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What does it mean to be a better model? One conceptualization, indeed a common one in Educational Data Mining, is that a better model is the one that fits the data better, that is, higher prediction accuracy. However, oftentimes, models that maximize prediction accuracy do not provide meaningful parameter estimates. Here we argue that models that provide meaningful parameters are better models and, indeed, often also provide higher prediction accuracy. To illustrate our argument, we investigate the Performance Factors Analysis (PFA) model and the Additive Factors Model (AFM). PFA often has higher prediction accuracy than the AFM; however, PFA申fs parameter estimates are ambiguous and confounded. We propose more interpretable models (AFMh and PFAh) designed to address the confounded parameters and demonstrate PFA申fs confounding issues with synthetic data. The results from the experiment with 27 real-world dataset also support our claims and show that the more interpretable models can produce better predictions. 

Computational models of learning can be powerful tools to test educational technologies, automate the authoring of instructional software, and advance theories of learning. These mechanistic models of learning, which instantiate computational theories of the learning process, are capable of making predictions about learners’ performance in instructional technologies given only the technology itself without fitting any parameters to existing learners’ data. While these so call “zero-parameter” models have been successful in modeling student learning in intelligent tutoring systems they still show systematic deviation from human learning performance. One deviation stems from the computational models’ lack of prior knowledge—all models start off as a blank slate—leading to substantial differences in performance at the first practice opportunity. In this paper, we explore three different strategies for accounting for prior knowledge within computational models of learning and the effect of these strategies on the predictive accuracy of these models.