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Record W2017638705 · doi:10.5539/ies.v3n3p52

Effects of Modality and Redundancy Principles on the Learning and Attitude of a Computer-Based Music Theory Lesson among Jordanian Primary Pupils

2010· article· en· W2017638705 on OpenAlexvenueno aff
Osamah Mohammad Ameen Aldalalah, Soon Fook Fong

Bibliographic record

VenueInternational Education Studies · 2010
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPsychologyModality (human–computer interaction)Redundancy (engineering)Analysis of covarianceMode (computer interface)Music theoryMathematicsComputer scienceStatisticsArtificial intelligence

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate the effects of modality and redundancy principles on the attitude and learning of music theory among primary pupils of different aptitudes in Jordan. The lesson of music theory was developed in three different modes, audio and image (AI), text with image (TI) and audio with image and text (AIT). The study sample consisted of 405 third-grade pupils. Analyses of covariance (ANCOVA) and Post hoc were carried out to examine the main effects as well as the interaction effects of the independent variables on the dependent variables. The findings of this study showed that pupils using the AI mode performed significantly better than those in the TI mode. Pupils using the TI mode did not perform significantly better than those in the AIT mode. There were no significant differences in the preference or dislikes towards the three modes of courseware. Overall, the modality and redundancy principles need to be considered in the design and development of music theory learning so as to promote better learning.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.052
GPT teacher head0.394
Teacher spread0.342 · 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

Citations13
Published2010
Admission routes1
Has abstractyes

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