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Record W1554621693 · doi:10.19173/irrodl.v4i2.153

Differences Between Traditional and Distance Education Academic Performances: A Meta-Analytic Approach

2003· article· en· W1554621693 on OpenAlexvenueno aff
Mickey Shachar, Yoram Neumann

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPercentileDistance educationMeta-analysisConfidence intervalStatisticsStandard deviationMathematicsMathematics educationInclusion (mineral)PsychologyDemographyMedicineInternal medicineSocial psychology

Abstract

fetched live from OpenAlex

<P class=abstract>This meta-analysis research estimated and compared the differences between the academic performance of students enrolled in distance education courses relative to those enrolled in traditional settings, as demonstrated by their final course grades/ scores within the 1990-2002 period.<BR> <BR> Eighty-six experimental and quasi-experimental studies met the established inclusion criteria for the meta-analysis (including data from over 15,000 participating students), and provided effect sizes, clearly demonstrating that: (1) in two thirds of the cases, students taking courses by distance education outperformed their student counterparts enrolled in traditionally instructed courses; (2) the overall effect size d+ was calculated as 0.37 standard deviation units (0.33 < 95% confidence interval < 0.40); and (3) this effect size of 0.37 indicates the mean percentile standing of the DE group is at the 65th percentile of the traditional group (mean defined as the 50th percentile). </P>

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)0.034
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.064
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.031
Bibliometrics0.0070.006
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.301
GPT teacher head0.488
Teacher spread0.188 · 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 designMeta-analysis
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

Citations294
Published2003
Admission routes1
Has abstractyes

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