Quality rating of learning objects using Bayesian Belief Networks

The unceasing growth of the Internet has led to new modes of learning in which students routinely interact online with instructors, other students and more frequently, digital resources. Much recent research has focused on building infrastructure for these activities, especially to facilitate searching, filtering and recommending online resources known as learning objects [I]. Although newly defined standards for learning object metadata [2] are expected to greatly improve searching and filtering capabilities, students, teachers, and instructional developers may still be faced with choosing from many pages of result listings returned from a single learning object query. The listed objects tend to vary widely in quality. Without proper recommendation, the learning object enquirers not only grope in the dark in front of overwhelming information, but also easily fall for poorly designed and developed instructional materials, wasting time and effort. Hence, there is a clear need for quality evaluations prior to recommendation that can be communicated in a coherent, standardised format to measure the quality of learning objects. Consequently, we need certain criteria to obtain this evaluation. In the last few years, a number of quality rating standards have been developed. As different evaluation instruments are deployed in learning object repositories serving specialised communities of users, what methods can be applied for translating evaluative data across instruments to allow this data to be shared among different repositories? How can the large number of possible explicit and implicit measures of preference and quality be combined to recommend objects to users?