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Record W1514294285 · doi:10.19173/irrodl.v11i2.772

Length of online course and student satisfaction, perceived learning, and academic performance

2010· article· en· W1514294285 on OpenAlexvenueno aff
Janet Ferguson, Amy DeFelice

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationDistance educationOnline learningPsychologyOnline courseStatistical analysisCourse evaluationCourse (navigation)Higher educationComputer scienceGraduate studentsMedical educationMultimediaPedagogyStatisticsMathematicsEngineeringMedicine

Abstract

fetched live from OpenAlex

This research presents findings from a two-part study. In the first part, graduate students taking online courses were given a course evaluation form. Student responses from online abbreviated summer sessions were compared to student responses from online full-semester courses. Both the intensive and full-semester courses were taught by the same professor and both had identical requirements in terms of assignments and exams. The independent variable was the length of time taken to complete the requirements, with the dependent variables being satisfaction with the course, perceived learning, and academic performance. A statistical analysis of the data found significant differences in a number of areas.

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)0.003
metaresearch head score (Gemma)0.018
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.064
GPT teacher head0.495
Teacher spread0.431 · 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

Citations109
Published2010
Admission routes1
Has abstractyes

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