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Using Web Services for Personalised Web-based Learning

Using Web Services for Personalised Web-based Learning. 運用網路服務於個人化網路的學習. Abstract. A multi-agent, personalised learning system operating over the Web. The system is called Web F-SMILE Help novice users learn how to manipulate the file store of their personal computer.

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Using Web Services for Personalised Web-based Learning

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  1. Using Web Services for Personalised Web-based Learning 運用網路服務於個人化網路的學習

  2. Abstract • A multi-agent, personalised learning system operating over the Web. • The system is called Web F-SMILE • Help novice users learn how to manipulate the file store of their personal computer. • Web F-SMILE has assigned an agent to constantly observe the user and collect information about him/her • A Learner Modelling Server • The agents of the client applications interact with the Learner Modelling Server through Web Services. • learner modelling on web standards Independently of their underlying platforms

  3. Abstract • 這篇文章描述一多重代理人、個人化學習系統在網路上的運作。這個系統是名為 Web F-SMILE，可以幫助初學者學習怎樣操作他們的個人電腦上的檔案儲存。 • 為了提供合適的幫助並且加以指導，Web F-SMILE 選派一代理人去經常觀察使用者並且收集關於他/她的訊息。這訊息被集中保存在學習模組伺服器上。以這種方法，每個學習模型從任何客戶端要求而得。客戶端的應用代理人與透過網路服務與學習模組伺服器的互動。 • 網路服務的主要特性是他們與他們產生的應用相互作用。使用網路標準， 基於學習模組在網路標準上有分散式應用程式的動態整合方面的優勢，而無關他們使用那種基礎平台

  4. Introduction • Intelligent Tutoring Systems (ITSs)智慧型的輔助學習系統 • Intelligent Learning Environments (ILEs )智慧型學習環境 • long-term user models 長期使用者模式 • short-term user models 短期使用者模式 • merge of ITSs and ILEs with Web-based education • In particular, Web F-SMILE is an ILE for novice users of a GUI that manipulates files • using web standards such as WSDL 、 SOAP、 UDDI

  5. Related Work • ITSs and ILEs on the Web has been based on Java and has been employed in the distributed client-server architecture-- ADIS • distributed client-server By Elliot(1997) • the HTML-CGI architecture --WITS , ELM-ART, PAT, CALAT and AlgeBrain • DCG • Web F-SMILE

  6. Operation of the system • Web F-SMILE (File-Store Manipulation Intelligent Learning Environment) is an intelligent learning environment for novice users of a GUI Web F-SMILE(檔案儲存操作智慧化的學習環境)是一為GUI(圖形用戶界面)初學者操作檔案的智慧化的學習環境 • The main feature of the system is that it can adapt its interaction to each individual learner 系統的主要特徵是它能使它的相互作用適應每個個別的學習者 • The system can work both as a Web-based application and as a standalone application when the learner’s computer is not connected to the Internet. 系統能兩種狀態下都工作作為網上應用和獨立應用當時那些學習者計算機的未連接網路。

  7. The learner’s initial file store state

  8. Multi-agent architecture(一) • Short Term Learner Modelling (STLM) Agent 短期的學習者模型(STLM)代理人 • Long Term Learner Modelling (LTLM) Agent長期學習者模型(LTLM)代理人 • Advising Agent 建議代理人 • Tutoring Agent 教導代理人 • Speech-driven Agent 演講驅動的代理人

  9. Intelligent Agent(智慧型代理人) • 一種軟體程式 • 主要是處理例行性、反覆性和資訊搜尋的工作 • 幫助使用者克服大量資訊的處理過程 • 可分析出多個替代方案，提供使用者制定決策之參考

  10. Intelligent Agent • 自主性 可判斷環境的變化，不需要其他人事物的介入即可自動進行事件的處理 • 時間持續性 針對目標持續檢視事件並在適當的時機執行工作 • 反應性 依據環境的變化來反應執行的動作 • 目標導向 代理人以滿足使用者需求為目標

  11. Multi-agent architecture(二) • The Short Term Learner Modelling (STLM) Agent captures the cognitive state, as well as the characteristics of the learner and identifies possible misconceptions. In case the STLM Agent suspects that the learner is involved in a problematic situation, it performs error diagnosis. 短期的學習者模型代理人懷疑學習者陷入一種有問題的況狀下，它會進行錯誤診斷。 The analysis engine is based on a limited goal recognition mechanism and the Human Plausible Reasoning theory (Collins & Michalski, 1989). • Advising Agent, which is responsible for selecting the alternative action that the learner was more likely to have intended. 建議代理人負責選擇學習者很可能的實行的行動 • The Tutoring Agent is responsible for forming an adaptive presentation of the lesson to be taught to the learner. 教學輔導代理人負責形成一適合教學習者的課程給學習者

  12. Multi-agent architecture(三) • Both the Advising Agent and the Tutoring Agent send their results to the Speech-driven Agent • The Speech-driven Agent is responsible for the overall communication with the learner. This usually involves the collection of the learner’s queries and the presentation of advice in case the learner is diagnosed to have been in a problematic situation. 演講驅動代理人用來對學習者提出系統的建議 • Every time the STLM Agent acquires new information about the learner that interacts with the system, it sends it to the LTLM Agent. Generally, the LTLM Agent, maintains and manages the learner profiles and provides relevant information to the STLM Agent whenever this is considered necessary. 演講驅動代理人對負責和學習者的聯繫

  13. Web F-SMILE's architecture

  14. Interaction of Client and Server Learner Models(一) • Web F-SMILE keeps two separate learner models for every learner, one locally on each computer and one on the Server. In case the computer is not connected Web F-SMILE在和上保持兩單獨學習模組者給每個學習者，一本地每台電腦，一在伺服器上、 • In case the computer is not connected,Web F-SMILE works as a standalone application with a local user model. 那些電腦未連結，Web F-SMILE用一個本地使用者模型來獨立應用工作。

  15. Interaction of Client and Server Learner Models(二) • the LTLM Agent sends the information about the learner to the Web Service, which creates a new learner model based on the information that was available from the learner model that was initialised locally. LTLM代理人傳送關於學習者的訊息 Web伺服器，創建一個基於可以從學習者中獲得的訊息的新學習者模型初始化的模型 • the LTLM Agent undertakes the task of updating both models with the latest information. LTLM代理人用最新的訊息不斷更新兩個模型 • Web F-SMILE registers each learner interaction separately using timestamps Web F-SMILE使用時間郵戳記錄每學習者的相互作用

  16. Learner Modelling on the client side(一) • In Web F-SMILE, users are classified into one of three major classes according to their level of expertise,namely, novice, intermediate and expert 在Web F-SMILE，使用者已經根據他們的專門技能的水準分類成為3 個主要的種類， 即初學者，中等程度者和專家。 • Stereotypes may serve as a tool to model the beliefs and preferences that the users of a system may have The main reason for the application of stereotypes is that they provide a set of default assumptions 樣板模式的應用是他們提供一套預設假定

  17. Learner Modelling on the client side(二) • In order to find the stereotype that the learner belongs, to the learner is given a questionnaire, which contains questions about his/her level of experience in GUIs, his/her experience in operating systems 為了找到學習者所屬的樣板模式，對學習者被給一張詢問表，內容包含在使用GUI檔案操作的經驗水準 • As the system collects more and more evidence about a learner,information is acquired in part by the stereotype and in part from the individual learner model. The percentage of information acquired by the stereotype diminishes as the percentage of acquisition by the individual learner model increases. The percentage of information acquired by the stereotype diminishes as the percentage of acquisition by the individual learner model increases. 系統收集越來越多關於一個學習者的訊息，資訊被部分透過樣板模式和部分從個別的學習者模型獲得，樣板模式獲得的訊息百分比和個別的學習者模組獲得的百分比減少。

  18. Learner Modelling on the Server Side(一) • The LTLM Agent makes a specific SOAP call (under HTTP), which contains a request about the particular learner model, to the WS-LM. L TLM代理人使用SOAP call(在HTTP下) 包括有關那些特別學習者模型對 WS -LM的請求 • the LTLM Agent on the client which is responsible for the maintenance of the learner models, creates a new record on the learner model for every time that a user interacts with Web F-SMILE.

  19. Learner Modelling on the Server Side(二) • the learner model is divided into two parts; the first part contains summative learner information that is older than three months and the second part contains a detailed description of the learner’s interactions with Web FSMILE for the last three months. 客戶端的LTLM 代理人負責維護學習者模組，每次使用者與Web F-SMILE相互作用時都會產生一新記錄 . • The DM Module is assigned the daily task of joining the records of learner models 資料庫模件被指派加入學習者模型每日的任務的記錄的

  20. The architecture of WS-LM

  21. Conclusions • A multi-agent learning environment that helps users learn how to operate their file store 協助學習者怎樣操作檔案儲存的一種多重代理的學習環境。 • The system has assigned an agent (STLM Agent) to monitor users while working in a protected mode and in case it identifies a problematic situation, it tries to diagnose the cause of the problem and offers appropriate advice. STLM代理人監控使用者、診斷問題的原因並且提供適當的建議。

  22. Conclusions • Web F-SMILE keeps for every learner one learner model centrally on WS-LM and one learner model in each computer that the user uses to interact with Web F-SMILE. Web F-SMILE上的WS LM有每一使用者學習者模組可進行彼此交互作用 • This comparison has revealed that learner modelling based on Web Services introduces an improved interaction of the server with the client applications compared to other traditional 網路服務的學習模組改進了在客戶與伺服器之間的交互作用

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