RESEARCH ARTICLE
Adv. Sci. Lett. 20, 1936-1940, 2014
Copyright © 2014 American Scientific Publishers All rights reserved Printed in the United States of America
Advanced Science Letters Vol. 20,1936-1940, 20140–
A Student Modeling based on Bayesian Network Framework for Characterizing Student Learning Style Adhistya Erna Permanasari, Indriana Hidayah, Sapta Nugraha Department of Electrical Engineering and Information Technology Universitas Gadjah Mada, Jl. Grafika no. 2, Yogyakarta 55281, Indonesia
Students have different characteristics of learning, depending on knowledge, interest, learning style, and background. General learning materials for each student may not be properly accepted and will yield ineffective learning process. Therefore, implementation of suitable learning model in term of student characteristic is needed. Specifically, in conventional (face-toface) learning method where the number of student was large, knowing the character of each student is important yet difficult. Conventional learning is the main learning method applied in undergraduate program of Electrical Engineering and Information Technology, Universitas Gadjah Mada (UGM). Thus, finding an appropriate technique to understand student characteristic is important. In this paper, a student modeling based on Bayesian network method is constructed and presented in a conventional learning environment. Bayesian network method is a modeling tool based on causality in the set of random variables that can be used in variety of applications, for instance in the context of education. Bayesian network was implemented by applying K2 algorithm to determine the learning style of each student in English course held in undergraduate program of Electrical Engineering and Information Technology UGM as a case. The evaluation results showed that accuracy of the model reach a value of 80%. It indicated that the model was closely representing the real dataset. Keywords: Student Modeling, Learning Style, Bayesian Network.
1. INTRODUCTION Student modeling is an approach to characterize student as the learning object. It is important to construct a structural education system in order to develop such innovative and effective learning method. Thus, this method is able to solve many education problems. Learning model refers to a conceptual framework that illustrating systematic procedure to organize learning experience for a specific purpose1. This model can be used by learning designer and lecturer as a guideline in developing plans of learning activities. * Email Address:
[email protected] 1936 4, No. 2, 2011
In general, a lecturer gives the same learning format for all students. It is done based on the assumption that students have the similar characteristic. Unfortunately, each student has their own characteristic which influences their understanding to interpret learning material. The students must be provided with material models that meet with their personality. Otherwise, the optimal learning cannot be reached since there is no motivation from students. Students receive knowledges in different ways, including hearing and seeing; by reflecting and acting; reasoning either logically or intuitively; by memorizing or visualizing and drawing analogies; and, either steadily or in small bits and large pieces1. Normally, lecturer focuses
Adv. Sci. Lett. Vol. 20, No. 10-12, 2014 1936-6612/2011/4/400/008 doi:10.1166/asl.2011.1261
doi: 10.1166/asl.2014.5702
RESEARCH ARTICLE
Adv. Sci. Lett. 20, 1936-1940, 2014 the information delivery based on a specific principle or application. Thus, there is a need to develop an adaptive learning method based on students’ behaviour. A student modeling is an adaptive process of different students learning style associated to the necessity, level of understanding, learning style, and student knowledge. This method accommodates the different characteristic of student, so that each student gains the materials that meet their need. A number of approaches that support the student modeling have been applied such as neural network, fuzzy logic, naïve Bayes, and Bayesian network. Bayesian network is a compact representation that describes exact relationships between the parameters in the domain. Bayesian network is a direct acyclic graph where each node describes a probabilistic random variable and correlations between variables2. Bayesian network can represent a wide range of students in student modeling features3. In addition, Bayesian networks have high levels of accuracy and precision are high compared with other techniques in modeling learning systems4. In general, the technique informs the extent to which the characteristics of students to any given learning material concept. The aim of this paper is to develop a student model using Bayesian Network. This model focuses on the comparative evaluation of student model and questionnaire of the the student learning style. There are three kinds of student’s behaviors that are used in the model, including Visual, Auditory, and Kinesthetic (VAK). In this paper, each node on the Bayesian Network represents the relationship of student learning style. Every student has to fill in a questionnaire to determine their learning style based on VAK category. Finally, the information from the questionnaire is compared based on data from Bayesian Network Node. The remainder of the paper is organized as follows. Section 2 introduces the basic concepts of student modeling. Section 3 describes the proposed student modeling. Section 4 presents the student modeling framework. Section 5 reports results and discussion. Finally, Section 6 presents the conclusions of the study. 2. THE BASIC CONCEPTS OF STUDENT MODELING Students learned in different ways, namely hearing and seeing, reflecting and acting, memorizing and visualizing as well as drawing analogies1. Ability and compatibility of a teacher's teaching style should fit the student learning styles. Some researches revealed that greater learning can occur if there is a match between the characteristic of students with faculty teaching techniques 5,6 . Table 1 [7] shows learning style assessment models of VAK based on learning activities. VAK learning style determined three main human sensory, namely visual, auditory and kinesthetic. It is based on the theory of
modality, where there will be one or two dominant sensors from three sensors information. Table. 1. VAK learning styles [7] Learning styles Visual Auditory Kinesthetic
Learning preferences Text, Graphics, Tables Sound Practical related things
Recommended learning activity Figures, videos, lecture notes, animation, e-books, articles, charts, tables, maps. Speaking, seminars, music, group works, virtual lectures, sound samples. Design models, sensitivity, physical exercise, 3D-models, hands-on tests with specific programs.
In this study, utilization and development of potential students should consider the needs and learning styles of students. Visual students will feel comfortable to learn using visualization with the aid of two-dimensional media such as charts, pictures, models, and videos. Auditory students will learn easily by listening or using audio material. While students with kinesthetic type will learn faster by doing certain activities, such as experiments, knock-down, models design, objects manipulation, and others physical activities. Effective learning environments that support to the needs and knowledge of each student can be identified through student modeling. Learning style models classified students according to their ways of receiving and processing information1. Student modeling is very important for an intelligent learning environment to be able to adapt to the needs and knowledge of each student 8,9. There are some approaches for generating student models. Student models can be considered as a layer in the domain model. It is regarded as a core component of intelligent tutoring systems and processes to affective student learning progress. A wide range of student modeling approaches with computations involving some complex features are available, they are Bayesian network 9-11, Dempster-Shafer theory12, and fuzzy logic approach13. Student modeling works step-by-step through the process of solving problems to the systems involved in a process approach to students which is called a searching model. Student modeling is built works in through an understanding of the student’s interaction in a learning environment that is used to adapt according to the student desired model14. 3. THE PROPOSED APPROACH: BAYESIAN NETWORK Bayesian network derived from Bayes theorem, an approach to some uncertainties as measured by the probability theory15. This theorem is based on the generic formula:
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RESEARCH ARTICLE
Adv. Sci. Lett. 20, 1936-1940, 2014
where P (B | A) means the chance of event B if A occurs and P (A | B) is a probability of event A if B occurs. A Bayesian network is a Directed Acyclic Graph (DAG) that equipped by a distribution Conditional Probability Table (CPT) for each node. Every node shows a variable and each arc domain (arrow) between nodes represents a probabilistic dependency16. Bayesian network structure called the Directed Acyclic Graph (DAG) is a directed graph with no directed cycles (Fig. 1). DAG consists of nodes that represent variable attributes (x1, x2, x3, and x4) and variable edge (C) which represents the direct relationship17. Edge indicates the absence of conditional independence relationships among variables.
Fig. 1. Simple Bayesian network structure17 A Bayesian probabilistic approach is needed to obtain some inferences or conclusions18. Inference in a Bayesian network is obtained from each node relationships that exist in the Bayesian structure. Any changes18 on a node will affect the value of the probability on other nodes. K2 algorithm is one of several algorithms that can be used to determine the Bayesian network Structure (BS). This algorithm maximizes the probability P (BS, Databases) by the variable sequences assumptions19. K2 algorithm begins by assuming that each node has no parent. Then the addition into the parent will increase the chances of the final structure. The process will stop if there is no more addition into the parent. According to Cooper19, the function to increase the value of the opportunity structure is:
where Nijk is a calculated relative to πi which is the parent of yi and relative to the set of observations Database. Function Pred (yi) is a function that returns the set of nodes that precede node yi in order19. 4. THE STUDENT MODELING FRAMEWORK This study focused on an approach to English language learning characteristic using conventional methods. In English class, student was not only evaluated from their exam grade but also based on their activity in class. Therefore, it was important to develop an appropriate lecture model with student learning style. To achieve this goal, a Bayesian networks that represent VAK learning styles of a particular student is constructed. Factor analyzed to determine the student's learning style were auditory, visual, and kinesthetic. Evaluation to the auditory learning was conducted by comprehensive listening of students in the form of verbal delivered by remembering sounds and words. It was supported by the use of images, graphics, film and other visual forms. In the relationship between the variables nodes, each node represents a variable of different factors in analyzing each student's learning behaviors. Each node has different parameters variabel to define each learning styles. In this study, it is considered as three-dimensional theory of learning styles, namely Visual, Auditory, and Kinesthetic (VAK)7. The concept of VAK learning styles is recognized as a framework that provides systematic processes to design the learning model based on Bayesian network. Learning model framework includes components of the system according to the features of learning itself. This feature requires the design of a structure or model of Bayesian network topology based on VAK learning styles. To support the design of the database systems, Bayesian network topology is constructed and illustrated in Fig. 2.
1 Learning Style
2 Visual
Practical
Listening
3 Video
Reading
Drawing
Speaking
Seminar
Music
Fig. 2. Topology of Bayesian Network 1938
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Adv. Sci. Lett. 20, 1936-1940, 2014
Bayesian network topology (Fig. 2) is divided into three groups: 1. Group-1: variable that represents the relationship based on learning styles. 2. Group-2: nodes that represent every rule or every subtopic attribute. 3. Group-3: nodes that represent the attributes of learning styles that are used as input data in the student model. This model is designed based on learning style in Table 1 as well as the ease of data capture at the beginning of the English course. Application of learning materials and information were retrieved in the form of questionnaires and student data taken from the lectures based on the proposed attributes. Therefore, the design of this model is expected to support the learning style of the modeling system variables in the Bayesian network learning model. This model based on training data using conditional probability. Simple graphical Bayesian network topology is used to represent knowledge about a domain. In particular, each node in the graph is a random variable, while the edge between the nodes represent probabilistic depends on the random variables accordingly (Fig. 2). 5. RESULT AND DISCUSSION The study was conducted on English Class of undergraduate program in the Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada. Data were taken from 80 students through the courses and questionnaires. Data were processed using Weka Style.csv Learning tools. Weka computing software is a tool used to build a classification model. Bayes theorem algorithm was applied to determine the grouping of students learning style. Firstly, data were preprocessed. The dataset consisted of 9 attributes, including reading, drawing, video, and music. Weka would explore the attributes of dataset with superficial learning style. The grouping process was done based on selected learning style attributes, as presented in Fig. 3.
RESEARCH ARTICLE was chosen for discovering new data patterns that connected to the new data and the existing data. Classifion of the panel allowed user to configure and run one of the selected classifications to be applied to the existing datasets. The Classify panel of weka is showed in Fig.4.
Fig. 4. Classify Panel Computational results that mixed different learning styles are presented in Table 2. Table 2. Results of Learning Styles Students Learning Prediction Prediction Style Student Margin Questionnaire Modeling 00001 Auditory Auditory 0.659553 00002 Auditory Auditory 0.953838 00003 Visual Auditory -0.682147 00004 Visual Visual 0.350021 00005 Auditory Auditory 0.953838 Referring to Table 2, column two is a learning style results from questionnaire data, while column three is student learning styles results using Bayesian network modeling. Table 3 shows student outcomes using Bayesian network modeling. For example, students index 00001 have an auditory learning style type with prediction margin of 0.659553. Values of Prediction Margin are defined as the difference between the estimated probability of the true class ("1") and the prediction of the most likely class other than the true class ("0"). Results of the training set evaluation using weka can be seen in Fig. 5.
Fig. 5. The Result of Bayesian Network Evaluation Fig. 3. Grouping attributes on Weka Learning style datases was processed using Bayesian network learning algorithm outputs. K2 algorithm was selected to obtain learning style output. Training set test
It takes 0:01 seconds for training data using Weka. It is needed to obtain training data model. It results the learning style classification of each student. In the data evaluation, 80% data (64 data) is classified correctly 1939
RESEARCH ARTICLE (correctly classified instances) according to the applied algorithm. Furthermore, 20% data (16 data) is not appropriate according to the class (incorrectly classified instances). After processing the training data, the model accuracy is calculated through evaluation measurement. This evaluation measurement tool is used to determine the model performance. It calculated the number of classification model dataset for predicting the correct and incorrect record at the classification model. The results are tabulated in a confusion matrix as shown in Fig. 6.
Adv. Sci. Lett. 20, 1936-1940, 2014 Therefore, the Bayesian network performed well to classify students based on their learning style in English course which is held conventionally in classrooms. However, in order to achieve higher accuracy model, further study need to be performed to evaluate the questionnaire and involving more samples. For future work, the classification model can be used as a basis of adaptive educational software.
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Fig. 6. The Confusion Matrix Letters a, b, and c (Fig. 6) indicate the type of learning styles as auditory, visual, and kinesthetic respectively. Confusion matrix has the true prediction if the values of questionnaire with the student modeling prediction based on diagonally matrix. Refer to Fig. 7, the true prediction of auditory style is 46. It means that similar number of questionnaire data and student modeling result is 46. The true predictions of visual and kinesthetic are 18 and 0 respectively. The model accuracy can be calculated from the total number of true prediction divided by the total number of all prediction from all matrix elements. Then, the accuracy of learning style data from Fig. 7 as the following:
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From the above results, the accuracy reached 80%. So that, the learning style dataset has expressed a fairly high accuracy using Bayesian network. 6. CONCLUSIONS This paper presented the design of a Bayesian network classification for characterizing student learning style. The model was constructed based on VAK (Visual, Auditory, and Kinesthetic) learning method. The attributes of VAK learning styles are used to build the network topology. By implementing the network topology using computation software Weka, the developed classifier can be evaluated based on its accuracy parameter. Eighty students’ data were collected from an English course. The result showed that accuracy was 80% where the main learning tendency was auditory style.
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