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In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners' interactions is a time(More)
In this paper we investigate the role of reflection in simulation based learning by manipulating two independent factors that each separately lead to significant learning effects, namely whether students worked alone or in pairs, and what type of support students were provided with. Our finding is that in our simulation based learning task, students learned(More)
While human tutors typically interact with students using spoken dialogue, most computer dialogue tutors are text-based. We have conducted two experiments comparing typed and spoken tutoring dialogues, one in a human-human scenario, and another in a human-computer scenario. In both experiments, we compared spoken versus typed tutoring for learning gains and(More)
Phishing is a plague in cyberspace. Typically, phish detection methods either use human-verified URL blacklists or exploit Web page features via machine learning techniques. However, the former is frail in terms of new phish, and the latter suffers from the scarcity of effective features and the high false positive rate (FP). To alleviate those problems, we(More)
Large practical NLP applications require robust analysis components that can effectively handle input that is disfluent or extra-grammatical. The effectiveness and efficiency of any robust parser are a direct function of three main factors: (1) Flexibility: what types of disfluencies and deviations from the grammar can the parser handle?; (2) Search: How(More)
Many of the Intelligent Tutoring Systems that have been developed during the last 20 years have proven to be quite successful, particularly in the domains of mathematics, science, and technology. They produce significant learning gains beyond classroom environments. They are capable of engaging most students' attention and interest for hours. We have been(More)
While data from Massive Open Online Courses (MOOCs) offers the potential to gain new insights into the ways in which online communities can contribute to student learning, much of the richness of the data trace is still yet to be mined. In particular, very little work has attempted fine-grained content analyses of the student interactions in MOOCs. Survey(More)
The Why2-Atlas system teaches qualitative physics by having students write paragraph-long explanations of simple mechanical phenomena. The tutor uses deep syntactic analysis and abductive theorem proving to convert the student's essay to a proof. The proof formalizes not only what was said, but the likely beliefs behind what was said. This allows the tutor(More)
People come to online communities seeking information, encouragement, and conversation. When a community responds, participants benefit and become more committed. Yet interactions often fail. In a longitudinal sample of 6,172 messages from 8 Usenet newsgroups, 27% of posts received no response. The information context, posters' prior engagement in the(More)
We explore how features based on syntactic dependency relations can be utilized to improve performance on opinion mining. Using a transformation of dependency relation triples, we convert them into " composite back-off features " that generalize better than the regular lexicalized dependency relation features. Experiments comparing our approach with several(More)