EXPLORING INDIVIDUAL DIFFERENCES IN THE IMPACT OF WEB-BASED LEARNING TOOLS (WBLTS)
Bibliographic record
Abstract
The purpose of this study was to explore individual differences in middle and secondary school student attitudes and learning performance regarding Web-Based Learning Tools (WBLTs). The student characteristics assessed were gender, age, computer comfort level, subject comfort level, and average grade. Attitudes toward WBLTs were measured using a reliable, valid survey designed to gather data on student perceptions of learning, design, and engagement. Learning performance was assessed by comparing pre- and post-test scores on four knowledge categories (remembering, understanding, application, analysis) based on the revised Bloom’s taxonomy. Female students had significantly more positive attitudes toward WBLTs. Students who were more comfortable with using computers and the subject area addressed by a WBLT had significantly more positive attitudes toward WBLTs. Average grade was unrelated to student attitudes toward WBLTs. Student age was the only student characteristic that was significantly associated with learning performance. When older students use WBLTs (different from those used by younger students), learning performance is significantly greater than younger students. It is speculated that WBLTs may be better suited toward older students who have better self-regulation skills.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".