The purpose of this paper is to propose an adaptive system analysis for optimizing learning sequences. The analysis employs a decision tree algorithm, based on students’ profiles, to discover the most adaptive learning sequences for a particular teaching content. The profiles were created on the basis of pretesting and posttesting, and from a set of five student characteristics: gender, personality type, cognitive style, learning style, and the students’ grades from the previous semester. This paper address the problem of adhering to a fixed learning sequence in the traditional method of teaching English, and recommend a rule for setting up an optimal learning sequence for facilitating students’ learning processes and for maximizing their learning outcome. By using the technique proposed in this paper, teachers will be able both to lower the cost of teaching and to achieve an optimally adaptive learning sequence for students. The results show that the power of the adaptive learning sequence lies in the way it takes into account students’ personal characteristics and performance; for this reason, it constitutes an important innovation in the field of Teaching English as a Second Language (TESL).
In order to compete and survive in the twenty-first century global economy, it is essential that students acquire communication skills in English (Chen, Warden, & Chang, 2006). The goal of teaching English – including comprehension, listening, speaking, reading, and writing proficiency – is to facilitate students’ future academic and professional careers. Students’ learning depends on what happens in the classroom, and in different classrooms there may be different cognitive and learning styles. In the conventional learning systems of Taiwan, however, teachers of English teach the same content to all students, without taking into consideration the individual students’ gender, personality type, cognitive style, learning style, or previous knowledge. That is, the current courses are based on “static” learning material, not “dynamic” learning material (Romero, Ventura, Delgado, & Bra, 2007). In this type of learning system, if students wish to maximize their learning outcome, they must adapt themselves to the course content, the course content is never adapted to accommodate their individual needs and preferences.
Adapting what goes on in the classroom to students’ needs involves two important issues: how to tailor courses to each individual students’ characteristics and capabilities, and how to create, represent, and maintain the activity tree with the appropriate associated sequencing definition for different students. Unfortunately, because of the enormous costs universities have to pay for education in Taiwan, it is impossible to design personalized learning environments to accommodate each students’ learning needs. It is possible, however, by using a decision tree algorithm, for teachers to investigate students’ learning characteristics in advance, and on the basis of this information to extract students’ optimal learning sequences, and then maximize students’ learning outcome by grouping students with the same learning sequence together. This paper will apply a decision tree algorithm, a data mining technique, to investigate each students’ background and characteristics in order to optimize his or her learning sequence and maximize his or her learning outcome in the field of Teaching English as a Second Language (TESL).
The purpose of this paper was to propose an adaptive system analysis for optimizing learning sequences, in which a decision tree algorithm, based on student profiles, was used to extract the most adaptive learning sequences. By applying a decision tree data mining technique to the students’ profiles, nine optimal learning sequences for personalized learning were derived, and the students were grouped into fifteen optimal personalized learning groups. In order to cut teaching costs and facilitate the teaching process, after expert panel discussions, the hierarchical decision tree of optimal learning sequences/handouts was simplified, and the simplified optimal learning sequences/handouts were minimized to five learning sequences/handouts.
This study has outlined a way in which the process of learning English can be facilitated for Taiwanese students who, for cultural reasons, are lacking in both motivation and self-confidence. The decision tree algorithm technique and theory discussed in this study could be an important innovation in the field of TESL in Taiwan. The researchers hope that this paper may serve as an initial research model for using data mining techniques to adapt course content to the learning needs of individual students. A future study should focus on designing and developing a way to test the recommendation rules for optimal learning sequences outlined in this paper.
