{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T18:37:06Z","timestamp":1742927826396,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031147555"},{"type":"electronic","value":"9783031147562"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-14756-2_4","type":"book-chapter","created":{"date-parts":[[2022,8,20]],"date-time":"2022-08-20T08:03:05Z","timestamp":1660982585000},"page":"55-72","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Guidelines for\u00a0the\u00a0Application of\u00a0Data Mining to\u00a0the\u00a0Problem of\u00a0School Dropout"],"prefix":"10.1007","author":[{"given":"Veronica Oliveira","family":"de Carvalho","sequence":"first","affiliation":[]},{"given":"Bruno Elias","family":"Penteado","sequence":"additional","affiliation":[]},{"given":"Leandro Rondado","family":"de Sousa","sequence":"additional","affiliation":[]},{"given":"Frank Jos\u00e9","family":"Affonso","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,21]]},"reference":[{"key":"4_CR1","first-page":"3","volume":"16","author":"RL Ackoff","year":"1989","unstructured":"Ackoff, R.L.: From data to wisdom. J. Appl. Syst. Anal. 16, 3\u20139 (1989)","journal-title":"J. Appl. Syst. Anal."},{"key":"4_CR2","first-page":"161","volume":"15","author":"F Agrusti","year":"2019","unstructured":"Agrusti, F., Bonavolonta, G., Mezzini, M.: University dropout prediction through educational data mining techniques: a systematic review. J. e-Learn. Knowl. Soc. 15, 161\u2013182 (2019)","journal-title":"J. e-Learn. Knowl. Soc."},{"key":"4_CR3","doi-asserted-by":"publisher","unstructured":"Aiken, J.M., Bin, R.D., Hjorth-Jensen, M., Caballero, M.D.: Predicting time to graduation at a large enrollment american university. PLoS One 15(11) (2020). https:\/\/doi.org\/10.1371\/journal.pone.0242334","DOI":"10.1371\/journal.pone.0242334"},{"issue":"e1","key":"4_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1017\/S026988891800036X","volume":"34","author":"SAN Alexandropoulos","year":"2019","unstructured":"Alexandropoulos, S.A.N., Kotsiantis, S.B., Vrahatis, M.N.: Data preprocessing in predictive data mining. Knowl. Eng. Rev. 34(e1), 1\u201333 (2019)","journal-title":"Knowl. Eng. Rev."},{"key":"4_CR5","unstructured":"Anderson, C.: The end of theory: the data deluge makes the scientific method obsolete (2008). https:\/\/www.wired.com\/2008\/06\/pb-theory\/"},{"key":"4_CR6","unstructured":"Burkart, N., Huber, M.F.: A survey on the explainability of supervised machine learning. CoRR abs\/2011.07876 (2020)"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Cardona, T., Cudney, E.A., Hoerl, R., Snyder, J.: Data mining and machine learning retention models in higher education. J. Coll. Student Retention Res. Theor. Pract. 25p. (2020)","DOI":"10.1177\/1521025120964920"},{"issue":"1","key":"4_CR8","doi-asserted-by":"publisher","first-page":"17","DOI":"10.2190\/CS.13.1.b","volume":"13","author":"D Delen","year":"2011","unstructured":"Delen, D.: Predicting student attrition with data mining methods. J. Coll. Student Retention Res Theor. Pract. 13(1), 17\u201335 (2011)","journal-title":"J. Coll. Student Retention Res Theor. Pract."},{"issue":"11","key":"4_CR9","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1145\/240455.240464","volume":"39","author":"U Fayyad","year":"1996","unstructured":"Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: The KDD process for extracting useful knowledge from volumes of data. Commun. ACM 39(11), 27\u201334 (1996). https:\/\/doi.org\/10.1145\/240455.240464","journal-title":"Commun. ACM"},{"key":"4_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-98074-4","volume-title":"Learning from Imbalanced Data Sets","author":"A Fern\u00e1ndez","year":"2018","unstructured":"Fern\u00e1ndez, A., Garc\u00eda, S., Galar, M., Prati, R.C., Krawczyk, B., Herrera, F.: Learning from Imbalanced Data Sets. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-98074-4"},{"key":"4_CR11","doi-asserted-by":"publisher","unstructured":"Hasbun, T., Araya, A., Villalon, J.: Extracurricular activities as dropout prediction factors in higher education using decision trees. In: 2016 IEEE 16th International Conference on Advanced Learning Technologies, pp. 242\u2013244 (2016). https:\/\/doi.org\/10.1109\/ICALT.2016.66","DOI":"10.1109\/ICALT.2016.66"},{"key":"4_CR12","unstructured":"Hey, T., Tansley, S., Tolle, K.: The fourth paradigm: data-intensive scientific discovery. Microsoft Research (2009). https:\/\/www.microsoft.com\/en-us\/research\/publication\/fourth-paradigm-data-intensive-scientific-discovery\/"},{"key":"4_CR13","unstructured":"Kitchenham, B., Charters, S.: Guidelines for performing systematic literature reviews in software engineering. Technical report EBSE 2007-001, Keele University and Durham University Joint Report (2007)"},{"key":"4_CR14","volume-title":"The Structure of Scientific Revolutions","author":"T Kuhn","year":"1962","unstructured":"Kuhn, T.: The Structure of Scientific Revolutions. University of Chicago Press, Chicago (1962)"},{"key":"4_CR15","doi-asserted-by":"publisher","first-page":"1203","DOI":"10.1126\/science.1248506","volume":"343","author":"D Lazer","year":"2014","unstructured":"Lazer, D., Kennedy, R., King, G., Vespignani, A.: Big data. The parable of google Flu: traps in big data analysis. Science 343, 1203\u20131205 (2014). https:\/\/doi.org\/10.1126\/science.1248506","journal-title":"Science"},{"key":"4_CR16","doi-asserted-by":"publisher","unstructured":"Marb\u00e1n, O., Segovia, J., Menasalvas, E., Fern\u00e1ndez-Baiz\u00e1n, C.: Toward data mining engineering: a software engineering approach. Inf. Syst. 34(1), 87\u2013107 (2009). https:\/\/doi.org\/10.1016\/j.is.2008.04.003, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0306437908000355","DOI":"10.1016\/j.is.2008.04.003"},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"Mduma, N., Kalegele, K., Machuve, D.: A survey of machine learning approaches and techniques for student dropout prediction. Data Sci. J. 18(14), 10p. (2019)","DOI":"10.5334\/dsj-2019-014"},{"key":"4_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-94989-5","volume-title":"Machine Learning: A Practical Approach on the Statistical Learning Theory","author":"RF Mello","year":"2018","unstructured":"Mello, R.F., Ponti, M.A.: Machine Learning: A Practical Approach on the Statistical Learning Theory. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-94989-5"},{"key":"4_CR19","doi-asserted-by":"publisher","unstructured":"Metaxas, P.T., Mustafaraj, E., Gayo-Avello, D.: How (not) to predict elections. In: 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing, pp. 165\u2013171 (2011). https:\/\/doi.org\/10.1109\/PASSAT\/SocialCom.2011.98","DOI":"10.1109\/PASSAT\/SocialCom.2011.98"},{"key":"4_CR20","doi-asserted-by":"crossref","unstructured":"Molnar, C.: Interpretable Machine Learning (2019). https:\/\/christophm.github.io\/interpretable-ml-book\/","DOI":"10.21105\/joss.00786"},{"key":"4_CR21","doi-asserted-by":"publisher","first-page":"52","DOI":"10.5539\/hes.v9n3p52","volume":"9","author":"MC Nicoletti","year":"2019","unstructured":"Nicoletti, M.C.: Revisiting the Tinto\u2019s theoretical dropout model. High. Edu. Stud. 9, 52\u201364 (2019)","journal-title":"High. Edu. Stud."},{"key":"4_CR22","unstructured":"Pedroza, K.Y.D., Chasoy, B.Y.C., G\u00f3mez, A.: Review of techniques, tools, algorithms and attributes for data mining used in student desertion. In: Sixth International Meeting of Technological Innovation. Journal of Physics: Conference Series (2019)"},{"key":"4_CR23","doi-asserted-by":"publisher","first-page":"e267","DOI":"10.7717\/peerj-cs.267","volume":"6","author":"V Plotnikova","year":"2020","unstructured":"Plotnikova, V., Dumas, M., Milani, F.: Adaptations of data mining methodologies: a systematic literature review. PeerJ Comput. Sci. 6, e267 (2020). https:\/\/doi.org\/10.7717\/peerj-cs.267","journal-title":"PeerJ Comput. Sci."},{"key":"4_CR24","doi-asserted-by":"publisher","unstructured":"Reeves, S., Albert, M., Kuper, A., Hodges, B.D.: Why use theories in qualitative research? BMJ 337 (2008). https:\/\/doi.org\/10.1136\/bmj.a949, https:\/\/www.bmj.com\/content\/337\/bmj.a949","DOI":"10.1136\/bmj.a949"},{"key":"4_CR25","unstructured":"Rezende, S.O.: Sistemas Inteligentes: fundamentos e aplica\u00e7\u00f5es. Editora Manole Ltda (2003)"},{"key":"4_CR26","doi-asserted-by":"crossref","unstructured":"Rumberger, R.W.: The economics of high school dropouts. In: Bradley, S., Green, C. (eds.) The Economics of Education: A Comprehensive Overview, 2nd edn., chap. 12, pp. 149\u2013158. Academic Press (2020)","DOI":"10.1016\/B978-0-12-815391-8.00012-4"},{"key":"4_CR27","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.eswa.2018.07.042","volume":"114","author":"Y Shao","year":"2018","unstructured":"Shao, Y., Liu, B., Wang, S., Li, G.: A novel software defect prediction based on atomic class-association rule mining. Expert Syst. Appl. 114, 237\u2013254 (2018)","journal-title":"Expert Syst. Appl."},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"Sousa, L.R., Carvalho, V.O., Penteado, B.E., Affonso, F.J.: A systematic mapping on the use of data mining for the face-to-face school dropout problem. In: Proceedings of the 13th International Conference on Computer Supported Education CSEDU, pp. 36\u201347 (2021)","DOI":"10.5220\/0010476300360047"},{"key":"4_CR29","unstructured":"Taipe, M.S.A., S\u00e1nchez, D.M.: Prediction of university dropout through technological factors: a case study in Ecuador. Rev. Espacios 39(52) (2018)"},{"key":"4_CR30","volume-title":"Introduction to Data Mining","author":"PN Tan","year":"2018","unstructured":"Tan, P.N., Steinbach, M., Karpatne, A., Kumar, V.: Introduction to Data Mining, 2nd edn. Pearson, London (2018)","edition":"2"},{"key":"4_CR31","unstructured":"Tatman, R., VanderPlas, J., Dane, S.: A practical taxonomy of reproducibility for machine learning research. In: Reproducibility in Machine Learning Workshop at ICML 2018, p. 5p. (2018)"},{"key":"4_CR32","volume-title":"Leaving College: Rethinking the Causes and Cures of Student Attrition","author":"V Tinto","year":"1993","unstructured":"Tinto, V.: Leaving College: Rethinking the Causes and Cures of Student Attrition. University of Chicago Press, Chicago (1993)"}],"container-title":["Communications in Computer and Information Science","Computer Supported Education"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-14756-2_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T04:21:43Z","timestamp":1727842903000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-14756-2_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031147555","9783031147562"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-14756-2_4","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"21 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CSEDU","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Supported Education","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 April 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 April 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"csedu2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.csedu.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}