Applying the Matrix Model in an English for Presentation Online Class during COVID-19 Pandemic: A Case Study of an Undergraduate Class in Thailand
Bibliographic record
Abstract
During the COVID-19 pandemic, online learning was an important topic for scholars. A private university in Khon Kaen Province, Thailand followed a policy to create online courses for every subject to ensure that education could proceed effectively. To correspond with the policy, the Matrix Model was integrated with the online course development of an English for Presentation class at this private university. The Matrix Model is also known as SAMR which refers to Substitution, Augmentation, Modification, and Redefinition. The online course was presented in the third semester of the academic year of 2019 with 77 participants who volunteered to participate in this course. The research instruments used in this study were observation, surveying, and interview. The data collections were done at the beginning, during, and after the course to provide a comprehensive study of online learning. The data revealed both positive opinions and obstacles associated with this online learning. The results of using the SAMR model in this study do provide benefits to students and educators and show that 84% of the participants prefer online presentation over in-class presentation.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".