Academics’ “Why” of Knowledge-Building for the Fourth Industrial Revolution and COVID-19 Era
Bibliographic record
Abstract
Online teaching results in knowledge building. Knowledge building is the teaching and learning process that helps academics and students to generate specific personal values used to understand their personal identities. Academics have been forced by COVID-19 lockdowns to go online in teaching their students. The purpose of this study is to explore and understand academics’ knowledge of teaching for knowledge building in two higher-education institutions (HEIs) (RSA and USA) during the COVID-19 era and the 4IR. Reflective activities, focus-group discussions, and semi-structured interviews were used for data generation. Purposive with convenience sampling was used to select the twenty most accessible academics to participate in this study. The findings reveal that this situation compelled the academics to self-actualise on their knowledge-building to address the “why” questions of teaching that help students to understand and address their needs. The self-actualization was framed by “technological pedagogical content knowledge” which produced societal, personal, and professional knowledge building. It was interesting to note that the USA HEI participants were supported by educational technologists, while the RSA HEI participants helped themselves. This was because the RSA HEIs do not have educational technology centres. Consequently, this study recommends a follow-up study that can qualitatively and quantitatively compare the two HEIs. In this way it can be established whether the success of online teaching and learning is influenced by the presence of educational technology centres.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".