Understanding Factors that Influence the Effectiveness of Learning Objects in Secondary School Classrooms
Bibliographic record
Abstract
The design, development, reuse, and accessibility of learning objects has been examined in some detail for almost 10 years (Kay & Knaack, 2007c, 2007d), however, research on the effectiveness of learning objects is limited (Kay & Knaack, 2005; Nurmi & Jaakkola, 2006a, 2006b, Sosteric & Hesemeirer, 2004), particularly in the K-12 environment. Until recently, learning objects were solely used in higher education (Haughey & Muirhead, 2005; Kay & Knaack, 2005, 2007c). The purpose of the current chapter is to examine factors that influence the effectiveness of learning objects in secondary school classrooms. These factors will include learning object qualities, gender, self-efficacy, grade, subject area, and teaching strategies.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".