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  1. Abstract: Modeling student learning processes is highly complex since it is influenced by many factors such as motivation and learning habits. The high volume of features and tools provided by computer-based learning environments confounds the task of tracking student knowledge even further. Deep Learning models such as Long-Short Term Memory (LSTMs) and classic Markovian models such as Bayesian Knowledge Tracing (BKT) have been successfully applied for student modeling. However, much of this prior work is designed to handle sequences of events with discrete timesteps, rather than considering the continuous aspect of time. Given that time elapsed between successive elements in a student’s trajectory can vary from seconds to days, we applied a Timeaware LSTM (T-LSTM) to model the dynamics of student knowledge state in continuous time. We investigate the effectiveness of T-LSTM on two domains with very different characteristics. One involves an open-ended programming environment where students can self-pace their progress and T-LSTM is compared against LSTM, Recent Temporal Pattern Mining, and the classic Logistic Regression (LR) on the early prediction of student success; the other involves a classic tutor-driven intelligent tutoring system where the tutor scaffolds the student learning step by step and T-LSTM is compared with LSTM, LR,more »and BKT on the early prediction of student learning gains. Our results show that TLSTM significantly outperforms the other methods on the self-paced, open-ended programming environment; while on the tutor-driven ITS, it ties with LSTM and outperforms both LR and BKT. In other words, while time-irregularity exists in both datasets, T-LSTM works significantly better than other student models when the pace is driven by students. On the other hand, when such irregularity results from the tutor, T-LSTM was not superior to other models but its performance was not hurt either.« less
  2. In the domain of programming, intelligent tutoring systems increasingly employ data-driven methods to automate hint generation. Evaluations of these systems have largely focused on whether they can reliably provide hints for most students, and how much data is needed to do so, rather than how useful the resulting hints are to students. We present a method for evaluating the quality of data-driven hints and how their quality is impacted by the data used to generate them. Using two datasets, we investigate how the quantity of data and the source of data (whether it comes from students or experts) impact one hint generation algorithm. We find that with student training data, hint quality stops improving after 15–20 training solutions and can decrease with additional data. We also find that student data outperforms a single expert solution but that a comprehensive set of expert solutions generally performs best.