本論文依據 Bernanke, Boivin, and Eliasz (2005) 之模型，建立因子擴充向量自我迴歸 (factor-augmented vector autoregression,簡稱 FAVAR) 模型分析台灣之價格僵固性 (price stickiness) 問題。我們考慮了台灣 1998 年 1 月至 2014 年 2 月之時間序列月資料，總共包含 267 個變數，其中包含總合物價 (aggregated prices，又稱總合價格)與非總合物價 (disaggregated prices，又稱非總合價格、類別物價或類別價格)。總合物價指的是經濟體系中於一段時間內將許多商品與服務聚合成一能代表全體的物價，相較之下，分類較細項之商品或服務價格則視為非總合物價。同時，我們將價格所受到的影響來源分成總體項干擾 (common component shock，又稱總體項衝擊或總體項波動)與類別項干擾 (sector-specific shock，又稱非總體項衝擊或非總體項波動)兩項。總體項干擾是會影響整個經濟體系之波動，其中還可區分出貨幣政策干擾(monetary policy shock，又稱貨幣政策衝擊或貨幣政策波動)，亦即貨幣政策工具－隔夜拆款利率（以下簡稱利率）－的變動，而類別項干擾即為其餘不會同時影響整個經濟體系的波動。我們分別觀察總合與非總合物價對於總體項、類別項與貨幣政策干擾之衝擊反應 (impulse response)，藉此判斷台灣不同性質價格之僵固程度。此外，我們也透過分析貨幣政策干擾造成工業生產指數 (industrial production index，簡稱 IPI) 與消費者物價指數 (consumer price index，簡稱 CPI) 衝擊反應形成之價格困惑 (price puzzle) 現象，比較 FAVAR 與向量自我迴歸 (vector autoregression，簡稱 VAR) 模型何者較能描述真實經濟市場情況。 我們的實證結果顯示，非總合價格面對所有干擾都有立即且顯著的反應而不具僵固性。其中，類別項干擾造成的影響持續期間較短，很快收斂至新均衡；總體項干擾造成的持續期間則較長，而貨幣政策干擾更是到了第 48 個月仍無法達到新均衡。相對地，總合價格面對貨幣政策干擾的反應微小而呈現僵固性。此外， VAR 模型下，總合變數皆呈現明顯價格困惑現象。反觀 FAVAR 模型下， IPI 反應慢且小而穩定，而 CPI 的價格困惑現象顯著卻不如 VAR 模型下的反應久。因此我們獲得結論， FAVAR 模型對於本研究而言，考慮了更大量的經濟指標或許更能描述現實經濟狀況。

We investigate the spillover of industry effect in Taiwan stock market. Using a generalized vector autoregressive where forecast-error variance decompositions are invariant to variable ordering, we objectively propose measures of both total and directional spillovers on return and volatility daily data. In full-sample analysis, there is a heavy spillover effect in the interaction between stock market and industries. The stock market acts as a receiver from the information diffused from the industries, but the industries could not be confirmed as spillover outputer or inputer. The rolling-sample findings also pinpoint the high spillovers during the financial events. Finally, conducting the robustness test, we divide the sample periods into subperiods and switch the daily data toward weekly and monthly data, then obtaining the consistent results with prior inference.

歐元的問世，代表的是從1970年代固定匯率被打破以來，世界金融體系最大一次的變革，其對全球之金融及社會文化有很深遠的意義；因此，有關美國與歐元區間之匯率、利率及物價關係的探討遂成為國際金融市場所關心的焦點之一；本文以Frankel (1992)所提出衡量國際間資本移動性的三種利率平價說：拋補利率平價說(Covered Interest Parity，CIP) 、無拋補利率平價說 (Uncovered Interest Parity，UIP)、實質利率平價說 (Real Interest Parity，RIP)為基礎，來檢驗此三種利率平價說是否成立。在實證方法上，本文以Dickey & Fuller (1979,1981)之ADF單根檢定來確定變數之數列特性，再採Johansen (1988)之最大概似估計法，對CIP、UIP與RIP進行實證分析。實證結果發現，於1999 年 1 月至 2004 年 7 月期間，美國與歐元區間 CIP 與 UIP 同時成立，表示當兩國資產報酬率有差異時，可以經由國際間資本的移動，使得報酬率最後有趨於相等的傾向；並且接受遠期匯率為未來即期匯率的不偏估計值之虛無假設，顯示歐元與美元間外匯市場具有效率性。另外，本文之實證結果並不支持 RIP 的成立，其有可能歐元區與美國在編制物價指數時，所使用的物價項目和比重情況不同而異，因此難以表示出公正之匯價；再者由於現實之貨幣、商品市場之不完全，與人民不一定能完全預期及存在貨幣幻覺等許許多多未考慮因素下，故在諸多驗證 RIP之文獻中，亦大多顯示無法找到其均衡之平價關係。 / The emergence of Eurodollar exemplified a significant reformation in the world financial system since the fixed rate had been broken in 1970, which brings far-reaching significance to the global finance and social culture. Therefore some discussions on exchange rate, interest rate and price relationship in the range of US Dollar and Eurodollar are one of focuses the international financial market concerns; On the basis of the three kinds of interest rate parity Frankel brought forward (1992) including Covered Interest Parity (CIP), Uncovered Interest Parity (UIP) and Real Interest Parity (RIP), this research mainly proves their feasibility. For the empirical methods, the Dickey & Fuller (1979, 1981)’s ADF unit root test was used to confirm the characteristics of variable series in this research; additionally, Johansen’s maximum likelihood method (1988) was adopted to do the empirical analysis on CIP, UIP and RIP. Based on the empirical results, we found out that the CIP and UIP are tenable simultaneously in the range of tenable US Dollar and Eurodollar from 1999 January to 2004 July. That means when return on asserts between two counties has some differences, it would become towards equality lastly on the basis of international capital mobility. And the null hypothesis that the forward rate is an unbiased predictor of the future spot rate can be employed, revealing the foreign exchange market in the range of Eurodollar and US Dollar has certain efficiency. Additionally, The empirical results of this research do not support the RIP, because it would vary with different prices and proportion used while making the price index in the range of Eurodollar and US Dollar, and cannot present equitable exchange rate; furthermore, because of imperfect current currency and commodity markets, and many unconsidered factors such as people’ incompletely anticipation and money illusion, most researches for validating RIP fail to find out its balanced parity relation.

電子工業為台灣最具競爭力之產業,使得電子類股在集中市場成交比重高達 69.49%,可見電子類股的波動足以對整個台股市場造成相當大的影響。而許多研究指出,網路上的文本訊息藉由社會網路的催化而快速傳遞,會對群眾情緒造成影響,進而影響股價波動,故對於投資者而言,如果能快速分析大量網路財經文本來推測投資大眾情緒進而預測股價走勢,即可提升獲利。然而,每天有近百篇的財經文本產生,傳統的人工抽樣分析方式效率不彰且過於耗力, 已不足以負荷此巨量資料。 過去文本情感分析的研究中已證實監督式學習方法可以透過簡單量化的方式達到良好的分類效果,但監督式學習方法所使用的訓練資料集須有事先定義好的已知類別,故其有無法預期未知類別的限制,造成無法判斷文本中可能存在的未知主題,所以本研究提出一套針對財經文本的混合監督式學習與非監督式學習之情感分析方法,透過非監督式學習將 2014 整年度的電子工業財經文本進行文本主題判別、情緒指數計算與情緒傾向標注。之後配合視覺化工具作趨勢線圖分析,找出具有領先指標特性之主題,接著再用監督式學習將其結合國際指標、總體經濟指標、台股指標、技術指標等,建立分類模型以預測台灣電子類股價指數走勢。 在實驗結果中,主題標注方面,本研究發現因文本數量遠大於議題詞數量造成 TFIDF 矩陣過於稀疏,使得 TFIDF-Kmeans 主題模型分類效果不佳;而文本具有多主題之特性造成 NPMI-Concor 分群之議題詞過於複雜不易歸納,然而LDA 主題模型基於所有主題被所有文章共享的特性,使得在字詞分群與主題分類準確度都優於 TFIDF-Kmeans 和 NPMI-Concor 主題模型,分類準確度高達 98%,故後續採用 LDA 主題模型進行主題標注。情緒傾向標注方面,證實本研 究擴充後的情感詞集比起 NTUSD 有更好的字詞極性判斷效果,計算出的情緒 指數之趨勢線也較投資人常用的 MACD 之趨勢線更符合電子類股價指數之趨 勢。此外,亦發現並非所有文本的情緒指數皆具有領先特性,僅企業營運主題與總體經濟主題之文本的情緒指數能提前反應電子類股價指數趨勢,故本研究用此二主題之文本的情緒指數來建立分類模型。 接著,本研究透過比較情緒指數結合技術指標之分類模型與單純技術指標分類模型的準確率發現,前者較後者高出 7%的準確率。進一步結合間接情緒指標的分類模型更有高達 71%準確率,故證實了情感分析確實能有效提升電子股價類股指數趨勢預測準確度,以提升投資人之投資報酬率。 / The electronic industry is the most competitive industry in Taiwan, and its large volume could have strong influence on the whole stock market. Many research show that text documents on the Internet have great effect on public emotion, and the public emotion could also affect the stock price. For investors, it is important to know how to analyze the potential emotion in text documents then use this information to predict the stock trend. However, the traditional way to analyze text documents by human resource cannot afford the large volume of financial text documents on the Internet. In past Sentimental Analysis research, supervised method is proven as a method could reach high accuracy, but there are limits about predicting the future trend. This research found a solution which mixed supervised and unsupervised methods to deal with these large financial text documents. First, we use unsupervised method to find out the topic of documents, and then calculate the sentimental index to judge the document’s emotional direction. After that we will produce trend line charts by visualization tools to find out which theme documents’ sentiment index are leading indicators. Furthermore, we use supervised method to integrate the sentimental index with other 24 indirect sentimental index to build the prediction model. According to the result, we found that LDA model’s performance is better than TFIDF-Kmeans model and NPMI-Concor mode because of document characteristic. Besides, sentimental dictionary I build has higher accuracy than NTUSD on judging word polarity. The trend of sentimental index and Taiwan electronic sub-index(TE) to each other is more similar than MACD line and TE to each other. We also discover that the sentiment index produced from documents about enterprise operation and macroeconomics are leading indicators, so we use these to build prediction model. Moreover, we found that the prediction model which include the sentiment index better than which only include the technical indicators. As mentioned above, the sentimental index could make the prediction of Taiwan electronic sub-index trend be more accurate and promote the return of investment.

由於電腦科技的快速發展，網際網路(World Wide Web；簡稱WWW)使得資料共享及搜尋更為便利，其中的網路搜尋引擎(Search Engine)更是尋找資料的利器，最知名的「Google」公司就是藉由搜尋引擎而發跡。網頁搜尋多半依賴各網頁的特徵，像是熵(Entropy)即是最為常用的特徵指標，藉由使用者選取「關鍵字詞」，找出與使用者最相似的網頁，換言之，找出相似指標函數最高的網頁。藉由相似指標函數分類也常見於生物學及生態學，但多半會計算兩個社群間的相似性，再判定兩個社群是否相似，與搜尋引擎只計算單一社群的想法不同。 本文的目標在於研究若資料服從多項分配，特別是似幾何分配的多項分配（許多生態社群都滿足這個假設），單一社群的指標、兩個社群間的相似指標，何者會有較佳的分類正確性。本文考慮的指標包括單一社群的熵及Simpson指標、兩社群間的熵及相似指標(Yue and Clayton, 2005)、支持向量機(Support Vector Machine)、邏輯斯迴歸等方法，透過電腦模擬及交叉驗證(cross-validation)比較方法的優劣。本文發現單一社群熵指標之表現，在本文的模擬研究有不錯的分類結果，甚至普遍優於支持向量機，但單一社群熵指標分類法的結果並不穩定，為該分類方法之主要缺點。 / Since computer science had changed rapidly, the worldwide web made it much easier to share and receive the information. Search engines would be the ones to help us find the target information conveniently. The famous Google was also founded by the search engine. The searching process is always depends on the characteristics of the web pages, for example, entropy is one of the characteristics index. The target web pages could be found by combining the index with the keywords information given by user. Or in other words, it is to find out the web pages which are the most similar to the user’s demands. In biology and ecology, similarity index function is commonly used for classification problems. But in practice, the pairwise instead of single similarity would be obtained to check if two communities are similar or not. It is dislike the thinking of search engines. This research is to find out which has better classification result between single index and pairwise index for the data which is multinomial distributed, especially distributed like a geometry distribution. This data assumption is often satisfied in ecology area. The following classification methods would be considered into this research: single index including entropy and Simpson index, pairwise index including pairwise entropy and similarity index (Yue and Clayton, 2005), and also support vector machine and logistic regression. Computer simulations and cross validations would also be considered here. In this research, it is found that the single index, entropy, has good classification result than imagine. Sometime using entropy to classify would even better than using support vector machine with raw data. But using entropy to classify is not very robust, it is the one needed to be improved in future.

行人偵測為物件偵測領域中一個極具挑戰性的議題。其主要問題在於人體姿勢以及衣著服飾的多變性，加之以光源照射狀況迥異，大幅增加了辨識的困難度。吾人在本論文中提出利用共變異矩陣描述子及結合單純貝氏分類器與級聯支持向量機的線上學習辨識器，以增進行人辨識之正確率與重現率。 實驗結果顯示，本論文所提出之線上學習策略在某些辨識狀況較差之資料集中能有效提升正確率與重現率達百分之十四。此外，即便於相同之初始訓練條件下，在USC Pedestrian Detection Test Set、 INRIA Person dataset 及 Penn-Fudan Database for Pedestrian Detection and Segmentation三個資料集中，本研究之正確率與重現率亦較HOG搭配AdaBoost之行人辨識方式為優。 / Pedestrian detection is an important yet challenging problem in object classification due to flexible body pose, loose clothing and ever-changing illumination. In this thesis, we employ covariance feature and propose an on-line learning classifier which combines naïve Bayes classifier and cascade support vector machine (SVM) to improve the precision and recall rate of pedestrian detection in a still image. Experimental results show that our on-line learning strategy can improve precision and recall rate about 14% in some difficult situations. Furthermore, even under the same initial training condition, our method outperforms HOG + AdaBoost in USC Pedestrian Detection Test Set, INRIA Person dataset and Penn-Fudan Database for Pedestrian Detection and Segmentation.

本研究計畫探討以RN神經網路模型預測國內債券價格的效度。目前一般用於財務預測的神經網路論著主要為BPN模型，惟BPN模型有其限制，所以本研究計畫將（1）分析比較統計計量模型，BPN神經網路，RN神經網路系統對國內公債價格之預測績效。（2）分析不同時期的預測能力，找出景氣和預測變數的關係，同時將比較各個時期統計計量模型和神經網路模型是否同時有效， 抑或有些有效， 有些無效，以探討各工具是否具有互補性或替代性。並探討預測績效是否受到背後經濟環境的影響。 我們研究對象為國內公債，其每日交易資料取樣時間自民國八十一年開始。影響債券價格的因素可拆解成實質利率，預期通貨膨脹率和風險貼水三層面，本研究總體變數之選取，亦循此三項範疇以求周延。 本研究之研究成果對理論及實務應用將有下列三項預期貢獻：（1）比較不同其常的債券在不同景氣狀況下，各不同預測模型的預測效度差異，探討各時期各工具之預測能力，可提供投資實務界對預測工具之選擇，應用與搭配。（2）對債券報酬率預測研究，分析總體變數，利率風險等變數對債券報酬率的影響，可進一步暸解影響債券價格的相關因素及程度。（3）以往神經網路應用在財務預測領域上， 皆以BPN 神經網路為主，此處引進RN神經網路，比較兩者的表現，可提供學術理論界之驗證。 / This research project empirically investigates the accuracy of Reasoning Neural Networks (RN) in forecasting Taiwan's bond prices. We explore (1) the relative predictive abilities of Vector Error Correction Model (VECM), which serve as a representative econometric model, Back Propagation Neural Networks (BPN), which is adopted by most current studies in the application of neural networks in finance, and RN, and (2) th3 potential variations in the three models' predictive power in different phases of economic cycle. Specifically, we aim to study if the three models substitute or complementone another. In addition, we explore the extent to which the relativepredictive abilities of the three models varies with underlying macroecomonic factors. The explanatory variables adopted in this study include all potential drives to (real) risk-free rate, expected inflation rate, and riskspremiums. In this study, we examine the government bond terms to maturity,coupon rate, and prices of government bonds during 1992-1995. This project would contribute to both academic and application researchin the following three aspects : (1) Few, if any , prior study explores whether and how various neuralnetworks and/or eco- nomic models perform under different macro-economicvariables. Our empirical results may indicate an appropriate model ( ormodels ) to improve forecasting of bond prices. (2) This study shows how RN, BPN, and VECM models perform in forecastinggovernment bonds yields to maturity. (3) The BPN model prevails in financial forecasting. Nevertheless, BPNis subject to a few short comings and may thus be a sub-optimal model. This study analyzes if RN is more cost-effective in forecasting bond prices than BPN.

由於住宅價格變動毫無預警制度，人民往往憑著個人主觀的判斷而決定何時購屋或售屋，而此種主觀判斷住宅市場利多及利空的觀念，對住宅市場的供需會產生失衡現象，因此是否可從經濟面的訊息找到住宅價格變動的答案，使住宅價格在尚未變動前，政府即已掌握資訊，提前做好穩定住宅價格的因應對策，使民眾依其需要而購屋，則是本研究之主要目的。 　　本研究從文獻中整理出影響住宅價格變動的七個總體經濟變數，這些總體經濟變數包含工資、物價、所得、貨幣供給額、股價、匯率及利率等，並利用向量自我迴歸模式(VAR)進行實證，以便較客觀的獲得變數間的落後期數及暸解變數間雙向、單向及領先、同步、落後情形，且進一步探討住宅價格與每一個總體經濟變數間影響程度大小及影響情形，以釐清各變數之間的關係。 　　本研究利用VAR模型進行住宅價格與總體經濟變數關係的研究，經由實證，得到下列的結論： 　　一、實證結果方面 　　本研究之實證主要有因果關係檢定與分析、變異數分解之分析及衝擊反應之分析三方面，其實證結果如下所述。 　　（一）因果關係檢定與分析 　　由因果關係檢定與分析中，得到股價、物價、匯率、貨幣供給額及利率均能做為住宅價格變動的領先指標。 　　（二）變異數分解之分析 　　由住宅價格之變異數分解中，得知住宅價格自身的解釋程度僅占三分之一，另三分之二被其他的總體經濟變數所解釋，顯示住宅價格受總體經濟變數的影響相當大；而從其他總體經濟變數之變異數分解中，得知住宅價格變動會干擾到總體經濟變數，而使總體經濟變數受干擾而變動變動。 　　（三）衝擊反應之分析 　　從總體經濟變數對住宅價格的衝擊反應分析圖中可以明顯看出除工資外，其餘總體經濟變數變動對住宅價格造成的衝擊均相當明顯，但匯率及利率對住宅價格的衝擊是負向的。 　　住宅價格對所得、股價、匯率及利率的衝擊相當明顯，而其對匯率的衝擊是負向。 　　二、政策應用方面 政府的決策過程中常會有時間落後的現象，而本研究實證的目的則是要使政府能事先掌握住宅價格的變動，並提前做好穩定住宅價格的因應對策，減少政府決策過程的時間落後現象，而實證結果應用至政策方面的內容則由以下說明之。 　　（一）藉由因果關係檢定與分析的實證內容，可以縮短政府對住宅價格不合理變動問題認定落後的時間。 　　（二）從變異數分解之分析的實證內容中，可以使決策者在解決住宅價格問題時，將行動落後的時間減少。 　　（三）由衝擊反應之分析中，可以使政府在執行穩定住宅價格政策時，將衝擊落後的時間縮小。 / Since there is no alarm system in the change of housing prices, people often decide when to buy or when to sell based on personal and subjective judgement. Such concept to judge subjectively whether the housing market is bull or bear will cause unequilibrium in the supply and demend of the housing market. There it is possible to find out the answers to the change of housing prices from economic side so that the government can have enough information and can be prepared in the reaction to stabilizing the housing prices, and so that the public can buy house according to their needs is the main purpose of this project. 　　Seven variables in macroeconomics influencing the change of housing prices have been taken from reative literature, including wage, commodity price, income, money supply, stock price, exchange rate, and interest rate. VAR has been employed to verify so that the more objective lagging period among variable can be known, and the bi-directional, uni-directional, leading, contemporaneous, and lagging situation among variables can be understood. Furthermore, the degree and the status of influence of each macroeconomic variable to the housing price will be investigated to clarify the relations among the variables. 　　The present project investigate the relations between housing price and macroeconomic variables. We have the following findings: 　　I、In Empirical Study: 　　The empirical study in this project includes causal relation test and analysis, the analysis of variable decompositon, and the analysis of impact response. The results are shown in the following: 　　(I) Causality Test and Analysis 　　In the causality test and analysis, we find out that stock price, commodity price, exchange rate, money supply and interest rate all can be the leading indicators in the change of housing prices. 　　(II) The Analysis of Variable Decomposition 　　It is learned from the variable decomposition of housing prices that housing price can only explain one third of the cause in its change, the other two thirds are explained by other macroeconomic variables. It shows that housing prices are subject to the influence of macroeconomic variables greatly. 　　From the variable decomposition of other macroeconomic variables, we know that the change in housing prices will affect macroeconomic variables so that the macroeconomic variables will change. 　　(III) The Analysis of Impact Response 　　It can be obviously seen from the analysis figure of the impact response of the macroeconomics to housing prices, all macroeconomic variables will cause obvious impact to housing prices expect for wage. However, both exchange rate and interest rate have negative impact to housing prices. 　　Housing prices' impact to income, stock prices, exchange rate and interest rate is quite obvious, among which, the impact to exchange rate is negative. 　　II、Policy Application 　　It is a common phenomenon that there often will be lagging in time in government's decision making. The purise of the empirical study in this project is to let the government to know in advance the change of housing prices and to let the government to know in advance the change of housing prices and to let the government be prepared in the reaction of stabilizing the housing prices to minimize the lagging in the decision making process. The contents of application of the empirical study to policy are explained in the following: 　　(I) With the empirical results of the change of the causality test and analysis, the time for the government to recognize the unreasonable changes in housing prices can be shortened. 　　(II) With the empirical results of the analysis of variable decomposition, the decision makers' lagging in the action responding to housing pricescan be minimized. 　　(III) With the analysis in impact response, the lagging in impact will be minimized when the government executing her housing price stabilizing policy.

台灣幅員狹小，天然資源不足，唯有藉著大量出口才能換取外匯，情況使得台灣逐漸發展成一小型開放經濟。長久以來，美國一直是台灣最大的貿易夥伴，使得台灣產品對美輸出的多寡往往直接影響台灣總體經濟的表現。隨著政府外匯政策的逐漸自由化，匯率在總體經濟中所扮演的角色也越顯重要。近幾年來，台幣匯價在外匯市場上時有波動，不但影響政府政策的擬定、經貿活動的往來，外匯市場上的投炒作更造成熱錢的流動。是故，新台幣對美元匯率的決定及波動因素是值得我們深入探討的課題。基於此點，本文擬建立一個可供實証的小型開放經濟模型，試圖探討新台幣對美元匯率的決定因素。首先，參照Frankel(1979)所提出的實質利率差價模型(Real Interest Rate Differential Model)，作為實証研究的基礎。其次，利用Johansen(1988,1991)、Johansen & Juselius(1990)的共整合(cointegration)分析方法，以台灣地區1981年至1993年間的月資料，驗証縮減式的長期關係是否成立。最後，採用誤差修正模型(error correction model)，估計匯率的動態調整途徑，並對匯率變動率進行樣本後預測。 　　實証結果發現：(1)實質匯率差價模型所刻畫的匯率與其他經濟變數的長期關係在台灣是可以成立的；(2)傳統貨幣學派對兩國結構喜數相同的假設過於嚴苛，對於台灣及美國並不適用；(3)除了名目利率外，台灣及美國的貨幣供給、產出水準及通貨膨脹率具有一對一的關係；(4)以誤差修正模型預測台幣／美元匯率變動率，其效果優於隨機漫步模型。

本研究旨在探討分段式評量教學法對高二學生於學習數學時學習成就的影響。研究對象為台北縣某國立高中二年級理組學生，分為實驗組42位及控制組44位共86位學生，以20週的時間進行實驗，觀察分析其學習成就的改變。 本研究結果發現，在相同教學時間下： 1. 分段式評量教學法在整體學習成就方面有正面影響且結果達顯著差異。 2. 對不同學習風格學習成就之正面影響雖未達顯著水準，但學習成就相對改善值似乎有增加的趨勢。 3. 對不同學習程度分組而言，中分組與低分組學生之學習成就方面有正面影響且達顯著差異。 4. 實施分段式評量教學法的學生比使用傳統教學法的學生在學習態度方面似乎較不會有放棄數學的現象。 綜而論之，分段式評量教學法可提供教學者，在面對數學學習成就低落的學生一個有效的引導方法，讓這些學生不僅不會放棄數學，還能漸漸的建立良好的學習習慣。 / This research mainly aims at evaluating how divided assessment teaching method would effect junior students’ mathematics-learning performance in high school. A case study was conducted on science-team junior students in a Taipei-county high school, composed of 42 students in experimental group and 44 ones in control group respectively, amounting to 86. This experiment spanned as long as 20 weeks for analysis on how students’ learning performance would be benefited. It is thus concluded in this research after evaluating both 2 group’s learning performance in term of equal length of time as below: 1. Divided assessment teaching method would have positive effect on learning performance at significance level. 2. Although divided assessment teaching method has positive effects on learning performance for various learning style subgroups, these positive effects are not significant. Their relative improvements of learning performance seemed to be increased, too. 3. When evaluated in term of original-performance level, students’ learning performance in average-level and in inferior-level subgroups both would be benefited positively at significance level. 4. Students taking divided assessment teaching method would have more persistent learning attitude than those taking traditional teaching method. In summary, divided assessment teaching method could help teachers to offer more effective teaching-guidance to students who had inferior learning performance. As a result, students would not only persist in mathematics learning but also cultivate enthusiastic learning attitude gradually.