Students’ Task Understanding during Engineering Problem Solving in an Introductory Thermodynamics Course
Bibliographic record
Abstract
Understanding problems or tasks is a critical step in any problem-solving activity and the heart of self-regulated learning. When encountering a problem, students draw upon information available in the environment, along with knowledge, concepts, and perceptions derived from prior learning experiences, to interpret the demands of the task. Interpretation of tasks is, therefore, a key determinant of the goals set while learning, strategies selected to achieve those goals, and the criteria used to self-assess and evaluate outcomes. The purpose of this study is to better understand engineering students’ self-regulation in task interpretation processes while engaged in problem solving in an introductory engineering thermodynamics course. Two research questions guided the study: (1) What are the gaps, if any, between the instructor’s and students’ interpretation (explicit and implicit task features) of a problem-solving task?; and (2) How do students’ task interpretation (explicit and implicit) change after engaging in self-evaluation of their problem-solving processes? One hundred twelve (112) second year engineering undergraduates voluntarily participated in the study. Analysis of the data collected revealed a significant difference between the instructor’s and students’ task interpretation of the assigned problems. Furthermore, the analysis showed that students’ had a higher ability to identify the explicit parts of problem tasks than implicit ones. Students were able to grasp 63 to 77 percent and 39 to 49 percent, respectively, of the explicit and implicit information that was presented to them while engaged in problem-solving activities.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".