The Architecture of ILCMS

As mentioned above, LTSA can be layered into 4 layers according to the service function of each layer and classified into 5 knowledge types based on knowledge management concept. However, 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 how to represent the 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.

Therefore, in this dissertation, based on Knowledge Management concept [39] and layered IEEE LTSA [80], 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. Figure 3.3 shows the layered architecture of ILCMS consisting of six knowledge modules in corresponding layer respectively, i.e., 1) Knowledge Representation, which uses SCORM standard, and new proposed Instructional Activity Model (IAM) and Object Oriented Learning Activity (OOLA) model to represent and manage the learning content and activity, 2) Knowledge Resources, which stores all related learning resources in repositories, 3) Knowledge Manager, which efficient manages a large number of learning resources in repositories, 4) Knowledge Acquirer, which provides teachers with useful tools to create the SCORM and OOLA compliant learning content and activity, 5) Knowledge Controller, which intelligently delivers the desired learning contents, services, test sheet to learners according to her/his learning results and performance, and 6) Knowledge Miner, which analyzes the learning portfolio for constructing the adaptive learning course and the learning concept map automatically.

Figure 3.3: The Layered Architecture of Intelligent Learning Content Management System (ILCMS)

Each knowledge module of ILCMS can be described in details as follows:

1. Knowledge Representation (KR): it includes 3 data models: SCORM, Instructional Activity Model (IAM) [115], and Object Oriented Learning Activity (OOLA) [81], to represent the learning content and activity, respectively.

As state previously, in order to share and reuse the contents among various learning systems, we use the popular SCORM standard to represent the teaching materials so that the issue of uniform content format can be solved. Moreover, in order to

efficient manage and reuse the large-scale Activity Tree (AT) with complex sequencing rules in SCORM. Therefore, we propose an Instructional Activity Model (IAM), which extends and modularizes the structure of AT with inter-relation attributes by means of Pedagogical Theory and the concept of the Object Oriented Methodology, respectively. Furthermore, based on the modularized AT of IAM and object oriented concept, we further propose a model with sequencing rule definition, called Object Oriented Learning Activity (OOLA), to efficiently model a adaptive learning activity by means of three basic elements, that is, Content, Interaction, and Assessment. Thus, an adaptive learning activity can be easily created and offered to learners with a personalized learning contents, services, and assessment.

2. Knowledge Resources (KRes): it includes five types of learning resources, i.e., Learning Activity, Learning Object, Test Item, Application Program, and Learning Portfolio, which are stored in their respective repositories and can be managed, reused, delivered, and analyzed by the sub-module of ILCMS in higher layers.

3. Knowledge Manager (KM): it includes a Learning Object Repository (LOR) Manager, in which we analyze the content structure of SCORM and then apply clustering technique and load balancing strategies to propose a Level-wise Content Management Scheme (LCMS)[117]. LCMS can automatically analyze the SCORM compliant contents, group these related objects into a cluster, and then create the relation links among different clusters. Therefore, by means of LCMS, LOR manager can efficiently maintain, search, and retrieve the desired learning objects from the SCORM compliant LOR with a large number of learning objects.

4. Knowledge Acquirer (KA): it includes a Learning Content Editor (LCE) and an OOLA authoring tool [81]. The former proposes a Content Transformation

Scheme (CTS)[114], which can efficiently transform the traditional teaching materials, e.g., HTML and PPT file format, into SCORM compliant learning contents, and an SCORM 2004 compliant authoring tool with Object Oriented Course Modeling (OOCM) [117] approach based upon High Level Petri Nets (HLPN) theory [59] [60] [62] [70] [71] [73] [82] [84], which can help teachers or editors efficiently create the course with desired learning sequencing guidance of SCORM standard. These created SCORM compliant learning content will be stored in Learning Object Repository (LOR). In addition, in order to construct OOLA compliant learning activity, the latter is a user-friendly GUI authoring tool, by which teachers can efficiently edit desired learning activity with associated SCORM compliant course in LOR, test sheet in TIB, and application program (AP) in APR. AP like an interaction tool, e.g., chat room, browser, messenger, etc., offer learners to interact with other learners and teachers. These edited OOLA learning activities will be transformed into rule format and then stored in Learning Activity Repository (LAR).

5. Knowledge Controller (KC): includes a Learning Activity Controller (LAC), which includes a System Coordinator (SC) and an Inference Engine (IE) to provide learners with personalized learning contents, exercises, and test sheets according to different learner’s portfolios and teaching strategies.

6. Knowledge Miner (KMin): includes a Learning Portfolio Analyzer (LPA), which consists of Learning Portfolio Mining (LPM) [118] and Two-Phase Concept Map Construction (TP-CMC) [110] algorithm. According to learners’

characteristics, the former applies the clustering and decision tree approach to analyze the learning behavior 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.

After the explanation above, the relationship of five knowledge module in ILCMS are described as follows. First, LCE and OOLA authoring tool in KA module can offer teachers or editors to edit the new SCORM compliant learning contents or transforms existing traditional teaching materials into SCORM compliant ones, and construct an OOLA learning activity, respectively. Then, LOR Manager in KM module applies clustering approach and load balancing strategies to efficiently manage a large number of learning objects in LOR. When learners initiate a learning activity, the LAC in KC module will retrieve the appropriate learning objects in LOR, testing sheets in Testing Item Bank (TIB), or application program (AP) in APR according to the personalized learning activity in 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 tool in KA module. Besides, after the learners finished the learning activities, teachers can use the LPA in KMin module to analyze the learning portfolios of learners for refining their teaching strategies and contents.

The topics in this dissertation mentioned above will be detailedly discussed in following Sections.

Chapter 4 Knowledge Representation