ViSeq: Visual Analytics of Learning Sequence in Massive Open Online Courses

Chen, Qing; Yue, Xuanwu; Plantaz, Xavier; Chen, Yuanzhe; Shi, Conglei; Pong, Ting-Chuen; Qu, Huamin

doi:10.1109/tvcg.2018.2872961

Cited by 49 publications

(32 citation statements)

References 38 publications

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“…Sequential information on learning activities helps MOOC analysts characterize learning behaviors via establishing correlations between learning sequences and performance. Chen et al (2018) recently introduced ViSeq, a visual analytics system that facilitates information exploration on learning sequences in MOOC. ViSeq helps in mitigating sequential information loss and visualizing learner categorical learning sequence and causality of learning behaviors.…”

Section: Resultsmentioning

confidence: 99%

Problem characterization for visual analytics in MOOC learner's support monitoring: A case of Malaysian MOOC

Asli

Hamzah

Ibrahim

et al. 2020

Heliyon

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Malaysia and many other developing countries progressively adopting massively open online course (MOOC) in their national higher education approach. We have observed an increasing need for facilitating MOOC monitoring that is associated with the rising adoption of MOOCs. Our observation suggests that recent adoption cases led analyst and instructors to focus on monitoring enrolment and learning activities. Visual analytics in MOOC support education analysts in analyzing MOOC data via interactive visualization. Existing literature on MOOC visualization focuses on enabling visual analysis on MOOC data from forum and course material. We found limited studies that investigate and characterize domain problems or design requirements of visual analytics for MOOC. This paper aims to present the empirical problem characterization and abstraction for visual analytics in MOOC learner's support monitoring. Detailed characterization and abstraction of the domain problem help visualization designer to derive design requirements in generating appropriate visualization solution. We examined the literature and conducted a case study to elicit a problem abstraction based on data, users, and tasks. We interviewed five Malaysian MOOC experts from three higher education institutes using semistructured questions. Our case study reveals the priority of enabling MOOC analysis on learner's progression and course completion. There is an association between design and analysis priority with the pedagogical type of implemented MOOC and users. The characterized domain problems and requirements offer a design foundation for visual analytics in MOOC monitoring analysis.

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Section: Resultsmentioning

confidence: 99%

Problem characterization for visual analytics in MOOC learner's support monitoring: A case of Malaysian MOOC

Asli

Hamzah

Ibrahim

et al. 2020

Heliyon

View full text Add to dashboard Cite

show abstract

“…In addition, many studies presented numerous innovative visualizations, to examine the behavioral patterns of learners in MOOCs courses, such as node-link diagram [58], thread river [18], novel glyphs [64], and calendar-based heatmap [43]. Chen et al [6] developed a visual system called ViSeq, which visualized the learning sequence of different learner groups and helped to better understand the reasons behind learning behaviors. Recently, [55] offered a systematic literature review of visual learning analytics from multiperspectives: In terms of correlation approaches, contexts, audiences, objectives, and data sources that researchers employed to visualize educational data.…”

Section: Mooc Clickstream Visualizationmentioning

confidence: 99%

Visual analytics of video‐clickstream data and prediction of learners' performance using deep learning models in MOOCs' courses

Mubarak

Cao

Zhang

2020

Comp Applic In Engineering

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The big data stored in massive open online course (MOOC) platforms have become a posed challenge in the Learning Analytics field to analyze the learning behavior of learners, and predict their respective performance, related especially to video lecture data, since most learners view the same online lecture videos. This helps to conduct a comprehensive analysis of such behaviors and explore various learning patterns in MOOC video interactions. This paper aims at presenting a visual analysis, which enables course instructors and education experts to analyze clickstream data that were generated by learner interaction with course videos. It also aims at predicting learner performance, which is a vital decision-making problem, by addressing their issues and improving the educational process. This paper uses a long short-term memory network (LSTM) on implicit features extracted from videoclickstreams data to predict learners' performance and enable instructors to make measures for timely intervention. Results show that the accuracy rate of the proposed model is 89%-95% throughout course weeks. The proposed LSTM model outperforms baseline Deep learning (GRU) and simple recurrent neural network by accuracy of 90.30% in the "Mining of Massive Datasets" course, and the "Automata Theory" accuracy is 89%.

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“…However, the automatic feature extraction methods used by CNN cannot clearly distinguish the importance of different behavioral data. In addition, a large number of educational practice studies have shown that the sequence of behaviors has a great impact on students' learning performance and final grades [20]. However, current CNN studies rarely consider time as a key factor in model building.…”

Section: Introductionmentioning

confidence: 99%

MOOC Dropout Prediction Using FWTS-CNN Model Based on Fused Feature Weighting and Time Series

et al. 2020

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ViSeq: Visual Analytics of Learning Sequence in Massive Open Online Courses

Cited by 49 publications

References 38 publications

Problem characterization for visual analytics in MOOC learner's support monitoring: A case of Malaysian MOOC

Problem characterization for visual analytics in MOOC learner's support monitoring: A case of Malaysian MOOC

Visual analytics of video‐clickstream data and prediction of learners' performance using deep learning models in MOOCs' courses

MOOC Dropout Prediction Using FWTS-CNN Model Based on Fused Feature Weighting and Time Series

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