Development of Teaching Materials in the Form of Daras Books Student Management Courses
Bibliographic record
Abstract
This study aims to develop teaching materials in the Student Management Study Program to determine the feasibility of teaching materials in improving the quality of classroom learning. This research was development research adapted from the ADDIE model. There are five stages of this research, namely: 1. Analysis 2. Design, 3. Development, 4. Implementation, and 5. Evaluation. Validation was carried out by material experts, media experts, linguists, and practicality assessments from MPI students at UIN Mahmud Yunus Batusangkar. Based on the assessment of validators, the first material expert scored aspects of the material presentation 3.4 (85%), and the second material expert scored 3.6 (90%) of the maximum score of 4.0. The results from the first material expert validator in the aspects of content eligibility were 3.4 (85%), and from the second material expert aspects of content feasibility were 3.2 (80%) of the maximum value of 4.0. The validation results from the first media expert validator were 3.4 (85% and the second media expert was 3.4 (85%) from a maximum value of 4.0, namely in the aspect of language feasibility; all aspects of the validator regarding the textbook of student management have been suitable to use as learning media for student management courses in improving the quality of learning.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".