lectureLess: A Mobile Cloud Computing Approach to Near Real-time Teaching Assessment

摘要

In this paper, we introduce lectureLess, a mobile computing architecture designed to collect and manage near real-time learning assessment data reported by students. The goal of lectureLess is to leverage the ubiquitous nature of student mobile devices to monitor and collect self-reported learning trends as they occur during a typical classroom session. The system includes a mobile device application for the learner and a rich internet application for the teacher. The mobile device collects real-time feedback from students who use the mobile application to report attainment of three learning metrics: comprehension, motivation, and interaction. Assessment data is pushed into a cloud repository where it can be analyzed and projected to the teacher in near real-time or archived for analysis after class. We share our experiences with lectureLess as employed in two systems engineering courses taught by two different teachers. We include aggregate trends in overall student reporting and discuss the viability of mobile devices for near real-time assessment. We also include preliminary results from a pilot study linking self-reported trends in student learning to teaching techniques. In this study, we analyze assessment data from lectureLess and identify inflection points in the reported levels of comprehension, motivation and interaction. These inflection points are matched to corresponding video highlights of the assessed classroom session. The teacher then evaluates the video highlights and makes their own assessment about the students' attainment of the evaluated learning dimensions.