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From Adaptive Learning to Ubiquitous Learning

From Adaptive Learning to Ubiquitous Learning. Speaker : Judy C. R. Tseng Department of Computer Science and Information Engineering Chung Hua University. Agenda. A Survey of Adaptive Learning Related Reseaches Introduction to Ubiquitous Computing Introduction to U-Learning.

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From Adaptive Learning to Ubiquitous Learning

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  1. From Adaptive Learning to Ubiquitous Learning Speaker : Judy C. R. Tseng Department of Computer Science and Information Engineering Chung Hua University

  2. Agenda • A Survey of Adaptive Learning • Related Reseaches • Introduction to Ubiquitous Computing • Introduction to U-Learning

  3. A Survey of Adaptive Learning

  4. Adaptive Learning Environment • Bases on Intelligent tutoring systems (ITS) [Burns & Capps , 1988] and Adaptive hypermedia system [Brusilovsky,1996] • Categories of adaptive learning technologies • ITS technologies • Curriculum sequencing • Problem solving support • Adaptive hypermedia technologies • Adaptive navigation support • Adaptive presentation • Web-inspired technologies • Student model matching

  5. Curriculum Sequencing (1) • Also referred to as Instructional Planning Technology • Helps the student to find an "optimal path" through the learning material • Two levels of sequencing • High-level sequencing or knowledge sequencing • determines next learning subgoal: next concept, set of concepts, topic, or lesson to be taught • Low-level sequencing or task sequencing • determines next learning task (problem, example, test) within current subgoal

  6. Curriculum Sequencing (2) • Two kinds of sequencing • Active sequencing • implies a learning goal (a subset of domain concepts or topics to be mastered) • build the best individual path to achieve the goal • fixed learning goal • adjustable learning goal • Passive sequencing (which is also called remediation) • starts when the user is not able to solve a problem or answer a question (questions) correctly • offer the user a subset of available learning material, which can fill the gap in student's knowledge of resolve a misconception

  7. Problem Solving Support (1) • Main duty and main value of ITS technology • Three technologies • Intelligent analysis of student solutions • Interactive problem solving support • Example-based problem solving support

  8. Problem Solving Support (2) • Intelligent analysis of student solutions • deals with students' final answers • has to decide whether the solution is correct or not, find out what exactly is wrong or incomplete, and possibly identify which missing or incorrect • knowledge may be responsible for the error (the last functionality is referred as knowledge diagnosis) • provide student with extensive error feedback and update the student model (eg: PROUST [Johnson, 1986])

  9. Problem Solving Support (3) • Interactive problem solving support • more powerful technology • provide intelligent help on each step of problem solving Instead of waiting for the final solution • The level of help vary: from signaling about a wrong step, to giving a hint, to executing the next step for the student • The systems which implement this technology (often referred to as interactive tutors) can watch the actions of the student, understand them, and use this understanding to provide help and to update the student model. (eg: LISP-TUTOR [Anderson, 1985])

  10. Problem Solving Support (4) • Example-based problem solving support • helping students to solve new problems by suggesting them relevant successful problem solving cases from their earlier experience (eg: ELM-PE [Weber, 1996], ELM-ART [Brusilovsky, 1996] and ELM-ART-II [Weber, 1999])

  11. Adaptive Navigation Support (1) • Support the student in hyperspace orientation and navigation by changing the appearance of visible links • Generalization of curriculum sequencing technology in a hypermedia context • Has more options than traditional sequencing: it can guide the students both directly and indirectly • Three most popular ways • direct guidance • adaptive link annotation • adaptive link hiding

  12. Adaptive Navigation Support (2) • Direct guidance • Informs the student the links that will drive him or her to the "best" page • Almost equivalent to curriculum sequencing technology with some differences • existing page v.s. generated presentation • one level sequencing v.s. two-level sequencing • the difference between these two technologies starts to disappear in the Web context

  13. Adaptive Navigation Support (3) • Adaptive link annotation • The most popular form of ANS on the Web • used first in ELM-ART [Brusilovsky, 1996] and applied in all descendants (eg: InterBook, AST, ADI, ACE, and ART-Web) and WEST-KBNS and KBS HyperBook. • Adaptive link hiding/disabling • make the link completely non-functional (eg: the Remedial Multimedia System [Anjaneyulu, 1997] ), or • show the user a list of pages to be read before the goal page as done in Albatros [Lai, 1995].

  14. Adaptive Presentation (1) • Adapt the content of a hypermedia page to the user's goals, knowledge and other information stored in the user model • Pages are not static, but adaptively generated or assembled from pieces for each user • For example, expert users receive more detailed and deep information, while novices receive more additional explanation

  15. Adaptive Presentation (2) • Conditional text: PT [Kay, 1997] and AHA [De Bra, 1998] • Adaptive summary: Medtec [Eliot,1997] • Adaptive preface depending on where the student came from: MetaLinks • Adaptive insertable warnings about the educational status of a page: ELM-ART, AST, InterBook and other descendants of ELM-ART • Performed individualized presentation by a life-like agent : WebPersona project [André,1997]

  16. Student Model Matching • ability to analyze and match student models of many students at the same time • Naturally happens in WBE because student records are usually stored centrally on a server • two examples of student model matching • adaptive collaboration support • use system's knowledge about different students to form a matching group for different kinds of collaboration • intelligent class monitoring • identify the students who have learning records essentially different from those of their peers • to find students who need special attention

  17. Related Researches

  18. Related Research Projects • 智慧型個人化網路學習、測驗與診斷服務平台之研究 - 個人化學習資訊蒐集、分析與推薦模組 • 國科會三年期整合性計畫(NSC-90-2520-S-216-001, NSC-91-2520-S-216-002, NSC -92-2520-S-216-001) • 智慧型適性化網路學習、測驗與評量服務平台 • 階梯數位學習公司贊助數位內容產學合作計畫 (NSC93-2524-S-009-004-EC3) • 支援合作與適性學習之智慧型網路虛擬助教系統 • 國科會數位學習國家型科技計畫三年補助(NSC93-2524-S260-003,NSC 94-2524-S-024 -003)

  19. Related Research Papers • [1] Judy C.R. Tseng, Gwo-Jen Hwang (2005), “Development of an Intelligent Internet Shopping Agent based on a Novel Personalization Approach”, Journal of Internet Technology, Vol. 6, No. 4 , October 2005. (EI) • [2] Judy C.R. Tseng and Gwo-Jen Hwang (2004/11), “A Novel Approach to Diagnosing Student Learning Problems in E-Learning Environments”, WSEAS Transactions on Information Science and Applications, Vol. 1, No. 5, November 2004, pp. 1295-1300. (EI) • [3] Gwo-Jen Hwang, Tong C.K. Huang and Judy C.R. Tseng (2004), “A Group-Decision Approach for Evaluating Educational Web Sites”, Computers & Education, Vol. 42, No. 1, pp. 65-86. (SSCI) • [4]Gwo-Jen Hwang, Jia-Lin Hsiao and Judy C.R. Tseng (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 Expanded, EI) • [5] Gwo-Jen Hwang, Judy C.R. Tseng, Carol Chu and Jing-Wu Shiau (2002), “Analysis and Improvement of Test Items for a Network-based Intelligent Testing System”, Journal of Science Education, Vol. 10, No. 4, pp.423-439 • [6] Judy C. R. Tseng and Gwo-Jen Hwang (2006), “Development of an Automatic Customer Service System on the Internet”, accepted by Electronic Commerce Research and Applications. (EI) • [7]Judy C.R. Tseng, Wen-Ling Tsai and Gwo-Jen Hwang (2005), “A Novel Approach to Facilitating the Design of On-Line Engineering Courseware”, WSEAS Transactions on Advances in Engineering Education, Vol. 4, No. 2, pp. 309- 314. (EI) • [8] Judy C.R. Tseng and Gwo-Jen Hwang (2005/1), “Development of an Efficient Question-Answering System on the Internet”, Chung Hua Journal of Science and Engineering Special Issue on Information Systems and Applications for Next Generation, Vol. 3, No. 1 , January 2005, pp.161-168.

  20. 智慧型個人化網路學習、測驗與診斷服務平台之研究智慧型個人化網路學習、測驗與診斷服務平台之研究 多專家教學策略庫分析、協調與管理系統 網 際 網 路 介 面 教材資 料庫 資料庫管理模組 智慧型學習指引模組 教學策略擷取模組 概念關聯資料庫 測驗、評量與診斷系統 教師聯合 出題模組 題庫 學習障礙 診斷模組 學生 測驗模組 個人化 資訊 個人化資訊 推薦模組 學生基本資料 個人化分析 模組 學生上網記錄 個人化學習系統

  21. 子計劃二：個人化學習資訊蒐集、分析與推薦模組子計劃二：個人化學習資訊蒐集、分析與推薦模組 • 線上學習行為記錄與分析：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 • 學習意願 • 耐心度 • 專心度 • 適性化教材之規劃

  22. 系統架構 學習者 智慧型個人化網路學習、測驗與診斷服務平台 個人化學習資訊蒐集模組 個人化教材推薦模組 個人基本資料 個人化學習資訊分析模組 學習風格評量 學習狀態評量 教材庫 個人化學習資訊 教學媒體庫 個人化教材

  23. CS(Ti) Course Subjec There are four kinds of Course: Mathematics, Natural, Technique, and Others. SLT(Uj) Suggested Learning Time Suggested learning time given by the instructor. SQP(Si) Sequential Processing Skill Process information sequentially or verbally; to readily derive meaning from information presented in a step-by-step, linear fashion (text environment). DS(Si) Discrimination Skill Visualize the important elements of a task, to focus attention on required detail and avoid distractions (pay attention to the course). AS(Si) Analytic Skill Identifying simple figures hidden in a complex field, use the critical element of a problem in a different way (mathematics course and Science). SS(Si) Spatial Skill Identify geometric shapes and rotate objects in the imagination; to recognize and construct objects in mental space (mathematics course and technique). 個人化學習資訊蒐集-學習風格評量

  24. 個人化學習資訊蒐集-學習風格評量

  25. ULT(Si,Uj) Unit Learning Time The timing, which student Si learn the unit Ui without taking Free time and Testing time into considerations. IT(Si,Uj) Idle Time The break time when student Si are learning unit Uj RST (Si,Uj) Response time When learning frame is over Idle Time, IATSDT will show a window and ask the student to response it. Then IATSDT will calculate how long does the student take to response it. UPT(Si,Uj) Unit Post-test Score After student learning each unit, IATSDT will exam the student automatically. If the score is below 60, the learner must go back to the unit again. If the score is over 60 students can continue the next unit. EFU(Si,Uj) Unit Learning Efficiency EFU(Si,Uj)=SLT(Uj) / ULT(Si,Uj) ABS(Si,Uj) Absorbed The concentration of unit Uj learning CDU(Si,Uj) Course Difficult Level We have three kinds of material level that is Primary, Secondary and Advanced for each student. LST(Si) Learning Style We have Two kinds of learning style, one is text material and the other is multimedia material 個人化學習資訊蒐集-學習狀態評量

  26. 網路學習行為之即時分析

  27. 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(T×0.5) corresponding willingness frames. If willingness is average Then insert INT(T×0.25) corresponding willingness frames If willingness is high Then keep the current status.

  28. 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.

  29. 學生集中精神於瀏覽教材的程度 分析依據：回應時間 專心度分析 模糊推理法則 If concentration is low Then insert a corresponding concentration frame. If concentration is high Then keep the current status. If concentration is average Then keep the current status.

  30. 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 high Then record this status and warn the student. If chat is average Then keep the current status. If chat is low Then keep the current status

  31. 個人化教材推薦 - 教材風格 • 選擇適當的教材呈現方式 • 分析依據：學習者的循序處理技能(SQP) 模糊推理法則 If SQP is high Then provide sequential-frame material Else provide hypermedia material

  32. 個人化教材推薦-預設教材難度 • 選擇適當的預設教材難度 • 分析依據：課程屬性(CA)以及學習者的分析技能(AS)與空間概念技能(SS) 模糊推理法則 Case CA=Mathematics PS = min(AS, SS)Case CA=Science PS = AS Case CA=Technique PS = SS IF CA not in {Mathematics, Science,Technique} Then DL= Mid Else IF PS is High then DL = Hard IF PS is Low then DL = Easy IF PS is Average then DL = Mid End if

  33. 個人化教材推薦-重複學習教材難度 • 為後測未通過者選擇重複學習之教材難度 • 分析依據：學習效率(EFU)以及目前教材難度(CDL) 模糊推理法則 DL= CDL IF EFU is Low Then IF CDL is Hard then DL = Mid IF CDL is Mid then DL = Easy End if

  34. 個人化教材推薦- 晉級學習教材難度 • 為通過後測者選擇學習下一課程單元之教材難度 • 分析依據：學習效率(EFU)以及目前教材難度(CDL) 模糊推理法則 DL= CDL IF EFU is low Then IF CDL is Hard then DL = Mid IF CDL is Mid then DL = Easy End if IF EFU is high Then IF CDL is Mid then DL = Hard IF CDL is Easy then DL = Mid End if

  35. 實驗設計

  36. 成效評估方法 • Use One Way ANOVA • Tool for education research • F Prob < 0.5 show statistically significant results

  37. Group name Experiment Group Control Group 1 Control Group 2 Class number 1 2 3 BOY 19 14 21 GIRL 10 18 9 Total learner 29 32 30 實驗樣本

  38. 學習效果分析 Experiment Group 1 and Control Group 2 (significant difference) Control Group 1 and Control Group 2 (significant difference) Experimental Group 1 and Control Group 1 (not significant)

  39. 學習效率分析 Experimental Group 1 and Control Group 1 (significant difference)

  40. 實驗結果 (1) 適性化學習(Adaptive learning)環境比非適性化學習(non-adaptive learning)環境更能增進學習效果. (2) Multi-source (同時考慮學習能力與學習風格)的適性化學習環境比single-source (只考慮學習能力)的適性化學習環境更能增進學習效率.

  41. 支援合作與適性學習之智慧型網路虛擬助教系統支援合作與適性學習之智慧型網路虛擬助教系統 學生 教師 子系統三 問題解答知識庫 具自我調適能力之課業問題解答與輔導系統 適性化 線上 教學系統 資訊基礎課程教材庫 單元問題案例庫 子系統一 以概念關係為基礎之合作學習輔導系統 資訊基礎課程教學策略庫 線上學習歷程 智慧型 虛擬助教 學生基本資料 學習診斷與導引策略庫 資訊基礎課程素材庫及教學策略庫之建置 學習規劃與導引系統 測驗及障礙診斷系統 資訊基礎課程 測驗資料庫 子系統二

  42. 具自我調適能力之課業問題解答與輔導系統 • 自動分析學生的問題來即時提供解決方案 • 24小時即時的解決學生的學習困難，並可大幅的減少教師的負擔 • 具有記錄及自我檢討及調適機制，以提昇解答品質

  43. 系統架構圖 已完成 研究中

  44. 問題分析與解答機制之規劃

  45. 主要系統模組 • 問題分析機制 • 可接受學生透過網頁或e-mail發問，且提供即時回覆解答或提供指導，會經由斷詞、擷取關鍵詞、權重初始化、關鍵詞比對等對問題做出分析 • 解答判定機制 • 使用相似度比對等步驟取出適切的解答自動回覆給學生

  46. 範例：假設有一字詞為『資料探勘』，共有4個字，所以W1~W4分別為資、料、探、勘，與詞庫進行比對，發現某一詞開頭與[資]相同，將W1擴充為W1W2；發現某一詞開頭與[資料]相同，則繼續擴充W1W2W3；最後即為[資料探勘]，字詞比對結束範例：假設有一字詞為『資料探勘』，共有4個字，所以W1~W4分別為資、料、探、勘，與詞庫進行比對，發現某一詞開頭與[資]相同，將W1擴充為W1W2；發現某一詞開頭與[資料]相同，則繼續擴充W1W2W3；最後即為[資料探勘]，字詞比對結束 問題分析機制 • 斷詞演算法 • 斷詞 ：假設欲進行斷詞的句子共有n個字，令此句子為w1w2 w3…wn ，其中w1、w2、…、wn代表句子中的各個字元 • Step1：將w1與詞庫進行比對，若詞庫存在某一詞，使得w1與該詞的第一個字元相同，將w1擴充為w1w2 • Step2：與詞厙進行比對，若詞庫中仍然存在某一詞，該詞的第一、第二字元與w1w2相同，則再進行擴充 • Step3：反覆進行此過程，直到w1w2 …wi無法在詞庫中找到相同的字串

  47. 問題分析機制 • 關鍵詞擷取 • 在系統的資料庫中，事先由教師定義了一組與該課程相關的關鍵詞 • 透過這些關鍵詞和其所佔有的權值組合來描述不同的問題類型 • 每個問題類型可能與一個或多個關鍵詞有關，而這些關鍵詞在全部問題類型中分別佔有不同的出現比率

  48. 代表第j個關鍵詞Kj在Qi中所佔的權重是Wij 代表常問問題類型Qi的特徵向量 第j個關鍵詞在常見問題Qi裡出現的次數 第j個關鍵詞在全部常見問題裡出現次數的總和 問題分析演算法 • 問題分析演算法的主要功能在於針對常見問題與學生提出的問題進行分析，產生用來描述該問題的特徵向量（Character Vector，CV） • 常見問題的特徵向量表示式為： • 權重的計算公式如下：

  49. 問題分析演算法範例 • 問題：請問資料庫中各階層架構如何讓資料庫中的資料產生獨立性? • 所以本問題之CV(Q)= 詢問問題之權重 專業關鍵詞及總出現次數

  50. 答案判定機制 • 關鍵詞比對演算法 • Step1：利用斷詞演算法擷取問題的關鍵詞。 • Step2：利用問題的關鍵詞算出該問題的特徵向量CV(Q) • Step3：利用其CV(Q)與資料庫問題之特徵向量比對其相似度 • Step4：找出最接近的前五個解答；否則，轉送給老師回覆

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