Using Detailed Access Trajectories for Learning Behavior Analysis

Wang, Yanbang; Law, Nancy; Hemberg, Erik; O’Reilly, Una-May

doi:10.1145/3303772.3303781

Cited by 11 publications

(3 citation statements)

References 19 publications

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“…Chen et al [3] explored ways of visualizing learners' sequences in their access to different learning resources. Wang, et al [34] introduced the detailed access trajectory and used them to identify several learning behavior patterns in a MOOC. However, these works stop short of addressing one major challenge in learning analytics, that of informing appropriate pedagogical action.…”

Section: Learning Analyticsmentioning

confidence: 99%

“…Maiyuran et al [21] further explored how the doer effect varies with respect to different topics and learners' academic backgrounds. Wang et al [34] propose a novel approach to visualizing learner behavior through Detailed Access trajectories (DAT). The DAT offers both fine granularity and coarse-grained visualizations of learner behavior sequences based on their interactions with resources.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Understanding Learner Behavior Through Learning Design Informed Learning Analytics

Shen

Liang

Law

et al. 2020

Proceedings of the Seventh ACM Conference on Learning @ Scale

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A goal of learning analytics is to inform and improve learning design. Previous studies have attempted to interpret learners' clickstream data based on learning science theories. Many of these interpretations are made without reference to the specific learning designs of the courses being analyzed. Here, we report on a learning design informed analytics exploration of an introductory MOOC on Computer Science and Python programming. The learning resources (videos) and practice resources (short exercises and problem sets) are analyzed according to the knowledge types and cognitive process levels respectively, both based on a revised Bloom's Taxonomy. A heat map visualization of the access intensity on a learner resource access transition matrix and social network analysis are used to analyze learners' behavior with respect to the different resource categories. The results show distinctively different patterns of access between groups of students with different course performance and different academic backgrounds.

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Section: Learning Analyticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding Learner Behavior Through Learning Design Informed Learning Analytics

Shen

Liang

Law

et al. 2020

Proceedings of the Seventh ACM Conference on Learning @ Scale

Self Cite

View full text Add to dashboard Cite

show abstract

“…This set contains VA tools on urban science [XTYL18, LKJ*20, MZAD*20, MHL*20, BZQ*21, RMH*22, GZRP*22, SNP*22] and for the analysis of various sources of data, including social media [BEF17, XO21, WSP*21, AAM*21, AYL*22], news/creative writing [PS21, HGE22], literature/digital humanities [NKWW22, MWJ22], and human behaviors/gestures [WLHO19,ZWW*22].…”

Section: Categorization Of Va + Embedding Approachesmentioning

confidence: 99%

VA + Embeddings STAR: A State‐of‐the‐Art Report on the Use of Embeddings in Visual Analytics

Huang

Witschard

Kucher

et al. 2023

Computer Graphics Forum

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Over the past years, an increasing number of publications in information visualization, especially within the field of visual analytics, have mentioned the term “embedding” when describing the computational approach. Within this context, embeddings are usually (relatively) low‐dimensional, distributed representations of various data types (such as texts or graphs), and since they have proven to be extremely useful for a variety of data analysis tasks across various disciplines and fields, they have become widely used. Existing visualization approaches aim to either support exploration and interpretation of the embedding space through visual representation and interaction, or aim to use embeddings as part of the computational pipeline for addressing downstream analytical tasks. To the best of our knowledge, this is the first survey that takes a detailed look at embedding methods through the lens of visual analytics, and the purpose of our survey article is to provide a systematic overview of the state of the art within the emerging field of embedding visualization. We design a categorization scheme for our approach, analyze the current research frontier based on peer‐reviewed publications, and discuss existing trends, challenges, and potential research directions for using embeddings in the context of visual analytics. Furthermore, we provide an interactive survey browser for the collected and categorized survey data, which currently includes 122 entries that appeared between 2007 and 2023.

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A Review of Data Mining in Personalized Education: Current Trends and Future Prospects

Xiong,

Li,

Liu

et al. 2024

Front. Digit. Educ.

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Using Detailed Access Trajectories for Learning Behavior Analysis

Cited by 11 publications

References 19 publications

Understanding Learner Behavior Through Learning Design Informed Learning Analytics

Understanding Learner Behavior Through Learning Design Informed Learning Analytics

VA + Embeddings STAR: A State‐of‐the‐Art Report on the Use of Embeddings in Visual Analytics

A Review of Data Mining in Personalized Education: Current Trends and Future Prospects

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