Automatic Generation of Coherent Learning Pathways for Open Educational Resources

Learners and educators all over the world have been increasingly relying on the internet for education, thus generating and consuming vast amounts of online learning resources. Selecting appropriate learning resources among them and structuring them in a way that maximises comprehension and skill building is a challenging task. In this work, we propose a model to automatically generate learning pathways from available open learning resources, such that the generated pathways are semantically coherent and pedagogically progressive. The proposed model has two components- a Greedy Generator and a Validator based on Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) models respectively. The Greedy Generator chooses the next resource in the learning pathway based on local considerations and the Validator validates the learning pathway as a whole. They work in tandem with each other connected by a feedback loop. Since we work with open educational resources that lack standard meta-data, we also propose methods to generate metrics that compare a pair of learning resources. The learning pathways generated by our model from a corpus of open learning resources show promising results.