Course instructors need to be able to identify students in need of assistance as early in the course as possible. Recent work has suggested that machine learning approaches applied to snapshots of small programming exercises may be an effective solution to this problem. However, these results have been obtained using data from a single institution, and prior work using features extracted from student code has been highly sensitive to differences in context. This work provides two contributions: first, a partial reproduction of previously published results, but in a different context, and second, an exploration of the efficacy of neural networks in solving this problem. Our findings confirm the importance of two features (the number of steps required to solve a problem and the correctness of key problems), indicate that machine learning techniques are relatively stable across contexts (both across terms in a single course and across courses), and suggest that neural network based approaches are as effective as the best Bayesian and decision tree methods. Furthermore, neural networks can be tuned to be reliably pessimistic, so they may serve a complementary role in solving the problem of identifying students who need assistance.

We investigated Parameterized Poker Squares to approximate an optimal game playing agent. We organized our inquiry along three dimensions: partial hand representation, search algorithms, and partial hand utility learning. For each dimension we implemented and evaluated several designs, among which we selected the best strategies to use for BeeMo, our final product. BeeMo uses a parallel flat Monte-Carlo search. The search is guided by a heuristic based on hand patterns utilities, which are learned through an iterative improvement method involving Monte-Carlo simulations and optimized greedy search.