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Found 5 papers, 4 papers with code
Transition-Aware Multi-Activity Knowledge Tracing
TAMKOT is formulated as a deep recurrent multi-activity learning model that explicitly learns knowledge transfer by activating and learning a set of knowledge transfer matrices, one for each transition type between student activities.
Knowledge Tracing for Complex Problem Solving: Granular Rank-Based Tensor Factorization
In this paper, we argue that not all attempts are equivalently important in discovering students' knowledge state, and some attempts can be summarized together to better represent student performance.
Relaxed Clustered Hawkes Process for Procrastination Modeling in MOOCs
Hawkes processes have been shown to be efficient in modeling bursty sequences in a variety of applications, such as finance and social network activity analysis.
Stimuli-Sensitive Hawkes Processes for Personalized Student Procrastination Modeling
However, previous attempts on dynamic modeling of student procrastination suffer from major issues: they are unable to predict the next activity times, cannot deal with missing activity history, are not personalized, and disregard important course properties, such as assignment deadlines, that are essential in explaining the cramming behavior.
Modeling Knowledge Acquisition from Multiple Learning Resource Types
In this paper, we propose a student knowledge model that can capture knowledge growth as a result of learning from a diverse set of learning resource types while unveiling the association between the learning materials of different types.
