利用電腦輔助閱讀系統以提升低成就高職學生的英文學習興趣

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研究生：	陳素蓉 Su-jung Chen
論文名稱：	利用電腦輔助閱讀系統以提升低成就高職學生的英文學習興趣 Using CALL technology to motivate low-achieving vocational high school English classes
指導教授：	陳献忠 Shian-jung Chen
口試委員:	洪紹挺 Shao-ting Hung 許秀貞 Hsiu-chen Hsu
學位類別：	碩士 Master
系所名稱：	人文社會學院 - 應用外語系 Department of Applied Foreign Languages
論文出版年：	2014
畢業學年度：	102
語文別：	英文
論文頁數：	101
中文關鍵詞：	電腦輔助語言學習、英文興趣、自主學習、適性學習、英語低成就者、超連結、剖析器、電腦閱讀
外文關鍵詞：	CALL Technology, English motivation, parsing, English underachievers, hyperlinking
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上一筆

本研究主要探討如何利用電腦輔助閱讀系統以提升低成就高職學生的英文學習興趣。高職學生普遍對英文學習較無興趣, 甚至有的已經放棄英文學習。本研究以北台灣某私立高職94位高一學生做研究對象,一組為實驗組, 一組為控制組,參與的學生參加為期9週的電腦輔助閱讀系統教學, 利用超連結文本來進行實驗。以全民英檢初級和學校第二次段考做為前測, 用學校第三次段考做為後測; 實驗結束後,參與的學生完成一份問卷, 並隨機挑選5位學生自願性地參與訪談。本研究使用SPSS 獨立樣本檢定和成對樣本檢定以及Microsoft Excel 2007進行資料分析。
研究結果發現:
(1) 參與電腦輔助閱讀系統在後測成績上有進步, 顯示低成就高職學生可透過此軟體加強學校的課業成績以及提高英文學習成效。
(2) 問卷結果顯示: 大部分的實驗組學生對於「電腦輔助閱讀系統」在英文學習的成效上抱持肯定的態度, 尤其是引發學習興趣是大多數學生認為幫助最大的部分。此外, 透過創造語言環境也可以使學生學習能力更好。
(3) 老師透過引導方式, 讓學生依照個人程度去利用超連結選擇要練習的部分, 透過自主學習, 讓學生的學習態度從被動變成主動學習。
本研究建議利用「電腦輔助閱讀系統」幫助低學習成就的學生以提升英文學習成效, 並引起學生的學習興趣, 已達到有效的英文教學。最後, 根據本研究的發現，研究者在文末提出數點教學建議及未來相關研究方向。

The present study aims to explore the effects of using CALL technology to
motivate low-achieving vocational high school English classes. Most of vocational high school students feel bored in English class and frustrated about learning English and some of them have entirely given up learning English.
The participants of the study were 94 first year of vocational high students from two classes in a vocation high school in Taipei area. Students of the experimental group were majored in Information and Technology and control group were majored in Food and Beverage Management.
A nine-week CALL Technology instruction was given to all the participants in the experiment group. Hyperlinking annotated materials were used to conduct all the study. The reading comprehension test of GEPT elementary level and school midterm were used as the pretest and final exam of school’s academic performances was used as the posttest. After using a CALL system to help with students’ learning, a questionnaire about using CALL Technology was conducted to explore the effect of the use of CALL Technology. And five participants were randomly selected to have interview with the researcher to elicit more information and details about their response and feedback from using CALL Technology. The collected data were analyzed by SPSS Independent Sample t-test and Paired Sample t-test and Microsoft Excel 2007.
The major findings of the study are summarized as follows:
(1) Use of the digital learning system by the experiment group had a positive effect on low-achieving vocational high school students’ school academic performances. This shows that the participants in the experiment group make some progress after receiving CALL Technology. CALL Technology is really beneficial to bettering English learning.
(2) Most subjects participating in this study had a positive response toward CALL Technology. The findings imply that vocational high school students need a new way to arouse their motivation and help them not to give up learning English. Through the language exposure, it creates an environment for students to understand English better.
(3)Through using CALL Technology, students could decide what part they were going to learn. Their attitude of learning changed from passive role to active learning.
Results indicate that CALL Technology can facilitate their understanding of English text and arouse their motivation as well. Based on the findings of this study, some implications were provided and several suggestions for future studies were offered at the end of this thesis.

Table of Contents
Chinese Abstract	i
English Abstract	ii
Table of Contents	iv
List of Tables	vii
List of Chart	viii

CHAPTER 1 INTRODUCTION	1
1.1 Background of the study	1
1.2 Motivation	6
1.2.1 Where does low achievers’ language learning fatigue come from?...	6
      1.2.2 Unfavorable environment for learning English as a foreign language	8
1.2.3 Renewed motivation	12
1.3  Purpose of the study	14
1.4  Significance of the study	15
1.5  Research questions	17
1.6  Terminology	17
CHAPTER 2 LITERATURE REVIEW	20
2.1 Washback	20
2.2 Prescriptive and descriptive grammar	22
2.3 First language acquisition	24
   2.4 Language exposure	25
   2.5 Autonomous learning	26
   2.6 Scaffolding	27
   2.7 Computer-assisted Language Learning (CALL)	28
2.8 A CALL system using scaffolding coaching and increasing language
exposure	30
2.9 Parser and computer reading	31
2.10 Automatic annotation	31
2.11 Parsing to enable CALL to help students acquire descriptive grammar	33
   2.12 Meaning representation	34
2.13 Referential meaning, relational meaning and specificational meaning	35
   2.14 Case grammar	36
2.15 Permutation, Addition, Deletion, and Substitution (PADS) gaps	37
2.16 Natural language processing (NLP) technology	37
2.16.1 Clauses boundary detection	37
      2.16.2 Anaphor resolution and co-indexing resolution	38
2.17 Adaptive personalized learning	38
2.18 Ubiquitous learning	39

CHAPTER 3 METHODOLOGY	41
3.1 Participants	41
3.2 Instruments	42
3.2.1 Materials 	43
3.2.2 Autonomus learning supported by hyperlinks	52
3.2.3 A prestest 1 (GEPT) & Pretest 2 (Midterm 2) & Posttest (Final exam) 	66
3.2.4 Questionnaires I & II	66
3.3 Procedure	69

CHAPTER 4 RESULTS AND DISCUSSION	70
4.1 The effects of CALL Technology on students’ vocabulary growth	70
4.2 Students’ response after using CALL Technology-Questionnaire I	72
4.3 Students’ response after using CALL Technology –Questionnaire II	78
4.4 Answers to research questions	83


CHAPTER 5 CONCLUSION	85
5.1	Overview of Major Finding in this study	85
5.1.1Summary of Major Findings	85
5.1.2Discussion of the Findings	87
5.2 Pedagogical Implications Pedagogical Implications	87
5.3 Limitations of this Study	88
5.4 Suggestions for Further Research	89

REFERENCES	91

APPENDIXES
A. Questionnaire of Using CALL technology to motivate
low-achieving vocational high school	95
B. Interview Questions Questionnaire of Using CALL Technology Instruction 	99

List of Tables
Table 3.1 Portion of annotations added automatically by the parser.	44
Table 3.2 Portion of annotation added manually to the automatic annotations	46
Table 3.3 Portion of annotations sorted by the Head of NPS	49
Table 3.4 Portion of annotations sorted by the Modifier of NPs	50
Table 3.5 Portion of annotations sorted by the Agent of Events.	51
Table 4.1 Students’ pretest 1 in GEPT Elementary level in Independent Sample t-test.	70
Table 4.2 Students’ pretest 2 in Midterm 2 in Independent Sample t-test.  	71
Table 4.3 Students’ posttest in Final exam in Independent Sample t-test.	71
Table 4.4 Participants of experimental group in pretest 2 and posttest in Paired Sample t-test.	72
Table 4.5 Questionnaire I : 1-12.	73
Table 4.6 Questionnaire I: 13	75
Table 4.7. Questionnaire I: 14	76
Table 4.8 Questionnaire I: 15 .	76
Table 4.9 Questionnaire I: 16	77
Table 4.10 Questionnaire I: 17	77
Table 4.11 Interview Questions --- Item 1	78
Table 4.12. Interview Questions ---Item 2	79
Table 4.13 Interview Questions ---Item 3 .	79
Table 4.14 Interview Questions --- Item 4	80
Table 4.15 Interview Questions --- Item 5	80
Table 4.16 Interview Questions --- Item 6	81
Table 4.17 Interview Questions --- Item 7.	81
Table 4.18 Interview Questions --- Item 8	82

List of Charts
Chart 3.1 The procedures of this study's experiment.	69

                                

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