (Vytasek, Patzak and Winne, 2020). Further, due to the shift to online means of education, concepts of m-learning or mobile learning are increasingly explored.…”

Section: Student Attendance and Engagementmentioning

confidence: 99%

Examining the impact of students’ attendance, sketching, visualization, and tutors experience on students’ performance: a case of building structures course in construction management

Ullah¹,

Sepasgozar²,

Tahmasebinia³

et al. 2020

CEB

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The aim of this paper is to examine students’ performance in a computation-based course by evaluating the effects of key factors including sketching, visualization resources provided to them during the lectures, their attendance and tutors’ experience. A systematic review was conducted including 192 articles published during January 2010 to December 2019. Further, a case study has been conducted in which 633 students from non-engineering backgrounds were taught a core course of construction over three-yearly sessions from 2017 to 2019. The performance has been assessed through two quizzes of 10% weight each, assignment of 40% weight and a final exam with 30% weight in 2017-18 and 40% weight in 2019 were utilized with an attendance criterion of below 75% as low attendance. The statistical result highlights that a clear difference of 14% overall marks exist between the students with less than 75% attendance and the ones with 75% and above in 2017 and a 10% gap in 2018. Students with high marks in sketching secured higher overall marks as compared to others highlighting that the sketching skill is useful to construction students. The findings contribute to the body of education knowledge by evaluating key influential factors and provide a useful benchmark to other educators in the field.

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Section: Student Attendance and Engagementmentioning

confidence: 99%

Examining the impact of students’ attendance, sketching, visualization, and tutors experience on students’ performance: a case of building structures course in construction management

Ullah¹,

Sepasgozar²,

Tahmasebinia³

et al. 2020

CEB

View full text Add to dashboard Cite

show abstract

“…Student engagement has been associated with a variety of positive educational outcomes (Fredricks et al, 2016;Trowler, 2010;Vytasek et al, 2020); one such positive outcome is academic performance. The evidence linking behavioural engagement and aspects of cognitive engagement with performance is strong (Fredricks et al, 2004).…”

Section: Student Engagementmentioning

confidence: 99%

Exploring Student Engagement Factors in a Blended Undergraduate Course

et al. 2020

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Student engagement is an important factor in academic performance and comprises four dimensions: behavioural, cognitive, emotional (Fredericks et al., 2004), and agentic (Reeve, 2013). Blended courses provide unique opportunities for instructors to use trace data collected during learning to understand and support student engagement. This mixed-methods case study compared the student engagement of two groups of students with a history of low prior academic achievement. The groups were (a) students who ultimately did well in the course and (b) students who did poorly. Data came from two primary sources: (a) log file data from the course LMS, and (b) trace data derived from authentic learning tasks. Data represented five indicators: (a) behavioural engagement, (b) cognitive engagement, (c) emotions experienced during learning, (d) agency or proactive approaches to studying, and (e) overall academic engagement. Findings indicated students who moved achievement groups showed higher levels of behavioural engagement, cognitive engagement, and agentic or proactive approaches to studying and overall engagement. Additionally, students who remained in the low achievement group showed higher levels of positive deactivating emotions (e.g., relief). Implications for future research on student engagement and designing teaching to increase engagement in blended courses are discussed.

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“…Lecturers or student support teams increasingly use learning behaviour dashboards to determine the allocation of extra support to students, to personalise curricula and assessment (Leitner, Khalil, & Ebner, 2017;Vytasek, Patzak, & Winne, 2020), or, in an act of educational triage, to ease students' departure (Prinsloo & Slade, 2014). The analyses informing the visual representation of their learning behavior are not neutral but are informed by ideology, possible (mis) understandings of student learning, and beliefs pertaining to data.…”

Section: Educational Analytics As Performative Politics: a Tentative mentioning

confidence: 99%

A social cartography of analytics in education as performative politics

Prinsloo¹

2019

Brit J Educational Tech

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Data—their collection, analysis and use—have always been part of education, used to inform policy, strategy, operations, resource allocation, and, in the past, teaching and learning. Recently, with the emergence of learning analytics, the collection, measurement, analysis and use of student data have become an increasingly important research focus and practice. With (higher) education having access to more student data, greater variety and nuanced/granularity of data, as well as collecting and using real‐time data, it is crucial to consider the data imaginary in higher education, and, specifically, analytics as performative politics. Data and data analyses are often presented as representing “reality” and, as such, are seminal in institutional “truth‐making,” whether in the context of operational or student learning data. In the broader context of critical data studies (CDS), this social cartography examines and maps the “data frontier” and the “data gaze” within the context of the dominant narrative of evidence‐based management and the data imaginary in higher education. Following an analysis of the main assumptions in evidence‐based management and the power of metrics, this paper presents a social cartography of data analytics not only as representational, but as actant, and as performative politics. What is already known about this topic Student data in higher education are increasingly used not only to inform operational and strategic planning, but to inform and shape curricula, pedagogy and student learning. The collection, measurement, analysis and use of student data are informed by a particular data imaginary in service of evidence‐based management. Data and analytics are seen as objective, neutral representations of reality and used as basis for informing policy, operational planning, resource allocation, and, increasingly, learning analytics and student facing dashboards. What this paper adds Critically examines data analytics in higher education as “truth‐making” and never neutral, and always as provisional snapshots of “reality” serving particular, often undeclared, assumptions and interests. Situates learning analytics as the collection, measurement, analysis and use of student data in the broader context of the data imaginary and student data as “data frontier”. Provides evidence that data analytics is not only representational, but, increasingly, performative and political. Implications for practice and/or policy Makes explicit some of the theoretical, political, epistemological and ontological assumptions underpinning and sustaining the data imaginary in higher education. Foregrounds the need for more critical approaches to our expectations, collections, analyses and use of student data. Argues the need for policy and practitioners to recognise the inherent political and performative nature of data and data analysis, and the need for transparency, accountability and care.

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Analytics for Student Engagement

Cited by 42 publications

References 111 publications

Examining the impact of students’ attendance, sketching, visualization, and tutors experience on students’ performance: a case of building structures course in construction management

Examining the impact of students’ attendance, sketching, visualization, and tutors experience on students’ performance: a case of building structures course in construction management

Exploring Student Engagement Factors in a Blended Undergraduate Course

A social cartography of analytics in education as performative politics

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