COMPUTER-BASED LEARNING SOFTWARE FOR ENGINEERING STUDENTS
Bibliographic record
Abstract
The rapidly evolving technological knowledge required by engineers imposes important challenges on how to best prepare the engineering workforce of the future. Novice engineers are faced with formulating creative solutions to problems they have never seen before; they did not really learn to solve "real world messy problems". Problem-solving in engineering requires the use of disciplinary-based heuristics that are not immediately apparent to novices in the field. The aim of this study is to evaluate and expand engineering education using cognitive apprenticeship as an instructional framework. Cognitive apprenticeship outlines pedagogical strategies that makes visible experts` problem solving heuristics to support novices in becoming more proficient. This work will provide an overview of a computer-based learning environment in a classroom with third- and fourth-year undergraduate students. First, we discuss the implementation of cognitive apprenticeship in the classroom and evaluate the learning outcomes of students with individual differences in learning styles. Second, we apply cognitive apprenticeship principles to guide the design of a Civil Engineering tutor, a computer-based learning environment that serves as a cognitive tool outside the classroom. The design of the software is driven by a cognitive task analysis of several knowledge areas that mediates competency in calculating the snow loads on buildings. This teaching tool includes the main assumptions of the cognitive apprenticeship approach and also defines instructional methods for enhancing learning. We analyze user satisfaction towards the different features of the software and derive some implications for future innovative educational tools.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".