Development of interactive and reflective learning among Malaysian online distant learners: An ESL instructor’s experience
Bibliographic record
Abstract
Technology has brought tremendous advancements in online education, spurring transformations in online pedagogical practices. Online learning in the past was passive, using the traditional teacher-centred approach. However, with the tools available today, it can be active, collaborative, and meaningful. A well-developed task can impel learners to observe, to reflect, to strategize, and to plan their own learning. This paper describes an English as a Second Language (ESL) instructor’s attempt to foster interactive and reflective learning among distance learners at a public university in Malaysia, working within the framework proposed by Salmon (2004). The authors found that proper planning and close monitoring of a writing activity that incorporates interactive and reflective learning helped to raise the students’ awareness of their own learning process and consequently helped them to be more responsible for their learning. The students acquired significant cognitive benefits and also valuable practical learning skills through the online discussions. However, there were challenges in carrying out the writing task to promote this form of learning, including students’ professional and family commitments and cultural attitudes as well as communication barriers in the online environment. To overcome these challenges, the authors recommend the following: ensure tutor guidance, enforce compulsory participation, address technical problems quickly, commence strategic training prior to the beginning of a task, and implement team teaching with each instructor taking on certain roles.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".