A Data-Driven Fuzzy Rule-Based Approach for Student Academic Performance Evaluation Ernest Wu Paper Reading Outline  Basic concepts of academic performance evaluation.  Basic concepts of Fuzzy Rule-Based System  demonstration  Data Driven FRBS  Subsethood-Based Rule Generation Algorithm (SBA)  Weighted Subsethood-based Rule Generation Algorithm 衡量學業表現的原因  可以對學生的表現有更多瞭解  老師可以藉此給予學生幫助  學生也可以因此克服弱點，或是成為進步的誘因  藉由學業表現對學生作出相應的決策  學生成績不好，重修或留級  也能用來衡量老師的表現  老師對學生表現的幫助有多少 衡量學業表現的方式  Formative assessment  著重在教授的”過程”  日常的活動:平時表現、平時報告、小考  Summative assessment  最後總結的成績  通常都會綜合以上兩者  面對不同的衡量目標，採行不同的方式  Formative assessment可以提供feedback  更全面的瞭解 Assessment Components (method)  Series of tests and quizzes  Portfolios  Formal written examinations  Individual Assignments and Coursework  Group work  Observation  Theses and publishable materials  Posters and oral presentation 衡量學業表現的表示法  Single letter-grade (A, B, C, D, E, F)  Nominal score (1, 2, …, 10)  Single numerical score (100 percent)  Linguistic terms ("Pass" and "Fail“)  GPA (0.00~4.00) 目前來說，使用數值資料表示法來作進一步統計計算比較普遍。 階層式的衡量法  各種的assessment components，可以使用階 層的方式，將它們匯總起來。 新方法產生的原因  確認傳統衡量方式是否有問題。  傳統方式無法面對不確定的評分，然而老師打分數本 來就是大概，因此使用fuzzy concepts可以面對這種 狀況。  傳統方式只能處理數值資料，對於自然語言或是含糊 的詞彙比較不能處理，若能使用自然語言將會讓衡量 更有彈性。  傳統方式缺乏比較的資訊 (與他人或自己比較) criterion-reference evaluation Æ norm-referenced evaluation (Z-Score) 使用兩者的Combination以得到更多的資訊 使用Fuzzy Rule-Based System (FRBS) 新增的衡量方式 Fuzzy Rule-Based System  Fuzzy set Theory  Fuzzy membership functions  Fuzzy logical operators  Fuzzy IF-THEN rules [...]... Exam is (C3) THEN the Final Grade is Good Testing the ruleset for classification tasks For testing the ruleset trained using SAP-1 for classification of student performance, the SAP-2 dataset is used Data Driven FRBS—Steps Subsethood-Based Rule Generation Algorithm (SBA) handle classification problems classify training data into subgroups according to the underlying classification results calculate... calculate fuzzy subsethood values for every variable in each subgroup create rules Subsethood-Based Rule Generation Algorithm (SBA) (2) Fuzzy rules dependent on the fuzzy subsethood values and a prespecified threshold value α ∈ [0, 1] Any variables that have a subsethood value that is greater than or equal to α will automatically be chosen as an antecedent for the fuzzy rules Weighted Subsethood-based... criterion of evaluation It will be used to transform crisp values into fuzzy values Step 3: Calculate fuzzy subsethood values Calculate fuzzy subsethood values for each linguistic term in each subgroup Step 4: Calculate weights for each linguistic term Calculate weights for each linguistic term using subsethood values calculated in Step 3 Step 5: Create rule Rule 1: The Final Grade is Poor (X) IF Assignment.. .Fuzzy set theory 傳統set theory—everything is precise Fuzzy set theory Fuzzy Membership Functions measure linguistic variable A linguistic variable is defined as a variable whose values are words or sentences in a natural or synthetic language choosing or generating an appropriate fuzzy membership function to represent a linguistic term is very important Fuzzy Logical Operators Traditional logical... is (A1 OR 0.0 5A2 ) AND Test is (B1 OR 0.43B2) AND Final Exam is (C1 OR 0.04C2) THEN the Final Grade is Poor Rule 2: The Final Grade is Average (Y) IF Assignment is (0. 4A1 OR A2 OR 0. 3A3 ) AND Test is (0.27B1 OR B2 OR 0.59B3) AND Final Exam is (0.68C1 OR C2 OR 0.21C3) THEN the Final Grade is Average Rule 3: The Final Grade is Good (Z) IF Assignment is (0.1 2A2 OR A3 ) AND Test is (0.13B2 OR B3) AND Final... Humidity = Normal -> class Yes [66.2%] Rule 4: Outlook = Rain, Wind = Weak -> class Yes [63.0%] Data Driven FRBS with WSBA method Training Dataset Testing Set Labels used for each linguistic term Step 1: Divide dataset into subgroups The training dataset was divided into three subgroups according to the classification outcomes Step 2: Define fuzzy partition The fuzzy partition is pre-defined according... High Weak Yes Rain Mild High Weak Yes Rain Cool Normal Weak Yes Rain Cool Normal Strong No Overcast Cool Normal Strong Yes Sunny Mild High Weak No Sunny Cool Normal Weak Yes Rain Mild Normal Weak Yes Sunny Mild Normal Strong Yes Overcast Mild High Strong Yes Overcast Hot Normal Weak Yes Rain Mild High Strong No Traditional decision tree- generate tree Outlook = Overcast: Yes (4.0) Outlook = Sunny:... both are fuzzy linguistic fuzzy model Mamdani-type FRBS Takagi-Sugeno-Kang (TSK) type FRBS Fuzzy IF-THEN Rules(2) 條件規則：IF 溫度 is A, THEN 壓縮機 is B 狀態：溫度 is 高 動作：壓縮機 is 打開 其 A B為模糊集合。IF的部分稱為前件部，而 THEN的部分則稱為後件部。 Mamdani-type FRBS Demonstration Traditional decision tree- training set Outlook Temperature Humidity Wind play ball Sunny Hot High Weak No Sunny Hot High Strong No Overcast Hot High Weak Yes Rain... Traditional logical operators Complement negation Intersection conjunction Union disjunction Fuzzy Logical Operators(2) Fuzzy Negation: Fuzzy Conjunction (t-norm): Fuzzy Disjunction (t-conorm): Min-Max operators have been used widely probably because of their simplicity Fuzzy IF-THEN Rules “IF x is A THEN y is B" where A and B are fuzzy sets fuzzy IF-THEN rules are production rules whose antecedents, consequences... Humidity = Normal: Yes (2.0) Outlook = Rain: | Wind = Weak: Yes (3.0) | Wind = Strong: No (2.0) Traditional decision tree- testing Outlook: Sunny, Humidity: Normal Decision: Yes CF = 1.00 [ 0.50 - 1.00 ] Traditional decision tree- generate rules Rule 1: Outlook = Sunny ,Humidity = High -> class No [63.0%] Rule 5: Outlook = Rain , Wind = Strong -> class No [50.0%] Rule 3: Outlook = Overcast -> class Yes [70.7%] . A Data-Driven Fuzzy Rule-Based Approach for Student Academic Performance Evaluation Ernest Wu Paper Reading Outline  Basic concepts of academic performance evaluation.  Basic concepts. linguistic variable is defined as a variable whose values are words or sentences in a natural or synthetic language  choosing or generating an appropriate fuzzy membership function to represent a linguistic. tests and quizzes  Portfolios  Formal written examinations  Individual Assignments and Coursework  Group work  Observation  Theses and publishable materials  Posters and oral presentation 衡量學業表現的表示法 