{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T14:15:40Z","timestamp":1743084940263,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319676142"},{"type":"electronic","value":"9783319676159"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-67615-9_6","type":"book-chapter","created":{"date-parts":[[2017,9,6]],"date-time":"2017-09-06T07:20:29Z","timestamp":1504682429000},"page":"67-76","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Prognosis of Junior High School Students\u2019 Performance Based on Active Learning Methods"],"prefix":"10.1007","author":[{"given":"Georgios","family":"Kostopoulos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sotiris","family":"Kotsiantis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vassilios S.","family":"Verykios","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,9,7]]},"reference":[{"issue":"1","key":"6_CR1","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Machine Learning 45(1), 5\u201332 (2001)","journal-title":"Random forests. Machine Learning"},{"key":"6_CR2","unstructured":"Cortez, P., Silva, A.M.G.: Using data mining to predict secondary school student performance (2008)"},{"issue":"19","key":"6_CR3","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1016\/j.tcs.2010.12.054","volume":"412","author":"S Dasgupta","year":"2011","unstructured":"Dasgupta, S.: Two faces of active learning. Theoretical Computer Science 412(19), 1767\u20131781 (2011)","journal-title":"Theoretical Computer Science"},{"issue":"4","key":"6_CR4","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1007\/s11390-010-9357-6","volume":"25","author":"MFA Hady","year":"2010","unstructured":"Hady, M.F.A., Schwenker, F.: Combining committee-based semi-supervised learning and active learning. Journal of Computer Science and Technology 25(4), 681\u2013698 (2010)","journal-title":"Journal of Computer Science and Technology"},{"issue":"2","key":"6_CR5","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1214\/aoms\/1177704575","volume":"33","author":"JL Hodges","year":"1962","unstructured":"Hodges, J.L., Lehmann, E.L.: Rank methods for combination of independent experiments in analysis of variance. The Annals of Mathematical Statistics 33(2), 482\u2013497 (1962)","journal-title":"The Annals of Mathematical Statistics"},{"issue":"5","key":"6_CR6","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","volume":"2","author":"K Hornik","year":"1989","unstructured":"Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2(5), 359\u2013366 (1989)","journal-title":"Neural Networks"},{"key":"6_CR7","unstructured":"John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pp. 338\u2013345. Morgan Kaufmann Publishers Inc. (1995)"},{"key":"6_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1007\/978-3-319-23781-7_21","volume-title":"Model and Data Engineering","author":"G Kostopoulos","year":"2015","unstructured":"Kostopoulos, G., Kotsiantis, S., Pintelas, P.: Predicting student performance in distance higher education using semi-supervised techniques. In: Bellatreche, L., Manolopoulos, Y. (eds.) MEDI 2015. LNCS, vol. 9344, pp. 259\u2013270. Springer, Cham (2015). doi: 10.1007\/978-3-319-23781-7_21"},{"key":"6_CR9","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.knosys.2013.01.032","volume":"44","author":"Y Leng","year":"2013","unstructured":"Leng, Y., Xu, X., Qi, G.: Combining active learning and semi-supervised learning to construct SVM classifier. Knowledge-Based Systems 44, 121\u2013131 (2013)","journal-title":"Knowledge-Based Systems"},{"key":"6_CR10","first-page":"519","volume":"3","author":"CX Ling","year":"2003","unstructured":"Ling, C.X., Huang, J., Zhang, H.: AUC: a statistically consistent and more discriminating measure than accuracy. IJCAI 3, 519\u2013524 (2003)","journal-title":"IJCAI"},{"issue":"1","key":"6_CR11","first-page":"43","volume":"9","author":"IE Livieris","year":"2016","unstructured":"Livieris, I.E., Mikropoulos, T.A., Pintelas, P.: A decision support system for predicting students\u2019 performance. Themes in Science and Technology Education 9(1), 43\u201357 (2016)","journal-title":"Themes in Science and Technology Education"},{"key":"6_CR12","unstructured":"Mamitsuka, N.A.H.: Query learning strategies using boosting and bagging. In: Machine Learning: Proceedings of the Fifteenth International Conference (ICML 1998), vol. 1. Morgan Kaufmann Pub. (1998)"},{"issue":"3","key":"6_CR13","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1007\/s10489-012-0374-8","volume":"38","author":"C M\u00e1rquez-Vera","year":"2013","unstructured":"M\u00e1rquez-Vera, C., Cano, A., Romero, C., Ventura, S.: Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data. Applied Intelligence 38(3), 315\u2013330 (2013)","journal-title":"Applied Intelligence"},{"key":"6_CR14","volume-title":"Probabilistic Reasoning in Intelligent Systems","author":"J Pearl","year":"1988","unstructured":"Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco (1988)"},{"key":"6_CR15","unstructured":"Platt, J.: Sequential minimal optimization: A fast algorithm for training support vector machines (1998)"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Ramirez-Loaiza, M.E., Sharma, M., Kumar, G., Bilgic, M.: Active learning: an empirical study of common baselines. Data Mining and Knowledge Discovery, 1\u201327 (2016)","DOI":"10.1007\/s10618-016-0469-7"},{"issue":"95","key":"6_CR17","first-page":"1","volume":"17","author":"O Reyes","year":"2016","unstructured":"Reyes, O., P\u00e9rez, E., del Carmen Rodriguez-Hern\u00e1ndez, M., Fardoun, H.M., Ventura, S.: JCLAL: a Java framework for active learning. Journal of Machine Learning Research 17(95), 1\u20135 (2016)","journal-title":"Journal of Machine Learning Research"},{"issue":"1","key":"6_CR18","first-page":"12","volume":"3","author":"C Romero","year":"2013","unstructured":"Romero, C., Ventura, S.: Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3(1), 12\u201327 (2013)","journal-title":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"},{"key":"6_CR19","doi-asserted-by":"crossref","unstructured":"Settles, B., Craven, M.: An analysis of active learning strategies for sequence labeling tasks. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1070\u20131079. Association for Computational Linguistics (2008)","DOI":"10.3115\/1613715.1613855"},{"issue":"1","key":"6_CR20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2200\/S00429ED1V01Y201207AIM018","volume":"6","author":"B Settles","year":"2012","unstructured":"Settles, B.: Active learning. Synthesis Lectures on Artificial Intelligence and Machine Learning 6(1), 1\u2013114 (2012)","journal-title":"Synthesis Lectures on Artificial Intelligence and Machine Learning"},{"issue":"1","key":"6_CR21","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1145\/584091.584093","volume":"5","author":"CE Shannon","year":"2001","unstructured":"Shannon, C.E.: A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review 5(1), 3\u201355 (2001)","journal-title":"ACM SIGMOBILE Mobile Computing and Communications Review"},{"issue":"1","key":"6_CR22","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1007\/s10618-016-0460-3","volume":"31","author":"M Sharma","year":"2017","unstructured":"Sharma, M., Bilgic, M.: Evidence-based uncertainty sampling for active learning. Data Mining and Knowledge Discovery 31(1), 164\u2013202 (2017)","journal-title":"Data Mining and Knowledge Discovery"},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Slater, S., Joksimovi\u0107, S., Kovanovic, V., Baker, R.S., Gasevic, D.: Tools for Educational Data Mining A Review. Journal of Educational and Behavioral Statistics (2016)","DOI":"10.3102\/1076998616666808"},{"key":"6_CR24","unstructured":"Stapel, M., Zheng, Z., Pinkwart, N.: An ensemble method to predict student performance in an online math learning environment. In: Proceedings of the 9th International Conference on Educational Data Mining, International Educational Data Mining Society, pp. 231\u2013238 (2016)"},{"issue":"2","key":"6_CR25","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s10115-013-0706-y","volume":"42","author":"I Triguero","year":"2015","unstructured":"Triguero, I., Garc\u00eda, S., Herrera, F.: Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study. Knowledge and Information Systems 42(2), 245\u2013284 (2015)","journal-title":"Knowledge and Information Systems"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann (2016)","DOI":"10.1016\/B978-0-12-804291-5.00010-6"},{"key":"6_CR27","unstructured":"Zhang, H.: The optimality of naive Bayes. AA 1(2), 3 (2004)"},{"key":"6_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/978-3-540-36668-3_3","volume-title":"PRICAI 2006: Trends in Artificial Intelligence","author":"Z-H Zhou","year":"2006","unstructured":"Zhou, Z.-H.: Learning with unlabeled data and its application to image retrieval. In: Yang, Q., Webb, G. (eds.) PRICAI 2006. LNCS, vol. 4099, pp. 5\u201310. Springer, Heidelberg (2006). doi: 10.1007\/978-3-540-36668-3_3"},{"key":"6_CR29","unstructured":"Zhu, X., Lafferty, J., Ghahramani, Z.: Combining active learning and semi-supervised learning using gaussian fields and harmonic functions. In: ICML 2003 Workshop on The Continuum From Labeled To Unlabeled Data in Machine Learning and Data Mining, vol. 3 (2003)"}],"container-title":["Lecture Notes in Computer Science","Brain Function Assessment in Learning"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-67615-9_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,17]],"date-time":"2020-10-17T04:44:56Z","timestamp":1602909896000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-67615-9_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319676142","9783319676159"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-67615-9_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]}}}