Learning Reading Strategies for Science Texts in a Gaming Environment: iSTART vs iTG Learning Reading Strategies for Science Texts in a Gaming Environment: iSTART vs iTG An experiment will be conducted with high school students to compare the effectiveness of iSTARTME to iSTART. The study will be a pretest/post-extended-training comparison to examine the effects of extended strategy practice (i.e., including a total of 11 sessions) and include the between-subjects variable of Extended Training Environment (iSTART, iSTART-ME, Control). The study includes the withinsubjects variable of test (pretest, posttest) and the quasi-experimental variables of prior science knowledge and prior reading skill. The evaluation includes 11 sessions: pretest, training (regular iSTART), 7 additional practice sessions, a posttest, and a 1-week retention test. During the 7 additional practice sessions, students will engage in 40-60 minutes of practice per session. This amount of practice corresponds to one text of approximately 40 sentences; the student will self-explain approximately half of the sentences. The study will include approximately 120 high school students. This includes 40 students per training environment condition, a sufficient number to afford analyses of potential interactive effects emerging from individual differences. Additional details about this study can be found in the proposal. This study will be completed during this final year of the project at ASU. The data will be analyzed and disseminated. REU: Learning Reading Strategies for Science Texts in a Gaming Environment: iSTART vs ITG An overarching goal of this project is to develop tutoring technologies that support deeper learning. This project centers on processes that tend to result in deeper understanding of scientific concepts, including the pivotal notion of self-explanation. Students who self-explain text are more successful at solving problems, more likely to generate inferences, construct more coherent mental models, and develop a deeper understanding of the concepts covered in the text. However, not all students are able to successfully self-explain text. They either lack the reading skills or prior domain knowledge to do so. iSTART was developed to provide instruction in reading strategies that support the process of self-explanation. This project furthers our previous work on iSTART by examining whether a gaming environment for learning selfexplanation and reading strategies to understand science texts sustains students attention and engagement during training, and by consequence, improves learning of the strategies. iSTART is a web-based reading strategy trainer that provides young adolescent to college-aged students with reading strategy training to better understand challenging science texts. iSTART provides reading strategy instruction in an automated web-based system. In iSTART, pedagogical agents instruct trainees in the use of self-explanation and other active reading strategies to explain the meaning of science text while they read. The training was motivated by empirical findings that show that students who self-explain text are more successful at solving problems, more likely to generate inferences, construct more coherent mental models, and develop a deeper understanding of the concepts covered in the text. A technological goal of this project is to further develop a framework for a learning environment based on serious games. There is no doubt that games sustain the attention of many individuals. The notion behind serious or educational games is to capitalize on aspects of game environments that grab and sustain the attention of the users and make learning more fun. Nonetheless, it is also possible for entertainment to hinder learning by distracting the learner from the intended learning task. One of our tasks in the development and refinement of the iSTART game environment will be to optimize both learning and entertainment. Thus, one objective is to optimize the amount of entertainment in the learning situation to promote engagement and deeper learning.
|Effective start/end date||8/16/11 → 4/30/12|
- National Science Foundation (NSF): $87,599.00
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