Three cases in using conceptual maps for teaching engineering
Bibliographic record
Abstract
Much of traditional teaching in engineering focuses on procedural learning. However, future engineers will face a rapidly evolving and complex environment that will require reflexive learning and cognitive transfer skills which require a deep learning approach as opposed to surface or even strategic learning. Concept maps are powerful tools that encourage deep learning, expand students’ ability to generate and exchange knowledge. This paper presents three cases in which concept maps were used, first to develop students’ ability to analyse and synthesize information from multiple sources and enhance deep learning, second to facilitate the generation and sharing of collective knowledge. In the first two cases, students were asked to identify and organize key concepts from formal courses, presentations or texts within concept maps. It was found that students not only improved their ability to assimilate content from these sources, but also demonstrated better skills in analysing and communicating information. It was also found that they were more active in class, asked more questions and answered questions more frequently. In the last case, concept maps were used as boundary objects to mediate interactions and structuring collective knowledge in the design process of a sustainable house. The teaching in this last case was based on a socio-constructivist approach of situated learning, in which the students had to co-develop their understanding of sustainable construction within a design laboratory. Concept maps, used in combination with interactive boards and a knowledge portal proved to be quite effective in accelerating individual and collective learning of a complex topic, while also developing communication and transdisciplinary skills. The three cases demonstrate the power and value of using concept maps in engineering teaching, both from a cognitivist or a socio-constructivist perspective. Concept maps are powerful tools not only to learn how to learn individually and collectively, it increases students ability to structure their though and exchange knowledge.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.005 |
| 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".