Introduction

1. Bayesian Network Student Model for Adapting Learning Activity Tasks in Adaptive Course Generation System Viet Anh Nguyena, Viet Ha Nguyena, Si Dam Hoa, Hitoshi Sasaki aCollege oF Technology, Vietnam National University Hanoi, VietnambFaculty oF Engineering, Takushoku University, Japan Adaptive Course Generation System (ACGS) Introduction Adaptive educational hypermedia system (AEHS) aims to develop the course that can adapt to users. One important components of it is learner model. Learner model represents individual learner’s information such as knowledge, background, learning goals, learner’s preference, etc… that useful for adaptation. In this paper, we represent the learner modeling component of ACGS, and how to develop Bayesian Network (BN) learner model to manage overlay knowledge model and adapt learning activities based on task model. In addition, we describe an implementation of this model for computer science domain, a database course subject: “How to design relationship database?”. Learning activity adaptation Adaptation is process to select activity tasks for each learner based on learner’s model. The learner with different knowledge level needs to do different tasks in order to finish learning goal. This is composed by several tasks which include abstract tasks and activities task. Figure 1. Adaptive course generation architecture ACGS (NGUYEN & Dam, 2006) includes three modules: Learner Module (LM), Visualization Module (VM) and Adaptation Module (AM) as depicts in figure 1. Learner Module designed to get learner’s demand such as learning goals, preferences, etc. and to evaluate learner’s knowledge. Visualization Module takes adaptive course outlines for displaying them as annotated hypertext links in the website to learner. Adaptation Module asks domain concepts from Learning Object Database which includes learning resources and learning-task database, which contains tasks with all possible combinations of levels of support and complexity as well as enough variability over other task features to allow for generalization and abstraction by the learner as well as asks learner’s knowledge, and learner’s learning goals to generate course structure. Figure 4. Adaptation engine of Adaptive Course Generation System (ACGS) Captions to be set in Times or Times New Roman or equivalent, italic, between 18 and 24 points. Right aligned if it refers to a figure on its right. Caption starts right at the top edge of the picture (graph or photo). • Adaptation process selects learning resources through phases: • First of all, resources are evaluated and classified in one equivalence class according to class membership rules are selected base on learner profile and adaptation rules (NGUYEN & Dam, 2008b) which is a set of rules represented in first order logic. • Secondly, according to adaptive navigation technique, one ore more techniques is selected such as hiding, annotation or direct guidance in order to input for visualization module to display the course to choose for current learner. • Finally, student activities response will be updated in his/her profile which is basic for adaptation process in next run-time learning activities. • Our experiments • We design a course topic “How to design relationship database?” for third year student. In order to design database, first of all the student need to skim problem’s speciation and then participates four phrases: designing entities relationship diagram, transforming entities relationship diagram to tables physic, normalizing tables, and defining query to retrieve information. There are twenty six concept nodes in the course model. The task diagram is composed by twelve abstract tasks and twenty nice activity tasks. There are two kinds of activity tasks: consequent task and parallel task. • Background • This section describes several theoretical backgrounds which involved our research. What can be adapted? • macro-adaptive selecting a few components that define the general guidelines for the e-Learning process, such as learning objectives or levels of detail and mainly based on learner model. • aptitude-treatment proposing different types of instructions and/or different types of media for different students. • micro-adaptive, diagnosing the student’s specific learning needs during instruction, providing instructional prescriptions for these needs and monitoring the learning behavior of the student while running specific tasks and adapting the instructional design afterwards, based on quantitative information. • The domain model • Domain model is an object model of problem domain. In AEHS, domain model is set of elements about educational domain; each element is domain object class and the relationship between them. Domain model decompose knowledge of the subject into fragments such as: topic, sub-topic, atomic concepts. • Depending on the domain, designer strategies, there many kinds of domain model structure: vector model, network model, and ontology, etc. • The overlay knowledge model • The overlay model is one that supposes the student’s knowledge to be a subset of the system’s knowledge of the subject. As the student learns, the subset grows, and the modeler’s job is to keep trace of the subset. • This model assumes that the student will not learn anything that the expert does not know. The principle of the learner’s overlay model is that for each domain model concepts, individual user knowledge model store data that represent values which is an estimation of the user knowledge level of this concept. • The task model • A task statement refers to a set of coherent activities that are performed to achieve a goal in a given domain. Task models are documentation structures that are used for: i) documenting the result of a task design of proposed activities, ii) supporting personnel selection, iii) identifying needs for training. • Bayesian network • A BN is a directed graph whose nodes represent the (discrete) uncertain variables of interest and whose edges are the causal or influential links between the variables. Course domain model of ACGS. Course domain model includes several topics which include two objects are concepts and learning tasks as depicted in figure 2. In order to acquire a concept, learner need to work several related learning tasks. Figure 2. Course domain model of ACGS Otherwise in order to finish learning task, learner also needs to acquire some concepts corresponding. The course domain is represented as a directed acyclic graph (DAG) with several nodes and vertex connects between them. Node depicts an atomic concept while vertex depicts prerequisite relationship between the concepts. Conclusion The main contribution of this paper is a model to manage student model based on learner overlay knowledge model. As a result, model gathers information about learner current state of knowledge and modeling learner as unreliable source of concepts. Improving our previous work, adaptation process extends to adapt learning activity task based on task model in order to adapt for know-how and learner’s learning goals. For this, prerequisite relationship among activity task as taking into account for selecting learning material process. Finally, we developed ACGS architecture for generating adaptive course. For more detail, please contact with:Viet Anh NguyenEmail: vietanh@vnu.edu.vnWeb: http://www.coltech.vnu.edu.vn/~anhnv Figure 3. Partial learning activity task of “How to design relationship database” course Bayesian Network learner model To develop BN learner model, we assign a set of variables to measure learner’s knowledge with three states: not acquired, in progress, acquired. p(not-acquired(C)) represents probability value of not acquired state for concept C, p(in-progress(C)) denotes probability value of in progress state for concept C, and p(acquired(C)) denotes probability value of acquired state for concept C. there is p(not-acquired(C)) + p(in-progress(C)) + p(acquired(C)) =1. Selected References Nguyen Viet Anh, Nguyen Viet Ha, Ho Si Dam (2008). " Contructing a Bayesian Belief Network to Generate learning path in adaptive hypermedia system".Journal of Computer Science and Cybermetics Vol 1(24), 2008, p. 12-19. Viet Anh Nguyen, Si Dam Ho (2006), "Applying Weighted Learning Object to Build Adaptive Course in E-learning", Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding, Frontiers in Artificial Intelligence and Applications, Volume 151, p  647-648, Beijing, China. Viet Anh Nguyen, Si Dam Ho (2006)."ACGS: Adaptive Course Generation System- An efficient approach to build E-learning course".Proceeding of 6th IEEE International Conference  on Computers and Information Tecnology, 2006, p 259- 265,Seoul, Korea.