Student Model Adjustment Through Random-Restart Hill Climbing
نویسندگان
چکیده
ACTIVEMATH is a web-based intelligent tutoring system (ITS) for studying mathematics. Its course generator, which assembles content to personalized books, strongly depends on the underlying student model. Therefore, a student model is important to make an ITS adaptive. The more accurate it is, the better could be the adaptation. Here we present which parameters can be optimized and how they can be optimized in an efficient and affordable manner. This methodology can be generalized beyond ACTIVEMATH’s student model. We also present our results for the optimization based on two sets of log data. Our optimization method is based on random-restart hill climbing and it considerably improved the student model’s accuracy.
منابع مشابه
Plateaus and Plateau Search in Boolean Satis ability Problems: When to Give Up Searching and Start Again
We empirically investigate the properties of the search space and the behavior of hill-climbing search for solving hard, random Boolean satis ability problems. In these experiments it was frequently observed that rather than attempting to escape from plateaus by extensive search, it was better to completely restart from a new random initial state. The optimumpoint to terminate search and restar...
متن کاملAn iterated local search algorithm for learning Bayesian networks with restarts based on conditional independence tests
A common approach for learning Bayesian networks (BNs) from data is based on the use of a scoring metric to evaluate the fitness of any given candidate network to the data and a method to explore the search space, which usually is the set of directed acyclic graphs (DAGs). The most efficient search methods used in this context are greedy hill climbing, either deterministic or stochastic. One of...
متن کاملMulti-point Constructive Search
Multi-Point Constructive Search maintains a small set of “elite solutions” that are used to heuristically guide constructive search through periodically restarting search from an elite solution. Empirical results indicate that for job shop scheduling optimization problems and quasi-group completion problems, multi-point constructive search performs significantly better than chronological backtr...
متن کاملComputational annotation of eukaryotic gene structures: algorithms development and software systems
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 The MetWAMer system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 Data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 Stratified training and testing . . ...
متن کاملRandom Restarts in Minimum Error Rate Training for Statistical Machine Translation
Och’s (2003) minimum error rate training (MERT) procedure is the most commonly used method for training feature weights in statistical machine translation (SMT) models. The use of multiple randomized starting points in MERT is a well-established practice, although there seems to be no published systematic study of its benefits. We compare several ways of performing random restarts with MERT. We...
متن کامل