Identifying at-risk students in LIS distributed learning courses

The growth in the application of distributed learning in library and information science (LIS) programs has been dramatic. Students in the distributed environment are challenged by the content, as well as by teaching technologies and a radical change in pedagogy. Any negative impact of the distributed learning approach should be minimized. To do so, instructors need to identify students who are particularly at risk and to provide appropriate methods of intervention. This article reports a study in which an instrument to identify high-risk students was developed and tested in LIS distributed learning classes. The survey encompassed thirteen indicators of completion. Educational level, study habits, age, preparation for the course, number of hours worked, and grade point average (GPA) accounted for most of the predictive power in the discriminant analysis of the survey scores. The instrument predicted correctly the completion status of 87.5 percent of the students.