Exploring the use of web-based learning tools in secondary school classrooms
Why this work is in the frame
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Bibliographic record
Abstract
AbstractThis study explored the impact of Web-Based Learning Tools )WBLTs), also known as learning objects, in secondary school mathematics and science classrooms. Surveys, open-ended questions, and student performance data were collected from a sample of 8 teachers and 333 students. Teachers rated the learning benefits, quality, and engagement value of WBLTs very high. Students rated these same features moderately high. Student performance with respect to remembering, understanding, applying, and analyzing concepts increased significantly )28–53%) when WBLTs were used. Qualitative data suggested that a number of students reacted positively to the following qualities of WBLTs: visual supports, learning benefits, ease of use, animations, graphics, and engagement. Some students were concerned about pace )too fast), challenge level )too hard), and the quality of help features when using WBLTs. Overall, it appears that the WBLTs used in this study had a positive impact on teacher and student attitudes, as well as student learning performance.Keywords: evaluateassessqualityscaleeffectmiddle schoolWBLTweb-based learning toolsonline learning tools Notes on contributorRobin Kay has published over 50 articles or chapters in the area of computers in education, presented numerous papers at 15 international conferences, is a reviewer for five prominent computer education journals, and has taught computers, mathematics, and technology for over 18 years at the high school, college and university level. Current projects include research on laptop use in teacher education, learning objects, classroom response systems, gender differences in computer related behaviour, discussion board use, emotions and the use of computers, and factors that influence how students learn with technology. He completed his Ph.D. in Cognitive Science )Educational Psychology) at the University of Toronto, where he also earned his masters degree in Computer Applications in Education. He is currently an Associate Professor in the Faculty of Education at the University of Ontario Institute of Technology in Oshawa, Canada.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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