{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T20:16:56Z","timestamp":1742933816558,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031201011"},{"type":"electronic","value":"9783031201028"}],"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\/978-3-031-20102-8_39","type":"book-chapter","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T15:04:11Z","timestamp":1673535851000},"page":"507-519","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MOOC Performance Prediction and Online Design Instructional Suggestions Based on LightGBM"],"prefix":"10.1007","author":[{"given":"Yimin","family":"Ren","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Hao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianhou","family":"Gan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ken","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"key":"39_CR1","unstructured":"Luo, Y., Xibin, F., Han, S.: Exploring the interpretability of a student grade prediction model in blended courses. Distance Educ. China, 46\u201355 (2022)"},{"key":"39_CR2","unstructured":"Xian, Wei, F.: The evaluation and prediction of academic performance based on artificial intelligence and LSTM. Chinese J. ICT Educ. 123\u2013128 (2022)"},{"key":"39_CR3","doi-asserted-by":"crossref","unstructured":"Hao, J.F., Gan, J.H.: MOOC\u00a0performance\u00a0prediction\u00a0and personal\u00a0performance\u00a0improvement via Bayesian network. Educ. Inf. Technol. 1\u201324 (2022)","DOI":"10.1007\/s10639-022-10926-8"},{"key":"39_CR4","doi-asserted-by":"crossref","unstructured":"Hasan, F., Palaniappan, S., Raziffar, T.: Student academic performance prediction by using decision tree algorithm. In: IEEE 2018 4th International Conference on Computer and Information Sciences (ICCOINS), pp. 1\u20135 (2018)","DOI":"10.1109\/ICCOINS.2018.8510600"},{"key":"39_CR5","unstructured":"Zhao, H.Q., Jiang, Q., Zhao, W., Li, Y., Zhao, Y.: Empirical research of predictive factors and intervention countermeasures of online learning performance on big data-based learning analytics. e-Educ. Res. 62\u201369 (2017)"},{"key":"39_CR6","unstructured":"Li, S., Li, R., Yu, C.: Evaluation model on distance student engagement: based on LMS data. Open Educ. Res. 24(01), 91\u2013102 (2018)"},{"key":"39_CR7","unstructured":"Wei, S.F.: An analysis of online learning behaviors and its influencing factors:a case study of students\u2019 learning process in online course open education learning guide in the open university of China. Open Educ. Res. 81\u201390+17 (2012)"},{"key":"39_CR8","unstructured":"Qing W, Ru-guo L.: Predicting the students\u2019 performances and reflecting the teaching strategies based on the e-learning behaviors. Modern Educ. Technol. 6, 18\u201324 (2017)"},{"key":"39_CR9","unstructured":"Xu Xiaoyu, F.: Research on the prediction and early warning model of student achievement based on heterogeneous information network. Inf. Technol. Netw. Secur. 84\u201389 (2022)"},{"key":"39_CR10","doi-asserted-by":"crossref","unstructured":"M\u00e1rquez, C., Vera, F.: Early dropout prediction using data mining: a case study with high school students. Expert Syst. 31(1), 107\u2013124 (2016)","DOI":"10.1111\/exsy.12135"},{"key":"39_CR11","doi-asserted-by":"crossref","unstructured":"Lykourentzou, F., Giannoukos, S., Nikolopoulos, T.: Dropout prediction in e-learning courses through the combination of machine learning techniques. Comput. Educ. 53(3), 950\u2013965 (2009)","DOI":"10.1016\/j.compedu.2009.05.010"},{"key":"39_CR12","volume-title":"LightGBM: A Highly Effificient Gradient Boosting Decision Tree","author":"G Ke","year":"2017","unstructured":"Ke, G., Qi, F., Thomas, M.S., Finley, T.: LightGBM: A Highly Effificient Gradient Boosting Decision Tree. Curran Associates Inc, Neural Information Processing Systems (2017)"},{"key":"39_CR13","unstructured":"Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 30 (2017)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Cyber Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20102-8_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T15:35:28Z","timestamp":1673537728000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20102-8_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031201011","9783031201028"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20102-8_39","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"13 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ML4CS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning for Cyber Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","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":"2 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ml4cs2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/ml4cs2022\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}