As internet usage becomes more and more popular over the world, e-learning system including online learning, employee training courses, and e-book in the past ten years has been accepted globally [6] [13] [20] [52] [53] [61] [69] [86] [88] [99] [130]
[131] [138] [143]. In 2000, Urdan et al. [128] considered that e-learning is defined more narrowly than distance learning and defined it as “the delivery of content via all electronic media, including the Internet, intranets, extranets, satellite broadcast, audio/video tape, interactive TV, and CD-ROM“. E-learning system can make learner conveniently study at any time and any location. However, because the teaching materials in different e-learning systems are usually defined in specific data format, the sharing of the materials among these systems becomes difficult, resulting in increasing the cost of creating teaching materials. In order to solve the issue of the uniform teaching materials format, several standard formats including SCORM (Sharable Content Object Reference Model) of ADL [100], CP (Content Packaging) and QTI (Question & Test Interoperability) of IMS [56], CMI (Computer-Managed Instruction) of AICC [1], LOM (Learning Objects Metadata) of IEEE LTSC [79], etc. have been proposed by international organizations. By these standard formats, the teaching materials in different learning management systems can be shared, reused, and recombined.
SCORM is a set of specifications for developing, packaging and delivering high-quality education and training materials whenever and wherever they are needed.
SCORM-compliant courses leverage course development investments by ensuring that compliant courses are Reusable, Accessible, Interoperable, and Durable (RAID).
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 based on the pedagogical theory. For example, the work to create the SCORM compliant teaching materials is still hard, even using the authoring tools. This leads to that teachers or editors may be unwilling to use it.
As we know, if the same teaching materials are provided to all learners based on the predefined strategies or the predefined learning maps, the leaning efficiency will be diminished. Thus, in recent years, adaptive learning environments [22] [43] [98] [111]
[113] [122] [123] [140] have been proposed to offer different teaching materials for different students in accordance with their aptitudes and evaluation results. After students learn the teaching materials through the adaptive learning environment, the teachers can further analyze the historical learning records and then refine or reorganize the teaching materials and tests if needed. Therefore, more and more attention has been paid to the research of personalized instruction in computer education environment.
Moreover, because sequencing can help to generate teaching materials which can match the learner’s needs, (semi-)automatic sequencing of course materials also becomes an important research issue. However, although the personalized instruction scheme has been emphasized in most of existing e-learning systems, these systems, unfortunately, may not show good personalized and intelligent abilities.
Therefore, to sum up above, for the intelligent e-learning system, the following issues are needed to be solved.
z How to propose a scheme to efficiently create and manage the SCORM compliant learning contents with the desired learning sequencing.
z How to propose a scheme to efficiently create and manage the teaching strategies.
z How to propose an intelligent approach which can automatically generate appropriate learning activity for learners according to the individual learning portfolios, personal aptitudes, and teaching strategies.
z How to propose an efficient approach to evaluate the historical learning portfolio for understanding the mis-concept of learners.
At present, the international organization, IEEE LTSC, analyzed the basic requirements of e-learning system to propose a Learning Technology System Architecture (LTSA) [80] which is as a reference model and identifies the critical interoperability interfaces for the learning technology systems. LTSA, including 4 processes and 2 stores, that is, Learner Entity, 2) Coach, 3) Delivery, 4) Evaluation, 5) Learner Record, and 6) Learning Resource, can provide learners with an adaptive learning environment.
In addition, in order to support the interoperability and scalability of distributed e-learning system, IMS Abstract Framework (AF) [55] proposes a layered model, which defines the interface definition set. Also, E-Learning Framework (ELF) [35] also proposes a layered model, each layer of which defines different functionalities according to the different requirements of an e-learning system. Therefore, based on the layered models of IMS AF and ELF, LTSA reference model can be reorganized into 4 layers: Resources, Common Services, Learning Services, and Application, according to the functions of its components.
Besides, because IEEE LTSA is as a reference model of building an e-learning system in support of adaptive learning, it does not clearly specify and define the data format of learning content and activity. Therefore, in order to solve the issue of uniform data format among e-learning systems, how to define the data representation format of learning content and activity is a very important issue.
Furthermore, based on the Knowledge Management concept [39], 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
IEEE LTSA [80] 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 consisting of six knowledge modules in corresponding layer respectively, i.e., 1) Knowledge Representation (KR), which uses SCORM standard, and new proposed Instructional Activity Model (IAM) [115] and Object Oriented Learning Activity (OOLA) model to represent and manage the learning content and activity, 2) Knowledge Resources (KRes), which stores all related learning resources in repositories, 3) Knowledge Manager (KM), which efficiently manage a large number of learning resources in repositories, 4) Knowledge Acquirer (KA), which provide teachers with useful tools to create the SCORM and OOLA compliant learning content and activity, 5) Knowledge Controller (KC), which intelligently deliver the desired learning contents, services, test sheet to learners according to her/his learning results and performance, and 6) Knowledge Miner (KMin), which analyzes the learning portfolio to analyze the learning portfolio for constructing the adaptive learning course and the learning concept map automatically.
As mentioned above, the relationship of six knowledge modules in ILCMS are described as follows. First of all, KRes Module consists of five types of learning resources, i.e., Learning Activity, Learning Object, Test Item, Application Program, and Learning Portfolio, which are described by data formats: SCORM and OOLA model defined in KR Module and stored in their respective repositories. Then, KA module includes a Learning Content Editor (LCE) and an Object Oriented Learning Activity (OOLA) authoring tool [81]. In LCE, for reusing the existing traditional teaching materials, such as HTML and PPT file format, a Content Transformation Scheme (CTS) [114] has also been proposed. CTS approach can
divide a traditional teaching material into separate learning objects with SCORM metadata and then package them into one SCORM course. Moreover, in order to edit SCORM 2004 compliant learning contents, an Object Oriented Course Modeling (OOCM) [117] approach based upon High Level Petri Nets (HLPN) theory [59] [60]
[62] [70] [71][73] [82] [84] has been proposed. OOCM can provide teachers or editors with an authoring tool to efficiently construct the SCORM compliant course with desired sequencing behaviors. Furthermore, OOLA authoring tool can help teachers construct an OOLA learning activity with desired teaching strategy. Moreover, KM module includes a Learning Object Repository (LOR) Manager, where we apply clustering approach and load balancing strategies to propose a management approach, called Level-wise Content Management Scheme (LCMS) [116], to efficiently maintain, search, and retrieve the learning contents in SCORM compliant LOR. When learners initiate a learning activity, the Learning Activity Controller (LAC) in KC module will retrieve the appropriate learning objects in LOR, testing sheets in Testing Item Bank (TIB), or application program (AP) in AP Repository (APR) according to the personalized learning activity in Learning Activity Repository (LAR) for learners.
As mentioned above, the learning contents, test sheet, and AP will be retrieved and triggered according to the specific learning strategy. Those strategies are created by teachers using the authoring tools in KA module. Furthermore, KMin module includes a Learning Portfolio Analyzer (LPA), which consists of Learning Portfolio Mining (LPM) [118] and Two-Phase Concept Map Construction (TP-CMC) [110]
algorithms. According to learners’ characteristics, the former applies the clustering and decision tree approach to analyze the learning behaviors of learners with high learning performance for constructing the adaptive learning course. The latter applies Fuzzy Set Theory and Data Mining approach to automatically construct the concept map by learners’ historical testing records. Therefore, after the learners finished the learning
activities, teachers can use LPA module to analyze the learning portfolios of learners for refining their teaching strategies and contents.
The rest of this dissertation is organized as follows. Chapter 2 surveys the background knowledge of this work. Chapter 3 describes the layered architecture of LTSA and introduces the six modules of ILCMS. From Chapter 4 to Chapter 7, the details of Knowledge Representation (KR), Knowledge Acquirer (KA), Knowledge Manager (KM), Knowledge Controller (KC), and Knowledge Miner (KMin), are described. The system implementation and experimental results of ILCMS are shown in Chapter 9, and finally conclusion and future work are given in Chapter 10.