Identifying At-Risk Students: An Assessment Instrument for Distributed Learning Courses in Higher Education
Description: The current period of rapid technological change, particularly in the area of mediated communication, has combined with new philosophies of education and market forces to bring upheaval to the realm of higher education. Technical capabilities exceed our knowledge of whether expenditures on hardware and software lead to corresponding gains in student learning. Educators do not yet possess sophisticated assessments of what we may be gaining or losing as we widen the scope of distributed learning. The purpose of this study was not to draw sweeping conclusions with respect to the costs or benefits of technology in education. The researcher focused on a single issue involved in educational quality: assessing the ability of a student to complete a course. Previous research in this area indicates that attrition rates are often higher in distributed learning environments. Educators and students may benefit from a reliable instrument to identify those students who may encounter difficulty in these learning situations. This study is aligned with research focused on the individual engaged in seeking information, assisted or hindered by the capabilities of the computer information systems that create and provide access to information. Specifically, the study focused on the indicators of completion for students enrolled in video conferencing and Web-based courses. In the final version, the Distributed Learning Survey encompassed thirteen indicators of completion. The results of this study of 396 students indicated that the Distributed Learning Survey represented a reliable and valid instrument for identifying at-risk students in video conferencing and Web-based courses where the student population is similar to the study participants. Educational level, GPA, credit hours taken in the semester, study environment, motivation, computer confidence, and the number of previous distributed learning courses accounted for most of the predictive power in the discriminant function based on student scores from the survey.
Date: May 2000
Creator: Osborn, Viola
Partner: UNT Libraries