Course Associative Classification Rules (CourseACRs) to Evaluate Course Equivalency for Credits Transferring Process Jamornkul Laokietkul
Course Associative Classification Rules (CourseACRs) to Evaluate Course Equivalency for Credits Transferring Process. Jamornkul Laokietkul Information Technology Department Chandrakasem Rajabhat University, THAILAND
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Abstract This study was to create course associative classification rules called CourseACRs to evaluate course equivalency for a credit transferring process by using the associative classification technique. The purpose of this study was to create course evaluation tools for evaluating course equivalency. This study used course transfer history records of students in three bachelor’s degree programs at Chandrakasem Rajabhat Univeristy, Thailand-- Information Technology, Computer Science and Multimedia Technology-- as a training data set. The result of this study revealed that the CourseACRs performed at a good level of performance with an accuracy rate of 89.63% of the courses equivalency model for the university’s credit transferring process. Finally, the research discovered that the CourseACRs can be a guideline for academic staff and students to evaluate course equivalency in the credit transferring process and to manage an appropriate study plan after the credit transferring process in the future as well.
Keywords: Associative Classification, Course equivalency, Credit transferring. 1. Introduction A course equivalency in higher education is a term used to describe how a course offered by one college or university relates to a course offered by another. If a course is viewed as equal to or more challenging in terms of subject and course material than the course offered by the receiving college or university, the course can be noted as an equivalent course [1]. A course equivalency can be unilateral when it is deemed equivalent by the receiver. Alternatively, it could be bilateral when both sender and receiver acknowledge their acceptance of each other's courses as equivalent. The methods and measures used to determine course equivalency vary by institution, state, region and country [2]. The determination of transfer credit acceptance is made by the University Recorders and Registrar after a transfer applicant is admitted to the university. Student must be required to submit official transcripts of all completed coursework before a final decision can be made on credit transfer [1]. This research aimed to create an evaluation tool to evaluate course equivalency. The study also used course transfer history records of students in three bachelor’s degree programs at Chandrakasem Rajabhat University, Thailand--Information Technology, Computer Science and Multimedia Technology --in the testing.
2. Related work According to previous research, in order to apply the Associative Classification technique. The association classification can used to classification and prediction in various dimensions. In [5] they using of association rule with the training and testing approach for heart disease prediction, and [6] used generalized association rules to find library reuse patterns or [7] also applied an association rules to predict HIV-1 drug resistance. Many researchers have studied on the student classification, especially [4] [8] and [9] studied of critical factors that influenced students' success in education programs. Alternatively, [10] study about the factors that influenced university graduates to choose for self-employment or [11] researched on the differentiation of students who transferred from other universities. Furthermore in case of a student support, [12] study on decision support system for course and program assessment.
Journal of Next Generation Information Technology (JNIT) Volume 5, Number 4, November 2014
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Course Associative Classification Rules (CourseACRs) to Evaluate Course Equivalency for Credits Transferring Process Jamornkul Laokietkul
However, on the important of credit transferring [2] was designed an appropriated of curriculum for critically considers the European Credit Transfer System or [13] also study in the effective of credit transferring that surfaced in students' accounts and their lifelong learning journeys. As the associative classification technique was useful in many studies focusing on student classification, the researcher chose to apply it to evaluate the university’s credit transferring process.
3. Associative Classification Associative classification is one of the most interesting data classification techniques in data mining. The rules from this technique were called “Class Association Rules (CARs)” and this technique utilizes association rule discovery methods and data classification. [9]
3.1. Association Rules Discovery Association Rules Discovery is a branch in the data mining approach that is most famous in many research studies. The main characteristic of association rules mining is its use of the upward closure property of the support to confine the searching space [3]. The goal of the rule discovery is to find high confidence (or accurate) rules by computing a frequency of items set within the minimum Support threshold (Supp.) as in classification applications [4]. An applied Association Rules Discovery Method to Classification as CARs will cull from the affected rules with each class label.
3.2. CARs Generator & Classifier CARs are rules that focuses particularly on class and association with other items set, but these rules differ from traditional association rules that focus on any association between items set. The CARs pattern is represented as follows:
{itemi ,..., item j } Class : ( Supp.), (Conf .)
(1)
Items i-j are part of an associative data and Class functions as Classifier of them. The support (Supp.) of the item sets is defined as the proportion of transactions in the database that contains the item sets as illustrated in the following formula:
Supp.
(X Y) N
(2)
The confidence (Conf.) of an association rule is defined as the probability that a transaction contains Y given that it contains X, and yields the following formula:
Conf .
Supp.( X Y ) Supp.( X )
(3)
A number of rules and its iterations of this process are scale to the data sets. Afterward, the selection rules will specify with passes the MinSupp and MinConf thresholds and ordered rules by the precedence process considered by Conf. and Supp.
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Course Associative Classification Rules (CourseACRs) to Evaluate Course Equivalency for Credits Transferring Process Jamornkul Laokietkul
4. Course Associative Classification Rules The course associative classification rules as called CourseACRs in this research used a set of course transfer history records of students in 3 bachelor’s degree programs at Chandrakasem Rajabhat University, Thailand (Information Technology, Computer Science and Multimedia Technology) in the testing. The procedure of CourseACRs comprised 1) Data preparation 2) Generate CourseACRs and 3) classification.
4.1. Data preparation The data preparation process is to prepare the course transfer history of students in 3 bachelor’s degree programs at Chandraksem University, Thailand--Information Technology, Computer Science and Multimedia Technology-- as a training data set. It has transformed some attributes to an appropriate format and separated the data into a training set (49 cases 246 records) to generate rules for an evaluating model and a testing data set (24 cases with 134 records) for accuracy estimates.
Figure 1. Course transfer history in data transformation process.
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Course Associative Classification Rules (CourseACRs) to Evaluate Course Equivalency for Credits Transferring Process Jamornkul Laokietkul
4.2. Generate CourseACRs For this study, the researcher applied Course Associative Classification Rules (CourseACRs) to each course in 3 programs, and This process divided into 2 parts (as shown in Figure 1), First it operated to find-out all frequent rule items which passes of the Minimum of Supp (MinSupp) and minimum of Conf (MinConf) threshold. In this study, the MinSupp and MinConf were specified as 0.01% and as 30%. Then, the consideration method generates the specified rules with the class label items (Courses). This process starts after importing the training data set.
Figure 2. CourseACRs process
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Course Associative Classification Rules (CourseACRs) to Evaluate Course Equivalency for Credits Transferring Process Jamornkul Laokietkul
Figure 2 shows that the CourseACRs process that deviced into 2 main steps. 1) Find out frequent item sets from the training data set in the database and generate it into frequent item sets. Determine candidate item sets and continue to the next iterations. The iteration is upon attributes and items. 2) Generate CourseACRs from the complete frequent set with calculate Conf. and Supp. The CourseACRs are separated by course equivalency into 2 formats: An equivalence course referred to the course and relevant course as 1:1
RefCourseA CurrCours eX. RefCourseA RelCourse X. An equivalence course referred to the course and relevant course as 2:1
RefCourseA, RefCourseB CurrCours eX. RefCourseA, RefCourseB RelCourseΥ. The chosen rules were selected by minimum of Conf (Conf ≥ 30%) concern. Then, subsets of these rules that have high confidence are selected form relationship between items set and items in the class label. Finally, the CourseACRs were arranged by precedence in the descending order.
4.3. Classification After the CourseACRs generation process, the researcher chose CourseACRs by examining the precedence already used as a student classifier tool and the class label from the most precedence rules will select as results. The assort process can be presented as shown in pseudo-code in Figure 3. For (i=0; i CSSC1201
0.09
0.86
3901-1002 --> CSSC1201
0.02
0.50
3100-0003,3128-1001 --> CSSC1301
0.01
0.33
3128-1002,3128-2104 --> CSSC1301
0.01
0.50
…
…
3128-2206 --> ITEC3302
0.01
0.50
3901-2007 --> ITEC3302
0.01
1.00
3128-2005,3128-2204 --> ITEC4301
0.01
1.00
3128-2206 --> ITSC3301
0.01
0.50
…
…
3204-2002 --> MATH1606
0.05
0.88
3204-2002 --> MATH1606
0.05
0.88
…
…
CourseACRs
…
…
…
5.1. Testing CourseACRs This process used testing data sets (24 cases with 134 records) from the preparation process. The testing process consists of 3 steps and starts with 1) importing the testing data set in the role as appellants data with preparation and transformation before using the CourseACRs. 2) finding out the related results by using the CourseACRs assorting process and 3) evaluating an accuracy percentage compare with a number of data sets and repeat until all of the records. After, accuracy was measured in the testing process. The accuracy of CourseACRs to transfer course of Information Technology's student was the best-equalized model (96.00%) while the overall accuracy was a good performance (89.63%). Furthermore, the accuracy of other programs such as Computer Science, and Multimedia Technology students were simultaneous in good equalized model (87.30% and 80.95%). The results of this process are shown in Table 2.
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Course Associative Classification Rules (CourseACRs) to Evaluate Course Equivalency for Credits Transferring Process Jamornkul Laokietkul
Table 2. the experiments results of CourseACRs Accuracy
Curriculum
Appellants
Testing set (Request Course)
Transferred
%
E
Course
Computer Science
Information Technology
Multimedia Technology
1
7
6
85.71
14.29
2
5
4
80.00
20.00
3
5
5
100.00
-
4
4
3
75.00
25.00
5
5
5
100.00
-
6
6
6
100.00
-
7
8
7
87.50
12.50
8
9
7
77.78
22.22
9
3
3
100.00
-
10
5
4
80.00
20.00
11
6
5
83.33
16.67
11
63
55
87.30
12.70
12
4
4
100.00
-
13
4
4
100.00
-
14
3
3
100.00
-
15
6
5
83.33
16.67
16
5
5
100.00
-
17
7
6
85.71
14.29
18
7
6
85.71
14.29
19
6
6
100.00
-
20
8
8
100.00
-
9
50
47
96.00
4.00
21
4
3
75.00
25.00
22
6
5
83.33
16.67
23
5
5
100.00
-
24
6
4
66.67
33.33
4
21
17
80.95
19.05
Overalls
134
119
89.63
MSE 10.45
6. Conclusion This study used the course associative classification rules (CourseACRs) approach to evaluate course equivalency for the credit transferring process by using the associative classification technique. The purpose of this study was to create an evaluation tool for evaluating course equivalency. The samples use of this study was the course transfer history records of students in 3 bachelor ‘s degree programs at Chandrakasem Rajabhat University, Thailand: Information Technology, Computer Science and Multimedia Technology. The results of this study purposed that an evaluating course equivalency with CourseACRs perform at a good level of performance with accuracy rate at 89.63% of the course equivalency transferred model. The restriction of this study was a lack of data sets in curriculum class labels that affected its accuracy performances. Furthermore, the effort to reduce overfitting must be improved in the next research.
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Course Associative Classification Rules (CourseACRs) to Evaluate Course Equivalency for Credits Transferring Process Jamornkul Laokietkul
Finally, the research discovered that the CourseACRs can be used as a guideline for academic staff and students in an evaluation of course equivalency in the credit transferring process. The CourseACRs may also be used as a base model for research to develop a decision support system for the credit transferring process and to manage an appropriate study plan after the credit transferring process in the future as well.
7. References [1] Council for Higher Education Accreditation (CHEA). (2000). A Statement to the Community: Transfer and the Public Interest. Available at http://www.chea.org. Nov, 2000. [2] Gleeson, Jim. "The European Credit Transfer System and curriculum design: product before process?." Studies in Higher Education 38.6 : 921-938, 2013. [3] Di Paolo, Terry, and Ann Pegg. "Credit transfer amongst students in contrasting disciplines: examining assumptions about wastage, mobility and lifelong learning." Journal of Further and Higher Education 37.5 : 606-622, 2013. [4] E. Tovar, J. Carrillo, and R. Colomo, "Proposal of an educational model for technical courses in the context of the European Convergence in Higher Education," presented at Frontiers in education conference - global engineering: knowledge without borders, opportunities without passports, 2007. FIE '07. 37th annual, 2007. [5] C. Ordonez and C. Ordonez, "Association rule discovery with the train and test approach for heart disease prediction" Information Technology in Biomedicine, IEEE Transactions on, vol. 10, pp. 334-343, 2006. [6] A. Michail and A. Michail, "Data mining library reuse patterns using generalized association rules" presented at Software Engineering, Proceedings of the 2000 International Conference on, 2000. [7] A. Srisawat and B. Kijsirikul, "Using associative classification for predicting HIV-1 drug resistance," presented at Hybrid Intelligent Systems, 2004. HIS '04. Fourth International Conference on, 2004. [8] J. M. Brown, "Key factors that influence students' success in postsecondary vocational education programs "Journal of Career Development, vol. 13, 2005. [9] J. Laokietkul, N. Utakrit, and P. Meesad. " A Forecasting Model to Evaluate a Freshman’s Ability to Succeed by Using Particular Full-Scaled Class Association Rules (PFSCARs) " presented at IACSIT Spring Conference, Singapore, 2009. [10] P. Jongmesuk, "Factors that influenced graduates of srinakharinwirot university, Bangsaen campus to choose self-employment." [11] J. Stewart and F. Martinello, “Are transfer students different? An examination of first-year grades and course withdrawals,” Can. J. High. Educ., vol. 42, pp. 25–42, 2012. [12] I. E. Derivs Z. Deniz, "Using an Academic DSS for student, course and program assessment," presented at International Conference on Engineering Education, Oslo, Norway, 2001. [13] Pegg, Ann, and Terry Di Paolo. "Narrating unfinished business: adult learners using credit transfer to re-engage with higher education." Studies in Continuing Eduation 35.2 : 209-223, 2013.
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