Fostering Problem-Solving in a Virtual Environment
Bibliographic record
Abstract
This article investigates students’ perceptions of the relationship between Problem-Solving and the activities and resources used in a Web-based course on the fundamentals of Information Technology at a university in Montreal, Canada. We assess for the different learning components of the course, the extent of perceived problem-solving skills acquisition including research, creativity and critical thinking skills. The course entailed two categories of learning, namely resources-based and interactive components. The study aimed at answering the following questions: 1) To what extent do students understand the definitions of Problem-solving, Research, and Creative Idea Generation skills, and Critical Thinking skills? (2) What is the relative contribution of the various learning components (activities and resources) of the course to the perceived acquisition of Problem-Solving, Research, and Creative Idea Generations skills, and Critical Thinking skills; (3) Is the understanding of the definitions correlated with the perceived contributions of the learning components (activities and resources) of the course to the skills development? (4) To what extent is perceived Problem-solving skill acquisition explained by the acquisition of the other three skills?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".