台灣存託憑證(TDRs)從2009年起在台盛行，至今有三十五檔TDRs在台灣證劵交易所掛牌，各檔TDRs交易期間至今約半年至三年不等，目前TDRs檔數和交易時間已足以做比較和計量檢定，因此本文以在台灣證劵交易所掛牌之TDRs為對象，選取13檔TDRs做實證研究，資料期間為各檔TDRs上市日至2012年2月17日的日資料。研究內容包含兩部分：1.透過單根檢定、共整合檢定、VAR模型、Granger關係檢定和誤差修正模型等實證分析，觀察兩地股價是否具有長期的穩定關係和價格發現功能，以及股價波動是否具有因果關係；2.近年來台灣的相關法規是否對TDRs交易機制有正向幫助。實證結果主要如下：1.大部分TDRs結果顯示股價、匯率和大盤指數間具有長期的穩定關係，即是有共整合現象；誤差修正模型(VECM)中各變數的係數大多不顯著，也就是說各變數在短期並無法透過此模型來預測股價；2.在因果檢定方面，樣本中有5檔TDRs的原股股價對TDRs股價有領先效果，另外8檔TDRs的原股股價和TDRs股價間並沒有顯著的領先或落後效果；3.台灣近年陸續發布的相關法規確實改善市場的供需問題 和價格偏離現象，對投資人有正面幫助。

探究式教學法常被應用於科學學習活動中，有助於學生理解科學本質與推理過程，探究式學習歷程對於掌握影響探究式學習成效因素具有其重要意義，但是教師難以完全掌握學生的探究式學習歷程。因此，若能有目的、精確且真實的紀錄學習者在科學探究學習平台上的學習行為，將能更加全面性的掌控影響探究式學習成效的原因。 本研究以CWISE為輔助學習者進行科學探究學習之平台，並於平台中發展xAPI學習歷程記錄器模組，詳細的紀錄學生之學習歷程，以即時收集學生在科學探究學習過程之學習歷程資料，除了分析影響探究學習成效的個人因素與學習歷程行為外，並搭配序列探勘方法(sequential pattern mining)及序列分析(lag sequential analysis)，探討不同學習成效、探究能力、科學態度學習者之探究學習成效、整體學習時間與探究式模擬實驗學習時間，以及探究學習歷程是否具有顯著轉移與差異。 研究結果顯示：(1)探究能力越高者其探究學習成效越佳；(2)體驗探究模擬實驗活動時間越長，其探究能力與學習成效越佳；(3) 基於序列探勘，高低不同學習成效學習者在探究學習之整體課程瀏覽順序無顯著差異，均是依照課程設計的探究式學習流程順序進行學習；(4)基於序列分析，高學習成效與高探究能力學習者在進行浮力單元模擬實驗後，會再次調整先前設立之假說，而低學習成效與低探究能力學習者，則欠缺此一關鍵的探究學習行為；(5)探究模擬實驗活動有助於提高其探究能力與學習成效。最後，本研究依據研究結果針對探究式課程的課程設計提出建議，並提出未來研究方向。

近年來隨著電腦科技的進步，發展出了各種人機介面的應用。不同於傳統的滑鼠與鍵盤，新的人機介面往往在其他不同的領域能夠得到更好的發揮，眼動（eye movement）即是一個例子，利用眼球移動在螢幕上的眼動軌跡來操控電腦。另一方面，螢幕所呈現的內容也會影響使用者的眼動行為，例如閱讀文章時不同的文章內容就會影響使用者在看文章時的眼動行為，因此我們就可以從眼動軌跡去推測出使用者對於螢幕的內容（如文章或圖片）的反應。本論文的目的就是要找出閱讀文章時的眼動軌跡和閱讀者對該文章的理解程度之間的關連性，最後發展出一個系統，利用閱讀文章時的眼動軌跡就能去推測閱讀者對於文章的理解程度，而無需後續的閱讀測驗。 / 一般人通常對自身的眼動軌跡的進行方式不甚了解，認為閱讀文章的眼動應該是順著字句的進行而移動。其實仔細觀察細小的眼動軌跡會發現比想像中複雜的多，例如當我們閱讀到文意較為困難的片段時，通常都會放慢速度，或是反覆閱讀，又例如看到自己有興趣的主題時會凝視在某些區塊較久的時間等等行為，這些都是在各種不同的情境之下做出相對的反應。本論文提出了資料探勘的方法來分析複雜的眼動軌跡，能針對每個人不同的閱讀模式找出不同的眼動規則。此方法能適應各種使用者的閱讀習性，對閱讀理解程度的預測能達到更精確的效果。 / With the advance of the computer technology, there are many kinds of HCI (Human Computer interface) applications been developed in recent years. Different from mouse and keyboard, new interface will bring more benefits in different area, and eye-movement is an example. It extracts the trace of eye movement to control the computer. Furthermore, the screen content will also affect the eye-movement. For example, when the user reads the articles, the content will affect the eye-movement, so we can speculate the reaction of the user after seen the screen content (such as article or picture) by eye-movement. The goal of the paper is to find out the relationship between eye-movement and the degree of comprehension, and develop a system which can automatically measure reading comprehension without any follow-up comprehension test. / Most people don’t realize how there eye move. They think eye movements should be carried out along the words. In fact, eye movement will find more complex than imagined, for example, when we read the context of the article is more difficult, we often slow down or read over and over again. In another example, people will gaze at some of the subjects that they are interest in for a longer period of time. These reactions correspond to a variety of different situations. This paper presents a data mining approach to analyze complex eye movement. It can find out the different rules of eye movement for users who have different reading strategy. This method will be able to adapt to a variety of users reading habits, and make more accurate prediction with the degree of the comprehension.

昔天津的經濟發展都落後於在中國內地的其他一些城市，提到天津，人們往往聯想到，她只是北京光環下的一個毗鄰城市。為了在經濟成長中保持天津經濟的穩定增長，天津採取了吸引大規模工業建設項目的發展策略，再加上近年濱海新區的蓬勃發展，不僅讓該區的經濟表現成績亮眼，也進而讓天津此一城市向國際化大都市腳步邁進。 再者，地處環渤海經濟圈沿海區位之天津濱海新區對其經濟發展推動也著實重要，主因係大陸將開發天津濱海新區納入國家十一五規劃和國家發展戰略，並批准濱海新區成為國家綜合配套改革試驗區，相關重大工業項目都相繼落戶於濱海新區，進而促使天津濱海新區成爲國家級新區中發展最爲迅速的新區，所以本研究係從文獻探討回顧探討影響天津經濟發展的決定因素，並利用樣本資料與實證模型來推估假設變數與天津經濟成長率之間的關係與影響程度，俾利提供學術上與實務上濱海新區在天津經濟發展中所扮演角色之參考。

近來，臺灣教育部為招收更多國際學生而實行新政策。本研究分析政策實行的效果、學生來臺原因及在臺學生的滿意度。研究一以時間序列分析1954至2008年的國際學生人數，ARIMA模式的預測效果良好(RER=0.83%)，未來三年內國際學生人數將突破二萬人。研究二以問卷調查分析學生選擇留學國家及學校的選擇因素，同時分析學生來臺動機、所遇困難及在臺滿意度間的關係 (N=210)。結果顯示容易的簽證程序及臺灣的學術聲望影響學生來臺讀書；此外，財務、後勤及生活條件影響學生選擇留學學校。學生因臺學術聲望來臺且無食物適應困難者，願意再次來臺念書；學生因具吸引力課程及方便生活環境來臺且無硬體設備及學習適應方面困難者，願意建議他人來臺留學。根據研究結果，政府應加強國內教學品質，並協助提升學校學術聲望；增加赴海外任教教師，以教學專業提升我國國際形象。有興趣吸引國際學生的學校應可從健全學校行政體制著手，並聚焦於完善學生招生、入學諮詢輔導工作及協助生活適應。 / Recently, the Taiwanese Ministry of Education has implemented a new policy to recruit more international students. This study analyzes the policy’s effects and the reasons motivating students to study in Taiwan. The number of international students in Taiwan from 1954 to 2008 was used to generate a time series model to estimate the number of international students before and after the policy implementation. This ARIMA model (RER=0.83%) demonstrated a dramatic increase in the number of international students over the years. Using a questionnaire designed to examine determinants that affect international students’ decisions for choosing Taiwan and their respective schools (N=210), this study then study the relationship between the students’ motivations, the challenges they faced, and their degree of satisfaction with their experiences. Results demonstrated that an easy visa application process and Taiwan’s acclaimed academic reputation attracted students. Furthermore, financial, logistical, and living reasons affected the students’ choices in schools. Students who found living and educational conditions favorable and who felt intellectually stimulated would choose again to study in Taiwan and would recommend others to do so. Based on these results, the government should strengthen the quality of instruction and further promote Taiwan’s universities abroad. Schools interested in attracting international students should develop structured administrative systems focused on recruiting students and helping them transition into a new environment.

University of Macau / Faculty of Science and Technology / Department of Mathematics

本研究旨在探究學習者在wiki共筆、數位閱讀標註及知識論壇三種不同知識建構導向之電腦支援合作學習環境下，其合作知識建構與問題討論歷程差異，再則這三種不同學習環境是否營造不同知識建構氛圍，以及對支援知識建構是否有所欠缺。最後，針對研究結果提出有效知識建構教學的實施策略與建議。 本研究採實驗研究法，以某國立大學數位碩士在職專班19名研究生為研究對象，分別依序體驗包括wiki共筆、合作式數位閱讀標註及知識論壇三種各具特色的知識建構導向合作學習環境，進行知識建構討論與分享，藉由搜集觀察上述三種學習環境之知識建構歷程討論及成果記錄，進行知識建構及問題解決討論概念編碼後，進行序列分析，依此觀察學員們知識建構演進之歷程，並評估問題討論的互動程度，最後輔以半結構式訪談，與序列分析結果進行交互驗證。 結果發現學員在wiki共筆、合作式數位閱讀標註及知識論壇三種知識建構環境上，共同合作建構產出的知識建構成果均呈現一定品質水準，確實有助於輔助學生之共同知識建構成長。此外，三種電腦支援合作知識建構學習環境中，知識論壇在適當教學設計與學習策略實施下，較能發揮知識建構水準；知識論壇相當適合於問題解決討論；三種知識建構導向之電腦支援合作學習環境，對於完整支援知識建構仍有不足需要強化之處，特別是為促使學員更深入討論，以達更深層的知識建構，需要更好的教學設計與學習策略。 最後本研究根據研究結果，歸納出幾點建議，作為教師在進行合作知識建構教學時，選擇電腦支援合作學習環境之參考，並對未來研究方向提出建議。 / The major purpose of the present study was investigate the learners would have the differences of the progress of knowledge construction and problem discussion, which were under the three different guided knowledge construction in the computer-supported collaborative learning environments, the three learning environments including wiki, knowledge-based annotation learning system, and Knowledge Forum. A secondary purpose of this study was to examine if these three different learning environments would build the variety atmospheres of knowledge construction, and then the deficiency in the computer-supported collaborative learning. Finally, the conclusion drawn above should be proposed the efficient policy and suggestion in relation to the effective teaching of knowledge construction. The method to carry out this study was using an experimental research. The participant in this research were 19 postgraduate students enrolled in executive master of digital systems in one national university. In this experiment, all participants experienced the three different guided knowledge construction in the computer-supported collaborative learning environments in order, each differs from one another, including, wiki, knowledge-based annotation learning system, and Knowledge Forum. Furthermore, all subjects focused on the discussion and shared the progress of the knowledge construction and the results of the accomplishment by searching and observing the above three varied environments. The data of knowledge construction and problem solving were to conceptual encoded and processed the sequential analysis in order to observe the evolution of the progress of knowledge construction of all subjects, and to estimate the level of interaction of problem discussion. Lastly, we used the auxiliary semi-constructed interview and the result of sequential analysis to work with the cross-validation. The findings suggest that the participants produced the conclusions with coordinated knowledge construction which appeared in a certain quality, and this result indeed helped learners to grow-up in the coordinated knowledge construction. Additionally, Knowledge Forum firstly developed well standard under the congruent design and tactic of teaching in these three computer-supported collaborative learning environments. And then Knowledge Forum would be even more proper to discuss the problem solving. However, there is an insufficient part which needs to be strength of the three guided computer-supported collaborative learning environments for the complete computer-supported learning environments, most particularly, to be able to stimulate the subjects would go deep into the discussion in order to achieve the more depth of the knowledge construction, at the same time require the preferable instructional design and learning strategy. On the basis of the findings we sum up the few suggestions: As teacher, this study would be a reference to choose computer-supported collaborative learning environment when teach the coordinated knowledge construction, and then to address the suggestion in relation to the future research.

金融市場是個變化莫測的環境，看似隨機，在隨機中卻隱藏著某些特性與關係。不論是自然現象中的氣象預測或是金融領域中對下一時刻價格的預測, 都有相似的複雜性。 時間序列的預測一直都是許多領域中重要的項目之一, 金融時間序列的預測也不例外。在本論文中我們針對金融時間序列的非線性與非穩態關係引入類神經網路(ANNs) 與集合經驗模態分解法(EEMD), 藉由ANNs處理非線性問題的能力與EEMD處理時間序列信號的優點，並進一步與傳統上使用於金融時間序列分析的自回歸滑動平均模型(ARMA)進行複合式的模型建構，引入燭型圖概念嘗試進行高頻下的台指期貨TAIEX交易。在不計交易成本的績效測試下本研究的高頻交易模型有突出的績效，證明以ANNs、EEMD方法與ARMA組成的混合式模型在高頻時間尺度交易下有相當的發展潛力，具有進一步發展的價值。在處理高頻時間尺度下所產生的大型數據方面，引入平行運算架構SPMD(single program, multiple data)以增進其處理大型資料下的運算效率。本研究亦透過分析高頻時間尺度的本質模態函數(IMFs)探討在高頻尺度下影響台指期貨價格的因素。 / Financial market is complex, unstable and non-linear system, it looks like have some principle but the principle usually have exception. The forecasting of time series always an issue in several field include finance. In this thesis we propose several version of hybrid models, they combine Ensemble Empirical Mode Decomposition (EEMD), Back-Propagation Neural Networks(BPNN) and ARMA model, try to improve the forecast performance of financial time series forecast. We also found the physical means or impact factors of IMFs under high-frequency time-scale. For processing the massive data generated by high-frequency time-scale, we pull in the concept of big data processing, adopt parallel computing method ”single program, multiple data (SPMD)” to construct the model improve the computing performance. As the result of backtesting, we prove the enhanced hybrid models we proposed outperform the standard EEMD-BPNN model and obtain a good performance. It shows adopt ANN, EEMD and ARMA in the hybrid model configure for high-frequency trading modeling is effective and it have the potential of development.