iSTART: Reading Strategy Training Danielle S. McNamara (d.mcnamara@mail.psyc.memphis.edu) and Cognitive Studies for Educational Practice The University of Memphis Effectiveness of iSTART in Comprehension Next Stage: iSTART in High Schools to Help Students Introduction iSTART: Interactive Strategy Trainer Overarching Goals Improve student reading comprehension by teaching reading strategies. Design an automated reading strategy trainer that teaches reading strategies to students of differing ability levels. Work toward integrating the automated strategy trainer into high-school classrooms. Obtain a better understanding of the factors influencing the effectiveness of reading strategy training. Stage 1: Designing Self-Explanation Reading Training Self-Explanation Reading Training (McNamara & Scott, 1999; McNamara, 2004) incorporates: 1. Reading strategy training based on theories of reading comprehension and active thinking (Chi et al., 1994; Kintsch, 1988, 1998). 2. Interactive training based on educational research (Hacker & Graesser, in press). SERT training has been shown to improve textbook comprehension of low-knowledge college students (McNamara, 2004), and high-school students (O’Reilly, Best, & McNamara, 2004). Stage 2: Developing and Refining iSTART iSTART (Interactive Strategy Trainer for Active Reading and Thinking) is a Web-based computer program that uses automated agents to provide SERT training (McNamara, Levinstein, & Boonthum, 2004). Utilizes both vicarious and interactive features. Increased availability of the training to students. Stage 3: Testing the Effectiveness of iSTART Ran empirical studies with high-school and college students to assess impact of iSTART training on comprehension and course performance. Comprehension assessed using passage comprehension task and self-explanation task. Stage 4: Integrating iSTART in the Classroom Work with teachers to integrate iSTART into the classroom. SERT facilitates comprehension of science texts among college students (McNamara, 2004) and high-school students (O’Reilly, Best, & McNamara, 2004). iSTART training found to be as effective as live SERT training as assessed with a comprehension measure (O’Reilly, Sinclair, & McNamara, 2004). Introduction Students watch the teacher-agent explain the reading strategies to two student- agents. . Integrating iSTART into high-school science classes to help students improve reading comprehension of science textbooks. Implementing iSTART into high-school reading labs to help students with reading comprehension problems better understand science textbooks. Designing and evaluating extended self-explanation practice following iSTART training in classroom. To meet these goals we will: 1. Observe the way in which teachers use iSTART and incorporate self-explanation practice into regular classroom lessons. 2. Evaluate the role of extended self-explanation practice in students comprehension success and ability to self-explain science material. 3. Develop a teacher interface so that teachers can easily administer iSTART. 4. Increase the degree to which iSTART can adapt to the needs of the student by increasing the variety of practice texts, fine tuning the feedback. Demonstration Students are quizzed on various aspects of the SERT strategies. Practice Students practice generating explanations while the program provides feedback on performance. Conclusions Effectiveness of iSTART in Improving Self-Explanation Quality We have successfully developed an automated, interactive reading strategy trainer, iSTART. iSTART is effective for improving science understanding. The next goal is to evaluate the effectiveness of iSTART as a classroom teaching tool. Quality of self-explanations play a critical role in comprehension and learning (Chi et al., 1994). We analyzed paraphrases (reflects the students’ comprehension of the sentences) and elaborative inferences (indicate the students’ effort to integrate information provided in the text with prior knowledge). iSTART found to increase quality of self-explanations among high-school students. For example, students trained with iSTART produced more elaborations (M = .36) than controls (M = .25). Feedback during Practice Practice section incorporates feedback that is adaptive to the level of student performance. System utilizes both word-based and LSA-based algorithms to evaluate student responses. The accuracy of system feedback is highly correlated (r= .60 to .70) with human judgments of feedback accuracy. Acknowledgements We would like to thank the members of the ODU Lab (directed by I. Levinstein) and NIU lab (directed by K. Millis and J. Magliano) who helped to conduct our research. Our research was supported by the NSF (IERI Award number 0241144).