Evaluation of E-learning Activity Effectiveness in Higher Education Through Sentiment Analysis by Using Naïve Bayes Classifier

Eka Angga Laksana, Ase Suryana, Heri Heryono,




Abstract

Sentiment analysis as part of text mining research domain has been being recognized due to the successful implementation in social media analysis. Sentiment analysis methods had intelligent ability to classify texts into negative or positive. Classified texts concluded whole users respond and described opinion polarity about particular topic. Based on this idea, this research took e-learning’s users opinion as object to be measured through sentiment analysis. The results can be used to evaluate the e-learning activity. This research had been implemented in Widyatama University which had been running e-learning activity for several years. Qualitative method by given questioner to users and gather the feedback is commonly used as evaluation of e-learning system previously. Still, questioner doesn’t represent the conclusion about the whole opinion. Hence, it needs the method to identify opinion polarity from e-learning member. The e-learning opinion data sets were gathered from questioner filled by e-learning member included both student and lecturer as participants. The participants gave review about learning outcome after their participation in e-learning activity. Their opinion was needed to describe current situation about e-learning activity. Therefore, the conclusion could be used to make improvement and described few achievements about the e-learning system. The data sets trained by Naïve Bayes classifier to group each user respond into negative or positive. The classification results were also evaluated by a number of particular evaluation metric used in data mining to show the classifier performance such as accuracy, precision, and recall.


Save to Mendeley



Keywords

: classifier, evaluation, e-learning, sentiment, naïve bayes

Full Text:

PDF

References

. Guri-rosenblit, S. (2005). Eight Paradoxes in the Implementation Process of E-learning in Higher Education, 5–29. https://doi.org/10.1057/palgrave.hep.8300069.

. Garrison, D. R., & Anderson, T. (2011). E-Learning in the 21st Century A Framework for Research and Practice.pdf. Taylor & Francis.

. Dominici, G., & Palumbo, F. (2013). How to build an e-learning product : Factors for student / customer satisfaction. Business Horizons, 56(1), 87–96.

. Nichols, M. (2008). Institutional perspectives: The challenges of e-learning diffusion, 39(4), 598–609. https://doi.org/10.1111/j.1467-8535.2007.00761.x.

. Persico, D., Manca, S., & Pozzi, F. (2014). Computers in Human Behavior Adapting the Technology Acceptance Model to evaluate the innovative potential of e-learning systems. Computers in Human Behavior, 30, 614–622. https://doi.org/10.1016/j.chb.2013.07.045.

. Stoffregen, J., Pawlowski, J. M., & Pirkkalainen, H. (2015). Computers in Human Behavior A Barrier Framework for open E-Learning in public administrations. COMPUTERS IN HUMAN BEHAVIOR. https://doi.org/10.1016/j.chb.2014.12.024.

. Violante, M. G., & Vezzetti, E. (2013). Virtual Interactive E-Learning Application : An Evaluation of the Student Satisfaction, 72–91. https://doi.org/10.1002/cae.21580.

. Lee, K. (2005). E-Learning : The Quest for Effectiveness, 2.

. Novo-corti, I., Varela-candamio, L., & Ramil-díaz, M. (2013). Computers in Human Behavior E-learning and face to face mixed methodology : Evaluating effectiveness of e-learning and perceived satisfaction for a microeconomic course using the Moodle platform. Computers in Human Behavior, 29(2), 410–415. https://doi.org/10.1016/j.chb.2012.06.006.

. Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis, 2, 1–135. https://doi.org/10.1561/1500000001.

. Bringula, R. P. (2013). Computers & Education In fl uence of faculty- and web portal design-related factors on web portal usability : A hierarchical regression analysis. Computers & Education, 68, 187–198. https://doi.org/10.1016/j.compedu.2013.05.008.

. Escobar-rodriguez, T., & Monge-lozano, P. (2012). Computers & Education The acceptance of Moodle technology by business administration students. Computers & Education, 58(4), 1085–1093. https://doi.org/10.1016/j.compedu.2011.11.012.

. Navimipour, N. J., & Zareie, B. (2015). Computers in Human Behavior A model for assessing the impact of e-learning systems on employees ’ satisfaction, 53, 2011–2013.

. Hubackova, S. (2014). Effectiveness and evaluation of on-line courses. Procedia - Social and Behavioral Sciences, 143, 139–142. https://doi.org/10.1016/j.sbspro.2014.07.375.

. Laurillard, D., Wasson, B., M., & Hoppe, U. (2009). Implementing technologyenhanced learning. In Technology-enhanced learning.

. Capece, G., & Campisi, D. (2013). User satisfaction affecting the acceptance of an e- learning platform as a mean for the development of the human capital, (April), 37–41. https://doi.org/10.1016/j.bushor.2012.09.011.

. Lee, S. M., Kim, Y. R., & Lee, J. (2016). An Empirical Study of the Relationships among End-User Information Systems Acceptance , Training , and Effectiveness An Empirical Study of the Relationships among End-User Information Systems Acceptance , Training , and Effectiveness, 1222(April). https://doi.org/10.1080/07421222.1995.11518086.

. Yeh, Y., & Lin, C. F. (2015). Aptitude-Treatment Interactions during Creativity Training in E-Learning : How Meaning-Making , Self-Regulation , and Knowledge Management Influence Creativity, 18, 119–131.

. Graham, C. R. (2003). DEFINITION, CURRENT TRENDS, AND FUTURE DIRECTIONS.

. Liu, B. (2012). Sentiment Analysis and Opinion Mining, (May).

. Steven, B., Klein, E., & Loper, E. (2009). Natural Language Processing with Python


DOI: https://doi.org/10.24167/sisforma.v5i1.1450

Article Metrics

Abstract viewed : 31 times
PDF files downloaded : 7 times

Refbacks

  • There are currently no refbacks.


View My Stats