Evaluation of Emotional Students During the Final Project Writing Process

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2019 by IJCTT Journal
Volume-67 Issue-8
Year of Publication : 2019
Authors : Sulis Sandiwarno, Ariyani Wardhana
DOI :  10.14445/22312803/IJCTT-V67I8P110

MLA

MLA Style:Sulis Sandiwarno, Ariyani Wardhana"Evaluation of Emotional Students During the Final Project Writing Process" International Journal of Computer Trends and Technology 67.8 (2019):58-63.

APA Style Sulis Sandiwarno, Ariyani Wardhana.Evaluation of Emotional Students During the Final Project Writing ProcessInternational Journal of Computer Trends and Technology, 67(8),58-63.

Abstract
The dynamics of information technology has changed the way of thinking in life patterns, which originally carried out activities not yet using information technology but now in various layers must use information technology. The use of information technology can be applied in the field of education, where education is the right area to be targeted in the use of information technology. In the learning process carried out between students and lecturers, lecturers must be able to understand students` feelings or emotions verbally and nonverbally. In previous studies measuring the emotional level of students carried out during the learning process by using questionnaires. But this questionnaire will not be able to capture the emotional message of students in the learning process. We propose to use sentiment analysis to measure the emotional level of students in the learning process such as the preparation of the final assignment (TA). The sentiment analysis we use utilizes several methods of machine learning such as naïve Bayes (NB) and k-Nearest Neigbors (k-NN). In this study we have several stages such as: first, collecting data about the opinions of each student`s emotional. Second, we will conduct training data with NB and k-NN in measuring the emotional level of students. Third, we will compare methods to determine which method is best used to measure student emotional. The results obtained provide information, that k-NN is a good algorithm in evaluating student emotions based on text with an accuracy value of 86%.

Reference
[1] Hendon, M., Powell, L., Wimmer, H.: Emotional intelligence and communication levels in information technology professionals. Comput. Human Behav. 71, 165–171 (2017). doi:10.1016/j.chb.2017.01.048
[2] Brevik, L.M., Gunnulfsen, A.E., Renzulli, J.S.: Student teachers’ practice and experience with differentiated instruction for students with higher learning potential. Teach. Teach. Educ. 71, 34–45 (2018). doi:10.1016/j.tate.2017.12.003
[3] Michael A. Gottfried, J.J.K.: General education teachers’ math instructional practices in kindergarten classrooms with and without students with emotional and behavioral disabilities. Teach. Teach. Educ. 77, 309–320 (2019)
[4] Rachel E. Gaines, David J. Osman, Danika L.S. Maddocks, J.R.W., Jen L. Freeman, D.L.S.: Teachers’ emotional experiences in professional development: Where they come from and what they can mean. Teach. Teach. Educ. 77, 53–65 (2019)
[5] Yeh, Y. chu: Integrating collaborative PBL with blended learning to explore preservice teachers’ development of online learning communities. Teach. Teach. Educ. 26, 1630–1640 (2010). doi:10.1016/j.tate.2010.06.014
[6] Juutilainen, M., Metsäpelto, R.L., Poikkeus, A.M.: Becoming agentic teachers: Experiences of the home group approach as a resource for supporting teacher students’ agency. Teach. Teach. Educ. 76, 116–125 (2018). doi:10.1016/j.tate.2018.08.013
[7] Šumak, B., Heri?ko, M., Pušnik, M.: A meta-analysis of e- learning technology acceptance: The role of user types and e-learning technology types, (2011)
[8] Kangas, M., Siklander, P., Randolph, J., Ruokamo, H.: Teachers’ engagement and students’ satisfaction with a playful learning environment. Teach. Teach. Educ. 63, 274–284 (2017). doi:10.1016/j.tate.2016.12.018
[9] Hofer, M., Hüsser, A., Prabhu, S.: The effect of an avatar’s emotional expressions on players’ fear reactions: The mediating role of embodiment. Comput. Human Behav. 75, 883–890 (2017). doi:10.1016/j.chb.2017.06.024
[10] Dascalu, M.I., Bodea, C.N., Lytras, M., De Pablos, P.O., Burlacu, A.: Improving e-learning communities through optimal composition of multidisciplinary learning groups. Comput. Human Behav. 30, 362–371 (2014). doi:10.1016/j.chb.2013.01.022
[11] Mousas, C., Anastasiou, D., Spantidi, O.: The effects of appearance and motion of virtual characters on emotional reactivity. Comput. Human Behav. (2018). doi:10.1016/j.chb.2018.04.036
[12] Muntaner-Mas, A., Vidal-Conti, J., Sesé, A., Palou, P.: Teaching skills, students’ emotions, perceived control and academic achievement in university students: A SEM approach. Teach. Teach. Educ. 67, 1–8 (2017). doi:10.1016/j.tate.2017.05.013
[13] Islam, A.K.M.N.: E-learning system use and its outcomes: Moderating role of perceived compatibility. Telemat. Informatics. 33, 48–55 (2016). doi:10.1016/j.tele.2015.06.010
[14] Yang Lu, Savvas Papagiannidis?, E.A.: Exploring the emotional antecedents and outcomes of technology acceptance. Comput. Human Behav. 90, 153–169 (2019)
[15] Yubero, S., Navarro, R., Elche, M., Larrañaga, E., Ovejero, A.: Cyberbullying victimization in higher education: An exploratory analysis of its association with social and emotional factors among Spanish students. Comput. Human Behav. 75, 439–449 (2017). doi:10.1016/j.chb.2017.05.037
[16] Ortigosa, A., Martín, J.M., Carro, R.M.: Sentiment analysis in Facebook and its application to e-learning. Comput. Human Behav. 31, 527–541 (2014). doi:10.1016/j.chb.2013.05.024
[17] Muñoz, M.Á.C., Hernández, M.L.G.: Teacher and School Counsellor Training to Promote a Collaborative Family-school Relationship: An Empirical Study. Procedia - Soc. Behav. Sci. 23, 667–671 (2017). doi:10.1016/j.sbspro.2017.02.039
[18] López-Alcarria, A., Gutiérrez-Pérez, J., Poza-Vilches, F.: Sustainable Management of Pre-school Education Centers: A Case Study in the Province of Granada. Procedia - Soc. Behav. Sci. 237, 541–547 (2017). doi:10.1016/j.sbspro.2017.02.104
[19] Colace, F., Casaburi, L., De Santo, M., Greco, L.: Sentiment detection in social networks and in collaborative learning environments. Comput. Human Behav. 51, 1061–1067 (2015). doi:10.1016/j.chb.2014.11.090
[20] McGrath, K.F., Van Bergen, P.: Elementary teachers’ emotional and relational expressions when speaking about disruptive and well behaved students. Teach. Teach. Educ. 67, 487–497 (2017). doi:10.1016/j.tate.2017.07.016
[21] Zheng, L., Huang, R.: The effects of sentiments and co-regulation on group performance in computer supported collaborative learning. Internet High. Educ. 28, 59–67 (2016). doi:10.1016/j.iheduc.2015.10.001
[22] Skovholt, K.: Anatomy of a teacher–student feedback encounter. Teach. Teach. Educ. 69, 142–153 (2018). doi:10.1016/j.tate.2017.09.012
[23] Hopman, J.A.B., Tick, N.T., van der Ende, J., Wubbels, T., Verhulst, F.C., Maras, A., Breeman, L.D., van Lier, P.A.C.: Special education teachers’ relationships with students and self-efficacy moderate associations between classroom-level disruptive behaviors and emotional exhaustion. Teach. Teach. Educ. 75, 21–30 (2018). doi:10.1016/j.tate.2018.06.004
[24] Binali, H.H., Wu, C., Potdar, V.: A new significant area: Emotion detection in E-learning using opinion mining techniques. In: 2009 3rd IEEE International Conference on Digital Ecosystems and Technologies, DEST ’09. pp. 259–264 (2009)
[25] Song, D., Lin, H., Yang, Z.: Opinion mining in e-learning system. In: Proceedings - 2007 IFIP International Conference on Network and Parallel Computing Workshops, NPC 2007. pp. 788–792 (2007)
[26] Yoo, S.Y., Song, J.I., Jeong, O.R.: Social media contents based sentiment analysis and prediction system. Expert Syst. Appl. 105, 102–111 (2018). doi:10.1016/j.eswa.2018.03.055
[27] Cross-domain aspect extraction for sentiment analysis: A transductive learning approach. Decis. Support Syst. 114, 70–80 (2018). doi:https://doi.org/10.1016/j.dss.2018.08.009
[28] Ravi, K., Ravi, V.: A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Syst. 89, 14–46 (2015). doi:10.1016/j.knosys.2015.06.015
[29] Mining: Students Comments about Teacher Performance Assessment using Machine Learning Algorithms. Int. J. Comb. Optim. Probl. Informatics. 9, 26–40 (2018)
[30] Wen, M., Yang, D., Rosé, C.P.: Sentiment analysis in MOOC discussion forums: What does it tell us? In: Proceedings of Educational Data Mining. pp. 130–137 (2014)
[31] Schiller, S.Z.: CHAT for chat: Mediated learning in online chat virtual reference service. Comput. Human Behav. (2016). doi:10.1016/j.chb.2016.06.053
[32] Serrano-Guerrero, J., Olivas, J.A., Romero, F.P., Herrera-Viedma, E.: Sentiment analysis: A review and comparative analysis of web services. Inf. Sci. (Ny). 311, 18–38 (2015). doi:10.1016/j.ins.2015.03.040
[33] Bing Liu: Sentiment Analysis and Subjectivity. In: Handbook of Natural Language Processing. pp. 1–38 (2010)
[34] Pham, D.H., Le, A.C.: Learning multiple layers of knowledge representation for aspect based sentiment analysis. Data Knowl. Eng. 57, 26–39 (2018). doi:10.1016/j.datak.2017.06.001
[35] Aldo?an, D., Yaslan, Y.: A comparison study on active learning integrated ensemble approaches in sentiment analysis. Comput. Electr. Eng. 57, 311–323 (2017). doi:10.1016/j.compeleceng.2016.11.015
[36] Yadollahi, A., Shahraki, A.G., Zaiane, O.R.: Current State of Text Sentiment Analysis from Opinion to Emotion Mining. ACM Comput. Surv. 50, 1–33 (2017). doi:10.1145/3057270
[37] Madani, Y., Erritali, M., Bengourram, J.: Sentiment analysis using semantic similarity and Hadoop MapReduce, (2018)
[38] Ndenga, M.K., Ganchev, I., Mehat, J., Wabwoba, F., Akdag, H.: Performance and cost-effectiveness of change burst metrics in predicting software faults, (2018)
[39] Tripathy, A., Anand, A., Rath, S.K.: Document-level sentiment classification using hybrid machine learning approach. Knowl. Inf. Syst. 53, 805–831 (2017). doi:10.1007/s10115-017-1055-z
[40] Andi Nugroho. (2016). Aplikasi Web Informasi Dan Registrasi Peserta Seminar, Workshop, Talkshow Pada Acara Seminar Nasional Pengamplikasian Telematika (Sinaptika) Tahun 2016. Seminar Nasional Sistem Informasi Indonesia, 1-8.
[41] Raka Yusuf1, Yossi Susanto2. (2010). Pemanfaatan SMS Gateway untuk Absensi Sekolah Siswa. Seminar Nasional Pengaplikasian Telematika SINAPTIKA, 1-4.
[42] Fajar Masya1, Elvina2, Fitri Maria Simanjuntak3. (2012). Sistem Pelayanan Pengaduan Masyarakat pada Divisi HUMAS POLRI Berbasis Web. Seminar Nasional Aplikasi Teknologi Informasi, 1-6.
[43] Raka Yusuf1, Gilang Widi Darmawan2. (2016). Aplikasi Berbasis Web Dengan Menggunakan Pustaka Javascript Fabricjs Untuk Pembuatan Komik Strip Punakawan. Seminar Nasional Teknologi Informasi dan Multimedia, STMIK AMIKOM Yogyakarta, 1-6.

Keywords
learning, evaluation, machine learning classifiers