Adaptive learning for ESL based on computation

In the conventional English as a Second Language (ESL) class‐based learning environment, teachers use a fixed learning sequence and content for all students without considering the diverse needs of each individual. There is a great deal of diversity within and between classes. Hence, if students' learning outcomes are to be maximised, it is important to know how to provide learning content using students' preferences, learning characteristics and knowledge background as a basis. A five‐step algorithm was proposed that was based on the four factors (gender, learning motivation, cognitive style and learning style) as the different learner characteristics. The percentage increase between the pretest and posttest scores was used to determine optimal adaptive learning sequences to accommodate a variety of individual differences. The algorithm included the following five steps—to obtain the learning performance, to distinguish the learning performance of the lowest and highest groups, to use the different learning sequences as a basis for categorisation, to test the four factors between the lowest and highest performance, and to reduce the number of handouts. Finally, an empirical study for validating the adaptive learning sequence was conducted. By analysing the students' characteristics and the optimal learning sequences, an attempt was made to develop an adaptive learning sequence system to facilitate students' learning and to maximise their learning outcome, thus addressing the problem of fixed learning sequences in conventional ESL instruction.