T8: International Journal 1
Judul PI: Aplikasi Antrian Pada Klinik Menggunakan Visual Basic.Net dan Microsoft Access
Link Journal: https://pdfs.semanticscholar.org/df9f/94497b2cbc5485117049ab1e4e1bfeeb5693.pdf
Learning Content Recommendation
for Visual Basic.Net
Programming Language based on
Ontology
Abstract:
Nowadays, the quality of learning
and the expansion of education technology, motivate the researchers to work on learning area more than before. Problem statement: With the rapid
advance of learning contents on the web and also the variety of learning books,
finding suitable ones has become a very difficult and complicated task for learners.
Approach: This study aims to propose
a learning system includes the semantic recommender system. Students can employ
this application to learn learning content at anywhere. This system works based
on the learner’s knowledge level and also the learner’s request that system
asks from the learner at the beginning. Learner will be able to find and learn
the right learning materials to reach their request. Finally, all changes about
learner will store in the learner model in the ontology. The proposed architecture
comprises some subsystems and components. One of the most important of
subsystems is a knowledge based system, which covers the ontology which called
VBnet ontology. This ontology consists of three parts; LearnerModel, Domain
Concept and Learning Material. Moreover, we define two other subsystems;
Learner performance evaluation, recommendation system and some modules;
Availability checker, Knowledge evaluator, Exam generator, Request analyzer and
user interface. Results: Considering
to scope of research we develop the ontology for Visual Basic.Net programming
language and describe all available classes and subclasses step by step. Also
we create some query by SPARQL and show the information retrival from VB
ontology. Conclusion: This system
can help to student to learn materials of Visual Basic.Net with the good
quality without the place dependency.
Key words: E-learning
system, Computer-Based Training (CBT), knowledge based system, learning object,
recommender system, tutoring system, SQL database, Intelligent Tutoring System
(ITS), quiz generator
INTRODUCTION
E-learning systems help learners
to learn electronic course through computer or network as virtual classroom
instead of face to face learning. This type of learning has some advantages versus
conventional learning system. Face to face learning or conventional learning
systems will be time consuming for the learner and also learner should spend
more cost in contrast with the learning as virtual classroom(Yu et al., 2007). Schools and universities
have been looking for ways to increase learning efficiency for students and
improve learner performance. E-learning provides some advantages for the
learner by making access to educational material very fast and just-in-time at
any time or place (Ghaleb et al.,
2006). Due to increasing computer technologies, learning at home as distance learning can be effective similar to face to face
learning. So, developing tutoring system and the suitable learning way can
increase the learning efficiency and also can be an appropriate solution for
universities and schools problem(He, 2009). With the rapid growth of electronic
course contents and virtual classrooms, e-learning systems are efficiently used
for education and training in academic and non-academic places. Owing to
traditional teaching resources, such as textbooks, learner should follow fixed
sequences of learning materials. Learners with the same plan have the same
content lists for learning, even though they have different previous knowledge
about their plans but, the online education such as adaptive e-learning system
provide flexible curriculum sequencing control to adapt learners with learning
environments(Chen, 2008). One of the most important topics in the adaptive e-learning systems is personalization.
Personalization works based on user modeling and user profile. The information
that is included in the user profile is various for each learner. The user
profile has importance role in this proposed recommender system. Nowadays,
right information to the right person in the right way is a challenge in the
learning world Adaptive Content recommendation can solve this problem(Fischer,
2001; Yu et al., 2006). Sometime
student requires large volumes of material and spent a lot of time to learn
particular content or relevant information(Muna et al., 2005) . Recommender system can help students to spend less
time during the learning process.
Recommendation system offers
suitable learning contents based on user profile. Considering the
personalization in recommender systems and also their dependency on user
profile, recommended contents will be different for each learner. In the
current system, we mainly consider to knowledge evaluation and learner request
analysis. They play the most important role in recommendation. We are planning
to make the content recommendations using knowledge about the learner,
knowledge about the learning domain and knowledge about the learning materials.
Also, we offer the semantic recommender system based on ontology for
VisialBasic.Net programming language learning. In the structure of this system,
the use of ontology focuses mainly on learning materials and relation between
them and the learner model aspects, as well.
This study arises from two basic
research topics; First, the tutoring systems and learning systems and second,
recommender systems and knowledge representation.
Knowledge background:
Recommender
system: Many recommendation systems in various domains such as
movies, music, commerce and medicine have been developed but few in the
education field(Drachsler et al.,
2007). Compared with other fields, learning content recommendation is a new
topic with the appearance of e-learning. The most commonly used technique for
recommendation systems is collaborative filtering (Lemire, Boley et al. 2005;
Chiu et al., 2006; Manitsaris et al., 2007; Guo et al., 2008; Serradilla et
al., 2009). Content filtering is the second recommender technique.
Researchers employed this technique to develop their recommendation
systems(Ghauth and Abdullah, 2009). Another recommendation technique refers to
hybrid filtering. Hybrid filtering is a combination of collaborate filtering
and content filtering(Liang et al.,
2006; Khribi et al., 2008). The last
one is knowledge based technique. This technique also is used to develop a recommendation
system.
Ontologies:
Ontology comprises a set of
knowledge terms, including the
vocabulary, the semantic interconnections and some simple rules of inference
and logic for some particular topics (Hendler, 2001). Ontology is the backbone
of Semantic Web, a new form of Web content that is meaningful to computers and
will unleash a revolution of new possibilities(Berners-Lee et al., 2001). Gruber defines ontology as an explicit specification
of a conceptualization (Gruber, 1993). Two main properties of ontology such as
shareablility and reusability make them very attractive and powerful for
representing domain knowledge. Ontologies provide a shared understanding of a
domain of interest to support communication among human and computer agents(Al-Safadi
and Al-Abdullatif 2010).
Tutoring
system: Computers have been used for more than 35 years in education (Beck et al., 1996). The first tutoring
program designed in 1970 (Carbonell, 2007). Before Intelligent Tutoring
Systems, Computer-Based Training (CBT) and computer aided instruction (CAI)
were the first systems used to teach using computers. In these systems, the
learning environment is not individual, so the learning material is inflexible
while Intelligent Tutoring provides the personally learning environment. These
systems work similar to one-to-one tutoring(He, 2009).Tutoring systems discuss
about the course learning based on curriculum sequencing(Li, Tang et al.).
These systems are computer based program that represents the learning materials
in a flexible and personalized way(Brusilovsky and Peylo, 2003; Johnson, 2001).
Literature
review and current related work: Many related researches can be found
through the internet.. Domains, learning strategy and knowledge representation
are the main diffidences among them. We are discussing some of the learning
systems or tutoring systems as following.
He Xuechen has proposed a web-based intelligent Tutoring system for
English Dictation Automatic Correction, called EDAC. This system uses
JavaScript to correct the user’s dictation after executing a query to get the
answer from the SQL Server database(He, 2009).
Michael Negnevitsky has designed
a knowledge based tutoring system that supports the education of power
engineering students(Negnevitsky, 2002). He applied expert system shell in this
project. The students by using this system can understand and have received a
good introduction to fault analysis in power systems. (Song et al.,
1997) have described a system called C-Tutor, an Intelligent Tutoring System
(ITS) for novice C programmers. As a learning environment, Curriculum Network
constructs the knowledge base as genetic graphs to teach programming. The
knowledge base of C-Tutor is represented as frame structure. The system
evaluation is through some real students.
Aytu¨rk (Keles et al., 2009) have developed an
intelligent tutoring system for Mathematics Education, called ZOSMAT. ZOSMAT
can be used for the purpose of either individual learning or real classroom
environment. This system follows a student in each stage of the learning
process and they are guided about their needed following actions. This system
suggests some relevant examples from past experience also analyze student
solutions and explain errors then recommend some activity based on learning
goals. Know ledges about the learner and all learning materials store in the
ralational database.
Antonija Mitrovic in(Mitrovic,
2003) has designed a Web-enabled intelligent tutoring system for the SQL
database language. This system observes students’ actions and adapts to their
knowledge and learning abilities. The knowledge about the domain that SQLT-Web
contains is represented as a set of constraints.
Frigo et al.
(2005) have created a tutoring system is named MathTour. MathTutor is a
multi-agent system tutor. The architecture includes the Student, Domain and
Pedagogical Models. Application domain represented by using domain ontology
(Berners-Lee et al., 2001).
Adriana da Silva Jacinto et al,
have proposed an ontology based architecture for tutoring system. They have
designed ontology for each model in their system; ontology for learner model,
pedagogical model, structure model, adaption model and domain model. Adaptation
model works with a base of rules for decision making and pedagogical model
specifies the business rules of the system. Interaction model represents the
description of possible learner’s behavior. The Structure Model specifies how
the concepts of the Domain Model are grouped into semantic units, like,
learning units. Learner model consists of the learner information and domain
model represents the application domain structure. Each decision that is taken
by adaption model will be represented to a learner by presentation model(Frigo et al., 2005).
Thaw Ta Htaik et al have proposed
EGIP system to generate the explanations focused on teaching mathematical
integrations(Htaik and Phon-Amnuaisuk,(2005). The architecture of their system consists of
an expert module, the student model, the tutor module and Prolog based user
interface. The student model includes Name, ID, number of investigated
problems, and number of correct and incorrect answer. The tutor model provides
teaching and explaining integration rules such as problem and typing method,
and generates the correct answer. The expert model includes the domain
knowledge to be tutored. The system shell is being integrated with the Prolog
and HTML.
In addition to learning domain we
mention to some knowledge based intelligent system in other domain. The most of
these system apply the ontology to represent their knowledges.
Owaied et al., (2011) have developed the knowledge based tourism system.
This intelligence system guide the tourism to find the best attraction place
based on their need. This model follows the behaviour of the human guide.
Lu and Feng (2009)have proposed a
novel concept of intelligent topic map. This concept covers the related
characteristics of knowledge and realizes knowledge reasoning. They designed an
ontology which covers the multi-resource knowledge.
Ding and his colleague(Ding and
Sun, 2009) has designed the ontology based framework to provide the integration
among software resources. Their framework makes the semantic interoperability
between various software resources. Developed ontology in their research divide
to shared ontology and domain ontology which the shared ontology has an
important role in this model.
Wei et al. (2009) has proposed an ontological system for manufacturing
design. This system is trying to solve the available problem in manufacturing
design. The Knowledge Management (KM), a Product Knowledge Model (PKM) and the
Intelligent Application System (IAS) are the main subsystems in this system.
This system employs the semantic
technology to share knowledge among
multidisciplinary organizations and intelligent supporting the manufacturing
design.
As we described above, the first
difference refers to variety of the worked domains such as
mathematic(Berners-Lee et al., 2001;
Htaik and Phon-Amnuaisuk, 2005; Keles et
al., 2009), SQL language (Mitrovic, 2003), C programming languages(Song et al.,
1997), power engineering(Negnevitsky, 2002) and English dictation(He, 2009). The second difference points the
learning strategy. Some of them ask the learners about their problem then recommend the
suitable answers to them(Htaik and Phon-Amnuaisuk, 2005). Another control a
student in each stage of the learning process and guides him/her about what
he/she will need to do then based on their needs offer some activity(Keles et al., 2009). As we brought above, none
of them do not evaluate the learner’s knowledge level. In the previous research, we proposed the
recommender system(Shishehchi et al.,
2010) by using ontology. The most significant different between this study and
previous research(Shishehchi et al.,
2010) is about the knowledge representation. In the previous research
(Shishehchi et al., 2010) the learner’s
profile and all information about learner store in seperate storages. The
developed ontology in (Shishehchi et al.,
2010) consists of learning materials and semantic relationship among them while
in current study ontology covers both scopes of knowledge; learner’s knowledge
and know edges about learning materials. This approach integrates all of the
available know ledges.
Our strategy will be based on the
learner knowledge level and also the learner request. The knowledge level for
each new learner will evaluate and estimate through the system automatically.
This is one of the differences among our research and previous works. Another
one is about the domain that we work on. We select the Visual Basic.Net
programming language to teach. The majority of related works applied the
ontology to represent the knowledge about learner and domain, separately (Frigo
et al., 2005; Berners-Lee et al., 2001; Htaik and Phon-Amnuaisuk,
2005). Some of them applied rational database to store their knowledge(Song,
Hahn et al. 1997; Keles et al. 2009),
genetic graph (Song et al., 1997).
Furthermore, expert systems can use learning systems such as (Negnevitsky,
2002; Htaik and Phon-Amnuaisuk, 2005). Unlike the previous related works, we
prefer to have integrated knowledge consists of one ontology. We design the
ontology to represent knowledge related to learner model, learning materials
and domain.
MATERIALS
AND METHODS
System
architecture: Our
proposed architecture consists of
three subsystems; Recommendation system, semantic Knowledge based system and
learner performance evaluation system. In addition to these subsystems, we
define some various modules in this architecture; Exam generator, knowledge
evaluator, Availability checker, Graphical user interface, Learner’s request
analyzer.
Knowledge
based system: The
applied technique for representation
of knowledge is one of the most important ones in respective application. In
this system, we apply ontology to represent all knowledge in this system.
Various knowledge such as knowledge about the learner, knowledge about the
learning domain and knowledge about the learning materials have been
represented in this system.
VBnet
ontology: We create
the VBnet ontology by using Protégé
3.4.3 as ontology editor. This ontology has hierarchy structure and follows the
OWL ontologies. This ontology consists of some classes, instances for classes
and properties (object properties and data type properties). There is three
main classes in this ontology; LearnerModel class, ConceptDomain class and
LearningMaterial class. Each of these classes includes subclasses and also some
individuals. Some properties have been defined to make a relation among these
instances.
Recommendation
System: Each of these components has various tasks to do. Concept and
content are two words with different meaning and different using. Concept
refers to all topics and name of coerces in Visual Basic. Net that is available
in the DomainConcept class in ontology. Content points to all learning material
who system recommends them to a learner to learn. Concept can be loop statement
topic, and content can be an example of loop statement.
Concept
recommendation: Recommending
some suitable concepts based on
learner request and also the learner knowledge level.
Learning
path: When the system specifies the appropriate concepts to a learner,
then should extract the connection between these concepts and create the path.
In next stage learner use this learning path to learn some contents.
Learner
performance evaluation: When the
learner learns the textual contents,
system evaluates the learner performance. The system should evaluate the
current learner, whether the learner understands the current textual content or
not. This subsystem includes some components; Quiz Generator, Time Observer and
Learning Content recommendation. The output of Quiz generator and time Observer
components has an effect to the system specify the performance.
Quiz
Generator: This
component should generate quiz
according to current textual content which learner finishes the its reading.
This component will generate some questions from all topics of current textual
content randomly. Then the system evaluates the quiz and saves the obtained
score of a quiz for the learner on the learner’s model.
Time
Observer: When a
learner starts to read the current
textual content for each concept, system measure the reading time spent until
the learner finishes the content. This time is effective in performance
evaluation for the learner.
Learning
content recommendation: After performance evaluation through quiz
score and reading time spent, system recommends some learning content. Based on
the result, these learning contents are different. Then all changes related to
the learner will be stored in the learner’s model, and VBnet ontology will be
updated.
In this stage we are going to
describe the modules in the system architecture.
Availability
checker: This
module should check the learner’s
availability, whether the learner is new or old, since the process of the
system in the beginning for a new learner and old learners is different. The
system will search the username for the current learner, if the username is
valid, itmeans that the learner is the old learner, otherwise the learner is
new and should fill up the personal information form and make user name and password.
Knowledge
evaluator: Another
module of the system is the
knowledge evaluator. According to Fig. 2, when a learner logs in and the system
specifies whether s/he is a new or old learner, if he is new then this module
starts to evaluate the knowledge level of the current learner otherwise this
module will not need to work.
Exam
generator: When the
system specifies the knowledge level
for a learner, then the system automatically, generate the exam. The question
included in this exam is returned to result of knowledge evaluation. This exam
is different for each learner because their knowledge levels are different.
Learner
request analyzer: One of
the most effective components in
concept recommendation is the learner request. Each concept can be considered
as one request from the learner. Predefined concepts in the domain
have some prerequisites. In learning domain when the learner wants to learn the
specific concept is necessary to pass all prerequisites. Thus, this module
retrieves all prerequisites for the learner request from the ontology.
Creation
the ontology: This
ontology consists of three different
classes and 41 subclasses. VBnet ontology divides to some level:
•
The first
level of hierarchy structure consists of three main classes; learner Model,
ConceptDomain and Learningmaterials
•
The
second level comprises the all subclasses in these main classes. This level
covers all 41 subclasses
•
The third
level of the structure, includes the all individuals belong to these classes
and subclasses. Total of individuals are 50. 43 belong to ConceptDomain, 4
belongs to LearnerModel and 3 belongs to LearningMaterials
The first
level of VBnet ontology: As we
mentioned before, this level
consists of three main classes. Fig. 3 shows these classes.
The
second level of VBnet ontology: This
level comprises the subclasses. We
classify these subclasses based on their parent classes.
LearnerModel
class: The LearnerModel Class represents the knowledge about the
learner. This class consists of two subclasses;
•
learnerKnowledgeLevel
class: this class shows the knowledge level of a learner and includes three
individuals; Basic, Medium and Advance
•
LearnerInformation
class: this class covers all personal information about the learners. This
information can be first name, last name, username and password and alike.
Figure 4 presents the subclasses belong to this class
LearningMaterial class: The learningMaterial class represents the knowledge about the all learning object due to Concepts that we mention before in the ConceptDomain class. The learning materials in this paper are; Example, TextContent and Quiz.
Semantic
relationship as properties: To
extract the information from
ontology and make two way communication between learner and system, it needs to
provide some semantic relationships. We created
some properties (Bechhofer et al.,
2004) to make relation among materials in VBontology. These properties
according to OWL(Bechhofer, Van Harmelen et al. 2004) can be object property
and data type property.
RESULTS
As we mentioned before this
ontology follow the OWL ontologies(Bechhofer et al., 2004), so the samples of OWL file present in the Fig. 8.
During the processing, we need to retrieve some information from ontology, and
also we needed to add some information to ontology, that is why we also defined
some queries based on SPARQL language. Figure 9 shows the exmaple of query to
extract some information based on “HasExample ” property. The result of this
query present the avaiable exmples related to each TextContent in the
VBontology.
DISCUSSION
As we mention before, the
recommendation technique in the current study depends on some relationship
among the learning materials and learner’s knowledge level. To extract all
relationships among the learning materials is one of the significant tasks in
the current system when the learner asks the request. Figure 9 shows the
desired results related to the learner request about content ‘x’. Learner asks
some example for content ‘x’. This figure expresses the system operations
according to learner request. Specific learning materials as a desired
recommendation result need to some task in the system. Considering to ontology
performance that we mention before, it can improve the quality of knowledge
retrieval and increase the speed of search by using the semantic rule and
semantic query. This figure shows the knowledge retrieval from the VBOntology.
This information retrieval without the ontology is possible but with the lower
speed and complexity in structure of query.
CONCLUSION
We propose a learning system for VisualBasic.Net to
help learner to learn. This system plays the teacher role in the virtual class.
Learner can use this system at home or anywhere else. We represent our
knowledge by using ontology. VBnet ontology consists of some levels that we
implement them in details. This application comprises the recommendation
system. This part is one of the most important parts in this system. This
subsystem recommends the suitable learning
materials based on learner request and also learner
knowledge level. Knowledge level evaluation occurs for each new learner in the
system for the first time. This part prevents the time consuming during the
learning process. The system offers the suitable learning contents that learner
need to learn. Learners do not need to learn all prerequisite content to reach
their request. Other modules such as performance evaluation, exam generator and
availability checker play a specific role in this system. The knowledge based
system consists of our knowledge about learner, learning materials and domain.
This knowledge have represented by using Protégé 3.4.3.
Future
work: We are going to develop and
prototype the performance evaluation
module and recommendation module in this system. These modules have some
components. We will prototype them by using Java (Netbeans), the semantic
framework jena 2.6.3.
AKNOWLEDGEMENT
The authors would like to thank for funding this research under Assoc. Prof. Dr Nor Azan grant.
AKNOWLEDGEMENT
The authors would like to thank for funding this research under Assoc. Prof. Dr Nor Azan grant.
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