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<citation_list><citation key="ref0"><journal_title>In 2014 17th international conference on computer and information technology (ICCIT) (pp</journal_title><author>Ahmed</author><cYear>2014</cYear><doi>10.1109/iccitechn.2014.7073107</doi><article_title>Knowledge discovery from academic data using association rule mining</article_title><unstructured_citation>Ahmed, S., Paul, R., Hoque, M.L., Sayed, A. (2014). Knowledge discovery from academic data using association rule mining. In 2014 17th international conference on computer and information technology (ICCIT) (pp. 314-319): IEEE. [CrossRef]</unstructured_citation></citation><citation key="ref1"><journal_title>Cluster Computing</journal_title><author>Al-Obeidat</author><volume>21</volume><issue>1</issue><first_page>623</first_page><cYear>2017</cYear><doi>10.1007/s10586-017-0967-4</doi><article_title>Analyzing students' performance using multicriteria classification</article_title><unstructured_citation>Al-Obeidat, F., Tubaishat, A., Dillon, A., Shah, B. (2017). Analyzing students' performance using multicriteria classification. Cluster Computing, 21(1), 623-632. [CrossRef]</unstructured_citation></citation><citation key="ref2"><unstructured_citation>Bayer, J., Bydzovsk'a, H., G'eryk, J., Obsivac, T., Popelinsky, L. (2012). Pre-dicting drop-out from social behaviour of students. In International confer-ence on educational data mining (EDM).</unstructured_citation></citation><citation key="ref3"><journal_title>Journal of Educational Admin-istration</journal_title><author>Bendikson</author><volume>49</volume><issue>4</issue><first_page>433</first_page><cYear>2011</cYear><doi>10.1108/09578231111146498</doi><article_title>Identifying the comparative academic performance of secondary schools</article_title><unstructured_citation>Bendikson, L., Hattie, J., Robinson, V. (2011). Identifying the comparative academic performance of secondary schools. Journal of Educational Admin-istration, 49(4), 433-449. [CrossRef]</unstructured_citation></citation><citation key="ref4"><journal_title>Acm Sigkdd Explorations Newsletter</journal_title><author>Calders</author><volume>13</volume><issue>2</issue><first_page>3</first_page><cYear>2012</cYear><doi>10.1145/2207243.2207245</doi><article_title>Introduction to the special section on educational data mining</article_title><unstructured_citation>Calders, T., &amp; Pechenizkiy, M. (2012). Introduction to the special section on educational data mining. Acm Sigkdd Explorations Newsletter, 13(2), 3-6. [CrossRef]</unstructured_citation></citation><citation key="ref5"><journal_title>Expert Systems with Applications</journal_title><author>Campagni</author><volume>42</volume><issue>13</issue><first_page>5508</first_page><cYear>2015</cYear><doi>10.1016/j.eswa.2015.02.052</doi><article_title>Data mining models for student careers</article_title><unstructured_citation>Campagni, R., Merlini, D., Sprugnoli, R., Verri, M.C. (2015). Data mining models for student careers. Expert Systems with Applications, 42(13), 5508- 5521. [CrossRef]</unstructured_citation></citation><citation key="ref6"><doi>10.1145/3120259</doi><unstructured_citation>Carter, A.S., Hundhausen, C.D., Adesope, O. (2017). Blending measures of programming and social behavior into predictive models of student achieve-ment in early computing courses. ACM Transactions on Computing Educa-tion, 17(3),12. [CrossRef]</unstructured_citation></citation><citation key="ref7"><journal_title>In Proceedings of the 26th international conference on world wide web com-panion (pp</journal_title><author>Daud</author><cYear>2017</cYear><doi>10.1145/3041021.3054164</doi><article_title>Predicting student performance using advanced learning analytics</article_title><unstructured_citation>Daud, A., Aljohani, N.R., Abbasi, R.A., Lytras, M.D., Abbas, F., Alowibdi, J.S. (2017). Predicting student performance using advanced learning analytics. In Proceedings of the 26th international conference on world wide web com-panion (pp. 415-421). [CrossRef]</unstructured_citation></citation><citation key="ref8"><doi>10.18608/jla.2016.33.15</doi><unstructured_citation>Dvorak, T., &amp; Jia, M. (2016). Do the timeliness, regularity, and intensity of online work habits predict academic performance? Journal of Learning Ana-lytics, 3(3), 318-330. [CrossRef]</unstructured_citation></citation><citation key="ref9"><journal_title>Journal of Learning Ana-lytics</journal_title><author>Hart</author><volume>4</volume><issue>2</issue><first_page>129</first_page><cYear>2017</cYear><doi>10.18608/jla.2017.42.11</doi><article_title>Individual differences related to college students' course performance in calculus II</article_title><unstructured_citation>Hart, S., Daucourt, M., Ganley, C. (2017). Individual differences related to college students' course performance in calculus II. Journal of Learning Ana-lytics, 4(2), 129-153. [CrossRef]</unstructured_citation></citation><citation key="ref10"><journal_title>International Journal of Data Science and Analytics</journal_title><author>Helal</author><volume>7</volume><issue>3</issue><first_page>227</first_page><cYear>2018</cYear><doi>10.1007/s41060-018-0141-y</doi><article_title>Iden-tifying key factors of student academic performance by subgroup discovery</article_title><unstructured_citation>Helal, S., Li, J., Liu, L., Ebrahimie, E., Dawson, S., Murray, D.J. (2018). Iden-tifying key factors of student academic performance by subgroup discovery. International Journal of Data Science and Analytics, 7(3), 227-245. [CrossRef]</unstructured_citation></citation><citation key="ref11"><journal_title>International Journal of Innovations &amp; Advancement in Computer Science IJIACS ISSN</journal_title><author>Pal</author><volume>6</volume><issue>4</issue><first_page>2347</first_page><cYear>2017</cYear><doi>10.2139/ssrn.2991214</doi><article_title>Is alcohol affect higher education students' performance: searching and predicting pattern using data mining algorithms</article_title><unstructured_citation>Pal, S., &amp; Chaurasia, V. (2017). Is alcohol affect higher education students' performance: searching and predicting pattern using data mining algorithms. International Journal of Innovations &amp; Advancement in Computer Science IJIACS ISSN, 6(4), 2347-8616. [CrossRef]</unstructured_citation></citation><citation key="ref12"><journal_title>In Proceedings of the fourth interna-tional conference on learning analytics and knowledge (pp</journal_title><author>Papamitsiou</author><cYear>2014</cYear><doi>10.1145/2567574.2567609</doi><article_title>Temporal learning analytics for computer based testing</article_title><unstructured_citation>Papamitsiou, Z.K., Terzis, V., Economides, A.A. (2014). Temporal learning analytics for computer based testing. In Proceedings of the fourth interna-tional conference on learning analytics and knowledge (pp. 31-35): ACM. [CrossRef]</unstructured_citation></citation><citation key="ref13"><unstructured_citation>Saarela, M., &amp; K¨arkk¨ainen, T. (2015). Analysing student performance using sparse data of core bachelor courses. Journal of Educational Data Mining, 7(1), 3-32.</unstructured_citation></citation><citation key="ref14"><journal_title>In Proceedings of the Third (2016) ACM Conference on Learning Scale (pp</journal_title><author>Wang</author><cYear>2016</cYear><doi>10.1145/2876034.2893382</doi><article_title>The opportunity count model: a flexible approach to modeling student performance</article_title><unstructured_citation>Wang, Y., Ostrow, K., Adjei, S., Heffernan, N. (2016). The opportunity count model: a flexible approach to modeling student performance. In Proceedings of the Third (2016) ACM Conference on Learning@ Scale (pp. 113-116): ACM. [CrossRef]</unstructured_citation></citation><citation key="ref15"><unstructured_citation>Zimmermann, J., Brodersen, K.H., Heinimann, H.R., Buhmann, J.M. (2015). A model-based approach to predicting graduate-level performance using indi-cators of undergraduate-level performance. Journal of Educational Data Min-ing, 7(3), 151-176.</unstructured_citation></citation><citation key="ref16"><journal_title>In-ternational Journal of Artificial Intelligence in Education</journal_title><author>Beemer</author><volume>28</volume><issue>3</issue><first_page>315</first_page><cYear>2018</cYear><doi>10.1007/s40593-017-0148-x</doi><article_title>Ensemble learning for estimating individualized treatment effects in student success studies</article_title><unstructured_citation>Beemer, J., Spoon, K., He, L., Fan, J., Levine, R.A. (2018). Ensemble learning for estimating individualized treatment effects in student success studies. In-ternational Journal of Artificial Intelligence in Education, 28(3), 315-335. [CrossRef]</unstructured_citation></citation><citation key="ref17"><unstructured_citation>Bucos, M., &amp; Druagulescu, B. (2018). Predicting student success using data generated in traditional educational environments. TEM Journal, 7(3), 617- 625.</unstructured_citation></citation><citation key="ref18"><journal_title>In IEEE 7th international conference on engineering education ICEED 2015 (pp</journal_title><author>Buniyamin</author><cYear>2015</cYear><doi>10.1109/iceed.2015.7451491</doi><article_title>Educational data mining for prediction and classification of engineering students achievement</article_title><unstructured_citation>Buniyamin, N., Mat, U.B., Arshad, P.M. (2015). Educational data mining for prediction and classification of engineering students achievement. In IEEE 7th international conference on engineering education ICEED 2015 (pp. 49-53). [CrossRef]</unstructured_citation></citation><citation key="ref19"><journal_title>In World conference on in-formation systems and technologies (pp</journal_title><author>Fernandes</author><cYear>2017</cYear><doi>10.1007/978-3-319-56535-4_29</doi><article_title>Educational data mining: discovery standards of academic performance by students in pub-lic high schools in the Federal District of Brazil</article_title><unstructured_citation>Fernandes, E., Carvalho, R., Holanda, M., Van Erven, G. (2017). Educational data mining: discovery standards of academic performance by students in pub-lic high schools in the Federal District of Brazil. In World conference on in-formation systems and technologies (pp. 287-296). [CrossRef]</unstructured_citation></citation><citation key="ref20"><journal_title>International Journal of Data Science and Analytics</journal_title><author>Helal</author><volume>7</volume><issue>3</issue><first_page>227</first_page><cYear>2018</cYear><doi>10.1007/s41060-018-0141-y</doi><article_title>Iden-tifying key factors of student academic performance by subgroup discovery</article_title><unstructured_citation>Helal, S., Li, J., Liu, L., Ebrahimie, E., Dawson, S., Murray, D.J. (2018). Iden-tifying key factors of student academic performance by subgroup discovery. International Journal of Data Science and Analytics, 7(3), 227-245. [CrossRef]</unstructured_citation></citation><citation key="ref21"><unstructured_citation>Kamley, S., Jaloree, S., Thakur, R.S. (2016). A review and performance pre-diction of students' using association rule mining based approach. Data Min-ing and Knowledge Engineering, 8(8), 252-259.</unstructured_citation></citation><citation key="ref22"><journal_title>In E international conference on teaching assessment and learning for engineering (TALE) (pp</journal_title><author>Khan</author><cYear>2016</cYear><doi>10.1109/tale.2016.7851789</doi><article_title>Analysing the impact of poor teaching on student performance</article_title><unstructured_citation>Khan, A., &amp; Ghosh, S.K. (2016). Analysing the impact of poor teaching on student performance. In E international conference on teaching, assessment, and learning for engineering (TALE) (pp. 169-175): IEEE. [CrossRef]</unstructured_citation></citation><citation key="ref23"><journal_title>International Journal of Education and Management Engineering</journal_title><author>Kaviyarasi</author><volume>8</volume><issue>6</issue><first_page>15</first_page><cYear>2018</cYear><doi>10.5815/ijeme.2018.06.02</doi><article_title>Exploring the high potential factors that affects students' academic performance</article_title><unstructured_citation>Kaviyarasi, R., &amp; Balasubramanian, T. (2018). Exploring the high potential factors that affects students' academic performance. International Journal of Education and Management Engineering, 8(6), 15. [CrossRef]</unstructured_citation></citation><citation key="ref24"><journal_title>Journal of Learning Analytics</journal_title><author>O'Connell</author><volume>5</volume><issue>3</issue><first_page>167</first_page><cYear>2018</cYear><doi>10.18608/jla.2018.53.11</doi><article_title>Student ability best predicts final grade in a college algebra course</article_title><unstructured_citation>O'Connell, K.A., Wostl, E., Crosslin, M., Berry, T.L., Grover, J.P. (2018). Student ability best predicts final grade in a college algebra course. Journal of Learning Analytics, 5(3), 167-181. [CrossRef]</unstructured_citation></citation><citation key="ref25"><unstructured_citation>Tair, M.M.A., &amp; El-Halees, A.M. (2012). Mining educational data to improve students' performance: a case study. International Journal of Information, 2(2), 140-146.</unstructured_citation></citation><citation key="ref26"><unstructured_citation>Xiong, X., Adjei, S., Heffernan, N. (2014). Improving retention performance prediction with prerequisite skill features. In Educational data mining 2014.</unstructured_citation></citation><citation key="ref27"><doi>10.1109/IAdCC.2014.6779384</doi><unstructured_citation>G. Gray, C. McGuinness, P. Owende, An application of classification models to predict learner progression in tertiary education, in: Advance Computing Conference (IACC), 2014 IEEE International, IEEE, 2014, pp. 549-554 [CrossRef]</unstructured_citation></citation><citation key="ref28"><doi>10.1109/CNT.2014.7062736</doi><unstructured_citation>M. Mayilvaganan, D. Kalpanadevi, Comparison of classification techniques for predicting the performance of students academic environment, in: Com-munication and Network Technologies (ICCNT), 2014 International Confer-ence on, IEEE, 2014, pp. 113-118. [CrossRef]</unstructured_citation></citation><citation key="ref29"><unstructured_citation>B. M. Bidgoli, D. Kashy, G. Kortemeyer, W. Punch, Predicting student per-formance: An application of data mining methods with the educational web-based system lon-capa, in: Proceedings of ASEE/IEEE frontiers in education conference, 2003.</unstructured_citation></citation><citation key="ref30"><unstructured_citation>S. Sembiring, M. Zarlis, D. Hartama, S. Ramliana, E. Wani, Prediction of stu-dent academic performance by an application of data mining techniques, in: International Conference on Management and Artificial Intelligence IPEDR, Vol. 6, 2011, pp. 110-114.</unstructured_citation></citation><citation key="ref31"><doi>10.1007/11774303_52</doi><unstructured_citation>W. Ham¨ al¨ ainen, ¨ M. Vinni, Comparison of machine learning methods for intelligent tutoring systems, in: Intelligent Tutoring Systems, Springer, 2006, pp. 525-534. [CrossRef]</unstructured_citation></citation><citation key="ref32"><doi>10.1186/s40165-014-0010-2</doi><unstructured_citation>S. T. Jishan, R. I. Rashu, N. Haque, R. M. Rahman, Improving accuracy of students final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique, Decision Analytics 2 (1) (2015) 1-25. [CrossRef]</unstructured_citation></citation><citation key="ref33"><doi>10.5120/10489-5242</doi><unstructured_citation>V. Ramesh, P. Parkavi, K. Ramar, Predicting student performance: a statistical and data mining approach, International Journal of Computer Applications 63 (8) (2013) 35-39. [CrossRef]</unstructured_citation></citation><citation key="ref34"><unstructured_citation>E. Osmanbegovic, M. Sulji ' c, Data mining approach for predicting student performance, Economic Review 10 (1)</unstructured_citation></citation><citation key="ref35"><unstructured_citation>T. Wang, A. Mitrovic, Using neural networks to predict student's perfor-mance, in: Computers in Education, 2002. Proceedings. International Confer-ence on, IEEE, 2002, pp. 969-973</unstructured_citation></citation><citation key="ref36"><doi>10.1109/ICSIMA.2013.6717966</doi><unstructured_citation>P. M. Arsad, N. Buniyamin, J.-l. A. Manan, A neural network students' per-formance prediction model (nnsppm), in: Smart Instrumentation, Measure-ment and Applications (ICSIMA), 2013 IEEE International Conference on, IEEE, 2013, pp. 1-5. [CrossRef]</unstructured_citation></citation><citation key="ref37"><unstructured_citation>V. Oladokun, A. Adebanjo, O. Charles-Owaba, Predicting students academic performance using artificial neural network: A case study of an engineering course, The Pacific Journal of Science and Technology 9 (1) (2008) 72-79.</unstructured_citation></citation><citation key="ref38"><unstructured_citation>D. M. S.Anupama Kumar, Appraising the significance of self regulated learn-ing in higher education using neural networks, International Journal of Engi-neering Research and Development Volume 1 (Issue 1) (2012) 09-15.</unstructured_citation></citation><citation key="ref39"><unstructured_citation>C. Romero, S. Ventura, P. G. Espejo, C. Herv as,Datá mining algorithmsto classify students, in: Educational Data Mining 2008, 2008.</unstructured_citation></citation><citation key="ref40"><doi>10.1109/WOCN.2012.6335530</doi><unstructured_citation>K. Bunkar, U. K. Singh, B. Pandya, R. Bunkar, Data mining: Prediction for performance improvement of graduate students using classifica-tion, in:Wire-less and Optical Communications Networks (WOCN), 2012 Ninth Interna-tional Conference on, IEEE, 2012, pp. 1-5. [CrossRef]</unstructured_citation></citation><citation key="ref41"><unstructured_citation>G. Elakia, N. J. Aarthi, Application of data mining in educational database for predicting behavioural patterns of the students, Elakia etal,/(IJCSIT)International Journalof Computer Science and Information Technologies 5 (3) (2014) 4649-4652.</unstructured_citation></citation><citation key="ref42"><doi>10.1016/j.eswa.2014.04.024</doi><unstructured_citation>S. Natek, M. Zwilling, Student data mining solution-knowledge management system related to higher education institutions, Expert systemswith applica-tions41 (14) (2014) 6400-6407. [CrossRef]</unstructured_citation></citation><citation key="ref43"><doi>10.1109/ACCT.2014.105</doi><unstructured_citation>T. Mishra, D. Kumar, S. Gupta,Miningstudents' data for prediction perfor-mance, in: Proceedings of the 2014 Fourth International Conferenceon Ad-vanced Computing &amp; Communication Technologies, ACCT '14, IEEE Com-puter Society, Washington, DC, USA, 2014, pp. 255-262. [CrossRef]</unstructured_citation></citation></citation_list>
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