Developing an Instant Semantic Analysis and Feedback System to Facilitate Learning Performance of Online Discussion

Abstract

By applying two-mode social networks and Chinese word segmentation technologies, a novel visualization tool, the instant semantic analysis and feedback system (ISAFS), is designed in this study to present the semantic networks of co-words and non-co-words used in learners’ discussion processes and assist learners in grasping the discussion direction to enhance online learning effectiveness. By conducting a quasi-experimental research, the ISAFS-assisted discussion board was assigned to the 32 learners of one class as the experimental group, whereas the general online discussion board was assigned to the 32 learners of the other class as the control group to support the online discussion learning activities of socio-scientific issues (SSIs). Analytical results show that the mean scores of overall, complexity, and perspective of socio-scientific reasoning performance have no statistically significant differences between the two groups. However, the in-depth interview results suggest that the learners in the experimental group gave positive feedback on the design concepts of the ISAFS and could explore the complexity and various perspectives of the issue through co-words and non-co-words presented on the ISAFS graph. Several suggestions regarding the functions and user interface of the ISAFS were also collected from the interviews to provide the directions for future studies.