Connectivism in Learning Activity Design: Implications for Pedagogically-Based Technology Adoption in African Higher Education Contexts
Bibliographic record
Abstract
This paper examines the possible characteristics and the value of designing learning activities grounded in connectivism—an emerging learning theory. It is an exploratory attempt to connect the theory to the prevailing technology adoption archetypes used in African contexts with the aim of extracting influences that could shape pedagogical technology adoption in African higher education contexts. A reflection on the process of designing learning activities that employ blogging in an experimental training intervention provides a unique context in which to try and infuse connectivist principles while outlining the challenges that surface. The questions driving the argument in this paper include: What do connectivist perspectives offer learning activity design and practice? What can the prevailing technology adoption models used in African contexts offer to learning activity design? Can we combine connectivist perspectives and African-based technology adoption models to inform pedagogical technology adoption in African higher education contexts? These questions are exploratory and are based on one single subjective experience of the author. They are part of an argument put forward as a proposal which is yet to be tested in practice.
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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 | 0.028 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".