基於SCORM標準的智慧型學習內容管理系統之研製

Abstract

隨著網際網路的快速發展，網路學習(E-Learning)系統已廣為流行。為了解決教材無法在不同網路學習系統間分享與再利用之問題，國際組織已提出許多的國際標準格式，包含:ADL的SCORM、IMS的CP與QTI、IEEE LTSC的LOM、AICC的CMI等等。而SCORM在近幾年已成為最受廣泛使用的標準。SCORM為因應隨時隨地學習之需求，而提供可發展、包裝與傳遞高品質教育及訓練教材的教材標準。雖然SCORM具有分享、再利用、及重新組裝之優點，但對於製作、擷取與管理具個人化學習導引機制的SCORM教材來說，仍是相當困難。此外，如提供所有學習者，相同的學習課程與策略，則學習成效將無法有效提升。於是近幾年來，可根據不同學習者的學習能力與評量結果來提供不同學習課程的適性化學習環境便漸受重視。故對於基於SCORM標準的智慧型網路學習系統而言，如何有效地建立與管理具客製化學習導引與教學策略的SCORM課程、如何根據個人的學習歷程資訊、學習能力及教學策略，自動化提供學習者適當的學習活動、與如何評估及分析學習歷程資料來了解學習者的迷失概念等，便是本論文所關心的研究問題。目前IEEE LTSC組織提出一稱為學習科技系統架構(Learning Technology System Architecture, LTSA)的參考模型，用來定義學習科技系統中的關鍵互操作性介面。而為了支援分散式網路學習系統的互操作性(Interoperability)與延伸性(Scalability)，IMS 抽象架構(Abstract Framework, AF)與網路學習架構(E-Learning Framework, ELF)規劃出具分層概念的網路學習系統模型，其每ㄧ層皆根據網路學習系統不同的需求來定義其不同的功能。 此外，基於知識管理的概念，如何有效管理適性化網路學習系統中的各種資源與資訊，就如同於有效的管理不同的知識。因此，基於知識管理與具分層概念的LTSA架構，在此論文中，提出了智慧型學習內容管理系統(Intelligent Learning Content Management System, ILCMS)，來智慧地管理大量的學習內容與提供學習者適性化的學習策略，並藉由有效地學習歷程分析，做進ㄧ步的策略精練。ILCMS的分層架構具備6個知識模組: (1)知識表示(KR):使用SCORM標準、本論文提出的教學活動模型(IAM)與物件導向活動模型(OOLA)來表示與管理學習內容及活動、(2)知識資源(KRes):儲存學習活動、學習物件、試題、應用程式與學習歷程等學習資源於所屬之資源庫中、(3)知識管理(KM):應用叢集(Clustering)技術與負載平衡策略，提出階層式內容管理機制(Level-wise Content Management Scheme, LCMS)來有效管理大量的學習物件、(4)知識擷取(KA):提供教師有用的工具來製作SCORM與OOLA相容的課程與活動，其包含應用高階派翠網路(High Level Petri Nets, HLPN)來分析SCORM導引規則而提出的物件導向課程朔模(Object Oriented Course Modeling, OOCM)機制、(5)知識控制(KC):智慧地根據學生的學習成效來提供客製化的學習內容、服務、與試題，以進行適性學習、及(6)知識探勘(KMin):應用資料探勘技術來分析學習歷程資料以建構適性化學習課程與自動地建構學習概念圖。最後，為評估ILCMS，針對每ㄧ知識模組，發展各個相對應的系統功能與實際進行實驗驗證。而藉由實驗結果可證實ILCMS所架構的知識模組確實是可行的，且有益於學習者與教師進行有效的學習與教學。

With the vigorous development of the Internet, e-learning systems have become more and more popular. Currently, in order to solve the issue of sharing and reusing teaching materials in different e-learning systems, several standard formats, including SCORM of ADL, CP and QTI of IMS, LOM of IEEE LTSC, CMI of AICC, etc., have been proposed by international organizations. Among these international standards, the Sharable Content Object Reference Model (SCORM) has become the most popular standard in recent years. SCORM is a set of specifications for developing, packaging and delivering high-quality education and training materials whenever and wherever they are needed. Although SCORM has many advantages of reusing, sharing, and recombining teaching materials among different standards, it is difficult to create, retrieve, and manage the SCORM compliant course with personalized learning sequences. Moreover, if the same teaching materials are provided to all learners based on predefined strategies, the leaning efficiency will be diminished. Thus, in recent years, adaptive learning environments have been proposed to offer different teaching materials for different students in accordance with their aptitudes and evaluation results. Therefore, for the intelligent e-learning system based upon SCORM standard, how to efficiently create and manage the SCORM compliant learning contents with desired learning sequencing and teaching strategies, how to automatically generate appropriate learning activity for learners according to individual learning portfolio, personal aptitude, and teaching strategies, and how to evaluate the historical learning portfolio for understanding the mis-concept of learners are our concerns. Currently, the IEEE’s LTSC proposed a Learning Technology System Architecture (LTSA) which is as a reference model and identifies the critical interoperability interfaces for learning technology systems. In addition, in order to support the interoperability and scalability of distributed e-learning system, IMS Abstract Framework (AF) and E-Learning Framework (ELF) propose the e-learning system models with layering concept, each layer of which defines different functionalities according to the different requirements of an e-learning system. Furthermore, based on the Knowledge Management concept, how to efficiently manage the different resources and information in an adaptive e-learning system is similar to efficiently manage diverse knowledge. Therefore, based on this concept and LTSA with layering concept, in this dissertation, an Intelligent Learning Content Management System (ILCMS) is proposed to intelligently manage a large number of learning contents and offer learners an adaptive learning strategy which can be refined by means of efficient learning portfolio analysis. The layered architecture of ILCMS consists of six knowledge modules: 1) Knowledge Representation (KR), which uses SCORM standard, and proposed Instructional Activity Model (IAM) and Object Oriented Learning Activity (OOLA) model to represent and manage the learning content and activity, 2) Knowledge Resources (KRes), which stores related learning resources including Learning Activity, Learning Object, Test Item, Application Program, and Learning Portfolio in respective repositories, 3) Knowledge Manager (KM), which includes a Level-wise Content Management Scheme (LCMS), applying clustering approach and load balancing strategies, to efficiently manage a large number of learning resources, 4) Knowledge Acquirer (KA), which provides teachers with useful tools to create the SCORM and OOLA compliant learning content and activity by means of proposed Object Oriented Course Modeling approach based on High Level Petri Nets and OOLA model, 5) Knowledge Controller (KC), which intelligently delivers the desired learning contents, services, test sheet to learners according to her/his learning results and performance, and 6) Knowledge Miner (KMin), which applies data mining techniques to analyze the learning portfolio for constructing the adaptive learning course and the learning concept map automatically. Finally, in order to evaluate ILCMS, system implementations and experiments have been done for each knowledge module. Also, the experimental results shows that proposed knowledge modules of ILCMS are workable and beneficial for learners and teachers.