Understanding, evaluating, and supporting self-regulated learning using learning analytics

Roll, Ido; Winne, Philip H.

doi:10.18608/jla.2015.21.2

Cited by 163 publications

(100 citation statements)

References 5 publications

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“…Using data often but not exclusively gathered in online environments, learning analytics are intended to help administrators, teachers, and students themselves improve learning. When students are the audience for learning analytics, ideal learning analytics would provide information they can use to productively self-regulate learning (Roll & Winne, 2015;Winne, in press). …”

Section: Introductionmentioning

confidence: 99%

What if learning analytics were based on learning science?

Marzouk

Raković

Liaqat

et al. 2016

AJET

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Learning analytics are often formatted as visualisations developed from traced data collected as students study in online learning environments. Optimal analytics inform and motivate students' decisions about adaptations that improve their learning. We observe that designs for learning often neglect theories and empirical findings in learning science that explain how students learn. We present six learning analytics that reflect what is known in six areas (we call them cases) of theory and research findings in the learning sciences: setting goals and monitoring progress, distributed practice, retrieval practice, prior knowledge for reading, comparative evaluation of writing, and collaborative learning. Our designs demonstrate learning analytics can be grounded in research on self-regulated learning and self-determination. We propose designs for learning analytics in general should guide students toward more effective self-regulated learning and promote motivation through perceptions of autonomy, competence, and relatedness.

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Section: Introductionmentioning

confidence: 99%

What if learning analytics were based on learning science?

Marzouk

Raković

Liaqat

et al. 2016

AJET

View full text Add to dashboard Cite

show abstract

“…However, this collection is by no means complete in its coverage of relevant work nor in tackling the challenges in the Call for Papers. Approaches one would also want to consider include educational gaming analytics (e.g., Shute and Ventura, 2013;Shaffer, 2013), multimodal analytics on embodied presentation skills (e.g., Echeverría, Avendaño, Chiluiza, Vásquez, & Ochoa, 2014) and face-to-face collaboration (Martinez-Maldonado et al, 2016), computer-supported collaborative problem solving tests (Griffin et al, 2012), self-regulation (Roll & Winne, 2015), social learning analytics (e.g., Tan, Yang, Koh, & Jonathan, 2016), and "quantified self" personal data (e.g., Eynon, 2015).…”

Section: C21 Learning Analytics Are At An Early Stage Of Maturitymentioning

confidence: 99%

Learning Analytics for 21st Century Competencies

Shum

Crick

2016

Learning Analytics

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ABSTRACT. Many educational institutions are shifting their teaching and learning towards equipping students with knowledge, skills, and dispositions that prepare them for lifelong learning, in a complex and uncertain world. These have been termed "21 st centurycompetencies." Learning analytics (LA) approaches in general offer different kinds of computational support for tracking learner behaviour, managing educational data, visualizing patterns, and providing rapid feedback to both educators and learners. This special section brings together a diverse range of learning analytics tools and techniques that can be deployed in the service of building 21 st century competencies. We introduce the research and development challenges, and introduce the research and practitioner papers accepted to this section, before concluding with some brief reflections on the collection and relevance of a complex systems perspective for framing this topic.

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“…The importance of self-regulated learning is well cited (e.g., Zimmerman, 1990); however self-regulation is complex to define and is related to a number of other factors of learning (Roll & Winne, 2015). For example, in a longitudinal study on the causal dilemma between motivation and self-regulation, De Clercq, Galand, and Frenay (2013) concluded that a learning goal orientation resulted in a deep learning approach, which in turn resulted in better self-regulation.…”

Section: Factors Of Self-regulationmentioning

confidence: 99%

Learning Factor Models of Students at Risk of Failing in the Early Stage of Tertiary Education

Gray¹,

McGuinness²,

Owende³

et al. 2016

Learning Analytics

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This paper reports on a study to predict students at risk of failing based on data available prior to commencement of first year. The study was conducted over three years, 2010 to 2012, on a student population from a range of academic disciplines, n=1,207. Data was gathered from both student enrollment data and an online, self-reporting, learner-profiling tool administered during first-year student induction. Factors considered included prior academic performance, personality, motivation, self-regulation, learning approaches, age, and gender. Models were trained on data from the 2010 and 2011 student cohort, and tested on data from the 2012 student cohort. A comparison of eight classification algorithms found k-NN achieved best model accuracy (72%), but results from other models were similar, including ensembles (71%), support vector machine (70%), and a decision tree (70%). However, improvements in model accuracy attributable to non-cognitive factors were not significant. Models of subgroups by age and discipline achieved higher accuracies, but were affected by sample size; n<900 underrepresented patterns in the dataset. Factors most predictive of academic performance in first year of study at tertiary education included age, prior academic performance, and selfefficacy. Early modelling of first-year students yielded informative, generalizable models that identified students at risk of failing.Keywords: Learning analytics, learner profiling, academic performance, non-cognitive factors of learning, tertiary education INTRODUCTIONEnrollment numbers to tertiary education are increasing, as is diversity in student populations (OECD, 2013;Patterson, Carroll, & Harvey, 2014); however, significant numbers of students do not complete the courses in which they enroll, particularly courses with lower entry requirements (ACT, 2012;Mooney, Patterson, O'Connor, & Chantler, 2010). Factors predictive of academic performance have been the focus of research for many years (Farsides & Woodfield, 2003; Moran & Crowley, 1979), and continue as an active research topic (Jayaprakash, Moody, Lauria, Regan, & Baron 2014;Cassidy, 2011;Wise & Shaffer, 2015), indicating the inherent difficulty in generating accurate learning factor models (Knight, Buckingham Shum, & Littleton, 2013;Tempelaar, Cuypers, van de Vrie, Heck, & van der Kooij, 2013 331Tertiary education providers collect much data on students, including demographic data, academic activity, and log data from online campus activities. As a result, the application of data analytics to educational settings is an emerging and growing research discipline of data analytics (Campbell, deBlois, & Oblinger, 2007;Mirriahi, Gašević, Long, & Dawson, 2014;Sachin & Vijay, 2012;Siemens & Baker, 2012). The primary aim of learning analytics is to provide learning professionals, and students, with actionable information that can be used to enhance the learning process (Siemens, 2012;Chatti, Dyckhoff, Schroeder, & Thüs, 2012). Much of the published work in learning analytics is based on...

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Understanding, evaluating, and supporting self-regulated learning using learning analytics

Cited by 163 publications

References 5 publications

What if learning analytics were based on learning science?

What if learning analytics were based on learning science?

Learning Analytics for 21st Century Competencies

Learning Factor Models of Students at Risk of Failing in the Early Stage of Tertiary Education

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