Situated Learning And Motivation Strategies To Improve Cognitive Learning In Ce
Bibliographic record
Abstract
Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract SITUATED LEARNING AND MOTIVATION STRATEGIES TO IMPROVE COGNITIVE LEARNING IN CE Alexandre Cabral, Rolland Viau and Denis Bédard Université de Sherbrooke, Quebec, Canada Abstract This papers describes the results obtained and the main observations made during a year long research project whose main purpose was to integrate situated learning and some motivational tools in an undergraduate civil engineering course (Soil Mechanics I). New teaching material was developed almost from scratch around a main theme and several secondary themes. Oriented discussions and exercises were prepared in order to help the students acknowledge the new professional skills they had acquired. The motivational tools served as means to create a better teaching and learning environment in the classroom and in the laboratory. The response of the students was constantly monitored. The results show that the various activities strategically planned to motivate the students to become active learners and to situate them in the context of the practice of Civil Engineering had a positive effect on several aspects, including their perception the of the significance of the knowledge being acquired, of the reality of their future profession and of the importance of the tools they might need. Another significant increase relates to the perception the students ended up with of their capacity to transfer the knowledge acquired to other situations. 1. General problem Two of the challenges facing higher education, in particular professional education, are the capacity these programs have (1) to foster the students’ ability to use their newly acquired knowledge in other contexts such as practice training sessions and (2) to maintain the students’ motivation throughout each course. The programs should aim at encouraging the students to draw links between what they are learning in class and the more applied context of the profession for which they are being prepared. Many professors in universities and colleges orient their instruction so that the students achieve such a transfer of information. However, in regards to student learning and to capacity to use this knowledge, comparing goals to outcomes in terms of teaching does not present an encouraging perspective, especially in the context of professional training (Bédard and Turgeon, 1995). Higher education today is also struggling with students’ loss of motivation and engagement in the parts of the curriculum which tend to present a more abstract set of knowledge that cannot easily be linked to the profession. Students generally enter university programs with high expectations about the usefulness of the knowledge that will be acquired. What the students are offered in the first place are courses presenting abstract concepts, generally given in a lecturing format. Therefore, they tend to disengage from learning activities and from their study.
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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 | 0.001 | 0.015 |
| 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 | 0.001 |
| Open science | 0.000 | 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".