Can verbalisers learn as well as visualisers in simulation‐based CAL with predominantly visual representations? Preliminary evidence from a pilot study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it