Abstract
In the education sector, it has been a challenging task to identify the students individually to take appropriate actions to get a very deserving outcome from them. On the other hand, a student getting higher education should have the knowledge about the market demands and where are their weaknesses. If it is possible to get some data from students’ academic record and their percepts on some factors related to academic performances those may help to understand the reasons for success and failure which would be very useful in the educational environment and student’s success rate. We collect data from students from different institutes. First, we create an online survey form to get data, and then we process them to get some valuable information. After getting those data, we visualize then analyze them from different prospects. We try to get some exact knowledge which can be crucial for students’ success or failure in an academic environment. We apply the data mining technique decision tree algorithm (j48) to develop a model that shows us the hierarchy of different attributes related to students’ academic performance and their personal behaviors that affect a students’ academic status. Then we try to get information about which attributes have positive and which attribute has a negative impact on students’ academic growth.
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References
S. Hussain, N.A. Dahan, F.M. Ba-Alwib, N. Ribata, Educational data mining and analysis of students’ academic performance using WEKA. Indones. J. Electr. Eng. Comput. Sci. 9(2), 447–459 (2018)
E.B. Costa, B. Fonseca, M.A. Santana, F.F. de Araújo, J. Rego, Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Comput. Hum. Behav. 73, 247–256 (2017)
R. Asif, A. Merceron, S.A. Ali, N.G. Haider, Analyzing undergraduate students’ performance using educational data mining. Comput. Educ. 113, 177–194 (2017)
M. Kumar, A.J. Singh, D. Handa, Literature survey on student’s performance prediction in education using data mining techniques. Int. J. Educ. Manag. Eng. 7(6), 42–49 (2017)
A. Mueen, B. Zafar, U. Manzoor, Modeling and predicting students’ academic performance using data mining techniques. Int. J. Mod. Educ. Comput. Sci. 8(11), 36.s (2016)
E.A. Amrieh, T. Hamtini, I. Aljarah, Mining educational data to predict student’s academic performance using ensemble methods. Int. J. Database Theory Appl. 9(8), 119–136 (2016)
M.A. Yehuala, Application of data mining techniques for student success and failure prediction (The case of Debre_Markos University). Int. J. Sci. Technol. Res. 4(4), 91–94 (2015)
E.A. Amrieh, T. Hamtini, I. Aljarah, Preprocessing and analyzing educational data set using X-API for improving student’s performance, in 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) (IEEE, 2015), pp. 1–5
A.M. Shahiri, W. Husain, A review on predicting student’s performance using data mining techniques. Procedia Comput. Sci. 72, 414–422 (2015)
S. Borkar, K. Rajeswari, Attributes selection for predicting students’ academic performance using education data mining and artificial neural network. Int. J. Comput. Appl. 86(10) (2014)
A.B.E.D. Ahmed, I.S. Elaraby, Data mining: a prediction for student’s performance using classification method. World J. Comput. Appl. Technol. 2(2), 43–47 (2014)
V. Ramesh, P. Parkavi, K. Ramar, Predicting student performance: a statistical and data mining approach. Int. J. Comput. Appl. 63(8) (2013)
D. Kabakchieva, Predicting student performance by using data mining methods for classification. Cybern. Inf. Technol. 13(1), 61–72 (2013)
B.K. Baradwaj, S. Pal, Mining educational data to analyze students’ performance. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 2(6), 2011 (2012)
S.K. Yadav, S. Pal, Data mining: a prediction for performance improvement of engineering students using classification. World Comput. Sci. Inf. Technol. J. (WCSIT), 2(2), 51–56 (2012). ISSN: 2221-0741
E. Osmanbegovic, M. Suljic, Data mining approach for predicting student performance. Econ. Rev. J Econ. Bus. 10(1), 3–12 (2012)
R.R. Kabra, R.S. Bichkar, Performance prediction of engineering students using decision trees. Int. J. Comput. Appl. 36(11), 8–12 (2011)
M. Ramaswami, R. Bhaskaran, A CHAID based performance prediction model in educational data mining. IJCSI Int. J. Comput. Sci. Issues 7(1) (2010). ISSN (Online): 1694-0784, ISSN (Print): 1694-0814
O.J. Oyelade, O.O. Oladipupo, I.C. Obagbuwa, Application of k means clustering algorithm for prediction of students academic performance. Int. J. Comput. Sci. Inf. Secur. (IJCSIS) 7(1) (2010)
R.S. Baker, A.T. Corbett, S.M. Gowda, A.Z. Wagner, B.A. MacLaren, L.R. Kauffman, A.P. Mitchell, S. Giguere, Contextual slip and prediction of student performance after use of an intelligent tutor, in International Conference on User Modeling, Adaptation, and Personalization (Springer, Berlin, Heidelberg, 2010), pp. 52–63
N. Thai-Nghe, L. Drumond, A. Krohn-Grimberghe, L. Schmidt-Thieme, Recommender system for predicting student performance. Procedia Comput. Sci. 1(2), 2811–2819 (2010)
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Shetu, S.F., Saifuzzaman, M., Moon, N.N., Sultana, S., Yousuf, R. (2021). Student’s Performance Prediction Using Data Mining Technique Depending on Overall Academic Status and Environmental Attributes. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1166. Springer, Singapore. https://doi.org/10.1007/978-981-15-5148-2_66
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