Effectiveness of the ADDIE Model within an E-Learning Environment in Developing Creative Writing in EFL Students
Bibliographic record
Abstract
The present research aimed to examine the effectiveness of the ADDIE model as used in teaching online in the LMS of Blackboard® and its facilities such as discussion boards, forums and blogs for improving the creative writing skills of EFL college students. The researcher utilized a quasi-experimental method, involving a pretest, posttest and control group design. Sixty students were randomly selected from freshmen studying in the English department participated in the study and were assigned equally to the research groups. The experimental group was exposed to the e-learning environment, which sought to develop the students’ creative writing skills while the control group was exposed to the traditional teaching method. Using a creative writing checklist and a writing test designed to assess the specific features of creative writing (originality, accuracy, self-expression, fluency, flexibility and overall writing performance for assessing creative writing in the research participants, results of t-tests and eta square statistical tests demonstrated that there were statistically significant differences between the mean scores gained by the experimental group and those obtained by the control group writing performance post-testing to the good of the experimental group participants. Conclusions and pedagogical implications were forwarded at the end of the article.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".