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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".