{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T14:10:23Z","timestamp":1765807823975,"version":"3.38.0"},"reference-count":59,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2020,9,29]]},"abstract":"<jats:p>The lack of physical contact and the demanding need for personalized services has prompted stakeholders in distance learning to benefit from the enormous volume of students\u2019 online traces in the Learning Management Systems. Data mining methodologies are widely applied to analyze data logs and predict trends for early and efficient interventions. Thus, the retention of students in the educational process can be achieved with positive effects on the reputation and finances of the institutions. This work divides the moodle data sets from six different sections of an annual postgraduate program at the Hellenic Open University in six periods for each section, due to the number of written assignments. Then it implements data mining techniques to analyze the activity, polarity and emotions of tutors and students in order to predict students\u2019 grades. The results indicate the algorithm with the highest precision in each prediction. In addition, the research concludes that polarity and emotions as independent variables provide better performance in comparative models. Moreover, tutors\u2019 variables are highlighted as an important factor for more accurate predictions of student grades. Finally, a comparison of actual and predicted grades indicates which students have used a third party to fulfill their assignments.<\/jats:p>","DOI":"10.3233\/idt-190137","type":"journal-article","created":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T18:22:00Z","timestamp":1598379720000},"page":"409-436","source":"Crossref","is-referenced-by-count":14,"title":["Polarity, emotions and online activity of students and tutors as features in predicting grades"],"prefix":"10.1177","volume":"14","author":[{"given":"Andreas F.","family":"Gkontzis","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Patras, Greece"}]},{"given":"Sotiris","family":"Kotsiantis","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of Patras, Greece"}]},{"given":"Dimitris","family":"Kalles","sequence":"additional","affiliation":[{"name":"School of Science and Technology, Hellenic Open University, Patras, Greece"}]},{"given":"Christos T.","family":"Panagiotakopoulos","sequence":"additional","affiliation":[{"name":"Department of Primary Education, University of Patras, Patras, Greece"}]},{"given":"Vassilios S.","family":"Verykios","sequence":"additional","affiliation":[{"name":"School of Science and Technology, Hellenic Open University, Patras, Greece"}]}],"member":"179","reference":[{"key":"10.3233\/IDT-190137_ref1","unstructured":"Siemens G. Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning. 2004; 2(1)."},{"issue":"6","key":"10.3233\/IDT-190137_ref2","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1080\/03054985.2017.1343712","article-title":"Higher education, unbundling, and the end of the university as we know it","volume":"43","author":"Mccowan","year":"2017","journal-title":"Oxford Review of Education"},{"issue":"5","key":"10.3233\/IDT-190137_ref3","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1504\/IJTEL.2012.051816","article-title":"Learning analytics: Drivers, developments and challenges","volume":"4","author":"Ferguson","year":"2012","journal-title":"International Journal of Technology Enhanced Learning"},{"key":"10.3233\/IDT-190137_ref4","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.chb.2018.07.027","article-title":"The current landscape of learning analytics in higher education","volume":"89","author":"Viberg","year":"2018","journal-title":"Computers in Human Behavior"},{"key":"10.3233\/IDT-190137_ref5","unstructured":"Dalziel J. Implementing learning design: The learning activity management system (LAMS). 2003; 593-596."},{"issue":"1","key":"10.3233\/IDT-190137_ref6","first-page":"1","article-title":"Developing student support for open and distance learning: The EMPOWER project","volume":"2018","author":"Paniagua","year":"2018","journal-title":"Journal of Interactive Media in Education"},{"issue":"2","key":"10.3233\/IDT-190137_ref7","doi-asserted-by":"crossref","first-page":"109","DOI":"10.5944\/openpraxis.10.2.822","article-title":"Mapping the open education landscape: Citation network analysis of historical open and distance education research","volume":"10","author":"Weller","year":"2018","journal-title":"Open Praxis"},{"key":"10.3233\/IDT-190137_ref8","unstructured":"Schneller C, Holmberg C. Distance education in European higher education. UNESCO Institute for Lifelong Learning. International Council for Open and Distance Education and Study Portals BV: Oslo. 2014."},{"issue":"1","key":"10.3233\/IDT-190137_ref9","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1080\/01587919.2017.1299562","article-title":"Predicting student success by modeling student interaction in asynchronous online courses","volume":"38","author":"Shelton","year":"2017","journal-title":"Distance Education"},{"key":"10.3233\/IDT-190137_ref10","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.iheduc.2015.11.003","article-title":"Identifying significant indicators using LMS data to predict course achievement in online learning","volume":"29","author":"You","year":"2016","journal-title":"Internet and Higher Education"},{"key":"10.3233\/IDT-190137_ref11","doi-asserted-by":"crossref","unstructured":"Umer R, Susnjak T, Mathrani A, Suriadi S. On predicting academic performance with process mining in learning analytics. 2017; 10(2): 160-176.","DOI":"10.1108\/JRIT-09-2017-0022"},{"key":"10.3233\/IDT-190137_ref12","unstructured":"Lee JI, Brunskill E. The impact on individualizing student models on necessary practice opportunities. 2012; 118-125."},{"issue":"3","key":"10.3233\/IDT-190137_ref13","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s10639-016-9464-2","article-title":"Beyond engagement analytics: Which online mixed-data factors predict student learning outcomes","volume":"22","author":"Strang","year":"2017","journal-title":"Education and Information Technologies"},{"key":"10.3233\/IDT-190137_ref14","doi-asserted-by":"crossref","unstructured":"Romero C, Ventura S. Educational data mining: A review of the state of the art. 2010; 40(6): 601-618.","DOI":"10.1109\/TSMCC.2010.2053532"},{"key":"10.3233\/IDT-190137_ref15","unstructured":"Baker RS, Yacef K. The state of educational data mining in 2009: A review and future visions. 2009; 1(1): 3-17."},{"issue":"3","key":"10.3233\/IDT-190137_ref16","first-page":"98","article-title":"Educational data mining and learning analytics: differences, similarities, and time evolution","volume":"12","author":"Linan","year":"2015","journal-title":"International Journal of Educational Technology in Higher Education"},{"issue":"1","key":"10.3233\/IDT-190137_ref17","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s41239-017-0048-z","article-title":"The possibility of predicting learning performance using features of note taking activities and instructions in a blended learning environment","volume":"14","author":"Nakayama","year":"2017","journal-title":"International Journal of Educational Technology in Higher Education"},{"issue":"1","key":"10.3233\/IDT-190137_ref18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/1500000011","article-title":"Opinion mining and sentiment analysis","volume":"2","author":"Pang","year":"2008","journal-title":"Foundations and Trends in Information Retrieval"},{"issue":"2","key":"10.3233\/IDT-190137_ref19","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1111\/j.1750-8606.2011.00192.x","article-title":"Linking students\u2019 emotions and academic achievement: When and why emotions matter","volume":"6","author":"Valiente","year":"2012","journal-title":"Child Development Perspectives"},{"key":"10.3233\/IDT-190137_ref20","unstructured":"Chaplot DS, Rhim E, Kim J. Predicting student attrition in MOOCs using sentiment analysis and neural networks. 2015; 7-12."},{"issue":"5","key":"10.3233\/IDT-190137_ref21","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/MC.2017.133","article-title":"A sentiment analysis system to improve teaching and learning","volume":"50","author":"Rani","year":"2017","journal-title":"IEEE Computer"},{"issue":"2","key":"10.3233\/IDT-190137_ref22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3057270","article-title":"Current state of text sentiment analysis from opinion to emotion mining","volume":"50","author":"Yadollahi","year":"2017","journal-title":"ACM Computing Surveys"},{"issue":"2","key":"10.3233\/IDT-190137_ref23","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s11257-018-9203-z","article-title":"Student success prediction in MOOCs","volume":"28","author":"Gardner","year":"2018","journal-title":"User Modeling and User-adapted Interaction"},{"issue":"4","key":"10.3233\/IDT-190137_ref24","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1111\/jcal.12247","article-title":"Improving early prediction of academic failure using sentiment analysis on self-evaluated comments","volume":"34","author":"Yu","year":"2018","journal-title":"Journal of Computer Assisted Learning"},{"issue":"4","key":"10.3233\/IDT-190137_ref25","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.compedu.2003.09.005","article-title":"Prediction and assessment of student behaviour in open and distance education in computers using Bayesian networks","volume":"43","author":"Xenos","year":"2004","journal-title":"Computers & Education"},{"key":"10.3233\/IDT-190137_ref26","unstructured":"Kalles D, Pierrakeas C, Xenos M. Intelligently raising academic performance alerts. 2008; 37-42."},{"issue":"2","key":"10.3233\/IDT-190137_ref27","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1504\/IJTEL.2013.059088","article-title":"Using learning analytics to identify successful learners in a blended learning course","volume":"5","author":"Kotsiantis","year":"2013","journal-title":"International Journal of Technology Enhanced Learning"},{"key":"10.3233\/IDT-190137_ref28","doi-asserted-by":"crossref","unstructured":"Lotsari E, Verykios VS, Panagiotakopoulos C, Kalles D. A learning analytics methodology for student profiling. 2014; 300-312.","DOI":"10.1007\/978-3-319-07064-3_24"},{"issue":"2","key":"10.3233\/IDT-190137_ref29","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1515\/eurodl-2015-0014","article-title":"A learning analytics methodology for detecting sentiment in student fora: A case study in distance education","volume":"18","author":"Kagklis","year":"2015","journal-title":"The European Journal of Open, Distance and E-Learning"},{"key":"10.3233\/IDT-190137_ref30","doi-asserted-by":"crossref","unstructured":"Kostopoulos G, Kotsiantis S, Pintelas P. Predicting student performance in distance higher education using semi-supervised techniques. 2015; 259-270.","DOI":"10.1007\/978-3-319-23781-7_21"},{"key":"10.3233\/IDT-190137_ref31","doi-asserted-by":"crossref","unstructured":"Gkontzis AF, Karachristos CV, Panagiotakopoulos C, Stavropoulos EC, Verykios VS. Sentiment analysis to track emotion and polarity in student fora. 2017; p. 39.","DOI":"10.1145\/3139367.3139389"},{"key":"10.3233\/IDT-190137_ref32","doi-asserted-by":"crossref","unstructured":"Gkontzis AF, Panagiotakopoulos C, Kotsiantis S, Verykios VS. Measuring engagement to assess performance of students in distance learning. 2018; 1-7.","DOI":"10.1080\/10494820.2019.1709209"},{"key":"10.3233\/IDT-190137_ref33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/10494820.2019.1709209","article-title":"A predictive analytics framework as a countermeasure for attrition of students","author":"Gkontzis","year":"2019","journal-title":"Interactive Learning Environments"},{"key":"10.3233\/IDT-190137_ref34","doi-asserted-by":"crossref","unstructured":"Cerezo R, S\u00e1nchez-Santill\u00e1n M, Paule-Ruiz MP, N\u00fa\u00f1ez JC. Students\u2019 LMS interaction patterns and their relationship with achievement: A case study in higher education. Computers & Education. 2016; 96: 42-54. Available from: http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0360131516300264.","DOI":"10.1016\/j.compedu.2016.02.006"},{"key":"10.3233\/IDT-190137_ref35","doi-asserted-by":"crossref","unstructured":"Almeda MV, Zuech J, Baker RS, Utz C, Higgins G, Reynolds R. Comparing the factors that predict completion and grades among for-credit and open\/MOOC students in online learning. 2018; 22(1): 1-18.","DOI":"10.24059\/olj.v22i1.1060"},{"key":"10.3233\/IDT-190137_ref36","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.compedu.2016.03.016","article-title":"Exploring the factors affecting MOOC retention: A survey study","volume":"98","author":"Hone","year":"2016","journal-title":"Computers & Education"},{"key":"10.3233\/IDT-190137_ref37","first-page":"420","article-title":"Sentiment analysis: Towards a tool for analyzing real-time students feedback","author":"Altrabsheh","year":"2014","journal-title":"Proceedings of 26th International Conference on Tools with Artificial Intelligence"},{"key":"10.3233\/IDT-190137_ref38","doi-asserted-by":"crossref","unstructured":"Wang L, Hu G, Zhou T. Semantic analysis of learners\u2019 emotional tendencies on online mooc education. Sustainability. 2018; 10(6): 1921.","DOI":"10.3390\/su10061921"},{"key":"10.3233\/IDT-190137_ref39","doi-asserted-by":"crossref","unstructured":"Cummins S, Burd L, Hatch A. Using feedback tags and sentiment analysis to generate sharable learning resources investigating automated sentiment analysis of feedback tags in a programming course. 2010; 653-657.","DOI":"10.1109\/ICALT.2010.186"},{"issue":"6","key":"10.3233\/IDT-190137_ref40","doi-asserted-by":"crossref","first-page":"880","DOI":"10.1080\/02602938.2017.1416456","article-title":"Feeling feedback: Students\u2019 emotional responses to educator feedback","volume":"43","author":"Ryan","year":"2018","journal-title":"Assessment & Evaluation in Higher Education"},{"issue":"1","key":"10.3233\/IDT-190137_ref41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2200\/S00416ED1V01Y201204HLT016","article-title":"Sentiment analysis and opinion mining","volume":"5","author":"Liu","year":"2012","journal-title":"Synthesis Lectures on Human Language Technologies"},{"issue":"2","key":"10.3233\/IDT-190137_ref42","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.ijinfomgt.2014.10.007","article-title":"Beyond the hype: Big data concepts, methods, and analytics","volume":"35","author":"Gandomi","year":"2015","journal-title":"International Journal of Information Management"},{"issue":"3","key":"10.3233\/IDT-190137_ref43","doi-asserted-by":"crossref","first-page":"26","DOI":"10.5120\/ijca2015905866","article-title":"Approaches, tools and applications for sentiment analysis implementation","volume":"125","author":"Dandrea","year":"2015","journal-title":"International Journal of Computer Applications"},{"key":"10.3233\/IDT-190137_ref44","doi-asserted-by":"crossref","unstructured":"Hatzivassiloglou V, Mckeown KR. Predicting the semantic orientation of adjectives. 1997; 174-181.","DOI":"10.3115\/979617.979640"},{"issue":"11","key":"10.3233\/IDT-190137_ref45","doi-asserted-by":"crossref","first-page":"312","DOI":"10.15623\/ijret.2013.0211048","article-title":"A Survey on sentiment analysis and opinion mining","volume":"2","author":"Varghese","year":"2013","journal-title":"International Journal of Research in Engineering and Technology"},{"key":"10.3233\/IDT-190137_ref46","unstructured":"Socher R, Perelygin A, Wu JY, Chuang J, Manning CD, Ng AY, et al. Recursive deep models for semantic compositionality over a sentiment treebank. 2013; 1631-1642."},{"key":"10.3233\/IDT-190137_ref47","unstructured":"Jockers M. Package Syuzhet: Extracts Sentiment and Sentiment-Derived Plot Arcs from Text. 2017."},{"key":"10.3233\/IDT-190137_ref48","doi-asserted-by":"crossref","unstructured":"Manning CD, Surdeanu M, Bauer J, Finkel JR, Bethard S, Mcclosky D. The stanford corenlp natural language processing toolkit. 2014; 55-60.","DOI":"10.3115\/v1\/P14-5010"},{"key":"10.3233\/IDT-190137_ref49","unstructured":"Hu M, Liu B. Mining opinion features in customer reviews. 2004; 755-760."},{"key":"10.3233\/IDT-190137_ref50","doi-asserted-by":"crossref","unstructured":"Prollochs N, Feuerriegel S, Neumann D. Statistical inferences for polarity identification in natural language. Plos One. 2018; 13(12).","DOI":"10.1371\/journal.pone.0209323"},{"key":"10.3233\/IDT-190137_ref51","doi-asserted-by":"crossref","unstructured":"Breiman L. Random forests. 2001; 45(1): 5-32.","DOI":"10.1023\/A:1010933404324"},{"issue":"2","key":"10.3233\/IDT-190137_ref52","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1214\/aos\/1016218223","article-title":"Additive logistic regression: A statistical view of boosting","volume":"28","author":"Friedman","year":"2000","journal-title":"Annals of Statistics"},{"issue":"1","key":"10.3233\/IDT-190137_ref53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","article-title":"Top 10 algorithms in data mining","volume":"14","author":"Wu","year":"2007","journal-title":"Knowledge and Information Systems"},{"key":"10.3233\/IDT-190137_ref54","unstructured":"Smola AJ, Scholkopf B. A tutorial on support vector regression. Technical Report NeuroCOLT NC-TR-98-030. 1998."},{"issue":"3","key":"10.3233\/IDT-190137_ref55","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1162\/089976601300014493","article-title":"Improvements to Platt\u2019s SMO algorithm for SVM classifier design","volume":"13","author":"Keerthi","year":"2001","journal-title":"Neural Computation"},{"issue":"1","key":"10.3233\/IDT-190137_ref56","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3354\/cr030079","article-title":"Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance","volume":"30","author":"Willmott","year":"2005","journal-title":"Climate Research"},{"key":"10.3233\/IDT-190137_ref57","doi-asserted-by":"crossref","unstructured":"Romero C, Ventura S, Pechenizkiy M, De Baker RSJ. Handbook of educational data mining. 2010.","DOI":"10.1201\/b10274"},{"key":"10.3233\/IDT-190137_ref58","first-page":"3","article-title":"How common is commercial contract cheating in higher education and is it increasing? A systematic review","author":"Newton","year":"2018","journal-title":"Frontiers in Education"},{"issue":"4","key":"10.3233\/IDT-190137_ref59","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1111\/dsji.12140","article-title":"A conceptual framework for detecting cheating in online and take-home exams","volume":"15","author":"Dsouza","year":"2017","journal-title":"Decision Sciences Journal of Innovative Education"}],"container-title":["Intelligent Decision Technologies"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDT-190137","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T10:29:54Z","timestamp":1741688994000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDT-190137"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,29]]},"references-count":59,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/idt-190137","relation":{},"ISSN":["1872-4981","1875-8843"],"issn-type":[{"type":"print","value":"1872-4981"},{"type":"electronic","value":"1875-8843"}],"subject":[],"published":{"date-parts":[[2020,9,29]]}}}