Supporting content and language integrated learning through computer-based dual concept mapping

Abstract

<p>Among various approaches to teaching and learning English as a foreign language, content and language integrated learning (CLIL), i.e. learning subject content in a non-native language, has received wide attention. Despite its positive effects on language learning, CLIL poses cognitive challenges to learners. To deal with the challenge, prior studies applied concept maps to visualize knowledge structures to support content learning, but paid inadequate attention to visualizing text structure to support language learning. Some studies mixed knowledge structure and text structure in one concept map, failing to help students distinguish between knowledge structure and text structure. It is important to visualize both knowledge structure and text structure and present them in two parallel concept maps to facilitate CLIL. To this end, this study proposed a computer-based dual concept mapping approach visualizing both knowledge structure and text structure of learning content in two parallel concept maps to support science content and English language integrated learning. This study was conducted with 89 eighth-grade students from two classes, who were randomly assigned to either the experimental or control condition. Students’ achievements in English language and science knowledge were assessed by knowledge tests; their cognitive load was measured through a questionnaire survey. By comparing student performance in the two conditions, the study shows that the experimental group experienced a lower level of cognitive load and achieved better learning outcomes in both science knowledge and language performance. Further, there were more significant differences between experimental and control groups for low and medium achievers in English language than for high achievers, indicating that the proposed approach was more beneficial for the former. The findings provide new insights into the pedagogy and strategy for CLIL, that is, visualizing both knowledge structure and text structure of learning content in two parallel concept maps with the support of concept mapping technology.<br></p>