{'fraction_non_alphanumeric': 0.030985915492957747, 'fraction_numerical': 0.0018779342723004694, 'mean_word_length': 5.580246913580247, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Student models construction by using information criteria

Proposes a method of constructing student models for intelligent tutoring systems (ITSs) by using information criteria. This proposal provides a method to automatically construct the optimum student model from data. The main problem when traditional information criteria are employed to construct a model is that a large amount of data, which is difficult to obtain in actual school situations, needs to be obtained. This paper proposes a new criterion for using a smaller amount of data by utilizing a teacher's expert knowledge. Concretely, (1) the general predictive distribution is derived, and (2) a method of determining the hyper-parameters by using a teacher's expert knowledge is proposed. Finally, some Monte Carlo experiments comparing some information criteria [BIC (Bayesian information criterion), ABIC (Akaike's extension of BIC), MDL (minimum description length), and the exact predictive distribution] are performed. The results show that the proposed method provides the best performance.