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From the perception questionnaire, it was also found that some and low proficiency participants who had low motivation were unwilling to participate in data-driven learning activities. This phenomenon was from the influence of peers, not the instructors or the learning materials. Some participants stated that they felt anxious when they did the activity by themselves because they did not know how to perform the activity. To the researcher’s surprise, the majority of the participants did not think they could get higher grade by doing the data-driven learning and most felt data-driven learning was not suitable for them. In addition, they were reluctant to do another activity through the
data-driven learning. They thought data-driven learning was difficult and time-consuming.
However, most of the participants admitted the value of data-driven learning. They felt data-driven learning not only helped them avoid certain errors of vocabulary usages in the future, but also enhanced their motivation in learning English vocabulary. The loud and diverse suggestions given by the whole class often limited the time and imposed them with pressure. It was suggested by some high and low achievers that instead of putting four pairs of near-synonyms in one class, the course materials should be limited to one or two pairs only. They thought the contexts on course material were difficult for them as well.
5.3 Pedagogical implications
The present findings contribute to the field’s understanding of the forces acting on using data-driven learning to learn near-synonyms. Based on the findings, two
pedagogical implications for EFL corpus-based near-synonyms vocabulary teaching at college are proposed. It is hoped that the results of this research demonstrate an example of how to incorporate data-driven learning into English near-synonyms instructions and expand students’ vocabulary knowledge in English learning in Taiwan EFL context.
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5.3.1 Adaptive Data-driven Learning Approach in English Near-synonyms learning According to the results, the feedback of the high and low achievers on their perceptions of data-driven learning activities showed that different proficiency students possessed different thinking skills to master their vocabulary learning. Unlike traditional teaching which consists of lectures passively received by the students, data-driven learning offered an opportunity to make students participate actively in vocabulary learning through doing corpus-based hands-on activities. For example, instead of being told about the word meanings directly in the beginning, students could apply their word knowledge in different contexts in the data-driven learning induced by themselves. Hence, teachers should take different proficiency into consideration to arrange data-driven
learning vocabulary activities for better vocabulary learning.
5.3.2 Instructional Activities and Design of Lessons
Instructional activities and design of lessons are two important elements in vocabulary teaching and learning. The key to successful vocabulary learning is how the teaching activities are carried out and how the lesson plans are manipulated. If the instructor knows the learning features well, then the system can become more effective for learners. The followings are some instructional activities and lesson designs in order to provide effective learning environment:
1. The findings of this study showed that most of participants, especially low achiever were attracted to interesting examples and word meanings of near-synonyms, such as cross, across. They seemed to participate more in the activities and remembered the words. Therefore, when design near-synonyms vocabulary activities, it is suggested to integrate the fun aspects of the targets words such as showing interesting examples or
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some authentic news happened in Taiwan to engage students to participate. For example, in the present study, the teacher taught the words huge, big, large, and enormous in class. After the students induced the answers by themselves, the teacher explained the words’ differences and concluded
by the authentic materials which were extracted from Apple Daily News English Column (figure 5.1). The teacher used Apple Daily News English Column as a supplementary material to help the students understand that near-synonyms could be found not only in the textbook but also in some other reading materials such as magazines or newspapers.
Figure 5.1 Apple Daily English Column
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2. Dividing students into groups is an important technique when teachers want to conduct data-driven learning approach in their classrooms. Traditionally speaking, teachers may ask the whole class to answer in union, it is easy to carry out such activity in a
traditional deductive classroom than in a data-driven learning class. The students are passive learners. However, data-driven learning puts emphasis on students’ active participation. Hence, students are divided in groups so that every learner is given chances to practice decoding ability and cultivating language awareness. In addition, according to this research, in the group discussion, the results showed that low achievers learn vocabulary knowledge from high achievers. Before dividing students into several groups, the teacher has to carefully design the group members. Each group should include students with various levels of proficiency. For example, the instructor in present study divided learners into eight groups, six learners in one group. Each mix-level group has its own group leader who assigns the work to each member.
3. Based on the concept of zone proximal development (ZPD), which proposed by
Vygotsky (1963), learners are encouraged to be immerged in the environment, which is full of materials that bridge the gap between what they know and what they know after trying (Chang, 2013). The challenge could be fulfilled by providing learners with various scaffolding materials when learning. In the study, the course materials, corpus-based concordances, have positive effect on EFL learners’ learning of the near-synonyms. Therefore, in the process of concordance selection and course material compilation, teachers have to choose concordance lines carefully. They have to
conform to the principle “i+1” and try to avoid picking up ambiguous sentences from the corpus before class. Doing research and compiling course materials are the trickiest part for teachers who want to use data-driven approach to teach near-synonym in class.
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When doing concordance lines selection and edition, teachers are recommended to select the most frequent words, collocations and the marked usages which are based on the salience by corpus tool such as Sketch Engine or Word Smith. Moreover, teachers are also suggested to think thoroughly possible words based on EFL learners’ prior knowledge to decide what kind of near-synonyms are more appropriate for EFL college students’ needs, which bridge the gap between what they are learning and what they are trying to pick up by applying what they have known. For example, in this study, when the instructor selected near-synonyms and designed the course materials, the instructor took the two versions of senior high school English textbooks in Taiwan into reference. Last but not least, the course materials have to be arranged by a gradable sequence from easy, simple words or sentences to difficult or tough ones. Because learners are novice at data-driven learning approach, they must spend few times getting with new learning methodology.
4. Good classroom management is conducive to manipulating data-driven learning in class. Data-driven approach needs a large amount of time in observing and discussing the examples. In the process of discussing, learners are easily distracted and chat with their classmates. How to keep good order in class is also the crucial element in this approach. The researcher suggests that teachers can introduce a bonus system like
giving credits to enhance learners’ attention even though they are college students.