Improving the integrated experience of in-class activities and fine-grained data collection for analysis in a blended learning class

Abstract

Blended learning has steadily gained in popularity at the higher levels of education. This marks a change in pedagogical approaches from one-directional instruction to an interactive and technology-aided class. However, to manage fluent in-class activities and proper data analysis, real-time and fine-grained data collection activities are still needed. We propose an approach which provides rich information about student activities and automates processes which are time-consuming and which otherwise require extraneous effort. First, we implemented a program to collect real-time and fine-grained data and to provide an integrated experience during in-class activities. Second, we undertook a data analysis with the collected real-time, fine-grained data. Our blended learning is a type of flipped learning with personal response systems (PRSs) of the type commonly known as clickers. We used clickers for attendance, quizzes, and daily surveys, and collected the resulting data. Our outcome shows that the blended learning approach improves student achievement levels with a relatively small standard deviation compared to traditional classes. In addition, the present findings are factors related to student satisfaction and seat position, as analyzed from the data collected using the clickers.