MI-IDEM: A model to evaluate instructional design using Bayesian Belief Networks

In this paper, we propose a model to evaluate instructional design for distance learning, based on Bayesian Belief Networks (BBN) that combine data from student feedback, peer review as well as data extracted from Moodle and parsed content from student learning environment interaction. For the purpose of our model, we use a commercial BBN tool developed by Norsys that provides the flexibility of interacting with the model using the GUI, as well as programmatically. This approach can be used to evaluate learning experience gained through online interaction, improving the quality of instructional design methodology and tools used. The model will also be tested with actual data for an online Java courses being developed in Athabasca University.