A Formative Analysis of Instructional Strategies for Using Learning Objects
Bibliographic record
Abstract
To date, limited research has been done examining and eval uatingtheinstructionalwrapforusinglearningobjectseffec tively. The current study examined instructional strategies used by 15 teachers to integrate learning objects into 30 sec ondary school classrooms (510 students). Four key areas were examined: preparation time, purpose for using a learn ingobject,integrationstrategies,andtimespentusingalearn ing object.A small, but significant, correlation was observed between preparation time and student attitudes toward learn ing objects.When the purpose of using a learning object was to introduce a concept before a formal lesson, motivate stu dents, or teach a new concept, student attitudes and perfor manceweresignificantlyhigher.Ontheotherhand,choosing to use a learning object after a formal lesson or to review a concept resulted in significantly lower student attitudes and performance. Regarding integration strategies, providing a guiding set of questions was associated with more positive student attitudes and increased performance, whereas allow ing students to explore on their own (without direction) and class discussion after use led to significantly lower student attitudes and performance. Finally, time spent using learning objects was inversely correlated with student attitudes and performance.Itisreasonabletoconcludethatdecisionsabout instructional wrap had a significant impact on the effective ness of learning objects in a secondary school environment. Learning objects are operationally defined in this study as interactive web-basedtoolsthatsupportthelearningofspecificconceptsbyenhancing, amplifying, and/or guiding the cognitive processes of learners (Bennett & Jl. of Interactive Learning Research (2009) 20 (3) , 295-315
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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 | 0.006 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".