Indian Journal of Science and Technology

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Development of Guidelines for the Academic Support of Students by using the a Priori Algorithm


Affiliations
1 Suan Sunandha Rajabhat University, Bangkok, Thailand
2 Ubon Ratchathani University, Ubon Ratchathani, Thailand
 

Objective: To examine the relationship of courses and tendency to attain weak study outcome to assist and protect students from school expulsion in higher education. Statistical Analysis: The data used for operating the Apriori algorithm in association rule was the grade points of the students from the Department of Information Technology, Faculty of Science and Technology, Suan Sunandha Rajabhat University, who each studied 23 courses in the general education category and the information technology category in total. This data had been recorded between 2011 and 2016 (3,200 records). Findings: The accuracy of thirty association rules with test data of 1,200 student records was real study outcome of all students from academic year of 2017 to 2018. Data from these 2 academic years were not used to formulate the rules. According to evaluation of rules’ efficiency defined by accuracy was 89%. Application/Improvement: The association rules to provide, assistance, approaches and mentorship to students with risk via online social media. These students would be able to adapt themselves and attained better study outcome.
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  • Ranumas M. Effective teaching and learning in higher education, Journal of Southern Technology. 2011; 9(2):169−76.

  • Nawarat K, Varoonkanya K. Factors Affecting The Drop Out of Rajamangala University of Technology Phra Nakhon Students in 2014 Academic Year. Research Funded by Rajamangala University of Technology Phra Nakhon; 2015. p. 1−97 .

  • Nuttid S, Anthika S. The use of association rules to find the factors effected to the risk of low proficiency students Case study: Prince of Songkla University, Journal of Management Sciences. 2011; 28(1):65−79.

  • Data Mining. Date accessed: 17/09/2018. https://en.wikipedia.org/wiki/Data_mining.

  • Larose DT. Discovering knowledge in data: an introduction to data mining. John Wiley and Sons; 2005. PMCid: PMC1157106.

  • Agrawal R, Srikant R. Fast Algorithms for Mining Association Rules in Large Databases. Proceedings of the 20th International Conference on Very Large Data Bases, Morgan Kaufmann Publishers Inc.; 1994. p. 487−99.

  • Han J, Pei J, Yin Y. Mining frequent patterns without candidate generation. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data; 2000. p. 1−12. https://doi.org/10.1145/342009.335372.

  • Goswami DN, Chaturvedi A, Raghuvanshi CS. An algorithm for frequent pattern mining based on apriori, International Journal on Computer Science and Engineering. 2010; 2(4):942−47.


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  • Development of Guidelines for the Academic Support of Students by using the a Priori Algorithm

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Authors

Sumitra Nuanmeesri
Suan Sunandha Rajabhat University, Bangkok, Thailand
Wongkot Sriurai
Ubon Ratchathani University, Ubon Ratchathani, Thailand

Abstract


Objective: To examine the relationship of courses and tendency to attain weak study outcome to assist and protect students from school expulsion in higher education. Statistical Analysis: The data used for operating the Apriori algorithm in association rule was the grade points of the students from the Department of Information Technology, Faculty of Science and Technology, Suan Sunandha Rajabhat University, who each studied 23 courses in the general education category and the information technology category in total. This data had been recorded between 2011 and 2016 (3,200 records). Findings: The accuracy of thirty association rules with test data of 1,200 student records was real study outcome of all students from academic year of 2017 to 2018. Data from these 2 academic years were not used to formulate the rules. According to evaluation of rules’ efficiency defined by accuracy was 89%. Application/Improvement: The association rules to provide, assistance, approaches and mentorship to students with risk via online social media. These students would be able to adapt themselves and attained better study outcome.

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DOI: https://doi.org/10.17485/ijst%2F2018%2Fv11i39%2F131268