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Record W1481853694 · doi:10.19173/irrodl.v4i2.151

An E-Learning Experience: A written analysis based on my experience in an e-Learning Pilot Project

2003· article· en· W1481853694 on OpenAlexvenueno aff
Kabita Bose

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2003
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsnot available
Fundersnot available
KeywordsUnit (ring theory)Educational technologyExperiential learningMedical educationMathematics educationPsychologyE learningPedagogyComputer scienceMedicine

Abstract

fetched live from OpenAlex

This article reports the experience of an e-Learning pilot project selected by the Educational Technology Unit (EduTech) of the Centre for Academic Development (CAD), University of Botswana (UB). This e-Learning package was designed and delivered in connection with the three-credit course “Issues and Trends in Early Childhood Education” (EPI-642), which is required for the first year of the master’s program in the Department of Primary Education. The course was taught via a flexi-time, gradual, phase-by-phase transition from traditional face-to-face teaching to the electronic medium of an e-Learning lab (called SMART classroom). This course utilized a student centred e-Learning package that retained the learning qualities of traditional teaching, personal guidance, and mentoring, while seeking to enhance students’ research and computer skills.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0040.003
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.115
GPT teacher head0.475
Teacher spread0.360 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations15
Published2003
Admission routes1
Has abstractyes

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