Tristan Nixon

Independent Researcher · Research

Generalizability of models of student learning is a highly desirable feature. As new students interact with educational systems, highly predictive models, tuned to increasing amounts of data from previous learners, presumably allow such systems to provide a more individualized, optimal learning path, give better feedback, and provide a more effecti...

Most work on learning curves for ITSs has focused on the knowledge components (or skills) included in the curves, aggregated across students. But an aggregate learning curve need not have the same form as subsets of its underlying data, so learning curves for subpopulations of students may take different forms. We show that disaggregating a skill’s...

Deep analysis of domain content yields novel insights and can be used to produce better courses. Aspects of such analysis can be performed by applying AI and statistical algorithms to student data collected from educational technology and better cognitive models can be discovered and empirically validated in terms of more accurate predictions of st...

Time pressure helps students practice efficient strategies. We report strong effects from using games to promote fluency in mathematics.

Creating an educational curriculum is a difficult task involving many variables and constraints [Wang 2005]. In any curriculum, the order of the instructional units is partly based on which units teach prerequisite knowledge for later units. Historically, psychologists and cognitive scientists have studied the dependency structure of information in...

One function of a student model in tutoring systems is to select future tasks that will best meet student needs. If the inference procedure that updates the model is inaccurate, the system may select non-optimal tasks for enhancing students' learning. Poor selection may arise when the model assumes multiple knowledge components are required for a s...

We describe a methodology for simulating student behavior to predict the effects of skill-learning parameter changes on system behavior. Validation against data collected after the changes were made shows that accurate predictions can be made despite a different cohort of students. Furthermore, deviations from the predictions may help explain unexp...

In Cognitive Tutors, student skill is represented by estimates of student knowledge on various knowledge components. The estimate for each knowledge component is based on a four-parameter model developed by Corbett and Anderson [Nb]. In this paper, we investigate the nature of the parameter space defined by these four parameters by modeling data fr...