Abstract
As differing evaluation instruments are adopted in learning object repositories serving specialized communities of users, what methods can be adopted 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? In this research we studied the application of Bayesian Belief Networks (BBN) to the problem of translating and integrating data among different quality evaluation instruments and measures. A BBN was constructed to probabilistically model relationships between items in the Learning Object Evaluation Instrument (LORI) and items used for evaluation in the MERLOT repository. Initial testing using evaluation data from objects in the domain of computer programming showed that the model was able to make potentially useful inferences about different dimensions of learning object quality.

Citation
Han, K., Kumar, V. & Nesbit, J. C. (2003, November). Rating learning object quality with bayesian belief networks. E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, & Higher Education, Phoenix, AZ.

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