Learning Technologies, Science and Mathematics Education, and Online Learning
Bibliographic record
Abstract
In this issue, we focus on learning technologies, science and mathematics education, online learning, and several additional topics. We begin with four articles related to learning technology. The articles include an examination of the ways to integrate immersive-learning tools into practice-oriented learning, a bibliometric analysis of global trends in learning technology within the field of psychology, the intersection of generational characteristics and AI integration in master’s education, and a description of an innovative initiative that created a publicly accessible e-book comprising digital media research assignments. Then, we present two articles on science and mathematics education, including one that discusses the results of an environmental scan of secondary science education programs across Canada regarding the inclusion of the nature, history, and philosophy of science in course descriptions, and another that presents a meta-analysis examining the effect of technology on statistics learning. We then share two articles on online learning, including one that reviews the obstacles students and lecturers faced during the COVID-19 pandemic regarding online learning and teaching at two institutions in Afghanistan and Indonesia, and another that examines the factors affecting the effectiveness of online learning. Four additional articles are presented on chronic absenteeism, teacher professional development, cross-cultural competence within teacher education programs, and the perspectives of early-career teachers on well-being practices. This issue concludes with four book reviews.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".