Evaluating self-explanations in iSTART

Word matching, latent semantic analysis, and topic models

Chutima Boonthum, Irwin B. Levinstein, Danielle McNamara

Research output: Chapter in Book/Report/Conference proceedingChapter

6 Citations (Scopus)

Abstract

iSTART (Interactive Strategy Trainer for Active Reading and Thinking) is a webbased, automated tutor designed to help students become better readers via multimedia technologies. It provides young adolescent to college-aged students with a program of self-explanation and reading strategy training [19] called Self-Explanation Reading Training, or SERT [17], [21], [24], [25]. The reading strategies include (a) comprehension monitoring, being aware of one's understanding of the text; (b) paraphrasing, or restating the text in different words; (c) elaboration, using prior knowledge or experiences to understand the text (i.e., domain-specific knowledge-based inferences) or common sense, using logic to understand the text (i.e., domain-general knowledge based inferences); (d) predictions, predicting what the text will say next; and (e) bridging, understanding the relation between separate sentences of the text. The overall process is called self-explanation because the reader is encouraged to explain difficult text to him- or herself. iSTART consists of three modules: Introduction, Demonstration, and Practice. In the last module, students practice using reading strategies by typing self-explanations of sentences. The system evaluates each self-explanation and then provides appropriate feedback to the student. If the explanation is irrelevant or too short, the student is required to add more information. Otherwise, the feedback is based on the level of overall quality.

Original languageEnglish (US)
Title of host publicationNatural Language Processing and Text Mining
PublisherSpringer London
Pages91-106
Number of pages16
ISBN (Print)184628175X, 9781846281754
DOIs
StatePublished - 2007
Externally publishedYes

Fingerprint

Semantics
Students
Feedback
Demonstrations
Monitoring

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Boonthum, C., Levinstein, I. B., & McNamara, D. (2007). Evaluating self-explanations in iSTART: Word matching, latent semantic analysis, and topic models. In Natural Language Processing and Text Mining (pp. 91-106). Springer London. https://doi.org/10.1007/978-1-84628-754-1_6

Evaluating self-explanations in iSTART : Word matching, latent semantic analysis, and topic models. / Boonthum, Chutima; Levinstein, Irwin B.; McNamara, Danielle.

Natural Language Processing and Text Mining. Springer London, 2007. p. 91-106.

Research output: Chapter in Book/Report/Conference proceedingChapter

Boonthum, C, Levinstein, IB & McNamara, D 2007, Evaluating self-explanations in iSTART: Word matching, latent semantic analysis, and topic models. in Natural Language Processing and Text Mining. Springer London, pp. 91-106. https://doi.org/10.1007/978-1-84628-754-1_6
Boonthum C, Levinstein IB, McNamara D. Evaluating self-explanations in iSTART: Word matching, latent semantic analysis, and topic models. In Natural Language Processing and Text Mining. Springer London. 2007. p. 91-106 https://doi.org/10.1007/978-1-84628-754-1_6
Boonthum, Chutima ; Levinstein, Irwin B. ; McNamara, Danielle. / Evaluating self-explanations in iSTART : Word matching, latent semantic analysis, and topic models. Natural Language Processing and Text Mining. Springer London, 2007. pp. 91-106
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