{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T18:57:34Z","timestamp":1780599454145,"version":"3.54.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,5,30]],"date-time":"2022-05-30T00:00:00Z","timestamp":1653868800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,5,30]],"date-time":"2022-05-30T00:00:00Z","timestamp":1653868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000601","name":"De Montfort University","doi-asserted-by":"publisher","award":["De Montfort University"],"award-info":[{"award-number":["De Montfort University"]}],"id":[{"id":"10.13039\/501100000601","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Norwegian Computing Centre","award":["Norwegian Computing Centre"],"award-info":[{"award-number":["Norwegian Computing Centre"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["CCF Trans. Pervasive Comp. Interact."],"published-print":{"date-parts":[[2023,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation to engage in learning is essential for learning performance. Learners\u2019 motivation is traditionally assessed using self-reported data, which is time-consuming, subjective, and interruptive to their learning process. To address this issue, this paper proposes a novel framework for multimodal assessment of learners\u2019 motivation in e-learning environments with the ultimate purpose of supporting intelligent e-learning systems to facilitate dynamic, context-aware, and personalized services or interventions, thus sustaining learners\u2019 motivation for learning engagement. We investigated the performance of the machine learning classifier and the most and least accurately predicted motivational factors. We also assessed the contribution of different electroencephalogram (EEG) and eye gaze features to motivation assessment. The applicability of the framework was evaluated in an empirical study in which we combined eye tracking and EEG sensors to produce a multimodal dataset. The dataset was then processed and used to develop a machine learning classifier for motivation assessment by predicting the levels of a range of motivational factors, which represented the multiple dimensions of motivation. We also proposed a novel approach to feature selection combining data-driven and knowledge-driven methods to train the machine learning classifier for motivation assessment, which has been proved effective in our empirical study at selecting predictors from a large number of extracted features from EEG and eye tracking data. Our study has revealed valuable insights for the role played by brain activities and eye movements on predicting the levels of different motivational factors. Initial results using logistic regression classifier have achieved significant predictive power for all the motivational factors studied, with accuracy of between 68.1% and 92.8%. The present work has demonstrated the applicability of the proposed framework for multimodal motivation assessment which will inspire future research towards motivationally intelligent e-learning systems.<\/jats:p>","DOI":"10.1007\/s42486-022-00107-4","type":"journal-article","created":{"date-parts":[[2022,5,30]],"date-time":"2022-05-30T11:03:48Z","timestamp":1653908628000},"page":"64-81","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Multimodal motivation modelling and computing towards motivationally intelligent E-learning systems"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8025-549X","authenticated-orcid":false,"given":"Ruijie","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liming","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aladdin","family":"Ayesh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,5,30]]},"reference":[{"key":"107_CR1","first-page":"261","volume-title":"Studies in Computational Intelligence","author":"S Alghowinem","year":"2014","unstructured":"Alghowinem, S., Alshehri, M., Goecke, R., Wagner, M.: Exploring Eye Activity as an Indication of Emotional States Using an Eye-tracking Sensor. In: Chen, L., Kapoor, S., Bhatia, R. (eds.) Studies in Computational Intelligence, pp.&nbsp;261\u2013276. Springer, Cham (2014)"},{"key":"107_CR2","unstructured":"Arroyo, I., Cooper, D.G., Burleson, W., Woolf, B.P., Muldner, K., Christopherson, R.: Emotion sensors go to school. In: Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling. pp.&nbsp;17\u201324: (2009)"},{"key":"107_CR3","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1037\/0033-2909.91.2.276","volume":"91","author":"J Beatty","year":"1982","unstructured":"Beatty, J.: Task-evoked pupillary responses, processing load, and the structure of processing resources. Psychol. Bull. 91, 276\u2013292 (1982)","journal-title":"Psychol. Bull."},{"key":"107_CR4","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s11257-015-9167-1","volume":"26","author":"R Bixler","year":"2016","unstructured":"Bixler, R., D\u2019Mello, S.: Automatic gaze-based user-independent detection of mind wandering during computerized reading. User Model. User-Adapt. Interact. 26, 33\u201368 (2016)","journal-title":"User Model. User-Adapt. Interact."},{"key":"107_CR5","first-page":"197","volume":"20","author":"B du Boulay","year":"2010","unstructured":"du Boulay, B., Avramides, K., Luckin, R., Mart\u00ednez-Mir\u00f3n, E., M\u00e9ndez, G.R., Carr, A.: Towards systems that care: a conceptual framework based on motivation, metacognition and affect. Int. J. Artif. Intell. Educ. 20, 197\u2013229 (2010)","journal-title":"Int. J. Artif. Intell. Educ."},{"key":"107_CR6","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1111\/j.1469-8986.2008.00654.x","volume":"45","author":"M Bradley","year":"2008","unstructured":"Bradley, M., Miccoli, L., Escrig, M., Lang, P.: The pupil as a measure of emotional arousal and autonomic activation. Psychophysiology. 45, 602\u2013607 (2008)","journal-title":"Psychophysiology."},{"key":"107_CR7","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1016\/j.ijhcs.2009.03.005","volume":"67","author":"G Chanel","year":"2009","unstructured":"Chanel, G., Kierkels, J.J.M., Soleymani, M., Pun, T.: Short-term emotion assessmentin a recall paradigm. Int. J. Hum. Comput. Stud. 67, 607\u2013627 (2009). https:\/\/doi.org\/10.1016\/j.ijhcs.2009.03.005","journal-title":"Int. J. Hum. Comput. Stud."},{"key":"107_CR8","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/s40299-012-0011-7","volume":"22","author":"C-C Chang","year":"2013","unstructured":"Chang, C.-C., Liang, C., Yan, C.-F., Tseng, J.-S.: The impact of college students \u2019 intrinsic and extrinsic motivation on continuance intention to use english mobile learning systems. Asia Pac. Educ. Res. 22, 181\u2013192 (2013). https:\/\/doi.org\/10.1007\/s40299-012-0011-7","journal-title":"Asia Pac. Educ. Res."},{"key":"107_CR9","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1080\/08839510290030390","volume":"16","author":"C Conati","year":"2002","unstructured":"Conati, C.: Probabilistic assessment of user\u2019s emotions in educational games. Appl. Artif. Intell. 16, 555\u2013576 (2002)","journal-title":"Appl. Artif. Intell."},{"key":"107_CR10","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1016\/j.knosys.2007.04.010","volume":"20","author":"C Conati","year":"2007","unstructured":"Conati, C., Merten, C.: Eye-tracking for user modeling in exploratory learning environments: An empirical evaluation. Knowl. Based Syst. 20, 557\u2013574 (2007). https:\/\/doi.org\/10.1016\/j.knosys.2007.04.010","journal-title":"Knowl. Based Syst."},{"key":"107_CR11","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3389\/fnhum.2013.00261","volume":"7","author":"LD Crocker","year":"2013","unstructured":"Crocker, L.D., Heller, W., Warren, S.L., O\u2019Hare, A.J., Infantolino, Z.P., Miller, G.A.: Relationships among cognition, emotion, and motivation: implications for intervention and neuroplasticity in psychopathology. Front. Hum. Neurosci. 7, 2 (2013). https:\/\/doi.org\/10.3389\/fnhum.2013.00261","journal-title":"Front. Hum. Neurosci."},{"key":"107_CR12","doi-asserted-by":"publisher","first-page":"319","DOI":"10.2307\/249008","volume":"13","author":"FD Davis","year":"1989","unstructured":"Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13, 319\u2013340 (1989)","journal-title":"MIS Q."},{"key":"107_CR13","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1207\/S15327965PLI1104_01","volume":"11","author":"EL Deci","year":"2000","unstructured":"Deci, E.L., Ryan, R.M.: The \" What \" and \" Why \" of goal pursuits: Human needs and the self-determination of behavior. Psychol. Inq. 11, 227\u2013268 (2000). https:\/\/doi.org\/10.1207\/S15327965PLI1104_01","journal-title":"Psychol. Inq."},{"key":"107_CR15","doi-asserted-by":"publisher","unstructured":"Derbali, L., Frasson, C.: Prediction of Players\u2019 Motivational States using Electrophysiological Measures during Serious Game Play. In: 2010 10th IEEE International Conference on Advanced Learning Technologies. pp.&nbsp;498\u2013502. IEEE: (2010). https:\/\/doi.org\/10.1109\/ICALT.2010.143","DOI":"10.1109\/ICALT.2010.143"},{"key":"107_CR14","doi-asserted-by":"publisher","DOI":"10.1155\/2012\/624538","author":"L Derbali","year":"2012","unstructured":"Derbali, L., Frasson, C.: Assessment of learners&nbsp;motivation during interactions with serious games: a study of some motivational strategies in food-force. Adv. Hum. Comput. Interact. (2012). https:\/\/doi.org\/10.1155\/2012\/624538","journal-title":"Adv. Hum. Comput. Interact."},{"key":"107_CR17","doi-asserted-by":"publisher","unstructured":"Goldberg, M.E.: Parietal Lobe. In: International Encyclopedia of the Social & Behavioral Sciences. pp.&nbsp;11051\u201311054: (2001). https:\/\/doi.org\/10.1016\/B0-08-043076-7\/03471-9","DOI":"10.1016\/B0-08-043076-7\/03471-9"},{"key":"107_CR16","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1016\/S0169-8141(98)00068-7","volume":"24","author":"HJ Goldberg","year":"1999","unstructured":"Goldberg, H.J., Kotval, X.P.: Computer interface evaluation using eye movements: Methods and constructs. Int. J. Ind. Ergon. 24, 631\u2013645 (1999)","journal-title":"Int. J. Ind. Ergon."},{"key":"107_CR18","doi-asserted-by":"publisher","first-page":"73395","DOI":"10.1109\/ACCESS.2018.2881384","volume":"6","author":"A Hanif","year":"2018","unstructured":"Hanif, A., Jamal, F.Q., Imran, M.: Extending the technology acceptance model for use of e-learning systems by digital learners. IEEE Access. 6, 73395\u201373404 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2881384","journal-title":"IEEE Access."},{"key":"107_CR19","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1186\/s41239-019-0148-z","volume":"16","author":"TC Herrador-alcaide","year":"2019","unstructured":"Herrador-alcaide, T.C., Hern\u00e1ndez-Sol\u00eds, M., Galv\u00e1n, R.S.: Feelings of satisfaction in mature students of financial accounting in a virtual learning environment: an experience of measurement in higher education. Int. J. Educ. Technol. High. Educ. 16, 2 (2019). https:\/\/doi.org\/10.1186\/s41239-019-0148-z","journal-title":"Int. J. Educ. Technol. High. Educ."},{"key":"107_CR20","doi-asserted-by":"crossref","unstructured":"Horng, W.-B., Chen, C.-Y., Chang, Y., Fan, C.-H.: Driver fatigue detection based on eye tracking and dynamk, template matching. In: IEEE International Conference on Networking, Sensing and Control. pp.&nbsp;7\u201312., Taipei: (2004)","DOI":"10.1109\/ICNSC.2004.1297400"},{"key":"107_CR21","doi-asserted-by":"publisher","unstructured":"Hou, X., Liu, Y., Sourina, O., Mueller-wittig, W.: CogniMeter: EEG-based Emotion, Mental Workload and Stress Visual Monitoring. In: 2015 International Conference on Cyberworlds (CW). pp.&nbsp;153\u2013160: (2015). https:\/\/doi.org\/10.1109\/CW.2015.58","DOI":"10.1109\/CW.2015.58"},{"key":"107_CR22","doi-asserted-by":"publisher","unstructured":"Jenke, R., Peer, A., Buss, M.: Effect-size-based electrode and feature selection foremotion recognition from EEG. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. pp.&nbsp;1217\u20131221., Vancouver: (2013). https:\/\/doi.org\/10.1109\/ICASSP.2013.6637844","DOI":"10.1109\/ICASSP.2013.6637844"},{"key":"107_CR23","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1126\/science.3336779","volume":"239","author":"E John","year":"1988","unstructured":"John, E., Prichep, L., Easton, P.: Neurometrics: computer assisted differential diagnosis of brain dysfunctions. Science (80- ) 239, 162\u2013169 (1988)","journal-title":"Science (80- )"},{"key":"107_CR24","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/S0925-4927(00)00080-9","volume":"106","author":"V Knott","year":"2001","unstructured":"Knott, V., Mahoney, C., Kennedy, S., Evans, K.: EEG power, frequency, asymmetry and coherence in male depression. Psychiatry Res. Neuroimaging. 106, 123\u2013140 (2001). https:\/\/doi.org\/10.1016\/S0925-4927(00)00080-9","journal-title":"Psychiatry Res. Neuroimaging"},{"key":"107_CR25","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.tins.2015.02.002","volume":"42","author":"M Kodappully","year":"2016","unstructured":"Kodappully, M., Srinivasan, B., Srinivasan, R.: Towards predicting human error: eye gaze analysis for identification of cognitive steps performed by control room operators. J. Loss Prev. Process. Ind. 42, 35\u201346 (2016). https:\/\/doi.org\/10.1016\/j.tins.2015.02.002","journal-title":"J. Loss Prev. Process. Ind."},{"key":"107_CR26","unstructured":"National Institute of Mental: Health, J.A.: Gene Slows Frontal Lobes, Boosts Schizophrenia Risk, https:\/\/web.archive.org\/web\/20150404205032\/http:\/\/www.nih.gov\/news\/pr\/may2001\/nimh-29.htm, (2001)"},{"key":"107_CR27","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/S1071-5819(03)00017-X","volume":"59","author":"T Partala","year":"2003","unstructured":"Partala, T., Surakka, V.: Pupil size variation as an indication of affective processing. Int. J. Hum. Comput. Stud. 59, 185\u2013198 (2003)","journal-title":"Int. J. Hum. Comput. Stud."},{"key":"107_CR28","doi-asserted-by":"publisher","unstructured":"Poole, A., Ball, L., Phillips, P.: In search of salience: a response-time and eye-movement analysis of bookmark recognition. In: Fincher, S., Markopoulos, P., Moore, D., and Ruddle, R. (eds.) People and Computers XVIII \u2014 Design for Life SE \u2013 23. pp.&nbsp;363\u2013378. Springer London: (2005). https:\/\/doi.org\/10.1007\/1-84628-062-1_23","DOI":"10.1007\/1-84628-062-1_23"},{"key":"107_CR29","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/BF01135562","volume":"4","author":"L Prichep","year":"1992","unstructured":"Prichep, L., John, E.: QEEG profiles of psychiatric disorders. Brain Topogr. 4, 249\u2013257 (1992)","journal-title":"Brain Topogr."},{"key":"107_CR30","unstructured":"Rebolledo-mendez, G., De Freitas, S., Rafael Rojano-caceres, J., Garcia-gaona, R., A.: An Empirical Examination of the Relation Between Attention and Motivation in Computer-Based Education: A Modeling Approach. In: Proceedings of the Twenty-Third International Florida Artificial Intelligence Research Society Conference. pp.&nbsp;74\u201379: (2010)"},{"key":"107_CR31","first-page":"59","volume":"8","author":"RH Shroff","year":"2009","unstructured":"Shroff, R.H., Vogel, D.R.: Assessing the factors deemed to support individual student intrinsic motivation in technology supported online and face-to-face discussions. J. Inf. Technol. Educ. 8, 59\u201385 (2009)","journal-title":"J. Inf. Technol. Educ."},{"key":"107_CR32","doi-asserted-by":"publisher","first-page":"2723","DOI":"10.1016\/j.chb.2008.03.016","volume":"24","author":"S Sun","year":"2008","unstructured":"Sun, S.: An examination of disposition, motivation, and involvement in the new technology context computers in human behavior. Comput. Hum. Behav. 24, 2723\u20132740 (2008). https:\/\/doi.org\/10.1016\/j.chb.2008.03.016","journal-title":"Comput. Hum. Behav."},{"key":"107_CR33","doi-asserted-by":"publisher","first-page":"66481","DOI":"10.1109\/ACCESS.2018.2877760","volume":"6","author":"RM Tawafak","year":"2018","unstructured":"Tawafak, R.M., Romli, A.B.T., Bin, R., Arshah, A.: Continued intention to use UCOM: Four factors for integrating with a technology acceptance model to moderate the satisfaction of learning. IEEE Access. 6, 66481\u201366498 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2877760","journal-title":"IEEE Access."},{"key":"107_CR34","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1007\/s11042-013-1390-3","volume":"71","author":"T Walber","year":"2014","unstructured":"Walber, T., Scherp, A., Staab, S.: Benefiting from users \u2019 gaze: selection of image regions from eye tracking information for provided tags. Multimed Tools Appl. 71, 363\u2013390 (2014). https:\/\/doi.org\/10.1007\/s11042-013-1390-3","journal-title":"Multimed Tools Appl."},{"key":"107_CR36","doi-asserted-by":"publisher","unstructured":"Wang, R., Chen, L., Solheim, I., Schulz, T., Ayesh, A.: Conceptual Motivation Modeling for Students with Dyslexia for Enhanced Assistive Learning. In: SmartLearn \u201917 Proceedings of the 2017 ACM Workshop on Intelligent Interfaces for Ubiquitous and Smart Learning. pp.&nbsp;11\u201318. ACM New York, NY, USA \u00a92017: (2017). https:\/\/doi.org\/10.1145\/3038535.3038542","DOI":"10.1145\/3038535.3038542"},{"key":"107_CR37","doi-asserted-by":"publisher","unstructured":"Wang, R., Chen, L., Ayesh, A., Shell, J., Solheim, I.: Gaze-based Assessment of Dyslexic Students\u2019 Motivation within an E-learning Environment. In: 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation. pp.&nbsp;610\u2013617. IEEE, Leicester, UK (2019). https:\/\/doi.org\/10.1109\/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00142","DOI":"10.1109\/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00142"},{"key":"107_CR35","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1145\/3341197","volume":"10","author":"R Wang","year":"2020","unstructured":"Wang, R., Chen, L., Solheim, I.: Modeling dyslexic students\u2019 motivation for enhanced learning in e-learning systems. ACM Trans. Interact. Intell. Syst. 10, 21 (2020)","journal-title":"ACM Trans. Interact. Intell. Syst."}],"container-title":["CCF Transactions on Pervasive Computing and Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42486-022-00107-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42486-022-00107-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42486-022-00107-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T02:47:46Z","timestamp":1727318866000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42486-022-00107-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,30]]},"references-count":37,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["107"],"URL":"https:\/\/doi.org\/10.1007\/s42486-022-00107-4","relation":{},"ISSN":["2524-521X","2524-5228"],"issn-type":[{"value":"2524-521X","type":"print"},{"value":"2524-5228","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,30]]},"assertion":[{"value":"3 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 May 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 May 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The present work is part of the first author\u2019s PhD project conducted under the supervision of\u00a0the second author and the third author. The authors have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}