Exploring Through Simulation an Instructional Planner for Dynamic Open-Ended Learning Environments

Modern online courses can be characterized as

dynamic open-ended

learning environments (DOELEs). For instructional planning to work in DOELEs, an approach is needed that does not rely on data 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

$$s$$

). An experiment with simulated learners was used to explore what are the best values of

$$b$$

,

$$f$$

and

$$s$$

. Results showed that the CFLS planner should avoid sending a learner any further ahead (

$$s$$

) than they have been matched in the past (

$$b$$

), a prediction that can be applied to the real world.