Interactive learning with WeChat's Rain Classroom in a blended setting: The influence of three types of interactions on learning performance

Many advanced learning tools have been recently developed; however, demand for interactive environments that can enhance learning performance in a blended setting is growing. This study designed an interactive approach by integrating a learning tool, interactive sessions, and a variety of interaction types, and the effects of the types of interactions on learning performance were evaluated. Specifically, the WeChat-based app Rain Classroom was used to enhance learning. Rain Classroom has learner-learner, learner-teacher, and learner- content interaction sessions before, during, and after class; the method was denoted the Rain Classroom interactive approach. Analysis of covariance results revealed that the experimental group using Rain Classroom significantly outperformed the control group participating in conventional learning. A Pearson correlation analysis was conducted on Rain Classroom system log data, and learner interaction features, namely self-practice tests, Danmu discussion, and the instant response system (IRS), were positively related with learning performance; however, attendance was unrelated. Multiple regression analysis was conducted to determine which Rain Classroom interaction types predict learning performance; all interaction types predicted learning performance, but learner-content interactions had the greatest effect. Teachers could refer to the proposed Rain Classroom-based interactive approach when developing methods to improve student learning performance in a digital interactive environment.