{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:59:22Z","timestamp":1771376362379,"version":"3.50.1"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030804206","type":"print"},{"value":"9783030804213","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-80421-3_10","type":"book-chapter","created":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T19:03:04Z","timestamp":1625770984000},"page":"78-90","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums"],"prefix":"10.1007","author":[{"given":"Jialin","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laila","family":"Alrajhi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anoushka","family":"Harit","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongtian","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandra I.","family":"Cristea","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,9]]},"reference":[{"key":"10_CR1","unstructured":"Agrawal, A., Venkatraman, J., Leonard, S., Paepcke, A.: Youedu: addressing confusion in MOOC discussion forums by recommending instructional video clips (2015)"},{"key":"10_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/978-3-030-22244-4_20","volume-title":"Intelligent Tutoring Systems","author":"A Alamri","year":"2019","unstructured":"Alamri, A., et al.: Predicting MOOCs dropout using only two easily obtainable features from the first week\u2019s activities. In: Coy, A., Hayashi, Y., Chang, M. (eds.) ITS 2019. LNCS, vol. 11528, pp. 163\u2013173. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-22244-4_20"},{"key":"10_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1007\/978-3-030-49663-0_42","volume-title":"Intelligent Tutoring Systems","author":"A Alamri","year":"2020","unstructured":"Alamri, A., Sun, Z., Cristea, A.I., Senthilnathan, G., Shi, L., Stewart, C.: Is MOOC learning different for dropouts? a visually-driven, multi-granularity explanatory ML approach. In: Kumar, V., Troussas, C. (eds.) ITS 2020. LNCS, vol. 12149, pp. 353\u2013363. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-49663-0_42"},{"key":"10_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compedu.2017.11.002","volume":"118","author":"O Almatrafi","year":"2018","unstructured":"Almatrafi, O., Johri, A., Rangwala, H.: Needle in a haystack: Identifying learner posts that require urgent response in mooc discussion forums. Comput. Educ. 118, 1\u20139 (2018)","journal-title":"Comput. Educ."},{"key":"10_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1007\/978-3-030-49663-0_27","volume-title":"Intelligent Tutoring Systems","author":"L Alrajhi","year":"2020","unstructured":"Alrajhi, L., Alharbi, K., Cristea, A.I.: A multidimensional deep learner model of urgent instructor intervention need in MOOC forum posts. In: Kumar, V., Troussas, C. (eds.) ITS 2020. LNCS, vol. 12149, pp. 226\u2013236. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-49663-0_27"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Bakharia, A.: Towards cross-domain mooc forum post classification. In: Proceedings of the Third (2016) ACM Conference on Learning@ Scale, pp. 253\u2013256 (2016)","DOI":"10.1145\/2876034.2893427"},{"key":"10_CR7","unstructured":"Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)"},{"issue":"518","key":"10_CR8","doi-asserted-by":"publisher","first-page":"859","DOI":"10.1080\/01621459.2017.1285773","volume":"112","author":"DM Blei","year":"2017","unstructured":"Blei, D.M., Kucukelbir, A., McAuliffe, J.D.: Variational inference: a review for statisticians. J. Am. Stat. Assoc. 112(518), 859\u2013877 (2017)","journal-title":"J. Am. Stat. Assoc."},{"key":"10_CR9","unstructured":"Chandrasekaran, M.K., Kan, M.Y., Tan, B.C., Ragupathi, K.: Learning instructor intervention from mooc forums: Early results and issues. arXiv preprint arXiv:1504.07206 (2015)"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Chaturvedi, S., Goldwasser, D., Daum\u00e9 III, H.: Predicting instructor\u2019s intervention in MOOC forums. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (vol. 1, Long Papers), pp. 1501\u20131511 (2014)","DOI":"10.3115\/v1\/P14-1141"},{"key":"10_CR11","unstructured":"Clavi\u00e9, B., Gal, K.: Edubert: Pretrained deep language models for learning analytics. arXiv preprint arXiv:1912.00690 (2019)"},{"key":"10_CR12","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In: International Conference on Machine Learning, pp. 1050\u20131059 (2016)"},{"key":"10_CR13","unstructured":"Gal, Y., Ghahramani, Z.: A theoretically grounded application of dropout in recurrent neural networks. In: Advances in Neural Information Processing Systems, pp. 1019\u20131027 (2016)"},{"key":"10_CR14","first-page":"507","volume":"13","author":"Z Ghahramani","year":"2000","unstructured":"Ghahramani, Z., Beal, M.: Propagation algorithms for variational bayesian learning. Adv. Neural Inf. Process. Syst. 13, 507\u2013513 (2000)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10_CR15","first-page":"2348","volume":"24","author":"A Graves","year":"2011","unstructured":"Graves, A.: Practical variational inference for neural networks. Adv. Neural Inf. Process. Syst. 24, 2348\u20132356 (2011)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10_CR16","doi-asserted-by":"publisher","first-page":"120522","DOI":"10.1109\/ACCESS.2019.2929211","volume":"7","author":"SX Guo","year":"2019","unstructured":"Guo, S.X., Sun, X., Wang, S.X., Gao, Y., Feng, J.: Attention-based character-word hybrid neural networks with semantic and structural information for identifying of urgent posts in mooc discussion forums. IEEE Access 7, 120522\u2013120532 (2019)","journal-title":"IEEE Access"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez-Blanco, A., Herrera-Flores, B., Tom\u00e1s, D., Navarro-Colorado, B.: A systematic review of deep learning approaches to educational data mining. Complexity 2019, (2019)","DOI":"10.1155\/2019\/1306039"},{"key":"10_CR18","unstructured":"Hern\u00e1ndez-Lobato, J.M., Adams, R.: Probabilistic backpropagation for scalable learning of bayesian neural networks. In: International Conference on Machine Learning, pp. 1861\u20131869 (2015)"},{"key":"10_CR19","unstructured":"Hern\u00e1ndez-Lobato, J.M., Gelbart, M., Hoffman, M., Adams, R., Ghahramani, Z.: Predictive entropy search for bayesian optimization with unknown constraints. In: International Conference on Machine Learning, pp. 1699\u20131707. PMLR (2015)"},{"issue":"1","key":"10_CR20","first-page":"1303","volume":"14","author":"MD Hoffman","year":"2013","unstructured":"Hoffman, M.D., Blei, D.M., Wang, C., Paisley, J.: Stochastic variational inference. J. Mach. Learn. Res. 14(1), 1303\u20131347 (2013)","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"10_CR21","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1023\/A:1007665907178","volume":"37","author":"MI Jordan","year":"1999","unstructured":"Jordan, M.I., Ghahramani, Z., Jaakkola, T.S., Saul, L.K.: An introduction to variational methods for graphical models. Mach. Learn. 37(2), 183\u2013233 (1999)","journal-title":"Mach. Learn."},{"key":"10_CR22","unstructured":"Kendall, A., Badrinarayanan, V., Cipolla, R.: Bayesian segnet: Model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. arXiv preprint arXiv:1511.02680 (2015)"},{"key":"10_CR23","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in bayesian deep learning for computer vision? In: Advances in Neural Information Processing Systems, pp. 5574\u20135584 (2017)"},{"key":"10_CR24","unstructured":"Kim, Y., Wiseman, S., Rush, A.M.: A tutorial on deep latent variable models of natural language. arXiv preprint arXiv:1812.06834 (2018)"},{"key":"10_CR25","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"10_CR26","unstructured":"Kingma, D.P., Salimans, T., Welling, M.: Variational dropout and the local reparameterization trick. In: Advances in Neural Information Processing Systems, pp. 2575\u20132583 (2015)"},{"key":"10_CR27","unstructured":"Laila, A., Ahmed, A., Filipe, D.P., Alexandra, I.C.: Urgency analysis of learners\u2019 comments: an automated intervention priority model for mooc. Presented at the (2021)"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 (2015)","DOI":"10.18653\/v1\/D15-1166"},{"issue":"3","key":"10_CR29","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1162\/neco.1992.4.3.448","volume":"4","author":"DJ MacKay","year":"1992","unstructured":"MacKay, D.J.: A practical bayesian framework for backpropagation networks. Neural Comput. 4(3), 448\u2013472 (1992)","journal-title":"Neural Comput."},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"MacKay, D.J.: Probable networks and plausible predictions-a review of practical bayesian methods for supervised neural networks. Netw. Comput. Neural Syst. 6(3), 469\u2013505 (1995)","DOI":"10.1088\/0954-898X_6_3_011"},{"key":"10_CR31","unstructured":"Miao, Y., Yu, L., Blunsom, P.: Neural variational inference for text processing. In: International Conference on Machine Learning, pp. 1727\u20131736 (2016)"},{"key":"10_CR32","doi-asserted-by":"publisher","unstructured":"Neal, R.M.: Bayesian Learning for Neural Ntworks, vol. 118. Springer, New York (2012) https:\/\/doi.org\/10.1007\/978-1-4612-0745-0","DOI":"10.1007\/978-1-4612-0745-0"},{"key":"10_CR33","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"10_CR34","unstructured":"Ranganath, R., Gerrish, S., Blei, D.: Black box variational inference. In: Artificial Intelligence and Statistics, pp. 814\u2013822. PMLR (2014)"},{"issue":"1","key":"10_CR35","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"10_CR36","doi-asserted-by":"crossref","unstructured":"Sun, X., Guo, S., Gao, Y., Zhang, J., Xiao, X., Feng, J.: Identification of urgent posts in mooc discussion forums using an improved RCNN. In: 2019 IEEE World Conference on Engineering Education (EDUNINE), pp. 1\u20135. IEEE (2019)","DOI":"10.1109\/EDUNINE.2019.8875845"},{"key":"10_CR37","unstructured":"Tanaka, T.: A theory of mean field approximation. In: Advances in Neural Information Processing Systems, pp. 351\u2013360 (1999)"},{"key":"10_CR38","doi-asserted-by":"crossref","unstructured":"Wainwright, M.J., Jordan, M.I.: Graphical Models, Exponential Families, and Variational Inference. Now Publishers Inc, Boston (2008)","DOI":"10.1561\/9781601981851"},{"issue":"3","key":"10_CR39","doi-asserted-by":"publisher","first-page":"92","DOI":"10.3390\/info8030092","volume":"8","author":"X Wei","year":"2017","unstructured":"Wei, X., Lin, H., Yang, L., Yu, Y.: A convolution-LSTM-based deep neural network for cross-domain mooc forum post classification. Information 8(3), 92 (2017)","journal-title":"Information"},{"key":"10_CR40","doi-asserted-by":"crossref","unstructured":"Xiao, Y., Wang, W.Y.: Quantifying uncertainties in natural language processing tasks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7322\u20137329 (2019)","DOI":"10.1609\/aaai.v33i01.33017322"},{"issue":"8","key":"10_CR41","doi-asserted-by":"publisher","first-page":"2008","DOI":"10.1109\/TPAMI.2018.2889774","volume":"41","author":"C Zhang","year":"2018","unstructured":"Zhang, C., B\u00fctepage, J., Kjellstr\u00f6m, H., Mandt, S.: Advances in variational inference. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 2008\u20132026 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR42","doi-asserted-by":"crossref","unstructured":"Zhu, L., Laptev, N.: Deep and confident prediction for time series at Uber. In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 103\u2013110. IEEE (2017)","DOI":"10.1109\/ICDMW.2017.19"}],"container-title":["Lecture Notes in Computer Science","Intelligent Tutoring Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-80421-3_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T23:22:52Z","timestamp":1626132172000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-80421-3_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030804206","9783030804213"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-80421-3_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"9 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ITS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Tutoring Systems","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":"7 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 June 2021","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":"its2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/its2021.iis-international.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":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"87","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":"22","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":"22","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":"25% - 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":"4","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":"2","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}