4.2 Posttest
4.2.3 Qualitative Findings: Interview
Table 4.7 Results of Learners’ Feedback on Teacher’s teaching Value (N=39)
Factor 3 : Teacher’s teaching value Numabers of participant and frequency of
responses M SD
No. Item Description SA A D SD
27 I think I learn a lot of vocabulary knowledge from teacher’s teaching.
12 enthusiastic attitude in teaching.
20
29 I think the teacher is carefully well-prepared teaching materials
Table 4.6 shows over ninety-five percent (97.5%) of the participants agreed they learn a lot of vocabulary knowledge from teacher’s teaching. There were 89.8%
of the participants felt the teacher was enthusiastic when he taught near-synonyms in class. Moreover, all of them (100%) thought the teacher was well-prepared before class. To conclude, participants felt positive toward teacher’s teaching in three aspects: teaching skill (M= 3.28, SD= .52), teaching attitude (M= 3.42, SD= .68), teaching materials (M= 3.64, SD= .49).
4.2.3 Qualitative Findings: Interview
When the teacher uses DDL to teach students, they may find it hard to design the teaching materials for high and low achievers in a heterogeneously grouped class.
In Taiwan EFL context, heterogeneous grouping is a common way for school to arrange students in every class. Heterogeneous grouping refers to a way of grouping students with mixed achievement levels in a classroom setting. Hence, every student in Taiwan’s classrooms has different learning styles and skill levels. While some high
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achievers can speed-read through their text book, others may take longer to comprehend the teaching materials. When teaching mixed level classes using a flexible teaching approach such as data-driven learning approach, teachers may address the learning needs of their students with different English proficiency using various data-driven learning activities. By identifying both low and high achievers’
perceptions of DDL activities, teachers can desigh activities that not only accommodate their readiness level but also attract them to participate. Hence, different English proficiency students can benefit from the data-driven-based
activities. The researcher interviewed three high achievers and three low achievers in this study. The following section provides the information from the interviews with high and low achievers about their perception toward data-driven learning approach.
4.2.3.1 High Achievers: prefer challenging tasks
When interviewing with the high achievers about their learning experience of using data-driven approach to learn English near-synonyms, most of them were glad that they had the opportunities to learn a new methodology in this course. Through the vocabulary pretest in this study, the high achievers showed that they had a larger vocabulary size than the low achievers. Before learning data-driven learning, they had learned the near-synonyms when they were senior high school students. They pointed out that they merely learned near-synonyms through the translations. For example, the near-synonyms ‘devote’ and ‘dedicate’, the teacher directly elaborated the differences between those two words. This teaching approach is lacking of inductive thinking so that students forget the words. After eight weeks instructions, the high-achievers indicated that they felt challenged in using data-driven learning to learn
near-synonyms at first. They felt anxious because they were not fammilar with the
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new teaching approach and they wanted a clear and definite answer. They did not like ambiguous answers. One of the high achevers said : “ At first, I cannot tell the
differences between the two-near-synonyms because some words are diffcult to me when I looked into the meaning in the context. After eight weeks, I felt that I can distinguish a little bit but I still needed teacher’s help to confirm my answer. However, after teacher’s explanation, for me, I am confused with some words because I think the usages and meanings are the same. I need a correct answer.” In order to answer the high achiever’s question, teachers need to empahsize that the meanings of
near-synonyms are not two sides of the same coin. The meanings have gradable scale.
For example, the pairs of take part in, enter, and join, have a gradable differneces among those words, from the most formal to the least formal. Another high achievers continued contending that “ Althogh eight weeks seems like a very long time, it is actually a short period. It takes more than 8 weeks to get used to the data-driven learning.” One suggested the instuctor to lengthen the sentences on course materials so that they could see more contexts within the tagart word.
When asked the comparison between the traditional learning methology and data-driven leanring, all high-achevers had a positve attitude toward data-driven leanring. They thought this method could not only help them learn Englsh by
themselves when they were engaging in self-learning, but also improved their English proficiency. But they won’t let the traditional approach go. They liked the balanced approach which means both of data-driven learning and traditional deductive learning are emphasized. Using the data-driven learning to learn first and then concluded lessons by deductive learning. They enjoyed discussing near-synonyms with their group members, exchanging the information and sharing their knowledge with
teammates. Data-driven activities required participants to discuss the words’ meanings
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and usages in a new way and also determined the comparison and contrast of pairs of near-synonyms presented to the class. This activity seemed to satisfy high achievers’
need with higher level learning objective. Three high achievers expressed that they enjoyed learning English vocabulary through data-driven learning because they liked to challenge themsleves. They hoped that one day this method could help them get high scores on TOEIC or GEPT test. Moreover, one of high achievers said “When I learn some words that I feel interested in like date and appointment, I can remember them easily, having a long retention.” At the same time, in the group discussion, they mentioned that some of the low achievers would not like to take part in discussing because of the lack of confidence and basic vocabulary knowledge. One of the high achievers said: “low achievers didn’t want to join the group discussion until the teacher changed his way of presentation. Using ramdomly selection mechanism is a good way to force them (low achievers) to pay attention to the discussion. I think this activity is diffcult for them .”
In short, high achievers in this study enjoyed the corpus-based data-driven activities that provided them with challenges as well as opportunity to expand their vocabulary knowledge. Because of the lack of confidence, they tended to use data-driven learning first and then traditional deductive learning as a supplemtary method to learn English vocabulary. They also needed to look up in the dictionary in the long run to make sure the answers are right.
4.2.3.2 Low Achievers: prefer doing tasks with Chinese translations
Low achievers ususally have more difficulties in dealing with English learning process than the high achievers. They also are more passive toward learning. However, when interviewing the low achievers of this study, most of them revealed that they recognized the value of data-driven learning approach in learning English
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near-synonyms. They enjoyed the group discussion and expressed that this activities helped them learn a lot of vocabulary knowledge from high achievers because they had had few chances to discuss with peers in class. They found data-driven enhanced their memory because they spent a lot of time on observating and anlyzing the course materials. However, when it was their turn to present in class, they experienced difficulties. They felt anxious when coming to the board to perform group’s task.
They were afraid of mispronunciation and making grammar errors. Althogh their peers were shouting out answers to them, they still felt pressured that they could not complete the task quicker than others. They found that decoding the meaning from the given contexts was the most diffucult one when they had no Chinese translations to refer to. One of the members from his group said that “when I can see the Chinese traslations and I can use the dictionary in class, I feel much confident in performing the task.”
Although data-driven learning was difficult for them, some of them claimed that they are willing to attempt to use this method to learn vocabulary. One of the low achievers stated that eight weeks’ training was not enough, it needed a long time to cultivate his langauge obesrvational ability and language awareness. Moreover, low achievers remarked that they had never used this approach to learn English vocabulary.
They were novice learners.They felt this approach was difficult for them because they were not good at English, lacking in basic vocabulary knowledge.The problem of lacking of sufficient vocabulary made low achievers perform poorly in this activitiy.
One of the low achievers said that “ I think English is not important for me. I have low proficiency in English. It dosen’t matter because I can choose the job which is not related to English. For me, my learning target is to pass the course. I don’t need to take the TOEIC or GEPT test.” From the interview, the researcher understood the low
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achievers have low intrinsic motivation. Some of them were self-abandoned in learning English. Low achievers found that using data-driven learning was not
suitable for learning within whole English language textbooks. They thought it should be used in the textbooks which attach Chinese tranalstion as the supplementary. After eight weeks’ training, like high achievers, all of the low achievers admitted that they had improved their vocabulary knowledge.
To sum up, through the feedback from the low achievers, it found that low achievers were also attracted to the data-driven learning that involved high cognition.
However, without sufficient vocabulary and high motivation, most of them had difficulities in participating in activities that required them to decode the word
meanings and usages from the given contexts. They felt confidient in learning English when they were in the group discussion. They still need Chinese translation to lower their anxiety.
After examing the feedbacks from the high and low achievers about the perceptions of data-driven learning, we can see their perceptions differ significantly.
In terms of cognitive process, the low achievers preferred to engage in less challenging tasks such as passive vocabulary knowledge receiving and words’
meanings memorizing while high achievers perferred the challenging ones such as analyzing words’ meanings and observing sentence patterns actively. In terms of vocabulary learning, low achievers preferred activities that should involve Chinese translation as the supplementary note while as for the high achievers, they were looking for the teacher’s explanation of words’ usages and functions in the end.
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4.2.3.3 Summary of Interview
After analyzing the results of the vocabulary pretest and posttest, it showed that the data-driven learning approach helped participants pay attention to the word forms and retrieve the word meanings from their memory. Using corpus-based teaching materials was easier to make impression of the word meanings and usages rather than relying on L1 translations. The findings presented in this chapter showed that
participants have benefited from data-driven learning instruction in near-synonymous knowledge. Both high achievers and low achievers improved their vocabulary
knowledge. In addition, over 70% of the participants thought data-driven learning enhanced their memory and decoding ability. However, only 36% of the participants liked to intergrate of data-driven learning into their vocabulary lessons. Last but not least, the valuable feedback on the perceptions of data-driven learning activities from both the high and low achievers has provided the instructor a new insight. When the teacher designs activities, the teacher’s activities can involve different levels of course materials in order to meet the needs of different English proficiency students.
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CHAPTER 5
CONCLUSIONS AND IMPLICATIONS
This chapter is the summary of the main findings in the present study and is divided into four sections. First, the summary of the present study are presented. Next, the major findings of the present study are presented. Third, the limitations of this study are
addressed and finally, based on the findings of the study, some pedagogical implications and suggestions for future studies are proposed.
5.1 Summary of the study
In this study, three research questions were addressed, including (1) Does the data-driven learning (DDL) approach with manual concordances improve students’
near-synonyms knowledge and performance? (2) Are there any significant differences between high achievers in the experiment group and high achievers in the control group?
(3) What are learners’ reactions and preferences regarding using the corpus to learn vocabulary? The major findings are presented in the following sections.