The Effectiveness of Designing and Using a Practical Interactive Lesson based on ADDIE Model to Enhance Students’ Learning Performances in University of Tabuk
Bibliographic record
Abstract
Traditional teaching is one of the most common types of education, but with the explosive technologies, Traditional teaching could not be effective. Therefore, adopting and utilizing computer and communication technology in education became most important to make the learning more active, but before utilizing technology in education, instructors need to ensure of suiting the technology with the students’ abilities and characteristics based on Instructional design models, With the tremendous development of technology and the enhancement of student performance. In this paper, an interactive lesson designed based on ADDIE Model. Students divided into two groups; a control group and experimental group each group had 36 students to evaluate the effectiveness of using the interactive lesson and its role in enhancing students’ Learning Performance. The lesson had several stages; starting from having a useful design based on ADDIE Model, then provided a demo to the students to understand the knowledge. After that, the lesson presented in an interactive way, assistance’s hints provided to students during their learning process and reviewing the initial demo was available for the whole lesson. The effectiveness of this design has been measured, and results in both groups in this study compared, the experimental group showed statistically significant on students’ performances with a mean score of 5.45 versus 4.24 for the control group.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".