Canadian engineering students’ motivation in the contextof a shift toward student-centered teaching methods in an outcome-based education
Bibliographic record
Abstract
A recent transition to an outcome-based engineering education in Canada has prompted changes to instructional andpedagogical methods. Given that students can express a different degree of motivation depending on the course and on thelearning activities within a course, there is a need to examine the motivational dynamics that drive the students in thelearning process. Moreover, most studies on engineering students’ motivation have examined motivational componentsindependently. The purpose of this study is to analyze the joint contributions of student characteristics, their perception ofinstructors’ attitudes and behavior when interacting with students, as well as their perception of the nature of the learningactivities and their impact on student motivation within a course. The sample was composed of 215 students attending afrancophone engineering school in Canada. Participants completed a questionnaire composed of 42 items from variousexisting instruments. Multiple linear regression analysis was used to predict the set of motivational components for thisstudy. Instructors’ attitudes and behavior, as well as higher-order cognitive tasks are significantly related to studentmotivational components, resulting in a positive impact on mastery goal, performance goal, task value, control beliefs, andself-efficacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".