Principal Investigator: Noboru Matsuda
CoPrincipal Investigator(s):
Organization: Texas A&M University

Abstract:
This project focusses on equation solving in Algebra I for seventh and eighth grade students. This research extends research on an on-going project on developing an on-line, game-like learning environment called APLUS (Artificial Peer Learning environment Using SimStudent) in which students learn by teaching a synthetic peer called SimStudent. Learning by teaching is an exciting and innovative approach to learning and instruction that is often reported to be effective in classroom trials. However, little is known about the underlying cognitive and social theory on how and when students learn by teaching. Although some studies have shown a potential for scale learning by teaching through implementation in technology, very little is known about the transformative theory to successfully implement learning by teaching. To address this issue, the researchers build on their current work to further develop the theory of learning by teaching and to understand how best to achieve the theory development through advanced technology support. The researchers also investigate the similarities and differences between learning by teaching and learning by being tutored to better engineer effective advanced learning technology. In particular, the researchers propose to improve the effectiveness of APLUS by providing adaptive scaffolding for students to teach SimStudent correctly and appropriately. The researchers also propose to collect detailed process data that show fine-grained interactions between the student and SimStudent in addition to outcome data, which are test scores. By combining the process and outcome data, the researchers will explore the cognitive and social theory of learning by teaching. The researchers hypothesize that providing help on how to solve problems (cognitive help) and how to tutor (metacognitive help) will facilitate tutor learning. They also conjecture that the interaction between cognitive and metacognitive help will yield a rich learning environment that, in combination with the unique characteristics of learning by teaching, will be more effective than learning by being tutored. The following research questions will be addressed:

Q1: Does cognitive help facilitate tutor learning? If so, how and why?

Q2: Does metacognitive help facilitate tutor learning? If so, how and why?

Q3: Is learning by teaching with the meta-tutor assistance better than learning by being tutored?

Learning by teaching has been widely recognized to be effective for academically challenging populations, including African American students and underprivileged students. Therefore, the proposed intervention, if proven to be effective, would contribute to the development of effective STEM learning support system for all students regardless of their race/ethnicity, gender, socioeconomic status, or geographical locale in contemporary US classrooms. Since the entire proposed technology would run as a web-based application, the software products will be distributed widely and rapidly. The study data will be shared through the opendata repository, DataShop, under the direct supervision of the Pittsburgh Science of Learning Center. Thus, the research effort will contribute to the general learning research community by providing that community opportunities to conduct secondary data analyses.