Is concept mapping favourable for undergraduates with different learning styles?
Bibliographic record
Abstract
Background and objective: Concept mapping is a powerful metacognition and visual learning tool. However, human beings are born to understand and perceive the world using five basic senses. According to Neil Fleming’s VARK model, there are five different types of sensory modality groups which include visual, auditory, read/write, kinaesthetic and mixed modality. Therefore, this study aimed to investigate the effects of CM on students’ overall academic performance among visual, auditory, read/write, kinaesthetic and multi-modal dominant learners.Methods: This was a cross-sectional quantitative research study. The participants were nursing undergraduates in a private higher education institute and enrolled in the same course offered in the spring and summer semesters. At the beginning of the semester, the VARK questionnaire version 7.8 was used to identify students’ sensory modality groups. Concept mapping was adopted for teaching the course. After the semester, students’ overall academic performance was used to compare the differences between different sensory modality groups.Results: The mean grades of the spring students were: visual (M = 80.80, SD = 7.30), aural (M = 81.49, SD = 4.19), read/write (M = 81.16, SD = 8.69), kinaesthetic (M = 78.27, SD = 7.56) and multimodal (M = 79.56, SD = 7.65). The means grade of summer students were: visual (M = 74.68, SD = 8.59), aural (M = 78.79, SD = 9.38), read/write (M = 74.89, SD = 3.87), kinaesthetic (M = 77.10, SD = 9.69) and multimodal (M = 75.96, SD = 9.74). After comparing the average grades between different sensory modality groups in both spring and summer semesters using One-way ANOVA testing, there were no statistically significant differences found.Conclusions: The results of this study show that teaching with animated CM in PowerPoints and co-construction of CM seems to be applicable to learners with different sensory modality groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".