Learning Analytics as a Counterpart to Surveys of Student Experience

Borden, Victor M. H.; Coates, Hamish

doi:10.1002/he.20246

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…Since LA emerged around 2010, HEIs have directed data mining and analytic practices to novel ends in and out of the classroom. Before students even enroll, HEIs create profiles about prospective students based on information students disclose on their interest forms for SAT and ACT surveys, and in their applications for admission (Borden & Coates, ; Rienties, Cross, & Zdrahal, ). These profiles include a mixture of demographic, academic, financial, and familial information.…”

Section: Educational Data Mining and Learning Analyticsmentioning

confidence: 99%

A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics

Jones

Rubel

LeClere

2019

Asso for Info Science & Tech

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Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student's demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning analytics, consequently, raise serious issues concerning student privacy, autonomy, and the appropriate flow of student data. We argue that issues around privacy lead to valid questions about the degree to which students should trust their institution to use learning analytics data and other artifacts (algorithms, predictive scores) with their interests in mind. We argue that higher education institutions are paradigms of information fiduciaries. As such, colleges and universities have a special responsibility to their students. In this article, we use the information fiduciary concept to analyze cases when learning analytics violate an institution's responsibility to its students.

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Section: Educational Data Mining and Learning Analyticsmentioning

confidence: 99%

A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics

Jones

Rubel

LeClere

2019

Asso for Info Science & Tech

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“…Given the nascence of research into LA interventions -and in particular those that incorporate online student behavior and activity -a considerable limitation to the science in this area relates to the fact that there are very few empirically tested LA programs (Rienties et al, 2016). Indeed, the vast majority of the research to date comprises correlational studies focusing on particular variables and their predictive power, typically in terms of student success, retention, and/or experience (Borden & Coates, 2017;Papamitsiou & Economides, 2014;Saunders, Gharaie, Chester, & Leahy, 2017). In recent years, a number of qualitative and systematic review articles have appeared (Bienkowski, Feng, & Means, 2012;Ferguson, 2012;Papamitsiou & Economides, 2014;Romero & Venura, 2013), and a recent meta-analysis of published studies (Papamitsiou & Economides, 2016) has supported the use of LA in educational contexts.…”

Section: Rationale and Aimsmentioning

confidence: 99%

The efficacy of learning analytics interventions in higher education: A systematic review

Sønderlund¹,

Hughes²,

Smith³

2018

Brit J Educational Tech

160

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Educational institutions are increasingly turning to learning analytics to identify and intervene with students at risk of underperformance or discontinuation. However, the extent to which the current evidence base supports this investment is currently unclear, and particularly so in relation to the effectiveness of interventions based on predictive models. The aim of the present paper was to conduct a systematic review and quality assessment of studies on the use of learning analytics in higher education, focusing specifically on intervention studies. Search terms identified 689 articles, but only 11 studies evaluated the effectiveness of interventions based on learning analytics. These studies highlighted the potential of such interventions, but the general quality of the research was moderate, and left several important questions unanswered. The key recommendation based on this review is that more research into the implementation and evaluation of scientifically driven learning analytics is needed to build a solid evidence base for the feasibility, effectiveness, and generalizability of such interventions. This is particularly relevant when considering the increasing tendency of educational institutions around the world to implement learning analytics interventions with only little evidence of their effectiveness.

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“…The concept of intersectionality -that multiple aspects of discrimination co-exist and interact -should also be considered in analytical strategies. By analysing multiple intersecting data points about a student (for example, gender, disability, socio-economic status and race), we can harness data to identify and explore the combined effects (Borden & Coates, 2017). Encouraging research and evaluation with an intersectional approach is now an explicit focus for the Office for Students, the body responsible for Higher education provision in the UK (Office for Students, 2019).…”

Section: Harnessing Data To Understand Barriers and Improve Support Fmentioning

confidence: 99%

Accessible Inclusive Learning: Futures

Coughlan¹,

Lister²,

Tai-Seale³

et al. 2019

Educational Visions: The Lessons From 40 Years of Innovation

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The previous chapter on Accessible Inclusive Learning: Foundations outlined some key approaches and challenges when conducting research that seeks to make learning accessible to all. Here, we explore newer trends that are directing our current research and practice in this area. These promising directions include devising models for global networks, the potential to collect and use data to understand learning experiences in new ways, and new opportunities arising through artificial intelligence. By exploring current and recent projects around these areas, we also highlight some emerging tensions. Finally, we return to thinking about how we conduct research, considering how concepts of bricolage and guerrilla research are important in our methodological palette.

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Learning Analytics as a Counterpart to Surveys of Student Experience

Cited by 12 publications

References 17 publications

A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics

A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics

The efficacy of learning analytics interventions in higher education: A systematic review

Accessible Inclusive Learning: Futures

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