{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T20:51:50Z","timestamp":1760043110675,"version":"3.41.2"},"reference-count":17,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2016,4,4]],"date-time":"2016-04-04T00:00:00Z","timestamp":1459728000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,4,4]]},"abstract":"\n Purpose<\/jats:title>\n \u2013 The ultimate objective of the any e-Learning system is to meet the specific need of the online learners and provide them with various features to have efficacious learning experiences by understanding their complexities. Any e-Learning system could be much more improved by tracking students commitment and disengagement on that course, in turn, would allow system to have personalized involvements at appropriate times in order to re-engage learners. Motivations play a important role to get back the learners on the track could be done by analyzing of several attributes of the log files. This paper aims to analyze the multiple attributes which cause the learners to disengage from an online learning environment. <\/jats:p>\n <\/jats:sec>\n \n Design\/methodology\/approach<\/jats:title>\n \u2013 For this improvisation, Web based learning system is researched using data mining techniques in education. There are various attributes characterized for the disengagement prediction using web log file analysis. Though, there have been several attempts to include motivating characteristics in e-Learning systems are adapted, presently influence on cognition is acknowledged mostly. <\/jats:p>\n <\/jats:sec>\n \n Findings<\/jats:title>\n \u2013 Classification is one of the predictive data mining technique which makes prediction about values of data using known results found from different data sets. To find out the optimal solution for identifying disengaged learners in the online learning systems, Naive Bayesian (NB) classifier with Particle Swarm Optimization (PSO) algorithm is used which will classify the data set and then perform the independent analysis. <\/jats:p>\n <\/jats:sec>\n \n Originality\/value<\/jats:title>\n \u2013 The experimental results shows that the use of unrelated variables in the class attributes will reduce the accuracy and reliability of a any classification model. However, the hybrid PSO algorithm is clearly more apt to find minor subsets of attributes than the PSO with NB classifier. The NB classifier combined with hybrid PSO feature selection method proves to be the best feature selection capability without degrading the classification accuracy. It is further proved to be an effective method for mining large structural data in much less computation time.<\/jats:p>\n <\/jats:sec>","DOI":"10.1108\/prog-07-2015-0047","type":"journal-article","created":{"date-parts":[[2016,3,21]],"date-time":"2016-03-21T10:45:31Z","timestamp":1458557131000},"page":"215-224","source":"Crossref","is-referenced-by-count":7,"title":["A hybrid PSO with Na\u00efve Bayes classifier for disengagement detection in online learning"],"prefix":"10.1108","volume":"50","author":[{"given":"GopalaKrishnan","family":"T","sequence":"first","affiliation":[]},{"given":"P","family":"Sengottuvelan","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2020121700120702300_b8","doi-asserted-by":"crossref","unstructured":"Alshammari, M.\n , \n Anane, R.\n and \n Hendley, R.J.\n (2014), \u201cAdaptivity in e-learning systems\u201d, Proceedings of The Eighth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS 2014), Birmingham, pp. 79-86.","DOI":"10.1109\/CISIS.2014.12"},{"key":"key2020121700120702300_b15","unstructured":"Bouvier, P.\n , \n Lavou\u00e9, E.\n , \n Sehaba, K.\n and \n George, S.\n (2013), \u201cIdentifying learner\u2019s engagement in learning games: a qualitative approach based on learner\u2019s traces of interaction\u201d, 5th International Conference on Computer Supported Education (CSEDU 2013), pp. 339-350."},{"key":"key2020121700120702300_b16","doi-asserted-by":"crossref","unstructured":"Bouvier, P.\n , \n Sehaba, K.\n and \n Lavou\u00e9, E.\n (2014), \u201cA trace-based approach to identifying users\u2019 engagement and qualifying their engaged-behaviours in interactive systems: application to a social game\u201d, \n User Modeling and User-Adapted Interaction\n , Vol. 24 No. 5, pp. 413-451.","DOI":"10.1007\/s11257-014-9150-2"},{"key":"key2020121700120702300_b3","doi-asserted-by":"crossref","unstructured":"Chih-Ping, C.\n , \n Yi-Chun, C.C.\n and \n Cheng-Chang, T.\n (2011), \u201cPC2PSO: personalized e-course composition based on particle swarm optimization\u201d, \n Applied Intelligence\n , Vol. 34 No. 1, pp. 141-154.","DOI":"10.1007\/s10489-009-0186-7"},{"key":"key2020121700120702300_b4","doi-asserted-by":"crossref","unstructured":"Conati, C.\n , \n Jaques, N.\n and \n Muir, M.\n (2013), \u201cUnderstanding attention to adaptive hints in educational games: an eye-tracking study\u201d, \n International Journal of Artificial Intelligence in Education\n , Vol. 23 Nos 1-4, pp. 136-161.","DOI":"10.1007\/s40593-013-0002-8"},{"key":"key2020121700120702300_b7","doi-asserted-by":"crossref","unstructured":"Ghergulescu, I.\n and \n Muntean, C.H.\n (2014), \u201cA novel sensor-based methodology for learner\u2019s motivation analysis in game-based learning\u201d, \n Interacting with Computers\n , Vol. 26 No. 4, pp. 305-320.","DOI":"10.1093\/iwc\/iwu013"},{"key":"key2020121700120702300_b6","doi-asserted-by":"crossref","unstructured":"Jraidi, I.\n , \n 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