Facing a Changing World, Reinventing Technical Education and Learning Software Innovation 1969-1989: The Assembly of Two Learning Management Systems
Bibliographic record
Abstract
Using a sensibility from Actor-Network Theory, this research looks at the social-technical assembly of two early learning management systems from 1969 to approximately 1989. As such, it is more a story of change in post-secondary education rather than a story of software. The research looked at two related cases: Case A – SAIT Reinventing Technical Education: The Southern Alberta Institute of Technology, a polytechnic in Calgary, Canada, faced a changing world in 1969. In response, the President and people across the institution helped reinvent technical education as they created a new learning system by 1977 (competency-based technical education). Out of this assembled a learning management system (heavily influenced by mastery learning), one that was used from Calgary to Melbourne, and places between. Case B – CBTS Reinventing Learning Software Innovation: Facing a stall in innovation of the software at SAIT, key participants in Case A created a company, Computer Based Training Systems Ltd. in Calgary, to design and market a new learning management system. By the early 1990s, this software was being used by Technical Institutes, Colleges, Universities, K-12 education, and industry in Canada, Australia, USA, Ireland, England, South Africa and Zimbabwe. Of note, the users of the software held the first international conference on learning management systems at the University of Limerick, Ireland in 1989. This research makes visible the previously invisible story of the assembly of these two early learning management systems. It may clarify current academic understanding of the history of these systems. More important, this is a first draft of this tale and can act as a foundation for future research—particularly in these cases where there is more to tell. Also, post-secondary leaders and designers of change may find the models of the designers’ thinking (Focus, Flow, Frame and Formative) in Case A useful, with reflective transfer, for projects of designed change today. This design thinking focused on institutional change and contributed to the successful results seen in the two Cases, results that made a difference to students and institutions.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".