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Visualization of Course Discussion Forums: A Short Review from Online Learning Perspective

2019· review· en· W3014612091 on OpenAlexaff
Ming Ni Wu, M. Ali Akber Dewan, Fuhua Lin, Mahbub Murshed

Bibliographic record

Venuenot available
Typereview
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsAthabasca University
Fundersnot available
KeywordsVisualizationComputer scienceOnline discussionPerspective (graphical)Context (archaeology)Asynchronous communicationInformation visualizationStrengths and weaknessesFocus (optics)Data visualizationWorld Wide WebMultimediaData sciencePsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents a short review of existing visualization systems that focus on online course discussion forums. Discussion forums in online courses are used as an important means for students-students and students-instructors communications. However, a large amount of asynchronous and heterogeneous posts in the discussion forums and their complex relationships make it difficult to track and extract meaningful information about the students' activities and progress in the online courses. Information visualization has shown promise on this to facilitate online learning, especially for predicting students' performance on tasks and assessments, understanding students' affects and sentiments, and predicting students' social behavior pattern and awareness. In this review, we detailed the applications of the existing visualization systems, their design considerations, and their strengths and weaknesses in the context of discussion forum analyses. This information will provide insight into how to build an effective visualization system for the online courses' discussion forums.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score0.837

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.418
Teacher spread0.366 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2019
Admission routes1
Has abstractyes

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