Sequencing Strategy with Learning Portfolio Analysis for Personalized English Reading

Learning English is extremely popular in non-native English speaking countries, thus, the development of useful computer assisted learning programs and tools to support effective English learning are a critical issue in the educational field of learning the English-language. With the rapid growth of Internet technologies and easy access to information, the field of digital learning has stimulated innovation and diversified developments, and the emergence of guidance systems provide learners with adaptive learning paths that promote learning performance during learning processes. However, most guidance systems neglect to consider the level of difficulty, and recommended materials are proprietary in nature, and thus, do not fully utilize portfolio features of learners incorporated into personalized learning services when implementing personalized guidance. Therefore, this study proposed a guidance mechanism which utilizes the data of a learning portfolio to evaluate guidance parameters, and simultaneously considers information regarding the degree of relational reading, reading difficulty, and the average ability of learners, which can be entered into a genetic algorithm to construct a near optimal reading sequence during learning activities. Experiments were conducted to compare the free browsing reading mode without the guidance of a personalized reading path, as in most present systems. Based on the experimental results, this proposed guidance mechanism generates a high quality and concise learning path for individual learners, and the reading learning system results in an efficient and effective learning performance to promote learning motivation through appropriate learning paths.