Bayesian Network Student Model for Adapting Learning Activity Tasks in Adaptive Course Generation Sy

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 learners information such as
knowledge, background, learning goals, learners
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 learners 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
learners demand such as learning goals,
preferences, etc. and to evaluate learners
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
learners knowledge, and learners 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 problems 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 students 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
  students knowledge to be a subset of the
  systems knowledge of the subject. As the student
  learns, the subset grows, and the modelers 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 learners 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 learners
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 withViet Anh NguyenEmail
vietanh_at_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 learners 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.