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Record W2563319199 · doi:10.19173/irrodl.v17i6.2493

The Impact of Contact Sessions and Discussion Forums on the Academic Performance of Open Distance Learning Students

2016· article· en· W2563319199 on OpenAlexvenueno aff
Benjamin Olivier

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsSession (web analytics)AttendanceDistance educationPsychologyMedical educationOnline discussionComputer-mediated communicationMathematics educationComputer scienceThe InternetMedicineWorld Wide Web

Abstract

fetched live from OpenAlex

<p class="3">This study investigated the impact of face-to-face contact sessions and online discussion forums on the academic performance of students at an Open Distance Learning (ODL) university (N = 1,015). <em>t</em>-Tests for independent samples indicated that students who attended a written assignment preparation contact session performed significantly better in the written assignment than those students who did not attend this contact session [<em>t</em>(813) = 4.64, p = 0.00]; students who attended an examination preparation contact session did not perform significantly better in the examination than those students who did not attend this contact session [<em>t</em>(892) = 1.12, p = 0.26]; while students who used an online discussion forum performed significantly better in the final examination than those students who did not use this forum [<em>t</em>(1,013) = 4.04, p = 0.00]. Reasons for these mixed results are subsequently discussed. The study also found that the attendance of contact sessions and the utilisation of an online discussion forum by students were extremely low, and possible reasons for this are also given. Implications for the use of contact sessions and online discussion forums to improve the academic performance of ODL students are also discussed.</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 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)0.013
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation 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 score0.149
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.099
GPT teacher head0.530
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 teacher head, 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

Citations33
Published2016
Admission routes1
Has abstractyes

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