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Record W3158617949 · doi:10.5539/jel.v10n3p122

A Comparative Analysis on the Effects of Formal and Distance Education Students’ Course Attendance Upon Exam Success

2021· article· en· W3158617949 on OpenAlexvenueno aff
Recep ÖZ, Murat Tolga KAYALAR

Bibliographic record

VenueJournal of Education and Learning · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsAttendanceDistance educationMathematics educationVocational educationFormal educationMedical educationPsychologyAcademic achievementHigher educationPoint (geometry)PedagogyMedicinePolitical scienceMathematics

Abstract

fetched live from OpenAlex

The purpose of this study was to analyze the effect of associate degree formal and distance education students course attendance upon their course achievement. The data were obtained from the institutional records of 516 students who registered in formal education programs and 510 students who registered in distance education programs of a vocational school affiliated to a state university in Eastern Anatolian Region of Turkey. It was noticed that course attendance of formal education students was higher than those who registered in the distance education programs; on the other hand, formal education students were more successful rather than distance education students in terms of midterm, final exam and grade point average. It was determined that course attendance was a significant predictor of midterm, final exam and grade point average, and the achievement scores increased as the course attendance increased, as well. Taking measures to encourage students’ attendance in courses in distance education programs and optimizing access and technical infrastructure could positively contribute upon this issue.

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)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.385
Teacher spread0.370 · 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 designObservational
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

Citations5
Published2021
Admission routes1
Has abstractyes

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