Discovery and temporal analysis of latent study patterns in MOOC interaction sequences

Boroujeni, Mina Shirvani; Dillenbourg, Pierre

doi:10.1145/3170358.3170388

Cited by 40 publications

(21 citation statements)

References 11 publications

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“…Thus, informed by the instructors' pedagogical intentions, in total 15 sequence features were generated by the research team. The sequence of learner actions was previously noted in the MOOC literature as important to student learning (Boroujeni & Dillenbourg, 2018) Moreover, features were generated based on each particular content item in the LD. The first set of variables includes page-view features that convey student engagement in each specific learning component.…”

Section: Feature Engineering For Ld-driven Model (Mixing For Sequentimentioning

confidence: 99%

Aligning learning design and learning analytics through instructor involvement: a MOOC case study

Gómez‐Sánchez

Dimitriadis

et al. 2019

Interactive Learning Environments

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With the emergence of MOOCs, there is a growing interest in prediction research. Most existing predictive models do not consider the context for which they are intended, thus resulting in limited impact. Learning design (LD) can provide a contextual understanding for the design of predictive models in collaboration with the instructors, maximizing their potential for supporting learning. This paper presents the findings of a mixed-methods research that explored the potentials emerging from aligning LD and LA during the design of a predictive analytics solution and from involving the instructors in the design process. The context was a past MOOC, where the learner data and the instructors were accessible for posterior analysis and additional data collection. Through a close collaboration with the instructors, the details of the prediction task were identified, including the learning activity to focus on, the target variable to predict, and the practical constraints to consider. Later, two predictive models were built for the prediction task identified: LD-specific model, in which the features were based on the LD and pedagogical intentions, and a generic model, which was based on cumulative features, not informed by the LD. Although the LD-specific predictive model did not outperform the generic one, some features derived from the LD and pedagogical intentions were predictive. The quantity and the power of such features were associated with the degree to which the students acted as guided by the LD and pedagogical intentions. The leading instructor's opinion about the importance of the learning activities in the LD was compared with the results of the feature importance analysis. This comparison helped identify the parts of the LD that need improvement. That is, the results of the LA informed back the LD, where the instructor was a mediator. The implications for improving the LD are discussed.

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Section: Feature Engineering For Ld-driven Model (Mixing For Sequentimentioning

confidence: 99%

Aligning learning design and learning analytics through instructor involvement: a MOOC case study

Gómez‐Sánchez

Dimitriadis

et al. 2019

Interactive Learning Environments

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“…Reference [2] builds upon the work in [4,8,25] by applying clustering techniques to MOOC learner behavior. Clustering in this case enables the automatic identification of similar trajectories to be identified at scale, whereas prior work in this area was done manually [17,24].…”

Section: Learner Behavior Patternsmentioning

confidence: 99%

“…We apply this scalable clustering approach to MOOC course structures in the present research. Reference [2] employed both pattern-and datadriven approaches for analyzing and clustering MOOC learner activity data. They correlated learner engagement patterns with course learning outcomes as well-final course grades earned and each cluster's overall passing rate.…”

Section: Learner Behavior Patternsmentioning

confidence: 99%

“…The authors in [2] also introduce a second method to track latent learner activity patterns with an unsupervised processing pipeline. The pipeline is comprised of four phases: (i) activity sequence modeling, where a transition matrix is generated and used as a learner model, (ii) distance computation, (iii) Figure 1.…”

Section: Learner Behavior Patternsmentioning

confidence: 99%

“…Such process mining techniques include (i) visualization, where processes are plotted in a variety of graph types in order to make trends and patterns visually apparent, (ii) conformance checking, where actual/executed processes are compared to the normative/intended model, and (iii) process discovery, where a process model is learned from event log data. Whereas the authors in [2] were motivated by connecting behavior to learning outcomes, the authors in [16,19] are motivated to model learner behavior in order to develop targeted interventions to support learners in developing self-regulated learning skills.…”

Section: Learner Behavior Patternsmentioning

confidence: 99%

See 2 more Smart Citations

Toward large-scale learning design

Davis

Seaton

Hauff

et al. 2018

Proceedings of the Fifth Annual ACM Conference on Learning at Scale

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This paper applies theory and methodology from the learning design literature to large-scale learning environments through quantitative modeling of the structure and design of Massive Open Online Courses. For two institutions of higher education, we automate the task of encoding pedagogy and learning design principles for 177 courses (which accounted for for nearly 4 million enrollments). Course materials from these MOOCs are parsed and abstracted into sequences of components, such as videos and problems. Our key contributions are (i) describing the parsing and abstraction of courses for quantitative analyses, (ii) the automated categorization of similar course designs, and (iii) the identification of key structural components that show relationships between categories and learning design principles. We employ two methods to categorize similar course designs-one aimed at clustering courses using transition probabilities and another using trajectory mining. We then proceed with an exploratory analysis of relationships between our categorization and learning outcomes. * The author's research is supported by the Leiden-Delft-Erasmus Centre for Education and Learning.

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Detection of Learning Strategies: A Comparison of Process, Sequence and Network Analytic Approaches

Matcha

Gašević

Uzir

et al. 2019

Lecture Notes in Computer Science

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Discovery and temporal analysis of latent study patterns in MOOC interaction sequences

Cited by 40 publications

References 11 publications

Aligning learning design and learning analytics through instructor involvement: a MOOC case study

Aligning learning design and learning analytics through instructor involvement: a MOOC case study

Toward large-scale learning design

Detection of Learning Strategies: A Comparison of Process, Sequence and Network Analytic Approaches

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