A Lesson Plan Development Study for Higher Education Based on Needs Assessment “Graphics and Animation in Education” Course
Bibliographic record
Abstract
The aim of this study is to develop a lesson plan for the “Graphics and Animation in Education” course lectured in the department of Computer Education and Instructional Technology (CEIT). For this purpose, a “Needs Analysis Form for Graphics and Animation in Education Course” that includes open ended questions is produced by the program specialist and the researchers. The needs analysis form was applied to 10 instructors who have taught this course in the faculty of education. In the light of the findings derived from the needs analysis; the purpose of graphics and animation in education course was determined as: “creating e-materials which specifically comprise interactive features in order to use at various levels of education and providing students the skills to adapt these materials to be used in mobile devices”. The basic strategy to use for this course is stated as “Expository” and during the course demonstration and question-and-answer methods are used. As a result, a lesson plan was developed for a unit of the graphics and animation in education 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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".