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 JEE SELECTS

By PAUL S. STEIF AND ANNA DOLLÁR

CLUES TO ONLINE LEARNING

Students do best when monitoring themselves.


Girl accessing classes online

As many experienced educators can attest, good students seem to know when they’re learning. They are the ones in class who are able to formulate and ask questions that pinpoint an issue they don’t understand. How students monitor and reflect on their ability to absorb material takes on added significance with the growing availability of online instructional materials. Such courseware has enormous potential to engage students beyond the classroom and provides an important learning alternative for those who can’t be reached through lectures and expository texts. But online materials may be only as good as the extent to which they encourage students to regulate themselves and monitor what they learn.

Our research joined the concept of self-regulation, known to be relevant in other learning contexts, to courseware assessment. We tested whether learning gains are related to total usage of courseware or to usage suggestive of self-regulation. On-line interactive materials can support self-regulation more actively than purely expository materials and even traditional homework sets. They can prompt students at various points to respond to questions and perform various tasks, thus providing the students immediate feedback on their performance.

Our study used a Web-based Engineering Statics courseware that we had developed. Since statics is a subject that requires solving problems as well as understanding concepts, larger tasks have been dissected and then presented using carefully designed sequences of short text, graphics, and videos. Also embedded into the course are about 300 virtual “tutors.” These are online learning tools designed based on cognitive principles to interact with students in ways that mimic a human tutor – i.e., offering hints, giving feedback when the student errs, suggesting what to do next, and maintaining a low profile when the student is performing well.

The system on which the courseware runs maintains log files of all student interactions. To address the research question, students in a lecture-based statics course were assigned to use the courseware as part of homework assignments, and to take paper-and-pencil diagnostic quizzes both before and after online instruction. Learning gains of students on the quizzes, as well as in class exams, were analyzed and compared with usage patterns inferred from log files.

We found that learning gains, as well as performance on the relevant class exam, appeared to be more closely correlated with self-regulation of learning than with total usage of the courseware. Students who did no end-of-module self-assessment activities performed significantly worse on the corresponding class exam compared with those who did some or many self-assessment activities. In addition, we found that students who did few in-module learning activities performed significantly worse on in-class diagnostic quizzes compared with those who did medium and high numbers of activities.

Our findings are relevant to both courseware designers and engineering classroom instructors. Self-regulation of learning is likely to be critical to successful use of courseware. Therefore, designers should build into courseware means for users to gauge whether they have indeed learned what the courseware was intended to convey, as well as opportunities to seek more instruction. They could help instructors and students by trying to produce data that meaningfully track student learning. Such data could provide powerful insights to both instructors and students.

In evaluating courseware for adoption, instructors should consider whether it stimulates students to gauge and regulate their own learning. Instructors should also employ classroom activities that prompt more students to self-reflect more often.

We need to help students understand that they are the ones chiefly responsible for monitoring and regulating their learning and that in doing so, they can contribute significantly to their own success.

Paul S. Steif is a professor of mechanical engineering at Carnegie Mellon University. Anna Dollár is an associate professor in the Department of Mechanical and Manufacturing Engineering at Miami University in Oxford, Ohio. This article is adapted from “Study of Usage Patterns and Learning Gains in a Web-based Interactive Static Course,” in the October 2009 Journal of Engineering Education.

 

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