A Planning Algorithm to Support Learning in Open-ended, Unstructured Environments

The focus of this paper is a novel pedagogical planner that we have developed called the CFLS planner (Collaborative Filtering based on Learning Sequences). The CFLS planner has been designed for an open-ended and unstructured learning environment based on the ecological approach (EA) architecture (McCalla Journal of Interactive Media in Education, 7 , 2004 ). The EA-based learning environment represents its content as learning objects (LOs), maintains models of its learners, and keeps track of learner interactions with the LOs by attaching traces of their behaviour to the LOs they have interacted with. The CFLS planner creates pedagogical plans for a target learner by looking back at the sequence of the b (for “backward”) most recent LOs that the target learner has interacted with and finding a neighbourhood of other learners who in the past have interacted with a similar sequence of b LOs. The CFLS planner then recommends to the target learner a sequence of f (for “forward”) LOs that was the most successful sequence (in terms of learning outcomes) that had been carried out next among the neighbourhood of similar learners. We implemented and tested the CFLS planner using a very simple simulation, in which simulated learners interact with simulated learning objects. We experimented with various settings for the b and f parameters. Intriguing patterns in the relationship between b and f emerged. Further, the settings for b and f that led to the best learning outcomes (on two different measures of success) varied according to the aptitude levels of the learners. Finally, we compared the CFLS planner to two baseline planners: a simple prerequisite planner (SPP) and a planner that randomly recommended the next learning object (Random). The CFLS planner readily outperformed Random (as expected), but also, more surprisingly, with appropriate settings of b and f , it outperformed SPP even though the CFLS planner did not know about the prerequisite relationships among the LOs that the SPP was able to access. This shows promise that a CFLS planner can find niche learning paths to recommend to learners based on interaction traces left behind by the learners, without needing externally engineered metadata about the learning objects or knowing very much about the learners, perhaps even finding paths based on patterns of learning activity never considered by a human designer. This is exactly the kind of planning system needed in open-ended, unstructured learning environments.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.