Examination of the Text and Sentiment Analysis of the Opinions of the Students in the Social Service Departments regarding the Concept of Education
Öz
The main aim of this research is to examine the opinions and sentiments of university students in the department of social service regarding the concept of education. This research is based on a mixed research design where both qualitative and quantitative data are both used. In the first part of the study, students are asked to write an essay about what does the concept of education means to them. Then the data was analyzed in MATLAB and SPSS and different online text and sentiment analysis tools. The population of the study consists of the social services department in Iğdır University. The sample was selected from 16 third-grade students in the social services department of which 4 are male 12 of them are female students based on the convenient sampling method. The first finding shows that the sentimental tone of the whole responses is neutral but there is a slightly positive tone due to the higher slightly positive, positive, and very positive values. The second finding reveals that the range of female students regarding their sentiments is broader than the male ones. It seems that although students have a cognitive understanding regarding the importance of education they are less enthusiastic about it in the context of the affective domain.
Anahtar Kelimeler
Text Analytics sentiment analysis affective domain education
Kaynakça
- Altrabsheh, N. & Gaber, M. M. & Haig, E. (2013). SA-E: Sentiment Analysis for Education. Frontiers in Artificial Intelligence and Applications. The 5th KES International Conference on Intelligent Decision Technologies (KES-IDT): Portugal 255. 10.3233/978-1-61499-264-6-353.
- Altrabsheh N., Cocea M., Fallahkhair S. (2014) Learning Sentiment from Students’ Feedback for Real-Time Interventions in Classrooms. In: Bouchachia A. (eds) Adaptive and Intelligent Systems. ICAIS 2014. Lecture Notes in Computer Science, vol 8779. Springer, Cham. https://doi.org/10.1007/978-3-319-11298-5_5
- Baltacı, A. (2018). Nitel Araştırmalarda Örnekleme Yöntemleri ve Örnek Hacmi Sorunsalı Üzerine Kavramsal Bir İnceleme, Journal of Bitlis Eren University Institute of Social Sciences, 7(1), 231-274.
- Bloom, B. S. (1956). Taxonomy of educational objectives. In Handbook I: Cognitive domain. New York: David McKay.
- Dietz-Uhler, B., & Hurn, E. J. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective. Journal of Interactive Online Learning, 12, 17–26.
- Dolianiti F.S., Iakovakis D., Dias S.B., Hadjileontiadou S., Diniz J.A., Hadjileontiadis L. (2019) Sentiment Analysis Techniques and Applications in Education: A Survey. In: Tsitouridou M., A. Diniz J., Mikropoulos T. (eds) Technology and Innovation in Learning, Teaching and Education.
- TECH-EDU 2018. Communications in Computer and Information Science, vol 993. Springer, Cham. https://doi.org/10.1007/978-3-030-20954-4_31
- Frasson C., Heraz A. (2012) Emotional Learning. In: Seel N.M. (eds) Encyclopedia of the Sciences of Learning. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1428-6_120
- Johnson BR. Dialectical pluralism: A metaparadigm whose time has come. Journal of Mixed Methods Research. 2017;11:156–173. doi: 10.1177/1558689815607692.
- Imani, M., & Montazer, G. A. (2019). A survey of emotion recognition methods with emphasis on E-Learning environments. Journal of Network and Computer Applications, 147, 102423. https://doi.org/10.1016/j.jnca.2019.102423
- Liu, Z., Yang, C., Rüdian, S., Liu, S., Zhao, L., & Wang, T. (2019). Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums. Interactive Learning Environments, 27(5–6), 598–627. https:// doi.org/10.1080/10494820.2019.1610449
- Mandouit, L. (2016). Using student feedback to improve teaching. Educational Action Research, 26, 755– 769.
- Misuraca, M. Forciniti, A. Scepi, G. Spano, M. (2020). Sentiment Analysis for Education with R: packages, methods and practical applications, arXiv:2005.12840 retrieved from 28.03.2021
- Ortigosa, A., Martín, J. M., & Carro, R. M. (2014). Sentiment analysis in Facebook and its application to e-learning. Computers in Human Behavior, 31, 527–541. https://doi.org/10.1016/j.chb.2013.05.024
- Parkins, R. (2012). Gender and Emotional Expressiveness: An Analysis of Prosodic Features in Emotional Expression, Griffith Working Papers in Pragmatics and Intercultural Communication 5 (1), 46-54
- Text Analytics Toolbox (2020). The MathWorks, Inc.
- Zhou, J. Ye, J. (2020). Sentiment analysis in education research: a review of journal publications. Interactive Learning Environments, (), 1–13. doi:10.1080/10494820.2020.182698
Examination of the Text and Sentiment Analysis of the Opinions of the Students in the Social Service Departments regarding the Concept of Education
Öz
The main aim of this research is to examine the opinions and sentiments of university students in the department of social service regarding the concept of education. This research is based on a mixed research design where both qualitative and quantitative data are both used. In the first part of the study, students are asked to write an essay about what does the concept of education means to them. Then the data was analyzed in MATLAB and SPSS and different online text and sentiment analysis tools. The population of the study consists of the social services department in Iğdır University. The sample was selected from 16 third-grade students in the social services department of which 4 are male 12 of them are female students based on the convenient sampling method. The first finding shows that the sentimental tone of the whole responses is neutral but there is a slightly positive tone due to the higher slightly positive, positive, and very positive values. The second finding reveals that the range of female students regarding their sentiments is broader than the male ones. It seems that although students have a cognitive understanding regarding the importance of education they are less enthusiastic about it in the context of the affective domain.
Anahtar Kelimeler
Text Analytics sentiment analysis affective domain education
Kaynakça
- Altrabsheh, N. & Gaber, M. M. & Haig, E. (2013). SA-E: Sentiment Analysis for Education. Frontiers in Artificial Intelligence and Applications. The 5th KES International Conference on Intelligent Decision Technologies (KES-IDT): Portugal 255. 10.3233/978-1-61499-264-6-353.
- Altrabsheh N., Cocea M., Fallahkhair S. (2014) Learning Sentiment from Students’ Feedback for Real-Time Interventions in Classrooms. In: Bouchachia A. (eds) Adaptive and Intelligent Systems. ICAIS 2014. Lecture Notes in Computer Science, vol 8779. Springer, Cham. https://doi.org/10.1007/978-3-319-11298-5_5
- Baltacı, A. (2018). Nitel Araştırmalarda Örnekleme Yöntemleri ve Örnek Hacmi Sorunsalı Üzerine Kavramsal Bir İnceleme, Journal of Bitlis Eren University Institute of Social Sciences, 7(1), 231-274.
- Bloom, B. S. (1956). Taxonomy of educational objectives. In Handbook I: Cognitive domain. New York: David McKay.
- Dietz-Uhler, B., & Hurn, E. J. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective. Journal of Interactive Online Learning, 12, 17–26.
- Dolianiti F.S., Iakovakis D., Dias S.B., Hadjileontiadou S., Diniz J.A., Hadjileontiadis L. (2019) Sentiment Analysis Techniques and Applications in Education: A Survey. In: Tsitouridou M., A. Diniz J., Mikropoulos T. (eds) Technology and Innovation in Learning, Teaching and Education.
- TECH-EDU 2018. Communications in Computer and Information Science, vol 993. Springer, Cham. https://doi.org/10.1007/978-3-030-20954-4_31
- Frasson C., Heraz A. (2012) Emotional Learning. In: Seel N.M. (eds) Encyclopedia of the Sciences of Learning. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1428-6_120
- Johnson BR. Dialectical pluralism: A metaparadigm whose time has come. Journal of Mixed Methods Research. 2017;11:156–173. doi: 10.1177/1558689815607692.
- Imani, M., & Montazer, G. A. (2019). A survey of emotion recognition methods with emphasis on E-Learning environments. Journal of Network and Computer Applications, 147, 102423. https://doi.org/10.1016/j.jnca.2019.102423
- Liu, Z., Yang, C., Rüdian, S., Liu, S., Zhao, L., & Wang, T. (2019). Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums. Interactive Learning Environments, 27(5–6), 598–627. https:// doi.org/10.1080/10494820.2019.1610449
- Mandouit, L. (2016). Using student feedback to improve teaching. Educational Action Research, 26, 755– 769.
- Misuraca, M. Forciniti, A. Scepi, G. Spano, M. (2020). Sentiment Analysis for Education with R: packages, methods and practical applications, arXiv:2005.12840 retrieved from 28.03.2021
- Ortigosa, A., Martín, J. M., & Carro, R. M. (2014). Sentiment analysis in Facebook and its application to e-learning. Computers in Human Behavior, 31, 527–541. https://doi.org/10.1016/j.chb.2013.05.024
- Parkins, R. (2012). Gender and Emotional Expressiveness: An Analysis of Prosodic Features in Emotional Expression, Griffith Working Papers in Pragmatics and Intercultural Communication 5 (1), 46-54
- Text Analytics Toolbox (2020). The MathWorks, Inc.
- Zhou, J. Ye, J. (2020). Sentiment analysis in education research: a review of journal publications. Interactive Learning Environments, (), 1–13. doi:10.1080/10494820.2020.182698
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Erken Görünüm Tarihi | 25 Nisan 2022 |
| Yayımlanma Tarihi | 1 Haziran 2021 |
| Gönderilme Tarihi | 19 Mart 2021 |
| Yayımlandığı Sayı | Yıl 2021 Sayı: 23 |
- Makale Dosyaları