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Record W1595247267

Motivation and Cognitive Strategies in the Choice to Attend Lectures or Watch Them Online

2008· article· en· W1595247267 on OpenAlexaffvenue
John N. Bassili

Bibliographic record

VenueInternational journal of e-learning & distance education · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyCognitionClass (philosophy)Online learningMathematics educationOrientation (vector space)Social psychologyGoal orientationCognitive psychologyMultimediaComputer science
DOInot available

Abstract

fetched live from OpenAlex

This study explored relations between students' motivational and cognitive orientations as assessed by the Motivated Strategies for Learning Questionnaire (MSLQ), and their attitudes and choices relating to online lecture viewing. Examination performance was also assessed to determine if there were particular affinities between certain motivational or cognitive orientations and success in learning by attending lectures or watching them online. The results of regression analyses revealed that students who considered the course interesting and important and who were motivated extrinsically to do well in it, expressed particularly positive attitudes towards the option to watch lectures online. Students who did not particularly want to learn in interaction with their peers, and who were not inclined to monitor their learning, were particularly likely to watch lectures online rather than to attend them in class. The results suggest that attitudes towards the option to watch lectures by streaming video are related to students' motivational orientations whereas the actual choice to attend lectures or watch them online is related to their cognitive strategies. The extent to which students attended lectures or watched them online was not related to examination performance either alone or in interaction with any motivational orientation or cognitive strategy.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.230
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.043
GPT teacher head0.379
Teacher spread0.336 · 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

Citations56
Published2008
Admission routes2
Has abstractyes

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