Abstract
Data mining is a method to refine raw data to useful information. In education, data mining is a significant research part used to progress the value of education by observing students’ performance and understanding their learning patterns. Real-time student feedback would empower faculty and students to comprehend the teaching and learning problems in the most user-friendly way for the students. This paper uses a Lexicon based sentimental analysis technique to analyze students’ feelings and emotions through their feedback by correlating learning analytics to grounded theory. The sentiment analysis technique is a computational process to identify and classify subjective information such as positive, negative, and neutral from the source material. It can extract feelings and emotions from a piece of a sentence. Hence this paper aims to recognize the students’ positive or negative feelings and distinguished emotions, towards online teaching. The methodology undertakes four processes. The first process is data extraction from the feedback collected from the students through open-ended questions (Text) and is used as source material and imported to R studio. The second process is data cleaning /data preprocessing, removal of annoying data, and separation of data. The third process is sentimental analysis, which divides the data into positive, negative, and neutral categories/groups. This lexicon-based method of sentimental analysis is used to classify the sentiments. The results were estimated using sentiment scores and emotional variance. The sentiment scores result found that students have positive sentiments/emotions towards online teaching and emotions vary concerning the online class timing.
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References
Poulos, A., Mahony, M.J.: Effectiveness of feedback: the student’s perspective. Assess. Eval. High. Educ. 33(2), 143–154 (2008)
Baradwaj, B.K., Pal, S.: Mining educational data to analyze students’ performance. Int. J. Adv. Comput. Sci. Appl. 2(6), 63–69 (2011)
Rogers, D., Coughlan, P., Kearney, K.: Playschool for postgrads: facilitating creativity and innovation in doctoral education using digital resources. In: The Digital Learning Revolution in Ireland: Case Studies in Practice from ndlr, pp. 165–179 (2013)
Munezero, M., Montero, C.S., Sutinen, E., Pajunen, J.: Are they different? Affect, feeling, emotion, sentiment, and opinion detection in text. IEEE Trans. Affect. Comput. 5(2), 101–111 (2014)
Munezero, M., Montero, C.S., Mozgovoy, M., Sutinen, E.: Exploiting sentiment analysis to track emotions in students’ learning diaries. In: Proceedings of the 13th Koli Calling International Conference on Computing Education Research, pp. 145–152. ACM (2013)
Ortigosa, A., Martín, J.M., Carro, R.M.: Sentiment analysis in Facebook and its application to e-learning. Comput. Hum. Behav. 31, 527–541 (2014)
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. Adv. Learn. Technol. (ICALT) 10, 653–657 (2010)
Tian, Y., Galery, T., Dulcinati, G., Molimpakis, E., Sun, C.: Facebook sentiment: reactions and emojis. In: Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media, pp. 11–16 (2017)
Calders, T., Pechenizkiy, M.: Introduction to the special section on educational data mining. Assoc. Comput. Mach. Spec. Interest Group Knowl. Discov. Data Min. 13(2), 3–6 (2012)
Troussas, C.: Advances in Social Networking-based Learning: Machine Learning-based User Modelling and Sentiment Analysis, vol. 181. Springer, Cham (2020)
Troussas, C., Espinosa, K.J., Virvou, M.: Affect recognition through Facebook for effective group profiling towards personalized instruction. Inf. Educ. 15(1), 147–161 (2016)
Yoo, S., Song, J., Jeong, O.: Social media contents based sentiment analysis and prediction system. Expert Syst. Appl. 105, 102–111 (2018)
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PraveenKumar, T., Manorselvi, A., Soundarapandiyan, K. (2020). Exploring the Students Feelings and Emotion Towards Online Teaching: Sentimental Analysis Approach. In: Sharma, S.K., Dwivedi, Y.K., Metri, B., Rana, N.P. (eds) Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation. TDIT 2020. IFIP Advances in Information and Communication Technology, vol 617. Springer, Cham. https://doi.org/10.1007/978-3-030-64849-7_13
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DOI: https://doi.org/10.1007/978-3-030-64849-7_13
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