Early warning systems rely on behavioral and cognitive data drawn from student information systems, learning management systems, and courseware platforms; however, they often lack sufficient data on student attitudes, perceptions, and affective responses to effectively prevent student withdrawals, failures, and drop outs or to intervene early enough to improve institutional and student outcomes. To complement the behavioral and cognitive datastreams, researchers and designers are increasingly turning toward microsurveys—short questions or question sets that help researchers gather data at strategic points during a course—to enable earlier intervention and, therefore, improve outcomes. However, for microsurveys to be effective, researchers and designers may need to refactor their research, design, and evaluation processes to address considerations unique to microsurveys. This chapter considers how researchers may go about developing microsurveys by formulating a foundational research base, developing initial designs, and then refining those through formative evaluation.
Therapy Dogs Improve Student Affect but Not Memory
