%0 Journal Article %1 citeulike:6444776 %A Moridis, Christos N. %A Economides, Anastasios A. %C Los Alamitos, CA, USA %D 2009 %I IEEE Computer Society Press %J IEEE Trans. Learn. Technol. %K affective-computing, e-learning, empirical-study, personalization %N 1 %P 50--61 %R 10.1109/tlt.2009.12 %T Mood Recognition during Online Self-Assessment Tests %U http://dx.doi.org/10.1109/tlt.2009.12 %V 2 %X Individual emotions play a crucial role during any learning interaction. Identifying a student&\#x2019;s emotional state and providing personalized feedback, based on integrated pedagogical models, has been considered to be one of the main limits of traditional tools of e-learning. This paper presents an empirical study that illustrates how learner mood may be predicted during online self-assessment tests. Here a previous method of determining student mood has been refined based on the assumption that the influence on learner mood of questions already answered declines in relation to their distance from the current question. Moreover, this paper sets out to indicate that &\#x201C;exponential logic&\#x201D; may help produce more efficient models, if integrated adequately with affective modelling. The results show that these assumptions may prove useful to future research.

@article{citeulike:6444776, abstract = {{Individual emotions play a crucial role during any learning interaction. Identifying a student\&\#x2019;s emotional state and providing personalized feedback, based on integrated pedagogical models, has been considered to be one of the main limits of traditional tools of e-learning. This paper presents an empirical study that illustrates how learner mood may be predicted during online self-assessment tests. Here a previous method of determining student mood has been refined based on the assumption that the influence on learner mood of questions already answered declines in relation to their distance from the current question. Moreover, this paper sets out to indicate that \&\#x201C;exponential logic\&\#x201D; may help produce more efficient models, if integrated adequately with affective modelling. The results show that these assumptions may prove useful to future research.}}, added-at = {2017-11-15T17:02:25.000+0100}, address = {Los Alamitos, CA, USA}, author = {Moridis, Christos N. and Economides, Anastasios A.}, biburl = {https://www.bibsonomy.org/bibtex/2665b89baacdacd4b6672bd5f1454d24c/brusilovsky}, citeulike-article-id = {6444776}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1550398.1550513}, citeulike-linkout-1 = {http://dx.doi.org/10.1109/tlt.2009.12}, doi = {10.1109/tlt.2009.12}, interhash = {a3547c53e920706e00e926ffe7381e4e}, intrahash = {665b89baacdacd4b6672bd5f1454d24c}, issn = {1939-1382}, journal = {IEEE Trans. Learn. Technol.}, keywords = {affective-computing, e-learning, empirical-study, personalization}, number = 1, pages = {50--61}, posted-at = {2009-12-27 19:12:15}, priority = {2}, publisher = {IEEE Computer Society Press}, timestamp = {2017-11-15T17:02:25.000+0100}, title = {{Mood Recognition during Online Self-Assessment Tests}}, url = {http://dx.doi.org/10.1109/tlt.2009.12}, volume = 2, year = 2009 }