Chapter 2 Related Works
2.2 International Standards in E-Learning System
However, most existing e-learning systems represent student profile, learning management data, test bank and subject contents with different formats, which results in the difficulties of sharing, reusing, and recombining those e-learning resources.
Therefore, several international organizations have proposed teaching material standards, such as SCORM proposed by IMS, Simple Sequencing Specification and Content Packaging proposed by IMS, and LOM proposed by IEEE LTSC.
2.2.1 IMS (Instructional Management System)
In 1997, the IMS Project [56], which is part of the nonprofit EDUCAUSE [33], started its work and developed open, market-based standards including specifications of learning resource metadata for online learning. In the same year, the NIST (National Institute for Standards and Technology) and the IEEE P.1484 group, which now is the IEEE Learning Technology Standards Committee (LTSC) [79], also started to do a similar effort. Then, the IMS collaborated with NIST and ARIANDE project [3]. In 1998, IMS and ARIADNE submitted a joint proposal and specification to the IEEE, which is the basis of current IEEE Learning Object Metadata (LOM) base document.
Currently, the IMS project have proposed many standard specifications including learning metadata specification, content packaging specification, learner profiles specification, question and test interoperability, and simple sequence specification, etc.
2.2.2 IEEE LTSC
The international organization, IEEE LTSC [79], proposed the Learning Technology System Architecture (LTSA) and Learning Objects Metadata (LOM), described as follows.
LOM (Learning Objects Metadata):
The IEEE’s Learning Objects Metadata (LOM) [79] describes the semantics of learning object metadata. Here, a learning object is defined as any entity, including multimedia content, instructional content, and instructional software, which can be used, reused, shared, and recombined. To allow learning objects to be managed, located, and evaluated, the LOM standard makes efforts in the minimal set of properties needed.
The LOM describes learning resources by using the following categories.
z General: describe the general information of learning resource.
z LifeCycle: describe the history and current state of learning resource and its evolution information.
z Meta-MetaData: describe the specific information about the metadata record itself, e.g., who created this metadata record, etc.
z Technical: describe the technical requirements and characteristics of learning resource.
z Educational: describe the key educational or pedagogic characteristics of learning resource.
z Rights: describe the intellectual property rights and conditions of use for learning resource.
z Relation: define the relationships among this resource and other targeted resource.
z Annotation: provide comments on the educational use of learning resource, e.g., who created this annotation.
z Classification: describe classification criteria and hierarchy of learning resource.
Learning Technology System Architecture (LTSA):
IEEE LTSC analyzed the basic requirements of e-learning system to propose a Learning Technology System Architecture (LTSA) [80] which identify the critical interoperability interfaces for learning technology systems. LTSA mainly includes 4 processes and 2 stores, described as follows:
(1) Learner Entity: learners receive the multimedia learning contents delivered by system and the learning progress of learners will be tracked and recorded.
(2) Coach: teachers provide learning system with teaching materials and evaluate the learning performance of learners.
(3) Delivery: it is responsible for delivering the learning contents Coach indicates to learners.
(4) Evaluation: it evaluates the learning performance of learners and diagnoses the mis-concept.
(5) Learner Record: it records the learning behavior of learners, which can be used to analyze and track.
(6) Learning Resource: it stores the learning resources which were created by teachers and can be used to learn for learners.
Figure 2.1 illustrates the components of LTSA system.
Figure 2.1: The LTSA System Components
2.2.3 SCORM (Sharable Content Object Reference Model)
Among those existing standards for learning contents, SCORM [100], which was proposed by the U.S. Department of Defense’s Advanced Distributed Learning (ADL) organization in 1997, is currently the most popular one. The SCORM specifications are a composite of several specifications developed by international standards organizations, including the IEEE LOM [79], IMS [56], AICC [1] and ARIADNE [3]. In a nutshell, 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 "RAID:" Reusable: easily modified and used by different development tools, Accessible: can be searched and made available as needed by both learners and content developers, Interoperable: operates across a wide variety of hardware, operating systems and web browsers, and Durable: does not require significant modifications with new versions of system software [58]. The details of SCORM and its Sequencing & Navigation (SN) [109] will be described in Chapter 4.
2.3 The SCORM Compliant Authoring System
Recently, although many SCORM authoring tools have been developed by commercial companies, unfortunately, these tools support SCORM 1.2 only, for example, the Authorware 7 of Macromedia [64], Click2learn Unveils SCORM 1.2 Resource Kit [23], Seminar Author of Seminar Learning System [105], Elicitus Content Publisher [31], and more other SCORM 1.2 compliant authoring tools found in [32].
Because the complicated sequencing rule definitions of SN in SCORM 2004 make the design and creation of course hard, the article in [76] has proposed several document templates to construct SCORM compliant course according to the sequencing
definitions of SN. Teachers/authors can design their desired learning activities by modifying the sequencing definitions in document templates. Then, the SCORM course with sequencing definitions can be created by programming. However, for teachers/authors, creating the SCORM course with sequencing behavior rules by document templates is still hard. Moreover, it is time consuming and high cost to create SCORM course by programming.
Moreover, an open source tool, called Reload Editor, developed by [94] can be used to create the SCORM 2004 course. For setting the learning guidance, users have to edit the sequencing rules by clicking in the comboBox of sequencing rules. Although it offers the graphical user interface (GUI) to create SCORM course, the sequence of final course is hard to image and creating course is also time-consuming. Shih et al. [103]
also proposed a collaborative courseware authoring tool to edit the SCORM compliant course which can support collaborative authoring and suggest an optimal learning sequence. They analyzed the metadata of SCA in SCORM 1.3 to design the activity rules which can be used to generate lecture sequencing. This tool also offers users the sequencing rules definition page to define the sequencing behavior of courseware.
Besides, Yang et al. [144] developed a web-based authoring tool, called Visualized Online Simple Sequencing Authoring Tool (VOSSAT), to provide an easy-to-use interface for editing existing SCORM-compliant content packages with sequencing rules. Nevertheless, the disadvantages in [103][144] are the same as Reload Editor [94].
2.4 Applying Petri Nets in E-Learning System
Lin [70] applied Petri Nets theory to model online instruction knowledge for developing online training systems. Two-level specialized Petri nets including TP-net, which represents goal-oriented training plans, and TS-net, which represents
task-oriented training scenarios, are proposed. A Goal-Oriented Training Model Petri net (GOTM-net), which is combined by a TP-net and all TS-nets, is converted as a set of “if-then” rules representing the behaviors a learner may perform and the corresponding responses. However, GOTM-net may not be compatible with SCORM standard. Based on SCORM 1.2, Liu et al. [71] discussed meta-data structure which makes a base for reusing and aggregating learning resources in e-learning, and provided an aggregation model, called Teach net, based on High-Level Petri Nets (HLPN).
Several routing constructs in workflow are also modeled by HLPN for flexible navigation. However, the Teach net is mainly used to model the content aggregation without considering course sequencing. Besides, the modeled routing constructs may be not sufficient for modeling sequencing definition in SCORM 2004.
2.5 Structured Document Management
For fast retrieving the information from structured documents, Ko et al. [63] proposed a new index structure which integrates the element-based and attribute-based structure information for representing the document. Based upon this index structure, three retrieval methods including 1) top-down, 2) bottom-up, and 3) hybrid are proposed to fast retrieve the information from the structured documents. However, although the index structure takes the element and attribute information into account, it is too complex to be managed for the huge amount of documents.
How to efficiently manage and transfer document over wireless environment has become an important issue in recent years. The articles [75] [142] have addressed that retransmitting the whole document is expensive in faulty transmission. Therefore, for efficiently streaming generalized XML documents over the wireless environment, Wong et al. [133] proposed a fragmenting strategy, called Xstream, for flexibly managing the XML document over the wireless environment. In the Xstream approach, the structural characteristics of XML documents has been taken into account to fragment XML contents into an autonomous units, called Xstream Data Unit (XDU). Therefore, the XML document can be transferred incrementally over a wireless environment based upon the XDU. However, how to create the relationships between different documents and provide the desired content of document have not been discussed. Moreover, the above articles [63] [75] [133] [142] didn’t take the SCORM standard into account yet.
2.6 Learning Portfolio Analysis
In addition, for learning portfolio analysis, Chen [15][16] applied decision tree and data cube techniques to analyze the learning behaviors of students and discover the pedagogical rules on students’ learning performance from web logs including the
amount of reading article, posting article, asking question, login, etc. According to their proposed approach, teachers can easily observe learning processes and analyze the learning behaviors of students for pedagogical needs. However, although their proposed approaches can observe and analyze the learning behavior of students, they don’t apply education theory to model the learning characteristics of learners. Therefore, the learning guidance can not be provided automatically for the new learner. For providing the personalized recommendation from historical browser behavior in e-learning system, Wang [140] proposed a personalized recommendation approach which integrates user clustering and association-mining techniques. Based upon a specific time interval, they divided the historical navigation sessions of each user into frames of sessions. Then, a new clustering method, called HBM (Hierarchical Bisecting Medoids Algorithm) was proposed to cluster users according to the time-framed navigation sessions. In the same group, the association-mining technique was used to analyze those navigation sessions for establishing a recommendation model. Thus, this system can offer the similar students personalized recommendations. However, in this approach, the learning characteristics and sequential learning sequence of students were not considered, so that the personalized recommendation may be not appropriate. Of course, it doesn’t support SCORM 2004 standard yet.
2.7 Concept Map Construction
In 1984, Novak [85] proposed Concept Map to organize or represent the knowledge as a network consisting of nodes (points/vertices) as concepts and links (arcs/edges) as the relations among concepts. Thus, a wide variety of different forms of concept maps have been proposed and applied in various domains [8][45][46]. In the adaptive learning environment, the Concept Map can be used to demonstrate how the learning status of a concept can possibly be influenced by learning status of other concepts and give learners adaptive learning guidance. Thus, Appleby proposed an approach to create the potential links among skills in math domain [5]. The direction of a link is determined by a combination of educational judgment, the relative difficulty of skills, and the relative values of cross-frequencies. Moreover, a harder skill should not be linked forwards to an easier skill. As shown in Table 2.1,
f
AB represents the amount of learners with wrong answers of skill A and right answers of skill B. IfB
Table 2.1: Relative Skills Frequency
A is right A is wrong B is right fAB f AB B is wrong fAB fAB
Hsu also proposed a conceptual map-based notation, called Concept Effect Relationships (CER), to model the learning effect relationships among concepts [51]. In brief, for two concepts, Ci and Cj, if Ci is the prerequisite for efficiently learning the more complex and higher level concept Cj, then a CER Ci Æ Cj exists. A single concept
may have multiple prerequisite concepts, and can also be a prerequisite concept of multiple concepts. Thus, based upon CER, the learning guidance of necessary concepts to enhance their learning performance can be derived by analyzing the test results of students. Later, based upon statistical prediction and approach of Hsu [51], a CER Builder was proposed by Hwang [49]. Firstly, CER Builder finds the test item that most students failed to answer correctly and then collects the other test items failed to answer by the same students. Thus, CER Builder can use the information to determine the relationships among the test items. Though the CER Builder is easy to understand, only using single rule type is not enough to analyze the prerequisite relationship among concepts of test items, which may decrease the quality of concept map.
Tsai proposed a Two-Phase Fuzzy Mining and Learning Algorithm [126]. In the first phase, Look Ahead Fuzzy Mining Association Rule Algorithm (LFMAlg) was proposed to find the embedded association rules from the historical learning records of students. In the second phase, the AQR algorithm was applied to find the misconcept map indicating the missing concepts during students learning. The obtained misconcept map as recommendation can be fed back to teachers for remedy learning of students.
However, because the creating misconcept map, which is not a complete concept map of a course, only represents the missing learning concepts, its usefulness and flexibility are decreased. In addition, their approaches generate many noisy rules and only use single rule type to analyze the prerequisite relationship among learning concepts.
Chapter 3 Intelligent Learning Content Management System
(ILCMS)
3.1 The Layered Model of IEEE LTSA
In order to provide learners with an adaptive learning environment, the Learning Technology System Architecture (LTSA) of IEEE LTSC [80] as a reference model 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) [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. Figure 3.1 illustrates the layered LTSA model, where the module in higher layer will use the service provided from lower layer to offer more powerful and specific service. For example, the Delivery module in Common layer uses the resources in Resources layer to deliver to the learners.
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. Accordingly, each module in LTSA can be classified into five knowledge types according to its function, i.e., Knowledge Resources including learning resources and records, Knowledge Manager including the
Delivery, Knowledge Controller and Knowledge Acquirer including the Coach, and Knowledge Miner including the Evaluation, as shown in Figure 3.2.
Figure 3.1: The Layered Model of IEEE LTSA
Figure 3.2: Applying Knowledge Management Concept to Layered Model of LTSA
3.2 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
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