促成專業英文學生自主學習的語言鷹架學習模式｜國立臺灣科技大學博碩士論文系統

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研究生：	陳亮磬 Liang-Ching Chen
論文名稱：	促成專業英文學生自主學習的語言鷹架學習模式 A language instructional scaffolding model to help with ESP students’ autonomous learning
指導教授：	陳献忠 Shian-Jung Chen 賈繼中 Chi-Chung Chia
口試委員:	鄧慧君 Hui-Chun Teng 張秀珍 Hsiu-Chen Chang 楊琳鏗 Lin-Keng Yang
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：	2017
畢業學年度：	105
語文別：	英文
論文頁數：	159
中文關鍵詞：	鷹架理論、自主學習、接觸-內化-實際應用、電腦閱讀系統、谷歌翻譯、閱讀報告、電腦輔助人工翻譯、語意表達、專業英文
外文關鍵詞：	Exposure-Internalization-Use, Computer Reader, Reading Report, MAHT (Machine Aided Human Translate), Sentence Construction Rule, ESP (English for Specific Purpose)
相關次數：	點閱：569 下載：20
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上一筆

本研究探討如何以鷹架教學模式，訓練學生發覺閱讀與翻譯所需的語言知識，並學會如何內化語言知識，使學生能進行自主學習，讓他們脫離被動的記憶式學習，以彌補台灣學生無法按照接觸、內化、實際應用的方式學習英文，無法把學習交到學生手中的窘境。這個教學模式由陳献忠教授所發展，並實際應用在國立台灣科技大學體育英文的課程之中。研究者以觀察者及助教的角色實際參與該課程，觀察並記錄教學者如何利用他所開發的電腦閱讀系統以及谷歌機器翻譯系統，作為鷹架學習的輔助工具，帶領學生透過修改電腦閱讀系統的閱讀報告以及利用電腦輔助人工翻譯的機器翻譯後編譯，深入了解閱讀與翻譯所需的語言知識，並開始進行內化語言知識的工作。
陳献忠教授以他自創的語意表達設計開發電腦閱讀系統，由語言剖析器轉型而成的閱讀系統能逐句分析閱讀，以全自動的方式根據語意表達的核心三問，交出表格結構式的閱讀報告，這種閱讀報告模擬人類讀者的認知方式說出電腦認為句子的語義內涵，因為它具體又人性化，學生們樂於檢視評量電腦到底哪裡讀對，哪裡讀錯，不知不覺他們就走上內化語言知識的學習道路。期中之後，當陳教授開始要求學生完成電腦輔助人工翻譯的作業的時候，他們發現自己被要求內化的語言知識竟然派得上用場，他們很快就能看出谷歌機譯哪裡犯錯，原因何在，以及該如何改正。
電腦閱讀結果能輕易留下紀錄累積紀錄，合併許多電腦閱讀報告所建立的知識庫，因為其結構性與可調整性，正好可以支援教師與學生進行語言知識與專業知識，自然形成訓練專業英文師資理想的平台。

This study looks into how to develop an instructional scaffolding model to train students how to discover and internalize language knowledge that is needed in reading and translation so that they can depart from passive memorization-based study and do autonomous learning. The use of this model will change students’ learning style that fails to follow the natural learning procedure of exposure, internalization and use. This model will make sure that we can put learning into students’ own hands, i.e. letting them control their own language learning.
This teaching model is developed by Shian-Jung Chen and implemented by him in his class of “Sports in English” in National Taiwan University of Science and Technology. The researcher joined the class as a teaching assistant and observant. I participated in the class and helped with correcting of testing and students’ translations. I also help to monitor students’ activities and collecting students’ homework. Most of all, I observe, take notes and discuss with the professor how the class will proceed so that it will move forward as we expect. The main focus of the observation is on how Chen’s computer reading system (computer reader) and Google Translate are used as the scaffolding tools to help with students’ discovery of language knowledge and their internalization work. The tasks for students include evaluation of the computer reader’s reading reports and the post editing work of MAHT (machine aided human translation).
Chen developed the computer reader based on his concept of meaning representation. The computer system is instructed to parse the sentences of the text one after another and answer three questions of the meaning representation based on its understanding: (1) How many events in the sentence? (2) What is the case frame of Who Did What to Whom in each event? (3) What sentence construction rules are used to express inter-relations between events? Since the meaning representation asks for concrete answers to event-based questions, students asked to evaluate the computer’s reading reports are able to understand and feel that they can also do the same. Before they know, they find they now have a better idea of language knowledge should look like and they are internalizing the knowledge.
After the mid-term, students are asked to do post editing with Google Translate (GT). In no time, they find that when GT fails to translate something right, they can immediately tell where GT is missing certain language knowledge, especially when it misunderstands certain parts of the sentence. They realize that the knowledge they have internalized are useful for their translation quality control.
Since computer reading reports are records that we can keep and agglomerate and the reports’ XML can help with re-sorting and browsing from different angles, they can be put together as huge knowledge bases that support knowledge discovery and can be used as a platform for ESP teachers to learn language knowledge and expert knowledge in the given domain in relative short time. In other words, they will be very helpful for ESP teacher training.

Table of Contents    2
Acknowledgement    9
碩士學位論文指導教授推薦書     10
碩士學位考試委員審定書    11
Abstract     12
中文摘要    14
Chapter 1 Introduction     15
1.1 Background of the research    15
1.2 Motivation     15
1.3 Purpose of the research     17
1.4 Research questions     18
Chapter 2 Literature Review     19
2.1 Introduction     19
2.2 Second language acquisition     21
2.3 Language knowledge and internalization - What and How     23
2.4 Students’ motivation     24
2.5 ESP vs. EGP     25
2.6 PADS gaps and PADS restoration     26
2.7 Reading is a demonstration of a reader’s knowledge of the language     27
2.8 Meaning representation     30
2.9 Sentence construction rules     31
2.10 Case grammar and case frame of Who Did What to Whom     31
2.11 English parser used as a computer reader to do automatic annotation     32
2.12 Cumulative reading and knowledge discovery     33
2.13 Google Translate     34
2.14 Scaffolding Theory     35
2.15 Autonomous learning     36
Chapter 3 Methodology     38
3.1 Introduction     38
3.2 Participants     39
3.3 Sports news corpus and XML inputs for the computer reader as the data     40
3.3.1 Sports news from the Web    41
3.3.2 XML inputs of sports news for the computer reader as the data     43
3.4 Event-based computer reading reports resulted from the parser’s automatic annotation     44
3.4.1 Knowledge discovery using computer reading’s misinterpretation     45
3.5 Manual annotation to correct computer reader’s errors     51
3.6 Translated texts done by MT systems, students and teachers     53
3.7 Creation of a huge searchable knowledge base from cumulative computer reading     62
3.8 Creation of bilingual annotations     75
3.9 How to do autonomous learning with all the tools and knowledge bases     83
3.9.1 Autonomous learning aiming at local knowledge discovery     84
3.9.2 Autonomous learning aiming at global knowledge discover     86
3.10 Diagnosis test and final test     87
3.11 Experiment Process     88
Chapter 4 Course Analysis     89
4.1 Introduction     89
4.2 Importance of knowledge internalization     89
4.3 Diagnosis Test and language knowledge in reading     90
4.3.1 Diagnosis Test     90
4.3.2 Analysis of Test One     91
4.4 Necessary language knowledge in reading     95
4.4.1 Reasons for missing reference     95
4.4.2 What does it take for a reader to capture the referential meaning?     99
4.4.3 Importance of knowledge internalization     100
4.4.4 Language knowledge and domain knowledge     101
4.4.5 Chen's instructions related to knowledge internalization     101
4.5 Scaffolding to facilitate students’ language knowledge internalization     103
4.5.1 Chen's meaning representation at work     103
4.5.2 Chen’s meaning representation and language knowledge in question     104
4.5.3 A meaning representation proven to be working     105
4.6 Assignment to help students' knowledge internalization     108
4.6.1 Manual annotation is a good practice to internalize language knowledge     114
4.6.2 Basic language skills and language knowledge     115
4.7 Machine-aided human translation and post editing     115
4.7.1 Translation quality control and meaning representation     120
4.8 Domain knowledge and ESP learning/teaching     121
4.8.1 Computer reading can help with cumulative reading and building of knowledge base     121
4.8.2 Different kinds of knowledge bases     123
4.8.3 NP resolution     125
4.9 Computer’s cumulative reading     126
4.10 ESP learning and domain/expert knowledge     129
4.11 Bilingual corpus to support contrastive analysis between Chinese and English     131
4.12 Idiomatic expressions and variety in writing     135
4.13 Final Test     139
4.14 Improvement from the first test to the final test     143
Chapter 5 Conclusion     145
5.1 Introduction     145
5.2 Exposure-internalization-use tripartite learning     145
5.2.1 Exposure to natural L1 environment     145
5.2.2 Exposure to the target language as an official language but not the mother tongue     146
5.2.3 Exposure to the target language which is congenial to the mother tongue     147
5.2.4 Exposure to a target language which is not an official language to the native speakers of the area     147
5.3 Language knowledge internalization that is within reach     148
5.4 Chen’s meaning representation that makes computer reading possible     149
5.5 Internalization by constantly confirming and modifying     150
5.6 The knowledge is pivotal to reading comprehension and translation     151
5.7 Are the research questions properly answered?     151
5.7.1 Question 1: Why is it important to focus on exposure-internalization-use tripartite in foreign language learning?     151
5.7.2 Question 2: Why is knowledge internalization the key to successful language learning?     152
5.7.3 Question 3: How can we encourage students to do language knowledge internalization? Why?     153
5.7.4 Question 4: How can we help students to acquire domain knowledge in ESP teaching?     153
5.8 Conclusion     154
Reference     155
List of Tables
Table 2.1: The computer reader’s interpretation of three sentences     27
Table 2.2: The correct interpretation of the three sentences     28
Table 3.1: MLB news reports in XML     43
Table 3.2: NBA news reports in XML     44
Table 3.3: A computer reading report sample     45
Table 3.4: Error analysis of computer reading output     45
Table 3.5: Error analysis of Sentence 1     48
Table 3.6: Error analysis of Sentence 2     50
Table 3.7: Result of manual annotation     51
Table 3.8: NBA news reports in XML     54
Table 3.9: Translated text by Google Translate     54
Table 3.10: Original database in chronicle order (following the sequence of events)     62
Table 3.11: Knowledge base sorted in alphabetic order based on “Predicate (Did)”      66
Table 3.12: Knowledge base sorted in alphabetic order based on “Agent (WHO)”      67
Table 3.13: Knowledge base sorted in alphabetic order based on “Patient (To WHOM)”      69
Table 3.14: Knowledge base sorted in alphabetic order based on "Goal"      70
Table 3.15: Knowledge base sorted in alphabetic order based on “Event Relation”      70
Table 3.16: Bilingual annotations     76
Table 3.17: Scenario Case Frame (Threes company The 3-1 lead is officially sports latest hex)     85
Table 4.1: Diagnosis Test     91
Table 4.2: Diagnosis test (high missing rates)     94
Table 4.3: A computer reader's reading report     106
Table 4.4: A sample computer reading report     110
Table 4.5: Extra events crossed out and missed events added     112
Table 4.6: Adding or crossing out some case roles to get the right case frame     113
Table 4.7: Doing MAHT (Machine Aided Human Translation) with Google Translate     117
Table 4.8: Events presented in normal order     122
Table 4.9: Events arranged to the alphabetical order of the predicates     123
Table 4.10: Different phrases with “earn” in them     124
Table 4.11: Even numeric terms that are not in most dictionaries     125
Table 4.12: NP resolutions     126
Table 4.13: Numerous contexts for “score”      127
Table 4.14: Runs in NBA games and runs in MLB games     128
Table 4.15: Simple bilingual corpus     130
Table 4.16: Bilingual corpus arranged in stroke-number order     131
Table 4.17: Even Chinese sentences have the same meaning representation     132
Table 4.18: Bilingual corpus sorted in stroke-number order     136
Table 4.19: Final test on translation     139
Table 4.20: Answers to the final test on translation     142
Table 4.21: Final test (low missing rates)     142
List of Figures
Figure 3.1: MLB news reports     42
Figure 3.2: NBA news reports     42
Figure 4.1: Search “壓哨球” from the official web dictionary of MOE     96
Figure 4.2: Search “絕殺” from the official web dictionary of MOE     97

                                

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