Cognitive Technology for Academic Counselling in New Normal
Bibliographic record
Abstract
The objectives of this research on Cognitive Technology for Academic Counselling in the New Normal were (1) to design an architecture of cognitive technology for academic counselling in the new normal, (2) to develop a system of cognitive technology for academic counselling in the new normal, (3) to assess the academic performance of students using cognitive technology for academic counselling in the new normal, and (4) to assess the satisfaction of students using cognitive technology for academic counselling in the new normal. The sample group in this study were 30 students at a secondary school level 1 from Ongkharak Demonstration School, Srinakharinwirot University, in the 2021 academic year. The students were enrolled on a course in Design and Technology and selected by multistage randomization, i.e. 1) group randomization and, 2) simple random sampling. The research tools were, (1) a system of cognitive technology for academic counselling in the new normal, (2) a performance assessment form, (3) a test and worksheets on design and technology, and (4) a satisfaction assessment form. The research results found: (1) learning outcomes after using the system were significantly better than before at the .01 level, and (2) the overall satisfaction of students using cognitive technology for academic counselling in the new normal was at the highest level (Mean = 4.50, S.D. = 0.64).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".