{'fraction_non_alphanumeric': 0.022263450834879406, 'fraction_numerical': 0.0037105751391465678, 'mean_word_length': 5.829113924050633, '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}}
Contextualizing learning in a reflective conversational tutor

Contextualizing learning in an intelligent tutoring system is difficult for many reasons. Goals such as presenting material in an understandable manner, minimizing confusion and frustration, and helping the student reason about their actions all need to be balanced. Previous research has shown reflective discussions (with human tutors) occurring after problem-solving to be effective in helping students reason about their own actions (S. Katz et. al, 2003). However, leading a reflective discussion makes it difficult to present information in an understandable manner, and without contextualization, it is easy to create student confusion and frustration. This raises the question: how can intelligent tutoring systems effectively contextualize learning in a reflective discussion? In this paper, we describe the tutorial architecture of SCoT, a spoken conversational tutor that uses flexible, adaptive planning and multi-modal task modeling to support the contextualization of learning in reflective dialogues.