Values-Based Design of Learning Portals as New Academic Spaces
Bibliographic record
Abstract
Many guidelines for portal design tend to focus on the technical aspects of a portal or a network. However, as we continue to define portals as gateways for learning, we need to consider issues related to the social and cultural context in which portals are used. In this chapter we examine learning portals from both the instructors’ and the learners’ perspectives by synthesizing existing research and proposing a framework for quality guidelines. The Collaborative of Online Higher Education Research (COHERE), consisting of eight large research-intensive universities in Canada involved in Internet-based learning, was created to enhance learning and teaching through technology and to move toward a stronger culture of professional collaboration and scholarship in our educational practices (Carey, 2000). Based on our experience with COHERE, we have developed tools for the formative and summative evaluations of learning portals generally. These tools include usability studies, questionnaires and focus groups.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".