{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T02:29:16Z","timestamp":1772159356013,"version":"3.50.1"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030782696","type":"print"},{"value":"9783030782702","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\/978-3-030-78270-2_68","type":"book-chapter","created":{"date-parts":[[2021,6,11]],"date-time":"2021-06-11T17:03:12Z","timestamp":1623430992000},"page":"384-389","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Assessment2Vec: Learning Distributed Representations of Assessments to\u00a0Reduce Marking Workload"],"prefix":"10.1007","author":[{"given":"Shuang","family":"Wang","sequence":"first","affiliation":[]},{"given":"Amin","family":"Beheshti","sequence":"additional","affiliation":[]},{"given":"Yufei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jianchao","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Quan Z.","family":"Sheng","sequence":"additional","affiliation":[]},{"given":"Stephen","family":"Elbourn","sequence":"additional","affiliation":[]},{"given":"Hamid","family":"Alinejad-Rokny","sequence":"additional","affiliation":[]},{"given":"Elizabeth","family":"Galanis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,12]]},"reference":[{"key":"68_CR1","unstructured":"Dai, A.M., Le, Q.V.: Semi-supervised sequence learning. In: Advances in Neural Information Processing Systems, vol. 28, pp. 3079\u20133087 (2015)"},{"key":"68_CR2","unstructured":"Peters, M.E., et al.: Deep contextualized word representations. arXiv preprint arXiv:1802.05365 (2018)"},{"key":"68_CR3","unstructured":"Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding with unsupervised learning. Technical report, OpenAI (2018)"},{"key":"68_CR4","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1007\/978-981-15-3281-8_3","volume-title":"Web Information Systems Engineering","author":"A Beheshti","year":"2020","unstructured":"Beheshti, A., Benatallah, B., Sheng, Q.Z., Schiliro, F.: Intelligent knowledge lakes: the age of artificial intelligence and big data. In: U, L.H., Yang, J., Cai, Y., Karlapalem, K., Liu, A., Huang, X. (eds.) WISE 2020. CCIS, vol. 1155, pp. 24\u201334. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-3281-8_3"},{"issue":"6","key":"68_CR5","first-page":"1503","volume":"15","author":"X Yuan","year":"2019","unstructured":"Yuan, X., Wang, S., Wan, L., Zhang, C.: SSF: sentence similar function based on word2vector similar elements. J. Inf. Process. Syst. 15(6), 1503\u20131516 (2019)","journal-title":"J. Inf. Process. Syst."},{"key":"68_CR6","unstructured":"Zhou, T., Zhang, Y., Lu, v; Classifying computer science papers. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence, pp. 9\u201315 (2016)"},{"key":"68_CR7","doi-asserted-by":"crossref","unstructured":"Jaiswal, A., Babu, A.R., Zadeh, M.Z., Banerjee, D., Makedon, F.: A survey on contrastive self-supervised learning. Technologies 9(1), 2 (2021)","DOI":"10.3390\/technologies9010002"},{"key":"68_CR8","doi-asserted-by":"crossref","unstructured":"Fang, H., Xie, P.: CERT: contrastive self-supervised learning for language understanding. arXiv preprint arXiv:2005.12766 (2020)","DOI":"10.36227\/techrxiv.12308378.v1"},{"key":"68_CR9","doi-asserted-by":"crossref","unstructured":"Strubell, E., Ganesh, A., McCallum, A.: Energy and policy considerations for deep learning in NLP. arXiv preprint arXiv:1906.02243 (2019)","DOI":"10.18653\/v1\/P19-1355"},{"issue":"1","key":"68_CR10","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1007\/s40593-014-0026-8","volume":"25","author":"S Burrows","year":"2015","unstructured":"Burrows, S., Gurevych, I., Stein, B.: The eras and trends of automatic short answer grading. Int. J. Artif. Intell. Educ. 25(1), 60\u2013117 (2015)","journal-title":"Int. J. Artif. Intell. Educ."},{"key":"68_CR11","doi-asserted-by":"crossref","unstructured":"Dasgupta, T., Naskar, A., Dey, L., Saha, R.: Augmenting textual qualitative features in deep convolution recurrent neural network for automatic essay scoring. In: Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications, pp. 93\u2013102 (2018)","DOI":"10.18653\/v1\/W18-3713"},{"key":"68_CR12","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1007\/978-3-030-23204-7_39","volume-title":"Artificial Intelligence in Education","author":"C Sung","year":"2019","unstructured":"Sung, C., Dhamecha, T.I., Mukhi, N.: Improving short answer grading using transformer-based pre-training. In: Isotani, S., Mill\u00e1n, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds.) AIED 2019. LNCS (LNAI), vol. 11625, pp. 469\u2013481. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-23204-7_39"},{"key":"68_CR13","doi-asserted-by":"crossref","unstructured":"Taghipour, K., Ng, H.T.: A neural approach to automated essay scoring. In; Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1882\u20131891 (2016)","DOI":"10.18653\/v1\/D16-1193"},{"key":"68_CR14","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1007\/978-3-030-52237-7_44","volume-title":"Artificial Intelligence in Education","author":"M Uto","year":"2020","unstructured":"Uto, M., Okano, M.: Robust neural automated essay scoring using item response theory. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Mill\u00e1n, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12163, pp. 549\u2013561. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-52237-7_44"},{"key":"68_CR15","doi-asserted-by":"crossref","unstructured":"Uto, M., Xie, Y., Ueno., M.: Neural automated essay scoring incorporating handcrafted features. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 6077\u20136088, Barcelona, Spain (Online). International Committee on Computational Linguistics, December 2020","DOI":"10.18653\/v1\/2020.coling-main.535"},{"key":"68_CR16","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wei, Z., Zhou, Y., Huang, X.-J.: Automatic essay scoring incorporating rating schema via reinforcement learning. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 791\u2013797 (2018)","DOI":"10.18653\/v1\/D18-1090"},{"issue":"8","key":"68_CR17","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"68_CR18","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"68_CR19","unstructured":"Gunel, B., Du, J., Conneau, A., Stoyanov, V.: Supervised contrastive learning for pre-trained language model fine-tuning. arXiv preprint arXiv:2011.01403 (2020)"},{"key":"68_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1007\/978-3-030-34223-4_49","volume-title":"Web Information Systems Engineering \u2013 WISE 2019","author":"A Tabebordbar","year":"2019","unstructured":"Tabebordbar, A., Beheshti, A., Benatallah, B.: ConceptMap: a conceptual approach for formulating user preferences in large information spaces. In: Cheng, R., Mamoulis, N., Sun, Y., Huang, X. (eds.) WISE 2020. LNCS, vol. 11881, pp. 779\u2013794. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-34223-4_49"},{"key":"68_CR21","doi-asserted-by":"crossref","unstructured":"Beheshti, A., Tabebordbar, A., Benatallah, B.: iStory: intelligent storytelling with social data. In: Companion of The 2020 Web Conference 2020, Taipei, Taiwan, 20\u201324 April 2020, pp. 253\u2013256. ACM\/IW3C2 (2020)","DOI":"10.1145\/3366424.3383553"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Education"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-78270-2_68","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,11]],"date-time":"2021-06-11T17:12:13Z","timestamp":1623431533000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-78270-2_68"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030782696","9783030782702"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-78270-2_68","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":"12 June 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIED","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Education","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Utrecht","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aied2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aied2021.science.uu.nl\/","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":"209","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":"40","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":"76","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":"19% - 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":"5","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)"}},{"value":"Due to the COVID-19 pandemic the conference was held online.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}