Crossing Disciplinary Boundaries to Improve Technology-Rich Learning Environments
Bibliographic record
Abstract
Background The capacity of instructional technologies to personalize instruction has progressively improved over the last decade, in conjunction with changes in learning theories that dictate what, when, and how to support learners. Focus of Study This paper reviews several technology-rich learning environments that are investigated by members of the Learning Environments Across Disciplines partnership, including Newton's Playground, the War of 1812 iHistory tours, Crystal Island, BioWorld, and MetaTutor. The adaptive capabilities of these systems are discussed in terms of the metaphors of using computers as cognitive, metacognitive, and affective tools. Research Design Researchers rely on convergent methodologies to collect data via multiple modalities to gain a better understanding of what learners know, feel, and understand. The design guidelines of these learning environments are used to situate this understanding as a means to generalize best practices in personalizing instruction. Conclusions The findings of these investigations have significant implications for the metaphor of using technology as a tool to augment our thinking. The challenge is now to broaden learning theories while taking into consideration the social and emotional perspective of learning, as well as to leverage recent advances in learning analytics and data-mining techniques to iteratively improve the design of technology-rich learning environments.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".