The Effectiveness of a Multi-Dimensional Teaching Strategy on Students’ Attitudes and Level of Learned-Helplessness
Bibliographic record
Abstract
Background: Learned helplessness can become a persistent problem that develops in response to a student’s lack of confidence and frustration with a certain course of study.Aims: This study examined the effects that a teaching strategy that was designed to trigger intrinsic motivation components had on students’ attitudes toward an unpopular scientific subject and students’ learned helplessness as a general characteristic.Sample: The participants consisted of 157 female undergraduate students.Method: A teaching strategy that consisted of multi-dimensional intrinsic motivation components was delivered to one neuropsychology class of students, while a second class was taught exactly the same concepts via a traditional lecturing strategy. Two dependent variables were assessed: Attitude toward the neuropsychology course and learned helplessness. The self-rating scales were administered twice: Once at the beginning of the course and once at the end. An analysis of variance ANCOVA was used to compare the results of the two groups of students.Results: The performance of the students in the experimental group was higher than that of students in the control group in terms of course grades. Furthermore, these students exhibited a more positive attitude to neuropsychology than the students in the control group. Learned helplessness decreased in the experimental group, and this was indicative of the fact that the new teaching strategy had a positive effect on motivational levels.Conclusion: A teaching method that incorporates the components of intrinsic motivation can enhance students’ performance on a specific course and can improve their attitude toward completing that course.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".