Using learning analytics to explore help-seeking learner profiles in MOOCs

Corrin, Linda; Barba, Paula de; Bakharia, Aneesha

doi:10.1145/3027385.3027448

Cited by 38 publications

(29 citation statements)

References 13 publications

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“…Some of the studies focused on aggregating data from large data sets (usually blended courses or MOOCs) for the purpose of finding patterns within the data that can be applied in different contexts and among various modules [46], [5], [65], [90]. Moreover, these studies also aim to explore how learning design links to student online behavior [91]. Others, focused on actionable analytics in authentic learning settings [43], [42], [48], [49].…”

Section: Fig 5 Distribution Of Published Work By Pedagogical Approachmentioning

confidence: 99%

Learning Analytics for Learning Design: A Systematic Literature Review of Analytics-Driven Design to Enhance Learning

Mangaroska

Giannakos

2019

IEEE Trans. Learning Technol.

257

193

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As the fields of learning analytics and learning design mature, the convergence and synergies between these two fields became an important area for research. This paper intends to summarize the main outcomes of a systematic literature review of empirical evidence on learning analytics for learning design. Moreover, this paper presents an overview of what and how learning analytics have been used to inform learning design decisions and in what contexts. The search was performed in seven academic databases, resulting in 43 papers included in the main analysis. The results from the review depict the ongoing design patterns and learning phenomena that emerged from the synergy that learning analytics and learning design impose on the current status of learning technologies. Finally, this review stresses that future research should consider developing a framework on how to capture and systematize learning design data grounded in learning analytics and learning theory, and document what learning design choices made by educators influence subsequent learning activities and performances over time.

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Section: Fig 5 Distribution Of Published Work By Pedagogical Approachmentioning

confidence: 99%

Learning Analytics for Learning Design: A Systematic Literature Review of Analytics-Driven Design to Enhance Learning

Mangaroska

Giannakos

2019

IEEE Trans. Learning Technol.

257

193

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“…At its core, help‐seeking behavior is rooted in student self‐regulation and hinges on students’ seeking assistance from appropriate sources when they need it (Corrin, de Barba, & Bakharia, ). Baker and Corbett () describe a learning environment in which students are assessed not only on their learning of course material but also on their ability to improve “skills that cut across domains, such as … help seeking” behavior (p. 39).…”

Section: Applying Learning Analytics To Gateway Coursesmentioning

confidence: 99%

Guiding Early and Often: Using Curricular and Learning Analytics to Shape Teaching, Learning, and Student Success in Gateway Courses

Pistilli

Heileman

2017

New Drctns Hghr Edu

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“…[41] examined how student demographic indicators might correlated to their activities in MOOCs. [12] used dimension reduction and clustering techniques with affinity propagation to identify clusters and determine students' profiles based on their help-seeking behavior. [9] explored the effects of common MOOC discussion forum activities on peer learning and learner performance.…”

Section: Learning Analyticsmentioning

confidence: 99%

Temporal Analysis in Massive Open Online Courses – Towards Identifying at-Risk Students Through Analyzing Demographical Changes

Shi

Yang

Toda

2020

Lecture Notes in Information Systems and Organisation

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This chapter demonstrates a temporal analysis in Massive Open Online Courses (MOOCs), towards identifying at-risk students through analyzing their demographical changes. At-risk students are those who tend to drop out from the MOOCs. Previous studies have shown that how students interact in MOOCs could be used to identify at-risk students. Some studies considered student diversity by looking into subgroup behavior. However, most of them lack consideration of students' demographical changes. Towards bridging the gap, this study clusters students based on both their interaction with the MOOCs (activity logs) and their characteristics and explores their demographical changes along the MOOCs progress. The result shows students' demographical characteristics (membership of subgroups) changed significantly in the first half of the course and stabilized in the second half. Our findings provide insight into how students may be engaged in MOOCs and suggest the improvement of identifying at-risk students based on the temporal data.

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Using learning analytics to explore help-seeking learner profiles in MOOCs

Cited by 38 publications

References 13 publications

Learning Analytics for Learning Design: A Systematic Literature Review of Analytics-Driven Design to Enhance Learning

Learning Analytics for Learning Design: A Systematic Literature Review of Analytics-Driven Design to Enhance Learning

Guiding Early and Often: Using Curricular and Learning Analytics to Shape Teaching, Learning, and Student Success in Gateway Courses

Temporal Analysis in Massive Open Online Courses – Towards Identifying at-Risk Students Through Analyzing Demographical Changes

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