Developing Teaching Materials of Academic Writing Using Mobile Learning
Bibliographic record
Abstract
This study aimed to develop teaching material of academic writing skill using mobile learning.The innovation of this study was the teaching material of academic writing skill in form of application which could be easily installed on smartphones.This study implemented R&D method level 4 which includes 162 students and 7 lecturers.The techniques used in this study to collect data were tests, questionnaires, interviews, and focus group discussions (FGD).To design this teaching material, the researchers needed strategies that were started from the stages of 1) research, 2) designing product, and 3) development.Data was analyzed using Lilliefors technique.This study concluded that in the stage of research, students and lecturers needed mobile learning-assisted academic teaching materials in the form of application.The second stage was designing the product.In this stage, the design of the product was created in a storyboard, which was divided into scenes.Furthermore, design validation was carried out by material and media experts.It needed to revise the design.The third stage was developing.In this stage, the product was created using the Kodular website.Furthermore, it needed to conduct trials and revisions of product.The last step was conducting dissemination.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".