| DC 欄位 |
值 |
語言 |
| DC.contributor | 學習與教學研究所 | zh_TW |
| DC.creator | 徐若華 | zh_TW |
| DC.creator | Hsu, Ruo-Hua | en_US |
| dc.date.accessioned | 2015-6-22T07:39:07Z | |
| dc.date.available | 2015-6-22T07:39:07Z | |
| dc.date.issued | 2015 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=102127002 | |
| dc.contributor.department | 學習與教學研究所 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 近年來，在第二外語的研究中，搭配詞 (collocation) 受到許多關注。然而，搭配詞周圍的特定用法 (collocation-specific colligation) 卻長期被忽略，即使是學習者再熟悉不過的搭配詞，colligational details往往成為問題所在。Collocation-specific colligation是一種比搭配詞的用法更為複雜的使用模式。例如：我們使用搭配詞spend time時，必須用動名詞 [V-ing] 搭配使用：spend time doing something；然而，使用搭配詞take time時，卻可以使用不定詞 [to V] 作搭配：take time to do something，這些斜體字即為collocation-specific colligation。本研究旨在透過兩種資料驅動 (data-driven) 方式，探討學習英語collocation-specific colligation上的不同成效。
資料驅動方式以不同的系統設計和搜尋結果區分為兩種：一種是將搜尋字呈現在橫向關係 (syntagmatic relation) 的例句中，並讓學習者從中探索搭配詞和colligation；另一種是以使用模式的方式，並同時呈現橫向和縱向關係 (paradigmatic relation)，再分別連結到各個使用模式的例句。採用第一種方式 (example-driven approach) 的是BYU-BNC；採用第二種方式 (pattern-driven approach) 的則是StringNet。本研究共有八十二位受試者，分為控制組和實驗組，實驗組再分為兩組：採用範例驅動方式 (example-driven approach) 的BYU-BNC組和採用使用模式驅動方式 (pattern-driven approach) 的StringNet組。每組共進行三次測驗，包含前測、立即後測、以及延宕測驗，實驗組則另外進行訓練和實驗課程。此外，實驗組還必須完成一份包含十個問題和兩題開放性問題的問卷，以利於了解學習工具的容易使用程度、受試者的學習成效和使用意願。
研究結果顯示，兩種不同的資料驅動學習 (data-driven learning) 方式呈現出學習英語collocation-specific colligation上的不同成效。StringNet組在立即後測的進步幅度上，顯著高於控制組，而在延宕測驗的進步幅度上則是顯著高於其他兩組，意謂著colligational knowledge的學習成效，似乎無法在立即後測的成績上顯現出來。我們也可從結果得知，能輔助學習者學習collocation-specific colligation的工具，呈現出橫向和縱向關係是重要且不可或缺的。另一方面，不論透過質性或量化問卷，調查StringNet組的受試者和BYU-BNC組的受試者對於各自使用工具的評價指出，StringNet較為有幫助、有效率也更容易使用。總體而言，本研究指出教學者可使用能呈現出橫向和縱向關係的資料驅動學習工具，並採取使用模式驅動方式進行教學設計，以協助教學collocation-specific colligation。 | zh_TW |
| dc.description.abstract | While collocations have received substantial attention in second language research, the colligational details surrounding many of them have been neglected though they cause problems for many learners long after they have mastered the collocation. Colligation is a patterning more complex than collocation. For example, we should use spend time doing (not to do) something while we can use take time to do something. This thesis is an investigation of two data-driven approaches to learning English collocation-specific colligations.
The two data-driven approaches to learning these patterns differed with respect to whether the learner’s query results came in the form of concordance lines containing a key word in syntagmatic sequences and requiring the learner to detect collocations and colligational patterns from these exemplars or came in the form of patterns listed for the learner that showed both syntagmatic and paradigmatic relations which were in turn linked to concordance lines exemplifying each pattern separately. BYU-BNC was used for the first approach and StringNet for the second. Eighty-two students in college were divided into three groups, including one control group and two experimental groups: BYU-BNC Group in an example-driven approach and StringNet Group in a pattern-driven approach. Participants underwent a training session and an experiment session, as well as a pre-test, an immediate post-test, and a delayed post-test. A questionnaire containing ten Likert-scaled items solicited data from the experimental groups on three areas: (1) ease of use, (2) effect on learning, and (3) willingness to use and two open-ended questions on participants’ elicited perceptions of the two learning tools.
Based on the results reported in this thesis, the two different approaches caused different effects on learning collocation-specific colligation. The StringNet Group showed significantly greater gains than the Control Group in the immediate post-test and significantly greater gains than both the BYU-BNC Group and the Control Group in the delayed post-test. These results revealed that the syntagmatic and paradigmatic dimensions of a learning tool are both important in assisting learners to acquire collocation-specific colligations, and the colligational knowledge is likely to be so subtle that it was hard to attain the improvement immediately. On the other hand, both the qualitative and quantitative questionnaire show that StringNet was perceived by that group’s participants as more helpful, effective, and simpler to use compared with the BYU-BNC group’s perceptions of that tool. All in all, the findings suggest that it might be better for teachers to teach colligation using a pattern-driven approach with the DDL tools which show both the syntagmatic and paradigmatic dimensions. | en_US |
| DC.subject | 搭配詞 | zh_TW |
| DC.subject | Colligation | zh_TW |
| DC.subject | 資料驅動學習 | zh_TW |
| DC.subject | BYU-BNC | zh_TW |
| DC.subject | StringNet | zh_TW |
| DC.subject | Collocation | en_US |
| DC.subject | Colligation | en_US |
| DC.subject | Data-driven learning | en_US |
| DC.subject | BYU-BNC | en_US |
| DC.subject | StringNet | en_US |
| DC.title | The Missing Pieces around Collocation: A Comparative Study of Data-Driven Learning Resources for Learning Collocation-Specific Colligations | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | The Missing Pieces around Collocation: A Comparative Study of Data-Driven Learning Resources for Learning Collocation-Specific Colligations | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |