Abstract
The use of e-learning in education is an ever-increasing practice. E-learning could generate effective learning for education. There are several factors affecting the creation of successful e-learning for education as well as several criteria possibly applied to evaluate the effectiveness. The “traditional” way (questionnaire, interview, information system analysis) to measure effectiveness is not enough in e-learning measure of effectiveness because part of the information, that coming from social networks, will be lost. This paper, after identifying the Critical Success Factors (CSFs) of a synchronous e-learning system, and identifying the Key Performance Indicators (KPIs), proposes an approach for evaluation based on the analysis of information derived from social aspects. The paper proposes a set of CSFs and KPIs to study the students’ perception and highlights how to measure the KPIs using social software information.
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References
Kelly, G.: A collaborative process for evaluating new educational technologies. Campus-Wide Inf. Syst. 25(2), 105–113 (2008)
Dust, B.: The eLearning Guild’s Online Forums – Elearning Metrics That Work (2007). http://www.elearningguild.com/olf/olfarchives/index.cfm?id=356&action=viewonly
Haiyan, Z., Huanjing, W.: Survey and analysis of the use of social software in education. In: 2010 International Conference on E-Health Networking Digital Ecosystems and Technologies (2010)
Paiano R., Caione A., Guido A.L., Pandurino A., Pasanisi S.: E-learning project assessment: A new approach through the analysis of learners’ posts on social media. EAI Endorsed Transactions on e-Learning (in press)
Freund, Y.P.: Critical success factors. Plann. Rev. 16(4), 20–23 (1988)
Volery, T., Lord, D.: Critical success factors in online education. Int. J. Educ. Manage. 14(5), 216–223 (2000)
Bracchi, G., Francalanci, C., Motta, G.: Sistemi informativi aziendali in rete. McGraw-Hill, Milano (2001)
Cheawjindakarn, B., Suwannatthachote, P., Theeraroungchaisri, A.: Critical success factors for online distance learning in higher education: A review of the literature. Creative Educ. 3(08), 61 (2013)
Slim, H.M.: E-learning critical success factors: an exploratory investigation of student perceptions. Int. J. Technol. Mark. 2(2), 157–182 (2007)
Prougestaporn, P., Visansakon, T., Saowapakpongchai, K.: Key success factors and evaluation criterias of e-learning websites for higher education. Int. J. Inf. Educ. Technol. 5(3), 233 (2015)
Slizyte, A., Bakanauskiene, I.: Designing performance measurement system in organization. Organizacijø Vadyba: Sisteminiai Tyrimai, pp 135–148 (2007)
Hilgarth, B.: E-Learning success in action! From case study research to the creation of the cybernetic e-learning management model. e-Learning 2004, 1–2 (2009)
The eLearning Guild’s Online Forums – Strategies for Measuring the Value of e-Learning (2005). http://www.elearningguild.com/handouts/378_h1.pdf
Ravi, K., Ravi, V.: A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowl.-Based Syst. 89, 14–46 (2015)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)
Liu, B.: Sentiment analysis and subjectivity. Handb. Nat. Lang. Process. 2, 627–666 (2010)
Maynard, D., Funk, A.: Automatic detection of political opinions in tweets. In: García-Castro, R., Fensel, D., Antoniou, G. (eds.) ESWC 2011. LNCS, vol. 7117, pp. 88–99. Springer, Heidelberg (2012). doi:10.1007/978-3-642-25953-1_8
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224 N Project Report, Stanford, pp. 1–12 (2009)
Soong, M.B., Chan, H.C., Chua, B.C., Loh, K.F.: Critical success factors for on-line course resources. Comput. Educ. 36(2), 101–120 (2001)
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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Caione, A., Guido, A.L., Paiano, R., Pandurino, A., Pasanisi, S. (2017). A Social Metric Approach to E-Learning Evaluation in Education. In: Vincenti, G., Bucciero, A., Helfert, M., Glowatz, M. (eds) E-Learning, E-Education, and Online Training. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 180. Springer, Cham. https://doi.org/10.1007/978-3-319-49625-2_1
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DOI: https://doi.org/10.1007/978-3-319-49625-2_1
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