Some of the recent electronic learning environments have moved beyond the conventional delivery of text, multimedia, and objective tests. There are systems with animated conversational agents, intelligent adaptive tutoring, interactive simulations, and other features designed to engage learners and promote deeper comprehension. One system is AutoTutor, a learning environment that tutors students by holding a conversation in natural language. AutoTutor’s design was inspired by explanation-based constructivist theories of learning, intelligent tutoring systems that adaptively respond to student knowledge, and empirical research on dialogue patterns in tutorial discourse. AutoTutor presents challenging questions and then engages in mixed initiative dialogue that guides the student in building an answer. It provides feedback to the student on what the student types in (positive, neutral, negative feedback), pumps the student for more information, prompts the student to fill in missing words, gives hints, fills in missing information, identifies and

Research conducted in the laboratory and classroom has repeatedly found that self-explaining is a useful, self-directed learning strategy. Although the self-explanation effect has been replicated several times, the sources for its effectiveness are still under investigation. The present study attempts to address the question: Why does self-explaining work? Two alternative proposals are contrasted. The content account proposes that self-explaining is effective because of the additional information to which the learner is exposed. Alternatively, the generation account suggests that it is the activity of producing an explanation that is effective. The evidence, taken from learning curves collected in the classroom, predominantly supports the generation account of self-explanation, which highlights the benefit of actively processing the learning material, instead of simply attending to it. Keywords: Self-explanation; paraphrasing; physics education research; study strategies. To help smooth the transition from novice to expert-like performance, the cognitive and learning sciences have focused upon learning strategies that have proven to be effective across several different content domains. Among these domain-independent learning strategies is selfexplaining, which is defined as the sense-making process that an individual uses to gain a deeper understanding of some instructional material, including textbooks, workedout examples, diagrams, and other multimedia materials (Roy & Chi, 2005). Self-explaining has consistently been shown to be effective in producing robust learning gains in the

In this study, we compared Entailer, a computational tool that evaluates the degree to which one text is entailed by another, to a variety of other text relatedness metrics (LSA, lemma overlap, and MED). Our corpus was a subset of 100 self-explanations of sentences from a recent experiment on interactions between students and iSTART, an Intelligent Tutoring System that helps students to apply metacognitive strategies to enhance deep comprehension. The sentence pairs were hand coded by experts in discourse processing across four categories of text relatedness: entailment, implicature, elaboration, and paraphrase. A series of regression analyses revealed that Entailer was the best measure for approximating these hand coded values. The Entailer could explain approximately 50 % of the variance for entailment, 38 % of the variance for elaboration, and 23 % of the variance for paraphrase. LSA contributed marginally to the entailment model. Neither lemma-overlap nor MED contributed to any of the models, although a modified version of MED did correlate significantly with both the entailment and paraphrase hand coded evaluations. This study is an important step towards developing a set of indices designed to better assess natural language input by students in Intelligent Tutoring Systems.

Sense disambiguation for prepositions is used within a paraphrase recognition system. ‘With ’ is chosen to explore our approach of using information from WordNet (hypernym and meronym relations) and LCS (verb lexical information); similar concepts can be used for other prepositions. This paper describes the senses of ‘with’, the disambiguation model, evaluation process, and results. 1

Users of educational hypertext are faced with the challenge of creating meaning both within and between texts. Cohesion is an important factor contributing to whether a reader is able to capture meaning and comprehend text. When readers are required to fill in conceptual gaps in text, comprehension can fail if they do not have sufficient knowledge. Cohesion helps low-knowledge readers to create a more coherent mental representation of the text. However, text that is too cohesive can inhibit active processing, and thus reduce coherence for more knowledgeable readers. Similar patterns have been found for hypertext, which requires readers to create coherence between multiple electronic texts. Domain novices are in greater need of explicit pointers to important links between documents and gain from having less control over system navigation. Domain experts are in less need of scaffolding within the system. We discuss the use of a multimedia reading strategy training program to help low-knowledge readers better understand less cohesive text. Finally, we discuss four principles to guide hypertext development geared toward improving coherence and metacognitive engagement.

Prior neuroimaging studies of discourse comprehension and strategic reading comprehension have shown that there are at least two networks of brain regions that support strategic discourse comprehension. In particular, strategic reading comprehension leads to activation of a domain-general control network as well as a network of regions supporting coherence-building comprehension processes. The present study was designed to further examine the neural correlates of strategic reading comprehension by examining the brain regions associated with zoning out, or mind wandering, while performing reading strategies on expository texts and diagrams. The results show that a region of dorsal prefrontal cortex was associated with increased frequency of zoning out, and the results provide an important replication or prior work by showing a high degree of consistency in the areas that are active while using reading strategies.

9 Individuals with high-functioning autism and Asperger Syndrome find everyday social 10 interactions very challenging due to their impairments in language, communication and 11 social skills (Rapin & Tuchman, 2008). As their IQ scores are in the average and above range, 12 they are typically placed into mainstream schools and face complex social interactions with