Fostering Problem-Solving in a Virtual Environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it