  The quality of assessment determines the quality of learning, and is
characterized by validity, reliability and difficulty. Mastery of learning is
generally represented by the difficulty levels of assessment items. A very
large number of variables are identified in the literature to measure the
difficulty level. These variables, which are not completely independent of one
another, are categorized into learner dependent, learner independent, generic,
non-generic and score based. This research proposes a model for predicting the
difficulty level of assessment items in engineering courses using learner
independent and generic variables. An ordinal regression model is developed for
predicting the difficulty level, and uses six variables including three stimuli
variables (item presentation, usage of technical notations and number of
resources), two content related variables (number of concepts and procedures)
and one task variable (number of conditions). Experimental results from three
engineering courses provide around 80% accuracy in classification of items
using the proposed model.
