{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T05:45:20Z","timestamp":1743140720121,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819924455"},{"type":"electronic","value":"9789819924462"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-2446-2_48","type":"book-chapter","created":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T12:19:23Z","timestamp":1684153163000},"page":"533-540","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning Situation Risk Cognition and Measurement Based on Data-Driven"],"prefix":"10.1007","author":[{"given":"Chunqiao","family":"Mi","sequence":"first","affiliation":[]},{"given":"Qingyou","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Changhua","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Yiwen","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,14]]},"reference":[{"key":"48_CR1","doi-asserted-by":"crossref","unstructured":"Jishan, S.T., Rashu, R,I., Haque, N., et al. Improving accuracy of students\u2019 final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique. Decis. Anal. 2015(1), 1\u201325 (2015)","DOI":"10.1186\/s40165-014-0010-2"},{"key":"48_CR2","doi-asserted-by":"crossref","unstructured":"Mat, U.B., Buniyamin, N., Arsad, P.M., et al.: An overview of using academic analytics to predict and improve students\u2019 achievement: a proposed proactive intelligent intervention. In: ICEE. Proceedings of the IEEE 5th International Conference on Engineering Education, pp. 126\u2013130. IEEE, Selangor (2013)","DOI":"10.1109\/ICEED.2013.6908316"},{"key":"48_CR3","doi-asserted-by":"crossref","unstructured":"Christian, T.M., Ayub, M.: Exploration of classification using NB tree for predicting students\u2019 performance. In: ICODSE. Proceedings of the International Conference on Data and Software Engineering, pp. 1\u20136. IEEE, Bandung (2014)","DOI":"10.1109\/ICODSE.2014.7062654"},{"key":"48_CR4","doi-asserted-by":"publisher","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., et al.: Predicting students\u2019 final performance from participation in on-line discussion forums. Comput. Educ. 68, 458\u2013472 (2013)","journal-title":"Comput. Educ."},{"key":"48_CR5","first-page":"29","volume":"7","author":"WY Zhang","year":"2004","unstructured":"Zhang, W.Y.: Design and development of online learning environment evaluation model, index system and evaluation scale. China Educ. Technol. 7, 29\u201333 (2004)","journal-title":"China Educ. Technol."},{"key":"48_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"2252","DOI":"10.1007\/3-540-45110-2_119","volume-title":"Genetic and Evolutionary Computation\u2014GECCO 2003","author":"B Minaei-Bidgoli","year":"2003","unstructured":"Minaei-Bidgoli, B., Punch, W.F.: Using genetic algorithms for data mining optimization in an educational web-based system. In: Cant\u00fa-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2724, pp. 2252\u20132263. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/3-540-45110-2_119"},{"key":"48_CR7","unstructured":"Campbell, J.P.: Utilizing student data within the course management system to determine undergraduate student academic success: An exploratory study. Indiana: Purdue University (2007)"},{"key":"48_CR8","unstructured":"Baker, R.S., Lindrum, D., Lindrum, M.J., et al.: Analyzing early at-risk factors in higher education e-learning courses. In: International Educational Data Mining Society. Proceedings of the 8th International Conference on Educational Data Mining, pp. 150\u2013155. National University for Distance Education, Madrid (2015)"},{"key":"48_CR9","doi-asserted-by":"publisher","first-page":"170","DOI":"10.2197\/ipsjjip.26.170","volume":"26","author":"SJH Yang","year":"2018","unstructured":"Yang, S.J.H., Lu, O.H.T., Huang, A.Y.Q., et al.: Predicting students\u2019 academic performance using multiple linear regression and principal component analysis. J. Inform. Process. 26, 170\u2013176 (2018)","journal-title":"J. Inform. Process."},{"key":"48_CR10","unstructured":"Bravo, J., Sosnovsky, S., Ortigosa, A.: Detecting symptoms of low performance using prediction rules. International Working Group on Educational Data Mining. In: Barnes, T., Desmarais, M., Romero, C., et al. Proceedings of the 2nd Educational Data Mining Conference, pp. 31\u201340. Universidad de Cordoba, Cordoba (2009)"},{"key":"48_CR11","doi-asserted-by":"publisher","first-page":"6","DOI":"10.18608\/jla.2014.11.3","volume":"1","author":"MJ Sandeep","year":"2014","unstructured":"Sandeep, M.J., Erik, W.M., Eitel, J.M.L., et al.: Early alert of academically at-risk students: an open source analytics initiative. J. Learn. Anal. 1, 6\u201347 (2014)","journal-title":"J. Learn. Anal."},{"key":"48_CR12","first-page":"6","volume":"7","author":"AK Hamoud","year":"2017","unstructured":"Hamoud, A.K., Humadi, A.M., Awadh, W.A., et al.: Students\u2019 success prediction based on bayes algorithms. Int. J. Comput. Appl. 7, 6\u201312 (2017)","journal-title":"Int. J. Comput. Appl."},{"key":"48_CR13","unstructured":"Zhong, X.: Learning situation warning based on naive bayesian classifier based on feature weighting. J. Shanxi Datong Univ. (Nat. Sci.), 2019(2), 46\u201349 (2019)"},{"key":"48_CR14","doi-asserted-by":"publisher","first-page":"474","DOI":"10.21449\/ijate.435507","volume":"3","author":"HSY Aybek","year":"2018","unstructured":"Aybek, H.S.Y., Okur, M.R.: Predicting achievement with artificial neural networks: the case of anadolu university open education system. Int. J. Assess. Tools Educ. 3, 474\u2013490 (2018)","journal-title":"Int. J. Assess. Tools Educ."},{"key":"48_CR15","doi-asserted-by":"publisher","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., et al.: 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":"48_CR16","first-page":"639","volume":"4","author":"C Mi","year":"2018","unstructured":"Mi, C., Deng, Q., Lin, J., et al.: A dynamic early warning method of student study failure risk based on fuzzy synthetic evaluation. Int. J. Perform. Eng. 4, 639\u2013646 (2018)","journal-title":"Int. J. Perform. Eng."},{"key":"48_CR17","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1002\/abc.20025","volume":"3","author":"MD Pistilli","year":"2010","unstructured":"Pistilli, M.D., Arnold, K.E.: Purdue signals: Mining real-time academic data to enhance student success. About Campus 3, 22\u201324 (2010)","journal-title":"About Campus"},{"key":"48_CR18","doi-asserted-by":"crossref","unstructured":"Arnold, K.E., Pistilli, M.D.: Course signals at Purdue: Using learning analytics to increase student success. LAK12. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 267\u2013270. ACM, Vancouver (2012)","DOI":"10.1145\/2330601.2330666"},{"key":"48_CR19","unstructured":"Chen, X., Li, Y.: Interpretation of 2020 EDUCAUSE Horizon ReportTM (Teaching and Learning Edition) and its enlightenments: challenges and transformation of higher education under the epidemic situation. J. Distan. Educ. 2020(2), 3\u201316 (2020)"},{"key":"48_CR20","first-page":"5","volume":"7","author":"L Wang","year":"2016","unstructured":"Wang, L., Ye, Y., Yang, X.: Design of online learning early-warning model based on big data. Mod. Educ. Technol. 7, 5\u201311 (2016)","journal-title":"Mod. Educ. Technol."},{"key":"48_CR21","first-page":"54","volume":"10","author":"Z Hu","year":"2019","unstructured":"Hu, Z., Zhu, L., Wu, G.: Construction of postgraduate education management information platform for quality monitoring and early warning. Mod. Educ. Technol. 10, 54\u201359 (2019)","journal-title":"Mod. Educ. Technol."},{"key":"48_CR22","first-page":"3","volume":"1","author":"T Huang","year":"2021","unstructured":"Huang, T., Zhao, Y., Geng, J., Wang, H., Zhang, H., Yang, H.: Evaluation mechanism and method for data-driven precision learning. Mod. Distance Educ. Res. 1, 3\u201312 (2021)","journal-title":"Mod. Distance Educ. Res."},{"key":"48_CR23","first-page":"93","volume":"2","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Zheng, Y.: Multimodal data fusion: the core driving force to solve the key problems of intelligent education. Mod. Distance Educ. Res. 2, 93\u2013102 (2022)","journal-title":"Mod. Distance Educ. Res."}],"container-title":["Communications in Computer and Information Science","Computer Science and Education"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-2446-2_48","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T10:47:48Z","timestamp":1729421268000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-2446-2_48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819924455","9789819924462"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-2446-2_48","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"14 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Science and Education","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccse12022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ieee-iccse.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"510","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"168","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"43","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5.186","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}