Improving Academic Writing Skills through Online Mode of Task-Based Assignments
Bibliographic record
Abstract
This is to report a 2-Year Research Project (2015-2016) funded by the Directorate General of Higher Education of the Republic of Indonesia, which aims at justifying whether or not the online mode of task-based writing assignments (of various genres of English texts) could improve the writing skills of the students at higher education. An action research was conducted in College of Economics and Business Studies, Stikubank University (UNISBANK) Semarang, Central Java Indonesia in response to the lack of time allocated to students’ writing activities in their English class. Three cycles of treatments were employed—each with five phases, (1) identification of problem area, (2) collection and organization of data, (3) interpretation of data, (4) action based on data and (5) reflection of action. The findings showed that—compared with the initial condition— there was a mean increase of 31% and an increase of 121% in the students’ scores beyond the passing score of 61. Also, the students’ writing motivation increased considerably (>86% toward positive attitudes) as revealed in the survey at the end of the treatment program.
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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".