MétaCan
Menu
Back to cohort
Record W3011233240 · doi:10.5430/elr.v9n1p40

The Importance of Various Indicators of Active Learning on the Enhancement of Saudi Students’ Motivation and English Achievement: An Experimental Study

2020· article· en· W3011233240 on OpenAlexvenueno aff
Oqab Alrashidi

Bibliographic record

VenueEnglish Linguistics Research · 2020
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsIntervention (counseling)PsychologyTask (project management)Mathematics educationAcademic achievementValue (mathematics)SituatedApplied psychologyMedical educationMedicineComputer scienceMathematicsStatisticsEngineering

Abstract

fetched live from OpenAlex

This experimental study sought to examine the impact of four indicators of active learning (i.e., elaborated feedback, group work, situated learning, and videos and pictures in classroom instruction) on the enhancement of Saudi students’ various motivational constructs (i.e., self-efficacy, task value, and effort expenditure) and English achievement. Participants were 289 university students, and the data were collected at three time points: Time 1 (before the intervention), Time 2 (in the middle of the intervention), and Time 3 (after the intervention). The findings of repeated measures ANOVA and follow-up t-tests revealed that the intervention had small impacts on all the variables at Time 2 (in the middle of the intervention). However, at Time 3 (after the intervention), the intervention had small impacts on effort expenditure and task value, a moderate impact on academic achievement, and a large impact on self-efficacy. In general, the evidence obtained provides important implications for educational practices and further research development.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.106
GPT teacher head0.458
Teacher spread0.353 · 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 designNon-randomized trial
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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueEnglish Linguistics ResearchSame topicInnovative Teaching and Learning MethodsFrench-language works237,207