Assessment of Student Learning through Homework Intervention Method
Bibliographic record
Abstract
The work presented in this paper is based on a certain type of intervention strategy to the traditional college homework practice presented at the recent ASEE Conference in Vancouver [Akasheh and Davis, AC 2011-565, ASEE Conference, Vancouver, 2011].Following the modern cognitive theories of learning and motivation, the intervention strategies proposed in that preliminary showed potential to restore the effectiveness of homework as a learning tool which in turn reflected on better student academic achievement and attitude.Following similar strategies, this work seeks further validation of the influence of such interventions on student learning outcome.It also tests these interventions in different courses and in different classroom settings as well as a variation of the intervention which expands its applicability to large classes (the previous study was performed in small classroom setting).Data will be collected from these courses and analyzed to see if general conclusions can be drawn that support the cognitive model studies presented in the literature.The idea of this study is to enhance student motivation to complete the assigned homework more thoroughly, as originally intended by assigning homework, with the assumption that better learning will occur.To assess the effectiveness of the interventions, the performance of control and experimental student samples on exams is compared and student attitudes are surveyed.Results based on student learning and motivation survey show that a large majority of the students thought that the intervention helped their motivation and hence learning.The corresponding results based on performance on exams reflect this opinion but not in a definitive manner possibly due to the small sample size dictated by the nature of classrooms involved in the study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".