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Record W2003805897 · doi:10.1145/2090116.2090145

AAT

2011· article· en· W2003805897 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsAthabasca University
Fundersnot available
KeywordsLearning analyticsComputer scienceIdentification (biology)Online courseOnline learningAnalyticsMultimediaKnowledge managementData scienceMathematics educationPsychology

Abstract

fetched live from OpenAlex

In online learning environments, teachers and course designers often get little feedback about how students actually interact with and learn in online courses. Most of the learning systems used by educational institutions store comprehensive log data associated with students' behaviours and actions. However, these systems typically reveal or report on very general and limited information based on this data. In order to provide teachers and course designers with more detailed and meaningful information about students' behaviour and their use of learning resources within online courses, an analytics tool has been developed. The tool incorporates functionality to access and analyse data related to students' behaviours in learning systems. This tool can provide valuable information about students' learning processes allowing the identification of difficult or inappropriate learning material, and can therefore significantly contribute to the design of improved student support activities and resources.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.807
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.228
Teacher spread0.191 · 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

Quick stats

Citations57
Published2011
Admission routes1
Has abstractyes

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