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Can verbalisers learn as well as visualisers in simulation‐based CAL with predominantly visual representations? Preliminary evidence from a pilot study

2011· article· en· W1930496101 on OpenAlexaff
Tzu‐Chien Liu, Kinshuk Kinshuk, Yi‐Chun Lin, Ssu‐Chin Wang

Bibliographic record

VenueBritish Journal of Educational Technology · 2011
Typearticle
Languageen
FieldPsychology
TopicLearning Styles and Cognitive Differences
Canadian institutionsAthabasca University
FundersNational Science Council
KeywordsThink aloud protocolMathematics educationPsychologyReading (process)MainstreamRepresentation (politics)Educational technologyComputer scienceMultimediaHuman–computer interactionLinguistics

Abstract

fetched live from OpenAlex

Abstract Simulation‐based computer‐assisted learning (CAL) is emerging as new technologies are finding a place in mainstream education. Dynamically linked multiple representations (DLMRs) is at the core of simulation‐based CAL. DLMRs includes multiple visual representations, and it enables students to manipulate one representation and to immediately receive feedback from others. An interesting and important research question is whether verbalisers, who prefer to process verbal material, have similar learning performance and learning features as visualisers, who prefer to process visual material. To answer this question, 28 undergraduate students were selected as participants from the 855 undergraduate students who were initially tested with the style of processing scale (SOP). They were representative of either visualisers or verbalisers (students who scored upper 10% and lower 10% on the SOP). A study was conducted using an experimental design that included pre‐ and posttest and thinking‐aloud methods. Simulation‐Assisted Learning Statistics (SALS) was adopted as the learning environment for both groups. The analysis results are based on the data of 25 participants because three participants had trouble thinking aloud while using SALS. The results indicated that the visualisers and verbalisers did not differ significantly in their learning performance, but they did exhibit significantly different learning features in their use of DLMRs, their methods of reading learning guides and their learning strategies. Additionally, the learning features of the verbalisers explained why their learning performance was similar to that of the visualisers. Finally, this study provides recommendations for future applications and studies of simulation‐based CAL. Practitioner Notes What is already known about this topic Simulation‐based computer‐assisted learning (CAL) is useful for conceptual learning and is increasingly being applied in many educational fields. Visual‐verbal is one important dimension of cognitive styles. A number of studies have examined the learning performance of visualisers and verbalisers using learning materials that emphasise either visual or verbal representations; however, the results are mixed. What this paper adds Investigating the differences between the learning effects of visualisers and verbalisers after learning with simulation‐based CAL. Investigating the learning process features of visualisers and verbalisers when learning with simulation‐based CAL. Investigating the differences between visualisers' and verbalisers' learning features when learning with simulation‐based CAL. Implications for practice and/or policy Practitioners could use simulation‐based CAL in teaching statistical concepts. Practitioners should consider the learning features of visualisers and verbalisers when they are learning with simulation‐based CAL. Practitioners should try to develop and use targeted instruction that is developed based on the learning strategies to enhance visualisers' and verbalisers' learning effects.

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)0.007
metaresearch head score (Gemma)0.024
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 score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.048
GPT teacher head0.376
Teacher spread0.327 · 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".

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Citations7
Published2011
Admission routes1
Has abstractyes

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