iSTART: Interactive strategy training for active reading and thinking

By Danielle S. McNamara, Irwin B. Levinstein, C. Boonthum. 2004

317 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Developmental and Educational Psychology, Natural Language Processing

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Citations in the corpus

Improving Question Answering with External Knowledge (2019, methodology): “This work was conducted when the two authors were at Tencent AI Lab, Bellevue, WA. obtained from sources outside of the text (McNamara et al., 2004; Salmerón et al., 2006).”
Improving Machine Reading Comprehension with General Reading Strategies (2018, background): “…human readers, especially those who lack adequate prior knowledge of the topic of the text (Mokhtari and Sheorey, 2002; Mokhtari and Reichard, 2002; McNamara et al., 2004), we propose three corresponding domain-independent strategies to improve MRC based on an existing pre-trained transformer…”
Scaffolding Deep Comprehension Strategies Through Point&Query, AutoTutor, and iSTART (2005, background): “comprehension ( McNamara et al., 2004; O’Reilly, Sinclair, & McNamara, 2004).”
Language comprehension is both embodied and symbolic (2008, methodology): “…the semantic associations between a student answer, and ideal good and bad answers (Graesser et al. 2000; Graesser, Chapter 3, this volume). iSTART uses LSA in its teaching of reading strategies to students by providing appropriate feedback to students’ self-explanations (McNamara et al. 2004).”
Instruction Based on Self-Explanation (2010, methodology): “For example, McNamara (2004b) developed a self-explanation reading training program (SERT) and found that training signif icantly improved text-based comprehension during training compared to reading aloud alone for a group of psychology undergraduate students studying science-based text passages.”
The impact of text coherence on learning by self-explanation (2007, background): “Finally, researchers have also found that computer coaches can be successful at helping students to self-explain (Aleven & Koedinger, 2002; Conati & VanLehn, 2000; McNamara, Levinstein, & Boonthum, 2004 ).”
A Comparison of Greedy and Optimal Assessment of Natural Language Student Input Using Word-to-Word Similarity Metrics (2012, methodology): “We provide experimental results on two datasets provided to us by researchers developing two world-class dialogue-based tutoring systems: AutoTutor (Graesser et al., 2005) and iSTART (McNamara et al., 2004).”