Integrating iSTART into a High School Curriculum

Integrating iSTART into a High School Curriculum Courtney M. Bell (cbell@mail.psyc.memphis.edu) Danielle S. McNamara (d.mcnamara@mail.psyc.memphis.edu) Institute for Intelligent Systems University of Memphis Memphis, TN 38152 Active Reading and Thinking (iSTART) into their science classrooms and curriculum. Abstract This study examines the viability in the classroom of a tutoring system called Interactive Strategy Training for Active Reading and Thinking (iSTART). This study investigated the effects of teacher-guided and iSTART- guided extended practice (following initial reading strategy training with iSTART) including 78 high school students from four biology classes. Eight high school teachers were also trained to administer reading strategy training. The results supported the conclusion that teachers can successfully integrate educational technology in such a way that it compliments their traditional teaching roles while helping to meet the needs of their students. Intelligent Tutoring Systems Intelligent tutoring systems are part of the advancements in educational technology aimed at promoting student- centered learning. As an outgrowth of earlier computer- aided instruction systems (Lajoie & Azevedo, 2005), intelligent tutoring systems are designed to capitalize on the power of one-to-one tutoring. Research by Cohen, Kulik, and Kulik (1982) indicates that when compared to traditional classroom instruction, one-to-one tutoring by untrained tutors, such as peers, cross-age tutors, or paraprofessionals, can produce an effect size for learning of . 4 sigma (i.e., .4 standard deviations). Research using trained tutors suggests varying effects. For example, Bloom (1984) reported an effect size of 2 sigma (2.0 standard deviations) for math skills training, whereas VanLehn and colleagues (2007) reported an effect size of only 1 sigma (1.0 standard deviation) for physics tutoring. Despite these variations, research indicates that overall, one-to-one tutoring is effective in producing learning gains but impractical to implement on a large scale because of cost and time requirements. Intelligent tutoring systems can provide cost-effective one-to-one tutoring which is adaptable to individual students’ needs and personalizable (Tsiriga & Virvou, 2004). With the help of a student model that is dynamically monitored and updated using assessment tasks, intelligent tutors are able to provide students with adaptive feedback (Lajoie & Azevedo, 2005). This feedback guides students in correcting misconceptions and errors while helping them to effectively progress through the system (Graesser et al., 2004). iSTART is based on a human-delivered intervention called SERT (McNamara, 2004). It is an automated reading strategy trainer that teaches students to self- explain texts using five reading comprehensions strategies: comprehension monitoring, paraphrasing, elaboration, prediction, and bridging (McNamara et al., 2004). The system consists of both vicarious and interactive components to enhance learning and also consists of three phases: an introduction, demonstration, and practice, which are guided by pedagogical agents. In the introduction, students vicariously learn the five reading strategies by watching the instructor agent, Dr. Julie, use examples and definitions to teach two student agents, Mike and Julie. A short quiz follows each Keywords: Intelligent tutoring systems; iSTART; AutoTutor; classroom; curriculum; educational technology; artificial intelligence; education; science. Introduction As a result of educational reforms and new standards, educational goals and instructional methods are changing. Traditionally, school districts defined their goals and methods in terms of students being exposed to large amounts of declarative knowledge - the more, the better. And, students’ understanding and knowledge have generally been assessed in terms of explicit recall, primarily relying on fill in the blank and multiple choice tests: assessments that fail to assess deep level understanding. These techniques have often been supplemented by skill-based technologies centering on repetitive drills and practices that lack the benefits of feedback, adaptation, and knowledge construction. More recently, school districts have begun adopting instructional measures that train students to learn, train students in methods of research, help students develop the motivation to pursue personal enrichment, and train students to be creative and innovative (Domingo et al., 2002). Consequently, educational technology is also changing, and must change to adapt to the evolving understanding of how to enhance the learning process. Although policy-makers, educators, and researchers have invested large amounts of resources to meet the technological changes, successful integration at the rates hoped for has yet to occur for a number of reasons including a lack of teacher training as well as teachers’ fears of replacement and threats to their traditional roles. Thus, in this study, we examine how training influences teachers’ ability to successfully integrate an intelligent tutoring system called Interactive Strategy Training for