[精品]人工智慧技术在数位学习研究之应用

人工智慧技術在數位學習研究之應用,1,人工智慧在數位學習研究之應用,國立臺南大學 黃國禎數位學習科技系 教授資訊教育研究所 所長理工學院 院長2005/10/5,人工智慧技術在數位學習研究之應用,2,突破時空限制提供個別化教學指導提高學習興趣促成同學互動建立完整的教學管理機制,數位學習的優點,人工智慧技術在數位學習研究之應用,3,數位學習的發展現況,行政院在九十年NICI(國家資訊通信發展方案)計畫中，將數位學習納入網路社會化的一環。國科會通過數位學習國家型科技計畫的構想，預計五年內投入四十億元進行跨部會計畫全民數位學習縮減數位落差行動學習載具與輔具多功能電子書包數位學習網路科學園區前瞻數位學習技術研發數位學習之學習與認知基礎研究政策引導與人才培育,人工智慧技術在數位學習研究之應用,4,將在數位學習的研究由應用層次(Alessi, S.M. & Trollip, S.R. 1991)提昇到功能層次應用層次建構測驗：試題的組合及題庫的共享實施測驗：利用電腦作為考試媒介功能層次被動(Chou, C. 1996)：記錄學習過程，統計、保存、報告學習評量之結果主動(Kumar, D.D., Helgeson, S.L. & White, A.L. 1994)：分析學習狀態、引導學生學習、偵查學習迷思，並預防學生犯錯,人工智慧扮演的角色,人工智慧技術在數位學習研究之應用,5,個人化學習路徑之規劃線上學習過程之記錄線上學習行為之分析,人工智慧應用(1)：個人化教學系統,人工智慧技術在數位學習研究之應用,6,個人化學習路徑之規劃,人工智慧技術在數位學習研究之應用,7,線上學習行為分析 藉由模糊專家系統來推論出此學習者的學習狀態，並給予適當的幫助學習效率(Efficiency of Learning) 學習意願(Willingness)耐心度(Patience)專心度(Concentration)閒置(Idleness)理解度(Comprehension) 聊天(Chat),人工智慧技術在數位學習研究之應用,8,(1) 學習意願分析,學生用心學習的意願分析依據：有效登入時間/登入時間,模糊推理法則If willingness is low Then insert INT(T0.5) corresponding willingness frames.If willingness is average Then insert INT(T0.25) corresponding willingness framesIf willingness is high Then keep the current status.,人工智慧技術在數位學習研究之應用,9,(2) 耐心度分析,學生瀏覽一個畫面的持續度分析依據：畫面學習時間/預估學習時間,模糊推理法則If patience is low Then record this status and warn the student.If patience is average Then keep the current status.If patience is high Then keep the current status.,人工智慧技術在數位學習研究之應用,10,(3) 專心度分析,學生集中精神於瀏覽教材的程度分析依據：回應時間,模糊推理法則If concentration is lowThen insert a corresponding concentration frame.If concentration is highThen keep the current status.If concentration is averageThen keep the current status.,人工智慧技術在數位學習研究之應用,11,(4) 聊天狀態分析,學生利用線上討論區來閒聊而不是討論課程分析依據：學習相關比率,模糊推理法則If chat is highThen record this status and warn the student.If chat is averageThen keep the current status.If chat is lowThen keep the current status,人工智慧技術在數位學習研究之應用,12,Gwo-Jen Hwang (1998), “A tutoring strategy supporting system for distance learning on computer networks”, IEEE Transactions on Education, Vol. 41, No. 4, pp. 343. (SCI & EI),人工智慧技術在數位學習研究之應用,13,人工智慧應用(2) ：多專家教學知識擷取與整合系統,人工智慧技術在數位學習研究之應用,14,模糊專家系統知識擷取,Step 1: Elicit all of the elements (concepts to be learned) from the domain expert.,人工智慧技術在數位學習研究之應用,15,Step 2: Elicit attributes ( properties or fuzzy variables).,人工智慧技術在數位學習研究之應用,16,Step 3: Fill all of the concept, attribute entries of the grid. A 7-scale (-3 to +3) rating and the degree of certainty(“S”,”N”).,Consider the ratings of fuzzy variable boiling point: 3 means VERY HIGH, 2 means HIGH, 1 means MORE OR LESS HIGH, 0 means MIDDLE,-1 means MORE OR LESS LOW, -2 means LOW,-3 means VERY LOWS means VERY SURE, N means NOT VERY SURE,人工智慧技術在數位學習研究之應用,17,Step 4: the first column of the above fuzzy table is translated to the following rule: IF boiling point is MORE OR LESS LOW, and atom radius is NARROW, and metalloid is MORE OR LESS STRONG, and negative charge is VERY WEAK THEN the element could be Li TRUTH = 0.8 TRUTH =,人工智慧技術在數位學習研究之應用,18,多專家知識擷取與整合的問題,While elicit expertise from multiple experts, someproblems may arise:,same element or attribute,different vocabularies,Unified vocabularies,Different ratings,Conflict happen,Insist their ratings,?,?,?,人工智慧技術在數位學習研究之應用,19,A. Interactive knowledge elicitation unit,人工智慧技術在數位學習研究之應用,20,NEGOTIATOR:Please give a set of elements(concepts to be learned or decisions to be made).EDUCATOR: Li, K, Fr, F, Cl, I. . . .NEGOTIATOR: Select a set of fuzzy values for fuzzy variable “boiling point”: 1. LOW/MIDDLE/HIGH 2. SHORT/MIDDLE/TALL 3. LIGHT/NORMAL/HEAVY 4. SMALL/MIDDLE/BIG 0. Other (user-defined)EDUCATOR: 1NEGOTIATOR: Select a set of fuzzy values for fuzzy variable “atom radius”: 1. LOW/MIDDLE/HIGH 2. SHORT/MIDDLE/TALL 3. LIGHT/NORMAL/HEAVY 4. SMALL/MIDDLE/BIG 0. Other (user-defined)EDUCATOR: 0NEGOTIATOR: Indicate the lower bound of the fuzzy values.EDUCATOR: NARROWNEGOTIATOR: Indicate the middle of the fuzzy values.EDUCATOR: NORMALNEGOTIATOR: Indicates the upper bound of the fuzzy values.EDUCATOR: WIDE,(1) Interactive user interface,人工智慧技術在數位學習研究之應用,21,(2) Fuzzy table editor,人工智慧技術在數位學習研究之應用,22,(3) Membership function builder,人工智慧技術在數位學習研究之應用,23,(4) Knowledge analysis unit,Li K Fr F Cl I boiling point -1/N 0/N 1/N 1/S 2/S 3/S LOW;MIDDLE;HIGHatom radius -2/S -1/S 1/N 1/S 2/S 3/S NARROW;NORMAL;WIDE metalloid 1/S 2/S 3/S -3/S -3/S -3/S WEAK;NORMAL;STRONGnegative charge -3/S -3/S -3/S 3/S 2/S 1/S WEAK;MIDDLE;STRONG,Check if conflict occurs and integrate tutoring strategies.The contents of a fuzzy table is represented as Fuzzy_value(Educator_ID, Object_name, Fuzzy_variable) and Certainty_Degree (Educator_ID, Object_name, Fuzzy_variable) for examples, the fuzzy table below can represented as Fuzzy_value(Educator1, Li, boiling point) = -1 Certainty_Degree(Educator1, Li, boiling point) = “N” .,人工智慧技術在數位學習研究之應用,24,Knowledge analysis rule:Rule_analysis_02IF (1) Current_Phase is Knowledge_Analysis and (2) Fuzzy_value(Expi, Gk, Vs)Fuzzy_value(Expj, Gk, Vs) 0 and (3) Certainty_Degree (Expi, Gk, Vs) is S and (4) Certainty_Degree(Expj, Gk, Vs) is ”N” andTHEN (a) Set Suggested_Fuzzy_Value be Fuzzy_value(Expi, Gk, Vs) and (b) Set Suggested_Certainty_Degree be ”N and (c) Set Current_Phase be Knowledge_Negotiation,人工智慧技術在數位學習研究之應用,25,Knowledge analysis rule:Rule_analysis_04IF (1) Current_Phase is Knowledge_Analysis and (2) Fuzzy_value(Expi, Gk, Vs)Fuzzy_value(Expj, Gk, Vs) 0 and (3) Certainty_Degree (Expi, Gk, Vs) is S and (4) Certainty_Degree(Expj, Gk, Vs) is S” and (5) Fuzzy_value(Expi, Gk, Vs) Fuzzy_value(Expj, Gk, Vs) 0 THEN (a) Set Suggested_Fuzzy_Value be Fuzzy_value(Expi, Gk, Vs) and (b) Set Suggested_Certainty_Degree be S and (c) Set Current_Phase be Knowledge_Negotiation,人工智慧技術在數位學習研究之應用,26,Knowledge analysis rule: Rule_analysis_03IF (1) Current_Phase is Knowledge_Analysis and (2) Fuzzy_value(Expi, Gk, Vs)Fuzzy_value(Expj, Gk, Vs) 0 and (3) Certainty_Degree (Expi, Gk, Vs) is S and (4) Certainty_Degree(Expj, Gk, Vs) is S” andTHEN (a) Set Suggested_Fuzzy_Value be “Conflict” and (b) Set Current_Phase be Knowledge_Negotiation,人工智慧技術在數位學習研究之應用,27,B. Tutoring Strategy Negotiation unit,Present suggestions by knowledge analysis unitWhen a conflict occurs, experts are asked to give suggestions.,“ over-general” happen,BearAmerican gray bear bear of North Pole,invoke Object_Specialization procedure,An example,人工智慧技術在數位學習研究之應用,28,Perform knowledge integration procedure,人工智慧技術在數位學習研究之應用,29,Check conflict values and decide if Object_Specialization procedure should be invokedGenerate fuzzy rules.,人工智慧技術在數位學習研究之應用,30,(deffacts initial-state (is boiling-point MORE-OR-LESS LOW) (is atom-radius NARROW) (is metalloid MORE-OR-LESS STRONG) (is negative-charge VERY WEAK) (defrule Rule1 ?x1 (retract ?x1 ?x2 ?x3 ?x4) (assert (is Li -1-21-3) (assert (CF 0.8) (printout t ”Li is -1-21-3 with CF=0.8 crlf),C. Knowledge base generator(export to CLIPS),人工智慧技術在數位學習研究之應用,31,Experiment (1): Time for knowledge elicitation and integration,six topics of “Introduction to Computer Science”,人工智慧技術在數位學習研究之應用,32,Experiment (2): Frequency for correct inferences,250 test cases,Gwo-Jen Hwang (2002), “On the Development of a Cooperative Tutoring Environment on Computer Networks”, IEEE Transactions on System, Man and Cybernetic Part C, Vol. 32, No. 3, 2002, pp. 272-278. (SCI, EI),人工智慧技術在數位學習研究之應用,33,人工智慧應用(3)：多目標最佳化配題機制,從大量試題中，選取符合出題條件（題數、測試時間、概念最低配題比重等）且鑑別度最大的試卷指定測驗時間範圍的試題配置問題模型(Dedicated Range of Assessment Time Problem-DRAT)符合期望測驗時間最高界限和最低界限的多目標配題機制。固定題數的試題配置問題模型(Fixed Number of Test Items Problem FNTI)符合固定試卷試題數量的多目標配題機制。,人工智慧技術在數位學習研究之應用,34,Fuzzy Art and Dynamic Programming,Gwo-Jen Hwang (2003), “A Test Sheet Generating Algorithm for Multiple Assessment Requirements”, IEEE Transactions on Education, Vol. 46, No. 3, pp. 329-337. (SCI and EI),(1) Fuzzy Art: Classify test items into groups,(2) Dynamic Programming: Find optimal test item composition,人工智慧技術在數位學習研究之應用,35,Heuristic Algorithms,FTF (Feasible Time First) AlgorithmFind a solution to meet range of assessment timeReplace test items to find feasible solutions and to maximize average discrimination degree FNTF (Feasible Number of Test Item First) AlgorithmFind a solution to meet the number of test itemsReplace test items to find feasible solutions and to maximize average discrimination degree,Gwo-Jen Hwang, Tsung-Liang Lin, Bertrand M.T. Lin (2004), “An Effective Approach for Test-Sheet Composition from Large-Scale Item Banks”, accepted by Computers & Education. (SSCI, SCI, EI),人工智慧技術在數位學習研究之應用,36,Genetic Algorithms,源自於John Holland在1975 年出版的著作Adaptation in Nature and Artificial Systems仿效自然界生物進化過程透過基因的選擇(selection)交換(crossover)及突變(mutation)產生更好的下一代選擇(selection)過程較高合適值(fitness value)就有較大機會獲得保留較低合適值的解答，可能會遭到淘汰較不易陷入local optimal,人工智慧技術在數位學習研究之應用,37,Population (族體):Encoding (編碼):Crossover (交配):Mutation (突變):Selection (適者生存):Fitness Function (適合度公式):,Genetic Algorithm,人工智慧技術在數位學習研究之應用,38,基因演算法流程圖,基因演算法交配運算,基因演算法突變運算,Crossover randomly selects one-cut-point and exchanges the right parts of two parents to generate offspring.,Mutation alters one or more genes with a probability equal to the mutation rate.,人工智慧技術在數位學習研究之應用,39,DRAT目標函式： Maximize Z = DRAT限制式： 第i題告第j個概念的關係 Xi = 0 or 1, i = 1, 2, , nXi 代表第i題是否被選擇,指定測驗時間範圍的試題配置問題 (Dedicated Range of Assessment Time),指定概念的最小出題比重,指定測驗時間的下限,指定測驗時間的上限,指定鑑別度最大化,0,x1 x2 x3 x4 x98 x99 x100,1,1,0,0,1,0,人工智慧技術在數位學習研究之應用,40,DRAT的試題配置基因演算法 (1/4),概念程度下限先決基因演算法(Concept Lower-bound First Genetic approach CLFG)CLFG建立的母體(Encoding)X 為染色體，包含有 n 個基因X = x1 , x2 , , xnX = 0, 0, 1, , 0第i個試題被選取時，xi 為1 ；否則，為0；,人工智慧技術在數位學習研究之應用,41, = w dtu ipt_u dtu =,DRAT的試題配置基因演算法 (2/4),適配等級(Fitness ranking)適配函數v(Sk) =R =Kj = 0 if , Kj = 1 else = w dtl ipt_l w = ( in di xi) / average(u, l)dtl =,人工智慧技術在數位學習研究之應用,42,DRAT的試題配置基因演算法 (3/4),交配(Crossover)A1110011001 A1110011011B0100100011 B0100100001,Procedure: crossover Begin k = 0 while (k c / 2) do flag = 0 while flag = 0 do Generate random numbers R1 and R2 from discrete interval 1,K. If R1 R2 then flag=1 end while crossover function(R1,R2) end while End,Cut point,人工智慧技術在數位學習研究之應用,43,DRAT的試題配置基因演算法 (4/4),突變(Mutation)A1110011001 A1110011011P = ( 1 / n ),Procedure: mutation Begin for(i=1, i nk, i+) Generate random number yi from discrete interval 0, 1. Mutation function(P, yi) End,重覆25步驟，直到連續10代解無進步或已產生了1500代,人工智慧技術在數位學習研究之應用,44,固定題數的試題配置問題 (Fixed Number of Test Items),FNTI目標函式： Maximize Z = FNTI限制式：xi 1 Xi 代表題庫中的題號，最小題號為1 xi n 最大題號為n 1 i q_num 1 共選出 q_num 題,指定鑑別度最大化,指定概念的最小出題比重,12,x1 x2 x3 x4 x5 x6 x7 x8 x9 x10,18,9,45,82,6,2,34,65,71,人工智慧技術在數位學習研究之應用,45,FNTI的試題配置基因演算法 (1/3),試題數目先決基因演算法(Feasible Item First Genetic approach FIFG)FIFG的進行步驟建立母體 X 為染色體，包含有 q_num 個基因 X = x1 , x2 , , xq_numX = 25, 118, ., 803 基因值代表著一題試題的編號xi xj ，且 i j 和 1 i, j q_num,人工智慧技術在數位學習研究之應用,46,FNTI的試題配置基因演算法 (2/3),交配(Crossover) A12,15, 96,112,193,243 A12,15,96,185,256,356 B3,56,108,185,256,356 B3,56,108,112,193,243有兩相同基因值時，隨機更換其中一值，直到沒有相同基因值為止試卷中不可有二題相同的試題,Cut point,人工智慧技術在數位學習研究之應用,47,FNTI的試題配置基因演算法 (3/3),突變(Mutation) A3,8,56,66,256,515 A3,8,56,66,346,515P = ( 1 / n ),Procedure: mutation Begin for (m = 1, m q_num k, m+) Generate random number rm from discrete interval 0, 1 Generate random number RC from discrete interval 1, n mutation function(P, rm, RC) End,重覆25步驟，直到連續10代解無進步或已產生了1500代,人工智慧技術在數位學習研究之應用,48,實驗題庫樣本資料,每一個情況進行二十次實驗處理後，採用平均求解時間和平均鑑別度建立實驗樣本,人工智慧技術在數位學習研究之應用,49,CLFG實驗結果及分析 (1/3),l = 30,人工智慧技術在數位學習研究之應用,50,CLFG實驗結果及分析 (2/3),l = 60,人工智慧技術在數位學習研究之應用,51,CLFG實驗結果及分析 (3/3),l = 120,人工智慧技術在數位學習研究之應用,52,CLFG與最佳解的實驗數據圖表,l = 30,Gwo-Jen Hwang et al. (2004), “On the Development of a Computer-Assisted Testing System with Genetic Test Sheet-Generating Approach”, accepted by IEEE Transactions on Systems, Man, and Cybernetics: Part C.,人工智慧技術在數位學習研究之應用,53,人工智慧應用(4)：學習診斷與導引機制,概念影響關係圖的數學課程範例,人工智慧技術在數位學習研究之應用,54,Conceptual effect table (CET)概念影響關係表,e.g. C3 C4,人工智慧技術在數位學習研究之應用,55,Test item relationship table (TIRT)試題概念關係表,O: Not relevant 1: Very strongly relevant,人工智慧技術在數位學習研究之應用,56,Student answer sheet table (AST)學生答題狀況表,O: The student has correctly answered the test item 1: The student failed to correctly answer the test item,人工智慧技術在數位學習研究之應用,57,建立補救學習路徑,C1,C3,C6,C1,C3,C7,C1,C4,C8,C10,C2,C4,C8,C10,0.16,0.6,0.66,0.16,0.6,1,0.16,0.16,0,0,0,0.16,0,0,C2,C5,C9,0,0.28,0,假設值為0.3，代表我們對於某個概念之錯誤比率的最大容忍程度為30，依上述原則，可得到補救學習路徑：PATH1：C3C6PATH2：C3C7,人工智慧技術在數位學習研究之應用,58,產生學習指引及建議,Gwo-Jen Hwang (2003), “A Concept Map Model for Developing Intelligent Tutoring Systems”, Computers & Education, Vol. 40. No. 3, pp. 217-235. (SSCI & SCI, EI),人工智慧技術在數位學習研究之應用,59,歸納式概念影響關係演算法,歸納式概念關係演算法產生的概念繼承關係圖(Support0.1 & Belief=0.7 數學科),人工智慧技術在數位學習研究之應用,60,教師修改後的概念繼承關係圖(Support0.1 & Belief=0.7 數學科),人工智慧技術在數位學習研究之應用,61,教師輔助介面:產生概念關係,人工智慧技術在數位學習研究之應用,62,教師輔助介面:設定題目與概念關係,Gwo-Jen Hwang et al. (2003), “A Computer-Assisted Approach for Diagnosing Student Learning Problems in Science Courses”, Journal of Information Science and Engineering, Vol. 19, No.2, pp. 229-248. (SCI, EI),人工智慧技術在數位學習研究之應用,63,數位學習的實驗設計,比較答案的品質及演算法的效率-以大量資料模擬測試瞭解系統的滿意度及使用意願-問卷調查驗證系統或方法的效果-以實驗組及對照組進行3-6個月的測試及分析(前測及後測)比較新系統(方法)與舊系統(方法)的效果-多人交叉使用兩系統(方法)並比較結果比較對象：舊系統(方法)、使用與未使用、Random、Heuristic、Optimal方法產生的結果,人工智慧技術在數位學習研究之應用,64,導入基因演算法、模糊專家系統、資料探勘演算法及其他人工智慧技術發展線上智慧型虛擬助教系統發展線上自律學習管理機制對數位學習標準工具及延伸的探討對行動學習(M-Learing)與普化學習(U-Learing)平台與教學策略的探討,未來研究方向,人工智慧技術在數位學習研究之應用,65,相關著作,Gwo-Jen Hwang, et al. (2004), “On the Development of a Computer-Assisted Testing System with Genetic Test Sheet-Generating Approach”, accepted by IEEE Transactions on Systems, Man, and Cyberne