Bayesian and probabilistic networks are claimed to offer powerful approaches to inferring an individual’s knowledge state from evidence of mastery of concepts or skills. A typical application where such tools can be useful is Computer Adaptive Testing (CAT). Bayesian networks have been proposed as an alternative to the traditional Item Response Theory (IRT), which has been the prevalent CAT app...

We present a generalisation of the IRT framework that allows to discriminate between examinees. Our model therefore introduces examinee parameters that can be optimised with Expectation Maximisation-like algorithms. We provide empirical results on PISA data showing that our approach leads to a more appropriate grouping of PISA countries than by test scores and socio-economic indicators.

An accurately calibrated item bank is essential for a valid computerized adaptive test. However, in some settings, such as occupational testing, there is limited access to examinees for calibration. As a result of the limited access to possible examinees, collecting data to accurately calibrate an item bank in an occupational setting is usually difficult. In such a setting, the item bank can be...

Time on task is an important predictor for how much students learn. However, students must be focused on their learning for the time invested to be productive. Unfortunately, students do not always try their hardest to solve problems presented by computer tutors. This paper explores student disengagement and proposes an approach, engagement tracing, for detecting whether a student is engaged in...