基於語意關係之社會性標籤學習模式對改善英語閱讀成效之影響

Abstract

For students of English as a Foreign Language (EFL), extensive and effective English reading exercises are key techniques for improving general reading comprehension. However, opportunities for English students to collaboratively practice reading comprehension are rarely seen; moreover owing to resource constraints and the lack of accurate evaluation methods, English instructors rarely assess student performance effectively. Furthermore, simultaneously assisting many students in a course is a heavy burden for the teacher, especially at this time when digital learning is becoming more and more popular. It is indicated that not only teachers of a network-based course suffer from a heavy work load without any aid, but also students suffer from helplessness without proper guidance. To cope with these problems, we propose a semantic relationship-based social tagging learning approach that allows students to participate, learn, and create knowledge in ways that are personally meaningful and engaging. A knowledge network of reading clues is constructed based on collaborative filtering by combining both article and learner similarities and this can provide the adaptive and necessary supplementary knowledge as well as personal topic construction to every learner. Moreover, our novel approach takes semantic structure into consideration, and attempts to elicit reading behaviors from EFL students and assist the teachers in more effectively tracing and evaluating the student reading ability. A tag-based learning and teaching evaluation system based on the proposed approach has also been developed; moreover, an experiment has been conducted to evaluate the effectiveness of the approach. The experimental results of two case studies demonstrate that our approach can significantly improve the performance of learning through tag-based design practices.