INFLUENCE OF LEARNING PREFERENCE ON SELF-EFFICACY AND PERFORMANCE IN MIXED-MODALITY FIRST-YEAR ENGINEERING DESIGN
Bibliographic record
Abstract
All students have preferences for the way they receive and distribute information when the objective is learning. These preferences can be shown to have an effect on self-efficacy and on performance. The relationships between learning preference, self-efficacy and performance were studied using survey and grade data obtained from a first-year Engineering Design and Graphics course. The students were placed in one of three groups according to the modality (type) of design project they were given; a Simulation-Based project (SIM) using a software simulation tool, a Prototyping project (PRT) using a 3D printer, or a Simulation and Prototyping project (SAP) where they had to complete a design using both tools. Participants were given a custom survey that assessed self-efficacy and the VARK learning styles inventory which assesses learners on Visual, Aural, Read / Write and Kinesthetic learning preferences. 97 students were surveyed representing a response rate of 22.6%. Student performance was assessed by examining scores on a subset of questions related to design visualization on the final examination for the course. Data analysis involved examining the correlation between learning style and self-efficacy, and scores on final examination for each of the three course modality groups. Findings from this study include higher performance for Kinesthetic learners assigned a simulation-based project and low performance for Read/Write learners with a prototyping project. This study supports the hypothesis that student performance may depend on learning preferences coupled with design project modality.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".