An Approach to Developing Instructional Planners for Dynamic Open-Ended Learning Environments.
Bibliographic record
Abstract
Instructional planning (IP) technology has begun to reach large online environments. However, many approaches rely on having centralized metadata structures about the learning objects (LOs). For dynamic open-ended learning environments (DOELEs), an approach is needed that does not rely on centralized structures such as prerequisite graphs that would need to be continually rewired as the LOs change. A promising approach is collaborative filtering based on learning sequences (CFLS) using the ecological approach (EA) architecture. We developed a CFLS planner that compares a given learner’s most recent path of LOs (of length b) to other learners to create a neighbourhood of similar learners. The future paths (of length f) of these neighbours are checked and the most successful path ahead is recommended to the target learner, who then follows that path for a certain length (called s). We were interested in how well a CFLS planner, with access only to pure behavioural information, compared to a traditional instructional planner that used explicit metadata about LO prerequisites. We explored this question through simulation. The results showed that the CFLS planner in many cases exceeded the performance of the simple prerequisite planner (SPP) in leading to better learning outcomes for the simulated learners. This suggests that IP can still be useful in DOELEs that often won’t have explicit metadata about learners or LOs.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".