{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T04:07:12Z","timestamp":1768968432904,"version":"3.49.0"},"publisher-location":"Cham","reference-count":46,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030782917","type":"print"},{"value":"9783030782924","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\/978-3-030-78292-4_20","type":"book-chapter","created":{"date-parts":[[2021,6,10]],"date-time":"2021-06-10T21:03:59Z","timestamp":1623359039000},"page":"241-254","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Predicting Co-occurring Emotions from Eye-Tracking and Interaction Data in MetaTutor"],"prefix":"10.1007","author":[{"given":"S\u00e9bastien","family":"Lall\u00e9","sequence":"first","affiliation":[]},{"given":"Rohit","family":"Murali","sequence":"additional","affiliation":[]},{"given":"Cristina","family":"Conati","sequence":"additional","affiliation":[]},{"given":"Roger","family":"Azevedo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,11]]},"reference":[{"key":"20_CR1","doi-asserted-by":"publisher","unstructured":"Wortha, F., Azevedo, R., Taub, M., Narciss, S.: Multiple negative emotions during learning with digital learning environments\u2013Evidence on their detrimental effect on learning from two methodological approaches. Front. Psychol. 10, 2678:1\u20132678:19 (2019). https:\/\/doi.org\/10.3389\/fpsyg.2019.02678","DOI":"10.3389\/fpsyg.2019.02678"},{"key":"20_CR2","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.ijhcs.2009.12.003","volume":"68","author":"R Baker","year":"2010","unstructured":"Baker, R., D\u2019Mello, S., Rodrigo, M.M., Graesser, A.C.: Better to be frustrated than bored: The incidence, persistence, and impact of learners\u2019 cognitive\u2013affective states during interactions with three different computer-based learning environments. Int. J. Hum.-Comput. Stud. 68, 223\u2013241 (2010). https:\/\/doi.org\/10.1016\/j.ijhcs.2009.12.003","journal-title":"Int. J. Hum.-Comput. Stud."},{"key":"20_CR3","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1504\/ijlt.2009.028804","volume":"4","author":"B Woolf","year":"2009","unstructured":"Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., Picard, R.: Affect-aware tutors: recognising and responding to student affect. Int. J. Learn. Technol. 4, 129\u2013164 (2009). https:\/\/doi.org\/10.1504\/ijlt.2009.028804","journal-title":"Int. J. Learn. Technol."},{"key":"20_CR4","doi-asserted-by":"publisher","unstructured":"Grawemeyer, B., Mavrikis, M., Holmes, W., Gutierrez-Santos, S., Wiedmann, M., Rummel, N.: Affecting off-task behaviour: how affect-aware feedback can improve student learning. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge. pp. 104\u2013113. ACM, Edinburgh (2016). https:\/\/doi.org\/10.1145\/2883851","DOI":"10.1145\/2883851"},{"key":"20_CR5","doi-asserted-by":"publisher","unstructured":"Lall\u00e9, S., Conati, C., Azevedo, R.: Prediction of student achievement goals and emotion valence during interaction with pedagogical agents. In: Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems, pp. 1222\u20131231. IFAAMAS, Stockholm (2018). https:\/\/doi.org\/10.1145\/2883851","DOI":"10.1145\/2883851"},{"key":"20_CR6","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1016\/j.procs.2014.08.151","volume":"35","author":"S Salmeron-Majadas","year":"2014","unstructured":"Salmeron-Majadas, S., Santos, O.C., Boticario, J.G.: An evaluation of mouse and keyboard interaction indicators towards non-intrusive and low cost affective modeling in an educational context. Procedia Comput. Sci. 35, 691\u2013700 (2014)","journal-title":"Procedia Comput. Sci."},{"key":"20_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/978-3-319-07221-0_4","volume-title":"Intelligent Tutoring Systems","author":"N Jaques","year":"2014","unstructured":"Jaques, N., Conati, C., Harley, J.M., Azevedo, R.: Predicting affect from gaze data during interaction with an intelligent tutoring system. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 29\u201338. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-07221-0_4"},{"key":"20_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-07221-0_1","volume-title":"Intelligent Tutoring Systems","author":"L Paquette","year":"2014","unstructured":"Paquette, L., et al.: Sensor-free affect detection for a simulation-based science inquiry learning environment. In: Trausan-Matu, S., Boyer, K.E., Crosby, M., Panourgia, K. (eds.) ITS 2014. LNCS, vol. 8474, pp. 1\u201310. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-07221-0_1"},{"key":"20_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1007\/978-3-642-24600-5_32","volume-title":"Affective Computing and Intelligent Interaction","author":"J Sabourin","year":"2011","unstructured":"Sabourin, J., Mott, B., Lester, J.C.: Modeling learner affect with theoretically grounded dynamic bayesian networks. In: D\u2019Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) ACII 2011. LNCS, vol. 6974, pp. 286\u2013295. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-24600-5_32"},{"key":"20_CR10","unstructured":"Baker, R.S., et al.: Towards sensor-free affect detection in cognitive tutor algebra. In: Proceedings of the 5th International Conference on Educational Data Mining, pp. 126\u2013133. IEDMS, Montr\u00e9al (2012)"},{"key":"20_CR11","unstructured":"Wixon, M., Arroyo, I., Muldner, K., Burleson, W., Rai, D., Woolf, B.: The opportunities and limitations of scaling up sensor-free affect detection. In: Proceedings of the International Conference on Educational Data Mining, pp. 145\u2013152. IEDMS, London (2014)"},{"key":"20_CR12","doi-asserted-by":"publisher","unstructured":"Litman, D.J., Forbes-Riley, K.: Predicting student emotions in computer-human tutoring dialogues. In: Proceedings of the Annual Meeting on Association for Computational Linguistics, pp. 351\u2013358, Barcelona, Spain (2004). https:\/\/doi.org\/10.3115\/1218955.1219000","DOI":"10.3115\/1218955.1219000"},{"key":"20_CR13","unstructured":"Bosch, N., D\u2019Mello, S.: Co-occurring affective states in automated computer programming education. In: Proceedings of the Workshop on AI-supported Education for Computer Science (AIEDCS) at the 12th International Conference on Intelligent Tutoring Systems, pp. 21\u201330 (2014)"},{"key":"20_CR14","unstructured":"Dillon, J., et al.: Student emotion, co-occurrence, and dropout in a MOOC context. In: Proceedings of the 9th International Conference on Educational Data Mining, pp. 353\u2013357. IEDMS, Raleigh (2016)"},{"key":"20_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1007\/978-3-642-30950-2_5","volume-title":"Intelligent Tutoring Systems","author":"JM Harley","year":"2012","unstructured":"Harley, J.M., Bouchet, F., Azevedo, R.: Measuring learners\u2019 co-occurring emotional responses during their interaction with a pedagogical agent in MetaTutor. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 40\u201345. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-30950-2_5"},{"key":"20_CR16","doi-asserted-by":"publisher","unstructured":"Gutica, M., Conati, C.: Student emotions with an edu-game: a detailed analysis. In: Proceedings of the Humaine Association Conference on Affective Computing and Intelligent Interaction. pp. 534\u2013539. IEEE, Geneva (2013). https:\/\/doi.org\/10.1109\/acii.2013.94","DOI":"10.1109\/acii.2013.94"},{"key":"20_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1007\/978-3-319-91464-0_20","volume-title":"Intelligent Tutoring Systems","author":"J Sinclair","year":"2018","unstructured":"Sinclair, J., Jang, E.E., Azevedo, R., Lau, C., Taub, M., Mudrick, N.V.: Changes in emotion and their relationship with learning gains in the context of MetaTutor. In: Nkambou, R., Azevedo, R., Vassileva, J. (eds.) ITS 2018. LNCS, vol. 10858, pp. 202\u2013211. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-91464-0_20"},{"key":"20_CR18","series-title":"Springer International Handbooks of Education","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1007\/978-1-4419-5546-3_28","volume-title":"International Handbook of Metacognition and Learning Technologies","author":"R Azevedo","year":"2013","unstructured":"Azevedo, R., et al.: Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems. In: Azevedo, R., Aleven, V. (eds.) International Handbook of Metacognition and Learning Technologies. SIHE, vol. 28, pp. 427\u2013449. Springer, New York (2013). https:\/\/doi.org\/10.1007\/978-1-4419-5546-3_28"},{"key":"20_CR19","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/j.procs.2017.01.157","volume":"104","author":"S Petrovica","year":"2017","unstructured":"Petrovica, S., Anohina-Naumeca, A., Ekenel, H.K.: Emotion recognition in affective tutoring systems: collection of ground-truth data. Procedia Comput. Sci. 104, 437\u2013444 (2017). https:\/\/doi.org\/10.1016\/j.procs.2017.01.157","journal-title":"Procedia Comput. Sci."},{"key":"20_CR20","doi-asserted-by":"crossref","unstructured":"Ekman, P.: Basic emotions. In: Handbook of Cognition and Emotion. pp. 45\u201360. Wiley (1999)","DOI":"10.1002\/0470013494.ch3"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Pekrun, R., Frenzel, A.C., Goetz, T., Perry, R.P.: The control-value theory of achievement emotions: An integrative approach to emotions in education. In: Emotion in Education, pp. 13\u201336. Elsevier (2007)","DOI":"10.1016\/B978-012372545-5\/50003-4"},{"key":"20_CR22","doi-asserted-by":"publisher","first-page":"1268","DOI":"10.1080\/02699931.2016.1204989","volume":"31","author":"R Pekrun","year":"2017","unstructured":"Pekrun, R., Vogl, E., Muis, K.R., Sinatra, G.M.: Measuring emotions during epistemic activities: the epistemically-related emotion scales. Cogn. Emot. 31, 1268\u20131276 (2017). https:\/\/doi.org\/10.1080\/02699931.2016.1204989","journal-title":"Cogn. Emot."},{"key":"20_CR23","unstructured":"Ocumpaugh, J., Baker, R.S., Rodrigo, M.M.: Baker rodrigo ocumpaugh monitoring protocol (BROMP) 2.0 technical and training manual. Technical Report. Teachers College, Columbia University, New York. Ateneo Laboratory for the Learning Sciences, Manila (2015)"},{"key":"20_CR24","unstructured":"Malekzadeh, M., Mustafa, M., Lahsasna, A.: A review of emotion regulation in intelligent tutoring systems. Educ. Technol. Soc. 18, 435\u2013445. https:\/\/www.jstor.org\/stable\/10.2307\/jeductechsoci.18.4.435"},{"issue":"5","key":"20_CR25","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1007\/s11423-017-9521-6","volume":"65","author":"A Jarrell","year":"2017","unstructured":"Jarrell, A., Harley, J.M., Lajoie, S., Naismith, L.: Success, failure and emotions: examining the relationship between performance feedback and emotions in diagnostic reasoning. Educ. Tech. Res Dev. 65(5), 1263\u20131284 (2017). https:\/\/doi.org\/10.1007\/s11423-017-9521-6","journal-title":"Educ. Tech. Res Dev."},{"key":"20_CR26","unstructured":"Paquette, L., et al.: Sensor-free or sensor-full: a comparison of data modalities in multi-channel affect detection. In: Proceedings of the 8th International Conference on Educational Data Mining, pp. 93\u2013100. IEDMS, Madrid (2016)"},{"key":"20_CR27","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.bspc.2018.05.034","volume":"46","author":"J Kim","year":"2018","unstructured":"Kim, J., Seo, J., Laine, T.H.: Detecting boredom from eye gaze and EEG. Biomed. Sig. Process. Control 46, 302\u2013313 (2018). https:\/\/doi.org\/10.1016\/j.bspc.2018.05.034","journal-title":"Biomed. Sig. Process. Control"},{"key":"20_CR28","unstructured":"Lall\u00e9, S., Conati, C., Carenini, G.: Predicting confusion in information visualization from eye tracking and interaction data. In: Proceedings on the 25th International Joint Conference on Artificial Intelligence, pp. 2529\u20132535. AAAI Press, New York (2016)"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Henderson, N., Emerson, A., Rowe, J., Lester, J.: Improving sensor-based affect detection with multimodal data imputation. In: Proceedings of the 8th International Conference on Affective Computing and Intelligent Interaction, pp. 669\u2013675. IEEE, Cambridge (2019)","DOI":"10.1109\/ACII.2019.8925538"},{"key":"20_CR30","unstructured":"Hutt, S., Mills, C., White, S., Donnelly, P.J., D\u2019Mello, S.K.: The eyes have it: gaze-based detection of mind wandering during learning with an intelligent tutoring system. In: Proceedings of the 9th International Conference on Educational Data Mining, pp. 86\u201393. IEDMS, Raleigh (2016)"},{"key":"20_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/978-3-642-38844-6_18","volume-title":"User Modeling, Adaptation, and Personalization","author":"S Kardan","year":"2013","unstructured":"Kardan, S., Conati, C.: Comparing and combining eye gaze and interface actions for determining user learning with an interactive simulation. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds.) UMAP 2013. LNCS, vol. 7899, pp. 215\u2013227. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-38844-6_18"},{"key":"20_CR32","doi-asserted-by":"crossref","unstructured":"Pekrun, R., B\u00fchner, M.: Self-report measures of academic emotions. In: International Handbook of Emotions in Education. Routledge, London (2014)","DOI":"10.4324\/9780203148211"},{"key":"20_CR33","doi-asserted-by":"publisher","unstructured":"Tan, P.-N., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: Proceedings of the 8th ACM International Conference on Knowledge Discovery and Data Mining, pp. 32\u201341. ACM, Edmonton (2002). https:\/\/doi.org\/10.1145\/775047.775053","DOI":"10.1145\/775047.775053"},{"key":"20_CR34","doi-asserted-by":"publisher","unstructured":"Villarica, R., Richards, D.: Intelligent and empathic agent to support student learning in virtual worlds. In: Proceedings of the Conference on Interactive Entertainment, pp. 1\u20139. ACM, Newcastle (2014). https:\/\/doi.org\/10.1145\/2677758.2677761","DOI":"10.1145\/2677758.2677761"},{"key":"20_CR35","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1109\/t-affc.2012.6","volume":"3","author":"CN Moridis","year":"2012","unstructured":"Moridis, C.N., Economides, A.A.: Affective learning: empathetic agents with emotional facial and tone of voice expressions. IEEE Trans. Affect. Comput. 3, 260\u2013272 (2012). https:\/\/doi.org\/10.1109\/t-affc.2012.6","journal-title":"IEEE Trans. Affect. Comput."},{"key":"20_CR36","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1080\/1358165042000283101","volume":"29","author":"S Craig","year":"2004","unstructured":"Craig, S., Graesser, A., Sullins, J., Gholson, B.: Affect and learning: an exploratory look into the role of affect in learning with AutoTutor. J. Educ. Media. 29, 241\u2013250 (2004). https:\/\/doi.org\/10.1080\/1358165042000283101","journal-title":"J. Educ. Media."},{"key":"20_CR37","unstructured":"Liu, Z., Pataranutaporn, V., Ocumpaugh, J., Baker, R.: Sequences of frustration and confusion, and learning. In: Proceedings of the International Conference on Educational Data Mining, pp. 114\u2013120. IEDMS, Memphis (2013)"},{"key":"20_CR38","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1080\/02699931.2011.613668","volume":"25","author":"S D\u2019Mello","year":"2011","unstructured":"D\u2019Mello, S., Graesser, A.: The half-life of cognitive-affective states during complex learning. Cogn. Emot. 25, 1299\u20131308 (2011). https:\/\/doi.org\/10.1080\/02699931.2011.613668","journal-title":"Cogn. Emot."},{"key":"20_CR39","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/j.chb.2016.05.034","volume":"63","author":"X Huang","year":"2016","unstructured":"Huang, X., Mayer, R.E.: Benefits of adding anxiety-reducing features to a computer-based multimedia lesson on statistics. Comput. Hum. Behav. 63, 293\u2013303 (2016). https:\/\/doi.org\/10.1016\/j.chb.2016.05.034","journal-title":"Comput. Hum. Behav."},{"key":"20_CR40","unstructured":"Meyer, D.K.: Emotion regulation in K\u201312 classrooms. In: Handbook of Social Influences in School Contexts: Social-Emotional, Motivation, and Cognitive Outcomes. Routledge (2016)"},{"key":"20_CR41","doi-asserted-by":"publisher","unstructured":"Kardan, S., Lall\u00e9, S., Toker, D., Conati, C.: EMDAT: eye movement data analysis toolkit (1.x). The University of British Columbia (2021). https:\/\/doi.org\/10.5281\/zenodo.4699774","DOI":"10.5281\/zenodo.4699774"},{"key":"20_CR42","doi-asserted-by":"publisher","first-page":"104","DOI":"10.5281\/zenodo.3554613","volume":"5","author":"F Bouchet","year":"2013","unstructured":"Bouchet, F., Harley, J.M., Trevors, G.J., Azevedo, R.: Clustering and profiling students according to their interactions with an intelligent tutoring system fostering self-regulated learning. J. Educ. Data Min. 5, 104\u2013146 (2013). https:\/\/doi.org\/10.5281\/zenodo.3554613","journal-title":"J. Educ. Data Min."},{"key":"20_CR43","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"20_CR44","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1109\/TPAMI.2008.52","volume":"31","author":"Z Zeng","year":"2009","unstructured":"Zeng, Z., Pantic, M., Roisman, G.I., Huang, T.S.: A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Trans. Pattern Anal. Mach. Intell. 31, 39\u201358 (2009). https:\/\/doi.org\/10.1109\/TPAMI.2008.52","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"20_CR45","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002). https:\/\/doi.org\/10.1613\/jair.953","journal-title":"J. Artif. Intell. Res."},{"key":"20_CR46","unstructured":"Holm, S.: A simple sequentially rejective multiple test procedure. Scand. J. Stat. 65\u201370 (1979)"}],"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\/978-3-030-78292-4_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T17:22:08Z","timestamp":1672420928000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-78292-4_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030782917","9783030782924"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-78292-4_20","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":"11 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)"}}]}}