Workshop Approaching Twenty Years of Knowledge Tracing (BKT20y)

Knowledge Tracing is an extremely popular method for student modeling because of its capability to infer a student’s dynamic knowledge state in real time as the student is observed solving a series of problems (Corbett & Anderson, 1995). After its introduction in 1995, many extensions to the original technique have been proposed to improve its predictive accuracy. Variants include: fitting model parameters to individuals rather than populations (e.g., Lee & Brunskill, 2012; Yudelson, Koediger, & Gordon, 2010), contextualizing model parameters based on past and current usage of an intelligent tutoring system (Baker, Corbett, & Aleven, 2008, Baker et al., 2010; GonzálezBrenes, 2014; Pardos et al., 2010) and on latent characteristics of students and problems (Khajah et al, 2014), clustering similar students and sharing parameters among them (Pardos et al, 2012), soft sharing of parameters via hierarchical Bayesian inference (Beck & Chang, 2007; Beck, 2007), and considering knowledge state as a continuous variable (SohlDickstein, 2013; Smith et al., 2004).