Measurement of Learning Evaluation Against Assisted by Laboratory Assistants

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-V67I8P111


MLA Style:Sulis Sandiwarno,Ariyani Wardhana"Measurement of Learning Evaluation Against Assisted by Laboratory Assistants" International Journal of Computer Trends and Technology 67.8 (2019):64-68.

APA Style Sulis Sandiwarno,Ariyani Wardhana .Measurement of Learning Evaluation Against Assisted by Laboratory Assistants International Journal of Computer Trends and Technology, 67(8),64-68.

The development of good information now triggers the use of very broad and very easy to use technology. In its development this technology is known as Information Technology (IT), where IT is a good tool in connecting the giver and recipient of information. An example of the application of IT that can measure the level of user satisfaction with the system is by using sentiment analysis. Measuring the level of satisfaction by using this sentiment analysis can also be applied in the field of learning. Previous research has been conducted to measure the level of student satisfaction with the learning process assisted by laboratory assistants based on questionnaires. However, the assessment of student satisfaction in previous studies is said to fail if only limited to questionnaires. We propose using sentiment analysis to evaluate the learning process for students assisted by laboratory assistants. In conducting research using the concept of sentiment analysis we use logistic regression (LR) and naïve Bayes (NB) methods. As for several stages such as: first, collecting data about opinions or reviews from students whose learning process is assisted by laboratory assistants. Second, we will conduct training data with both methods. Third, we will make conclusions, what methods are best used in measuring the evaluation of learning carried out by laboratory assistants. The results of this study will provide results that NB is a good algorithm in evaluating student opinion levels with an accuracy value of 80.32%.

[1] 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
[2] 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)
[3] 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
[4] 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
[5] 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
[6] 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
[7] 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
[8] 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
[9] 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
[10] 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)
[11] 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)
[12] 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
[13] Cross-domain aspect extraction for sentiment analysis: A transductive learning approach. Decis. Support Syst. 114, 70–80 (2018). doi:
[14] 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
[15] Mining: Students Comments about Teacher Performance Assessment using Machine Learning Algorithms. Int. J. Comb. Optim. Probl. Informatics. 9, 26–40 (2018)
[16] 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. 1–8 (2014)
[17] 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
[18] 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
[19] Bing Liu: Sentiment Analysis and Subjectivity. In: Handbook of Natural Language Processing. pp. 1–38 (2010)
[20] 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
[21] 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
[22] 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
[23] Madani, Y., Erritali, M., Bengourram, J.: Sentiment analysis using semantic similarity and Hadoop MapReduce, (2018)
[24] Ndenga, M.K., Ganchev, I., Mehat, J., Wabwoba, F., Akdag, H.: Performance and cost-effectiveness of change burst metrics in predicting software faults, (2018)
[25] 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.
[26] Raka Yusuf1, Yossi Susanto2. (2010). Pemanfaatan SMS Gateway untuk Absensi Sekolah Siswa. Seminar Nasional Pengaplikasian Telematika SINAPTIKA, 1-4.
[27] Fajar Masya1, Elvina2, Fitri Maria Simanjuntak3. (2012). Sistem Pelayanan Pengaduan Masyarakat pada Divisi HUMAS POLRI Berbasis Web. Seminar Nasional Aplikasi Teknologi Informasi, 1-6.
[28] 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.

learning, evaluation, machine learning classifiers