{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T06:47:18Z","timestamp":1725864438937},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319451527"},{"type":"electronic","value":"9783319451534"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-45153-4_29","type":"book-chapter","created":{"date-parts":[[2016,9,6]],"date-time":"2016-09-06T02:42:06Z","timestamp":1473129726000},"page":"370-376","source":"Crossref","is-referenced-by-count":6,"title":["Predicting Academic Performance Based on Students\u2019 Blog and Microblog Posts"],"prefix":"10.1007","author":[{"given":"Mihai","family":"Dascalu","sequence":"first","affiliation":[]},{"given":"Elvira","family":"Popescu","sequence":"additional","affiliation":[]},{"given":"Alexandru","family":"Becheru","sequence":"additional","affiliation":[]},{"given":"Scott","family":"Crossley","sequence":"additional","affiliation":[]},{"given":"Stefan","family":"Trausan-Matu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,9,7]]},"reference":[{"issue":"1","key":"29_CR1","first-page":"3","volume":"1","author":"RS Baker","year":"2009","unstructured":"Baker, R.S., Yacef, K.: The state of educational data mining in 2009: A review and future visions. J. Educ. Data Min. 1(1), 3\u201317 (2009)","journal-title":"J. Educ. Data Min."},{"key":"29_CR2","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.compedu.2013.06.009","volume":"68","author":"C Romero","year":"2013","unstructured":"Romero, C., L\u00f3pez, M.I., Luna, J.M., Ventura, S.: Predicting students\u2019 final performance from participation in on-line discussion forums. Comput. Educ. 68, 458\u2013472 (2013)","journal-title":"Comput. Educ."},{"key":"29_CR3","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1007\/s40593-013-0010-8","volume":"24","author":"J Yoo","year":"2014","unstructured":"Yoo, J., Kim, J.: Can online discussion participation predict group project performance? investigating the roles of linguistic features and participation patterns. Int. J. Artif. Intell. Educ. 24, 8\u201332 (2014)","journal-title":"Int. J. Artif. Intell. Educ."},{"key":"29_CR4","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.chb.2014.09.034","volume":"47","author":"W Xing","year":"2015","unstructured":"Xing, W., Guo, R., Petakovic, E., Goggins, S.: Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory. Comput. Hum. Behav. 47, 168\u2013181 (2015)","journal-title":"Comput. Hum. Behav."},{"key":"29_CR5","first-page":"586","volume":"1","author":"MD Calvo-Flores","year":"2006","unstructured":"Calvo-Flores, M.D., Galindo, E.G., Jim\u00e9nez, M.P., Pi\u00f1eiro, O.P.: Predicting students\u2019 marks from Moodle logs using neural network models. Curr. Dev. Technol. Assist. Educ. 1, 586\u2013590 (2006)","journal-title":"Curr. Dev. Technol. Assist. Educ."},{"key":"29_CR6","unstructured":"Romero, C., Ventura, S., Espejo, P.G., Herv\u00e1s, C.: Data mining algorithms to classify students. In: 1st International Conference on Educational Data Mining, pp. 8\u201317. Quebec, Canada (2008)"},{"key":"29_CR7","doi-asserted-by":"crossref","unstructured":"Zafra, A., Ventura, S.: Predicting student grades in learning management systems with multiple instance genetic programming. In: 2nd International Conference on Educational Data Mining, pp. 309\u2013319. Cordoba, Spain (2009)","DOI":"10.1109\/ISDA.2009.108"},{"key":"29_CR8","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1007\/978-3-540-73078-1_60","volume-title":"User Modeling 2007","author":"ZA Pardos","year":"2007","unstructured":"Pardos, Z.A., Heffernan, N.T., Anderson, B., Heffernan, C.L.: The effect of model granularity on student performance prediction using bayesian networks. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 435\u2013439. Springer, Heidelberg (2007)"},{"key":"29_CR9","doi-asserted-by":"crossref","unstructured":"Giovannella, C., Popescu, E., Scaccia, F.: A PCA study of student performance indicators in a Web 2.0-based learning environment. In: 13th IEEE International Conference on Advanced Learning Technologies (ICALT 2013), pp. 33\u201335. IEEE, Beijing, China (2013)","DOI":"10.1109\/ICALT.2013.15"},{"key":"29_CR10","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-03419-5","volume-title":"Analyzing discourse and text complexity for learning and collaborating, Studies in Computational Intelligence","author":"M Dascalu","year":"2014","unstructured":"Dascalu, M.: Analyzing discourse and text complexity for learning and collaborating, Studies in Computational Intelligence, vol. 534. Springer, Cham (2014)"},{"key":"29_CR11","first-page":"335","volume-title":"Educational Data Mining: Applications and Trends","author":"M Dascalu","year":"2014","unstructured":"Dascalu, M., Dessus, P., Bianco, M., Trausan-Matu, S., Nardy, A.: Mining texts, learner productions and strategies with Reader Bench. In: Pe\u00f1a-Ayala, A. (ed.) Educational Data Mining: Applications and Trends, pp. 335\u2013377. Springer, Cham, Switzerland (2014)"},{"key":"29_CR12","doi-asserted-by":"crossref","unstructured":"Popescu, E., Dascalu, M., Becheru, A., Crossley, S.A., Trausan-Matu, S.: Predicting student performance and differences in learning styles based on textual complexity indices applied on blog and microblog posts \u2013 a preliminary study. In: 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016). IEEE, Austin, Texas (in press)","DOI":"10.1109\/ICALT.2016.104"},{"issue":"2","key":"29_CR13","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1007\/s11280-012-0172-6","volume":"17","author":"E Popescu","year":"2014","unstructured":"Popescu, E.: Providing collaborative learning support with social media in an integrated environment. World Wide Web 17(2), 199\u2013212 (2014)","journal-title":"World Wide Web"},{"issue":"4","key":"29_CR14","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s11412-015-9226-y","volume":"10","author":"M Dascalu","year":"2015","unstructured":"Dascalu, M., Trausan-Matu, S., McNamara, D.S., Dessus, P.: ReaderBench \u2013 automated evaluation of collaboration based on cohesion and dialogism. Int. J. Comput. Support. Collaborative Learn. 10(4), 395\u2013423 (2015)","journal-title":"Int. J. Comput. Support. Collaborative Learn."},{"key":"29_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1007\/978-3-642-30950-2_46","volume-title":"Intelligent Tutoring Systems","author":"S Trausan-Matu","year":"2012","unstructured":"Trausan-Matu, S., Dascalu, M., Dessus, P.: Textual complexity and discourse structure in computer-supported collaborative learning. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 352\u2013357. Springer, Heidelberg (2012)"},{"key":"29_CR16","unstructured":"Allen, L.K., Jacovina, M.E., Dascalu, M., Roscoe, R., Kent, K., Likens, A., McNamara, D.S.: {ENTER}ing the time series {SPACE}: uncovering the writing process through keystroke analyses. In: 9th International Conference on Educational Data Mining (EDM 2016). International Educational Data Mining Society, Raleigh, NC (in press)"},{"key":"29_CR17","doi-asserted-by":"crossref","unstructured":"Klecka, W.R.: Discriminant analysis. Quant. Appl. Soc. Sci. Ser, 19. Sage Publications, Thousand Oaks, CA (1980)","DOI":"10.4135\/9781412983938"}],"container-title":["Lecture Notes in Computer Science","Adaptive and Adaptable Learning"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-45153-4_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T20:07:37Z","timestamp":1657224457000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-45153-4_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319451527","9783319451534"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-45153-4_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2016]]}}}