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Digital technologies in linguistic education: Experience of development and implementation

Year 2024, , 308 - 331, 31.10.2024
https://doi.org/10.19128/turje.1444808

Abstract

The aim of this study was to share our experience of developing a digital Natural Language Processing Tool and its implementation in the process of training future linguists. In this article, we demonstrate the process of creating the web application SENTIALIZER, which is a multilingual Sentiment Analysis Tool developed with the help of the Python programming language and its libraries NLTK, BS4, TextBlob, Googletrans. The integration of Sentiment Analysis Tools into the educational framework is relied on the Unified Theory of Acceptance and Use of Technology (UTAUT) as its foundation. The results show that students see the prospects of using Sentiment Analysis Tools in their educational and professional activities, are ready to use them in the future, but are not ready to participate personally in projects to develop and improve such technologies. The reasons for this attitude are discussed. The presented study has a clear focus on student learning outcomes, which is an important criterion for the successful integration of technology into the educational process.

References

  • Aliaño, Á., Hueros, A., Guzmán-Franco, M. D., & Aguaded, I. (2019). Mobile learning in university contexts based on the unified theory of acceptance and use of technology (UTAUT). Journal of New Approaches in Educational Research, 8, 7-17. https://doi.org/10.7821/naer.2019.1.317
  • Babu, N. V., & Kanaga, E. G. M. (2022). Sentiment analysis in social media data for depression detection using artificial intelligence: a review. SN computer science, 3(74). https://doi.org/10.1007/s42979-021-00958-1
  • Banea, C., Mihalcea, R., Wiebe, J., & Hassan, S. (2008). Multilingual subjectivity analysis using machine translation. In M. Lapata, & H. T. Ng (Eds), Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP '08) (pp. 127-135). Association for Computational Linguistics. http://dx.doi.org/10.3115/1613715.1613734
  • Barab S. A., Squire K. D., & Dueber W. (2000). A co-evolutionary model for supporting the emergence of authenticity. Educational Technology Research and Development, 48(2), 37-62. https://doi.org/10.1007/BF02313400
  • Baskara, R., & Mukarto, M. (2023). Exploring the implications of ChatGPT for language learning in higher education. Indonesian Journal of English Language Teaching and Applied Linguistics, 7(2), 343-358.
  • Basmmi, A. B., Halim, S. A., & Saadon, N. A. (2020). Comparison of web services for sentiment analysis in social networking sites. Proceedings of the IOP conference series: Materials science and engineering, Malaysia, 884, 012063. https://dx.doi.org/10.1088/1757-899X/884/1/012063
  • Ben Youssef, A., Dahmani, M., & Omrani, N. (2015). Information technologies, students’ e-skills and diversity of learning process. Education and Information Technologies, 20, 141-159. https://doi.org/10.1007/s10639-013-9272-x
  • Birjali, M., Kasri, M., & Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226, 107134. https://doi.org/10.1016/j.knosys.2021.107134
  • Bisio, F., Oneto, L., & Cambria, E. (2017). Sentic computing for social network analysis. In F.A. Pozzi, E. Fersini, E. Messina, & B. Liu (Eds.), Sentiment Analysis in Social Networks (pp. 71-99). Morgan Kaufmann. https://doi.org/10.1016/B978-0-12-804412-4.00005-X
  • Bond, M., Marín, V. I., Dolch, C., Bedenlier, S., & Zawacki-Richter O. (2018). Digital transformation in German higher education: student and teacher perceptions and usage of digital media. International Journal of Educational Technology in Higher Education, 15, 48. https://doi.org/10.1186/s41239-018-0130-1
  • Boukes, M., van de Velde, B., Araujo, T., & Vliegenthart, R. (2019). What’s the tone? Easy doesn’t do it: Analyzing performance and agreement between off-the-shelf sentiment analysis tools. Communication Methods and Measures, 14(2), 83-104. https://doi.org/10.1080/19312458.2019.1671966
  • Bouznif, M. (2018). Business students’ continuance intention toward blackboard usage: an empirical investigation of UTAUT model. International Journal of Business and Management, 13(1), 120-130. https://doi.org/10.5539/ijbm.v13n1p120
  • Boyd, R. L., & Schwartz, H. A. (2021). Natural language analysis and the psychology of verbal behavior: The past, present, and future states of the field. Journal of Language and Social Psychology, 40(1), 21-41. https://doi.org/10.1177/0261927X20967028
  • Bueno, I., Carrasco, R., Ureña, R., & Herrera-Viedma, E. (2022). A business context aware decision-making approach for selecting the most appropriate sentiment analysis technique in e-marketing situations. Information Sciences, 589, 300-320. https://doi.org/10.1016/j.ins.2021.12.080
  • Chapple, D. G., Weir, B., & Martin, R. S. (2017). Can the incorporation of quick response codes and smartphones improve field-based science education? International Journal of Innovation in Science and Mathematics Education, 25(2), 49-71.
  • Chauhan, P., Sharma, N., & Sikka, G. (2021). The emergence of social media data and sentiment analysis in election prediction. Journal of Ambient Intelligence and Humanized Computing, 12, 2601-2627. https://doi.org/10.1007/s12652-020-02423-y
  • Chernikova, O., Heitzmann, N., Stadler, M., Holzberger, D., Seidel, T., & Fischer F. (2020). Simulation-based learning in higher education: a meta-analysis. Review of Educational Research, 90(4), 499-541. https://doi.org/10.3102/0034654320933544
  • Contreras, D., Wilkinson, S., Alterman, E., & Hervás, J. (2022). Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquake. Nat Hazards, 113, 403-421 https://doi.org/10.1007/s11069-022-05307-w
  • Darmoroz, H. (2017). Professional training of computational linguists at the university of stuttgart. Comparative Professional Pedagogy, 7(3), 75-83. https://doi.org/10.1515/rpp-2017-0039
  • Faizi, R. (2023). Using sentiment analysis to explore student feedback: a lexical approach. International Journal of Emerging Technologies in Learning (iJET), 18(09), 259-267. https://doi.org/10.3991/ijet.v18i09.38101
  • Feng, T. (2023). The impact of cloud technology and the MatLab app on the academic performance and cognitive load of further mathematics students. Education and Information Technologies, 29, 13577-13593. https://doi.org/10.1007/s10639-023-12386-0
  • Fernández, A., Gómez, B., Binjaku, K., & Kajo Meçe, E. (2023). Digital transformation initiatives in higher education institutions: a multivocal literature review. Education and Information Technologies, 28, 12351-12382. https://doi.org/10.1007/s10639-022-11544-0
  • Gao, S., Krogstie, J., & Siau, K. (2011). Developing an instrument to measure the adoption of mobile services. Mobile Information Systems, 7(1), 45-67. http://dx.doi.org/10.3233/MIS-2011-0110
  • García-Vera, V. E., & Chiner Sanz, E. (2017). Factors influencing graduate students’ preference of software tools for building engineering applications. The International Journal of Engineering Education, 33(1), 128-137.
  • Gedik Bal, N. (2024). Unlocking online language education: Opportunities, challenges, and recommendations. Turkish Journal of Education, 13(2), 158-179. https://doi.org/10.19128/turje.1379149
  • Gujjar, J. P., & Kumar, H. P. (2021). Sentiment analysis: Textblob for decision making. International Journal of Scientific Research & Engineering Trends, 7(2), 1097-1099.
  • Gupta, B., Negi, M., Vishwakarma, K., Rawat, G., & Badhani, P. (2017). Study of Twitter sentiment analysis using machine learning algorithms on Python. International Journal of Computer Applications, 165(9), 29-34. http://dx.doi.org/10.5120/ijca2017914022
  • Hajba, G. L. (2018). Using Beautiful Soup. Apress. https://doi.org/10.1007/978-1-4842-3925-4_3
  • Halloran, L., & Friday, C. (2018). Can the universities of today lead learning for tomorrow? The university of the future. Australia: Ernst & Young. https://assets.ey.com/content/dam/ey-sites/ey-com/en_au/topics/government-and-public-sector/ey-university-of-the-future-2030.pdf
  • Hew, K. F., Hu, X., Qiao, C., & Tang, Y. (2020). What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach. Computers & Education, 145, 103724. https://doi.org/10.1016/j.compedu.2019.103724
  • Hsu, H. (2012). The acceptance of Moodle: An empirical study based on UTAUT. Creative Education, 3(8B), 44-46. http://dx.doi.org/10.4236/ce.2012.38B010
  • Hui, V., Eby, M., Constantino, R. E., Lee, H., Zelazny, J., Chang, J. C., He, D., & Lee, Y. J. (2023). Examining the supports and advice that women with intimate partner violence experience received in online health communities: Text mining approach. Journal of Medical Internet Research, 25(e48607). http://doi.org/10.2196/48607.
  • Ikram, M. T., Afzal, M. T., & Butt, N. A. (2018). Automated citation sentiment analysis using high order n-grams: a preliminary investigation. Turkish Journal of Electrical Engineering and Computer Sciences, 26(4), 1922-1932. https://doi.org/10.3906/elk-1712-24
  • Joshi, A., Kale, S., Chandel, S., & Pal, D. K. (2015). Likert scale: explored and explained. Current Journal of Applied Science and Technology, 7(4), 396-403. https://doi.org/10.9734/BJAST/2015/14975
  • Kanoksilapatham, B. (2022). Digital technology in English education: linguistic gain and pain points. International Journal of Information and Education Technology, 12(4), 346-351. https://doi.org/10.18178/ijiet.2022.12.4.1625
  • Kapočiūtė-Dzikienė, J., Damaševičius, R., & Woźniak, M. (2019). Sentiment analysis of Lithuanian texts using traditional and deep learning approaches. Computers, 8(1), 4. https://doi.org/10.3390/computers8010004
  • Kastrati, Z., Dalipi, F., Imran, A. S., Pireva Nuci, K., & Wani, M. A. (2021). Sentiment analysis of students’ feedback with NLP and deep learning: a systematic mapping study. Applied Sciences, 11(9), 3986. https://doi.org/10.3390/app11093986
  • Kemaloğlu, N., Küçüksille, E., & Özgünsür, M. (2021). Turkish sentiment analysis on social media. Sakarya University Journal of Science, 25(3), 629-638. https://doi.org/10.16984/saufenbilder.872227
  • Kiesler, N., & Pfülb, B. (2023). Higher education programming competencies: A novel dataset. In L. Iliadis, A. Papaleonidas, P. Angelov, & C. Jayne (Eds), Lecture notes in computer science: Vol 14261. Artificial Neural Networks and Machine Learning – ICANN 2023. (pp. 319-330). Springer, Cham. https://doi.org/10.1007/978-3-031-44198-1_27
  • Kim, S.-M., & Hovy, E. (2006). Identifying and analyzing judgment opinions. Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT-NAACL ‘06), 200-207. https://doi.org/10.3115/1220835.1220861
  • Kokkinogenis, Z., Filguieras, J., Carvalho, S., Sarmento, L., & Rossetti, R. J. (2015). Mobility network evaluation in the user perspective: real-time sensing of traffic information in twitter messages. In R.J.F. Rossetti, & R. Liu (Eds.), Advances in artificial transportation systems and simulation (pp. 219-234). Academic Press. https://doi.org/10.1016/B978-0-12-397041-1.00012-1
  • Konate, A., & Du, R. (2018). Sentiment analysis of code-mixed Bambara-French social media text using deep learning techniques. Wuhan University Journal of Natural Sciences, 23, 237-243. https://doi.org/10.1007/s11859-018-1316-z
  • Kumar, S., Nabeem, M., Manoj, C. K., & Jeyachandran, K. (2020). Sentimental analysis (opinion mining) in social network by using Svm algorithm. Proceedings of the 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC). India, 859-865. https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000159
  • Lehmann, T., Blumschein, P., & Seel, N. M. (2023). Accept it or forget it: mandatory digital learning and technology acceptance in higher education. Journal of Computers in Education, 10, 797-817. https://doi.org/10.1007/s40692-022-00244-w
  • Li, X., Zhang, J., Du, Y., Zhu, J., Fan, Y., & Chen, X. (2023). A novel deep learning-based sentiment analysis method enhanced with emojis in microblog social networks. Enterprise Information Systems, 17(5). https://doi.org/10.1080/17517575.2022.2037160
  • Mancillas, L. K., & Brusoe, P. W. (2016). Born digital: integrating media technology in the political science classroom. Journal of Political Science Education, 12(4), 375-386. https://doi.org/10.1080/15512169.2015.1096792
  • Mäntylä, M., Graziotin, D., & Kuutila, M. (2018). The evolution of sentiment analysis – a review of research topics, venues, and top cited papers. Computer Science Review, 27, 16-32. https://doi.org/10.1016/j.cosrev.2017.10.002
  • McQuistan, A. (2019, July 15). Building a text analytics app in Python with Flask, Requests, BeautifulSoup, and TextBlob. theCodingInterface. https://thecodinginterface.com/blog/text-analytics-app-with-flask-and-textblob/
  • Mertz, D. (2004, June). Charming Python #b18: the natural language toolkit. Using Python in computational linguistics. Gnosis. https://gnosis.cx/publish/programming/charming_python_b18.html
  • Mihalcea, R., Banea, C., & Wiebe, J. (2007). Learning multilingual subjective language via cross-lingual projections. Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics Czech Republic, 976–983.
  • Mohamed Hashim, M., Tlemsani, I., & Matthews, R. (2022). Higher education strategy in digital transformation. Education and Information Technologies, 27, 3171–3195. https://doi.org/10.1007/s10639-021-10739-1
  • Nandwani, P., & Verma, R. (2021). A review on sentiment analysis and emotion detection from text. Social Network Analysis and Mining, 11(81). https://doi.org/10.1007/s13278-021-00776-6
  • Özmen, B., & Altun, A. (2014). Undergraduate students’ experiences in programming: Difficulties and obstacles. Turkish Online Journal of Qualitative Inquiry, 5(3), 1-27. https://doi.org/10.17569/tojqi.20328.
  • Pagolu, V. S., Reddy, K. N., Panda, G., & Majhi, B. (2016). Sentiment analysis of Twitter data for predicting stock market movements. Proceedings of the International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), 1345-1350. https://doi.org/10.1109/SCOPES.2016.7955659
  • Pérez, J. M., Rajngewerc, M., Giudici, J. C., Furman, D. A., Luque, F., Alemany, L. A., & Martínez, M. V. (2023). pysentimiento: A Python toolkit for opinion mining and social NLP tasks. arXiv preprint arXiv:2106.09462. https://doi.org/10.48550/arXiv.2106.09462
  • Pinto, M., & Leite, C. (2020). Digital technologies in support of students learning in higher education: literature review. Digital Education Review, 37, 343-360. https://doi.org/10.1344/der.2020.37.343-360
  • Piryani, R., Madhavi, M., & Singh, V. K. (2017). Analytical mapping of opinion mining and sentiment analysis research during 2000-2015. Information Processing & Management, 53(1), 122-150. https://doi.org/10.1016/j.ipm.2016.07.001
  • Pooja, & Bhalla, R. A. (2022). Review paper on the role of sentiment analysis in quality education. SN Computer Science, 3(6), 469. https://doi.org/10.1007/s42979-022-01366-9
  • Pozzi, F. A., Fersini, E., Messina, E., & Liu, B. (2017). Challenges of sentiment analysis in social networks: an overview. In F.A. Pozzi, E. Fersini, E. Messina, & B. Liu (Eds.), Sentiment Analysis in Social Networks (pp. 1-11). Morgan Kaufmann. https://doi.org/10.1016/B978-0-12-804412-4.00001-2
  • Raffaghelli, J. E., Rodríguez, M., Guerrero, A.-E., & Baneres, D. (2022). Applying the UTAUT model to explain the students’ acceptance of an early warning system in Higher Education. Computers & Education, 182, 104468. https://doi.org/10.1016/j.compedu.2022.104468
  • Rathee, N., Joshi, N., & Kaur, J. (2018). Sentiment analysis using machine learning techniques on Python. Proceedings of the Second International Conference on Intelligent Computing and Control Systems (ICICCS), 779-785. https://doi.org/10.1109/ICCONS.2018.8663224
  • Romero, M., Lepage, A., & Lille, B. (2017). Computational thinking development through creative programming in higher education. International Journal of Educational Technology in Higher Education, 14(42). https://doi.org/10.1186/s41239-017-0080-z
  • Romero-Rodríguez, J. M., Alonso-García, S., Marín-Marín, J.-A., & Gómez-García, G. (2020). Considerations on the implications of the internet of things in Spanish universities: The usefulness perceived by professors. Future Internet, 12(8), 123. https://doi.org/10.3390/fi12080123
  • Sailer, M., Schultz-Pernice, F., & Fischer, F. (2021). Contextual facilitators for learning activities involving technology in higher education: The C♭-model. Computers in Human Behavior, 121, 106794. https://doi.org/10.1016/j.chb.2021.106794
  • Salloum, S. A., & Shaalan, K. (2019). Factors affecting students’ acceptance of E-Learning system in higher education using UTAUT and structural equation modeling approaches. In A. Hassanien, M. Tolba, K. Shaalan, & A. Azar (Eds.), Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018. AISI 2018: Vol. 845. Advances in Intelligent Systems and Computing (pp. 469-480). Springer. https://doi.org/10.1007/978-3-319-99010-1_43
  • Sarker, A., & Gonzalez, G. (2015). Portable automatic text classification for adverse drug reaction detection via multi-corpus training. Journal of biomedical informatics, 53, 196-207. https://doi.org/10.1016/j.jbi.2014.11.002
  • Saura, J. R., Palos-Sánchez, P. R., & Grilo, A. M. (2019). Detecting indicators for startup business success: sentiment analysis using text data mining. Sustainability, 11(3), 917. https://doi.org/10.3390/su11030917
  • Schneider, M., & Preckel, F. (2017). Variables associated with achievement in higher education: A systematic review of meta-analyses. Psychological Bulletin, 143(6), 565-600. https://doi.org/10.1037/bul0000098
  • Sinnott, R., Duan, H., & Sun, Y. (2016). A case study in big data analytics: exploring Twitter sentiment analysis and the weather. In R. Buyya, R. Calheiros, & A. Vahid Dastjerdi (Eds.), Big Data: Principles and Paradigms (pp. 357-388). Morgan Kaufmann. https://doi.org/10.1016/B978-0-12-805394-2.00015-5
  • Sobral, S. R. (2021). Teaching and learning to program: Umbrella review of introductory programming in higher education. Mathematics, 9(15), 1737. https://doi.org/10.3390/math9151737
  • Sufi, F. K., & Khalil, I. (2022). Automated disaster monitoring from social media posts using AI based location intelligence and sentiment analysis. IEEE Transactions on Computational Social Systems, 11(4), 4614-4624. https://doi.org/10.1109/TCSS.2022.3157142
  • Tikva, C., & Tambouris, E. (2021). Mapping computational thinking through programming in K-12 education: A conceptual model based on a systematic literature review. Computers & Education, 162, 104083. https://doi.org/10.1016/j.compedu.2020.104083
  • Tiwari, P., Yadav, P., Kumar, S., Mishra, B. K., Nguyen, G. N., Gochhayat, S. P., Singh, J., & Prasad, M. (2019). Sentiment analysis for airlines services based on Twitter dataset. In N. Dey, S. Borah, R. Babo, & A. S. Ashour (Eds.), Social Network Analytics. (pp. 149-162). Academic Press. https://doi.org/10.1016/B978-0-12-815458-8.00008-6
  • Troussas, C., Krouska, A., & Sgouropoulou, C. (2020). Collaboration and fuzzy-modeled personalization for mobile game-based learning in higher education. Computers & Education, 144, 103698. https://doi.org/10.1016/j.compedu.2019.103698
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
  • Vuorikari, R., Kluzer, S., & Punie, Y. (2022). DigComp 2.2: The digital competence framework for citizens - with new examples of knowledge, skills and attitudes. EUR 31006 EN, Publications Office of the European Union, Luxembourg. https://dx.doi.org/10.2760/115376
  • Wang, L. (2023). Adoption of the PICRAT model to guide the integration of innovative technologies in the teaching of a linguistics course. Sustainability, 15, 3886. https://doi.org/10.3390/su15053886
  • Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55, 5731-5780. https://doi.org/10.1007/s10462-022-10144-1
  • Wolff, R. (2020, December 11). Top 8 no-code machine learning tools & How to use them. MonkeyLearn. https://monkeylearn.com/blog/no-code-machine-learning/
  • Yadav, A., & Vishwakarma, D. K. (2020). Sentiment analysis using deep learning architectures: a review. Artificial Intelligence Review, 53, 4335-4385. https://doi.org/10.1007/s10462-019-09794-5
  • Yılmaz, A. (2021). The effect of technology integration in education on prospective teachers’ critical and creative thinking, multidimensional 21st century skills and academic achievements. Participatory Educational Research, 8(2), 163-199. https://doi.org/10.17275/per.21.35.8.2
  • Zahidi, Y., Younoussi, Y. E., & Al-Amrani, Y. (2021). Different valuable tools for Arabic sentiment analysis: a comparative evaluation. International Journal of Electrical and Computer Engineering, 11, 753-762. http://doi.org/10.11591/ijece.v11i1.pp753-762
  • Zhang, Y. Applying digital technology to linguistic education: a connectivism-based intelligent learning system (2021). Proceedings of the 3rd International Conference on Internet Technology and Educational Informization (ITEI), China, 111-115. https://doi.org/10.1109/ITEI55021.2021.00034
Year 2024, , 308 - 331, 31.10.2024
https://doi.org/10.19128/turje.1444808

Abstract

References

  • Aliaño, Á., Hueros, A., Guzmán-Franco, M. D., & Aguaded, I. (2019). Mobile learning in university contexts based on the unified theory of acceptance and use of technology (UTAUT). Journal of New Approaches in Educational Research, 8, 7-17. https://doi.org/10.7821/naer.2019.1.317
  • Babu, N. V., & Kanaga, E. G. M. (2022). Sentiment analysis in social media data for depression detection using artificial intelligence: a review. SN computer science, 3(74). https://doi.org/10.1007/s42979-021-00958-1
  • Banea, C., Mihalcea, R., Wiebe, J., & Hassan, S. (2008). Multilingual subjectivity analysis using machine translation. In M. Lapata, & H. T. Ng (Eds), Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP '08) (pp. 127-135). Association for Computational Linguistics. http://dx.doi.org/10.3115/1613715.1613734
  • Barab S. A., Squire K. D., & Dueber W. (2000). A co-evolutionary model for supporting the emergence of authenticity. Educational Technology Research and Development, 48(2), 37-62. https://doi.org/10.1007/BF02313400
  • Baskara, R., & Mukarto, M. (2023). Exploring the implications of ChatGPT for language learning in higher education. Indonesian Journal of English Language Teaching and Applied Linguistics, 7(2), 343-358.
  • Basmmi, A. B., Halim, S. A., & Saadon, N. A. (2020). Comparison of web services for sentiment analysis in social networking sites. Proceedings of the IOP conference series: Materials science and engineering, Malaysia, 884, 012063. https://dx.doi.org/10.1088/1757-899X/884/1/012063
  • Ben Youssef, A., Dahmani, M., & Omrani, N. (2015). Information technologies, students’ e-skills and diversity of learning process. Education and Information Technologies, 20, 141-159. https://doi.org/10.1007/s10639-013-9272-x
  • Birjali, M., Kasri, M., & Beni-Hssane, A. (2021). A comprehensive survey on sentiment analysis: Approaches, challenges and trends. Knowledge-Based Systems, 226, 107134. https://doi.org/10.1016/j.knosys.2021.107134
  • Bisio, F., Oneto, L., & Cambria, E. (2017). Sentic computing for social network analysis. In F.A. Pozzi, E. Fersini, E. Messina, & B. Liu (Eds.), Sentiment Analysis in Social Networks (pp. 71-99). Morgan Kaufmann. https://doi.org/10.1016/B978-0-12-804412-4.00005-X
  • Bond, M., Marín, V. I., Dolch, C., Bedenlier, S., & Zawacki-Richter O. (2018). Digital transformation in German higher education: student and teacher perceptions and usage of digital media. International Journal of Educational Technology in Higher Education, 15, 48. https://doi.org/10.1186/s41239-018-0130-1
  • Boukes, M., van de Velde, B., Araujo, T., & Vliegenthart, R. (2019). What’s the tone? Easy doesn’t do it: Analyzing performance and agreement between off-the-shelf sentiment analysis tools. Communication Methods and Measures, 14(2), 83-104. https://doi.org/10.1080/19312458.2019.1671966
  • Bouznif, M. (2018). Business students’ continuance intention toward blackboard usage: an empirical investigation of UTAUT model. International Journal of Business and Management, 13(1), 120-130. https://doi.org/10.5539/ijbm.v13n1p120
  • Boyd, R. L., & Schwartz, H. A. (2021). Natural language analysis and the psychology of verbal behavior: The past, present, and future states of the field. Journal of Language and Social Psychology, 40(1), 21-41. https://doi.org/10.1177/0261927X20967028
  • Bueno, I., Carrasco, R., Ureña, R., & Herrera-Viedma, E. (2022). A business context aware decision-making approach for selecting the most appropriate sentiment analysis technique in e-marketing situations. Information Sciences, 589, 300-320. https://doi.org/10.1016/j.ins.2021.12.080
  • Chapple, D. G., Weir, B., & Martin, R. S. (2017). Can the incorporation of quick response codes and smartphones improve field-based science education? International Journal of Innovation in Science and Mathematics Education, 25(2), 49-71.
  • Chauhan, P., Sharma, N., & Sikka, G. (2021). The emergence of social media data and sentiment analysis in election prediction. Journal of Ambient Intelligence and Humanized Computing, 12, 2601-2627. https://doi.org/10.1007/s12652-020-02423-y
  • Chernikova, O., Heitzmann, N., Stadler, M., Holzberger, D., Seidel, T., & Fischer F. (2020). Simulation-based learning in higher education: a meta-analysis. Review of Educational Research, 90(4), 499-541. https://doi.org/10.3102/0034654320933544
  • Contreras, D., Wilkinson, S., Alterman, E., & Hervás, J. (2022). Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquake. Nat Hazards, 113, 403-421 https://doi.org/10.1007/s11069-022-05307-w
  • Darmoroz, H. (2017). Professional training of computational linguists at the university of stuttgart. Comparative Professional Pedagogy, 7(3), 75-83. https://doi.org/10.1515/rpp-2017-0039
  • Faizi, R. (2023). Using sentiment analysis to explore student feedback: a lexical approach. International Journal of Emerging Technologies in Learning (iJET), 18(09), 259-267. https://doi.org/10.3991/ijet.v18i09.38101
  • Feng, T. (2023). The impact of cloud technology and the MatLab app on the academic performance and cognitive load of further mathematics students. Education and Information Technologies, 29, 13577-13593. https://doi.org/10.1007/s10639-023-12386-0
  • Fernández, A., Gómez, B., Binjaku, K., & Kajo Meçe, E. (2023). Digital transformation initiatives in higher education institutions: a multivocal literature review. Education and Information Technologies, 28, 12351-12382. https://doi.org/10.1007/s10639-022-11544-0
  • Gao, S., Krogstie, J., & Siau, K. (2011). Developing an instrument to measure the adoption of mobile services. Mobile Information Systems, 7(1), 45-67. http://dx.doi.org/10.3233/MIS-2011-0110
  • García-Vera, V. E., & Chiner Sanz, E. (2017). Factors influencing graduate students’ preference of software tools for building engineering applications. The International Journal of Engineering Education, 33(1), 128-137.
  • Gedik Bal, N. (2024). Unlocking online language education: Opportunities, challenges, and recommendations. Turkish Journal of Education, 13(2), 158-179. https://doi.org/10.19128/turje.1379149
  • Gujjar, J. P., & Kumar, H. P. (2021). Sentiment analysis: Textblob for decision making. International Journal of Scientific Research & Engineering Trends, 7(2), 1097-1099.
  • Gupta, B., Negi, M., Vishwakarma, K., Rawat, G., & Badhani, P. (2017). Study of Twitter sentiment analysis using machine learning algorithms on Python. International Journal of Computer Applications, 165(9), 29-34. http://dx.doi.org/10.5120/ijca2017914022
  • Hajba, G. L. (2018). Using Beautiful Soup. Apress. https://doi.org/10.1007/978-1-4842-3925-4_3
  • Halloran, L., & Friday, C. (2018). Can the universities of today lead learning for tomorrow? The university of the future. Australia: Ernst & Young. https://assets.ey.com/content/dam/ey-sites/ey-com/en_au/topics/government-and-public-sector/ey-university-of-the-future-2030.pdf
  • Hew, K. F., Hu, X., Qiao, C., & Tang, Y. (2020). What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach. Computers & Education, 145, 103724. https://doi.org/10.1016/j.compedu.2019.103724
  • Hsu, H. (2012). The acceptance of Moodle: An empirical study based on UTAUT. Creative Education, 3(8B), 44-46. http://dx.doi.org/10.4236/ce.2012.38B010
  • Hui, V., Eby, M., Constantino, R. E., Lee, H., Zelazny, J., Chang, J. C., He, D., & Lee, Y. J. (2023). Examining the supports and advice that women with intimate partner violence experience received in online health communities: Text mining approach. Journal of Medical Internet Research, 25(e48607). http://doi.org/10.2196/48607.
  • Ikram, M. T., Afzal, M. T., & Butt, N. A. (2018). Automated citation sentiment analysis using high order n-grams: a preliminary investigation. Turkish Journal of Electrical Engineering and Computer Sciences, 26(4), 1922-1932. https://doi.org/10.3906/elk-1712-24
  • Joshi, A., Kale, S., Chandel, S., & Pal, D. K. (2015). Likert scale: explored and explained. Current Journal of Applied Science and Technology, 7(4), 396-403. https://doi.org/10.9734/BJAST/2015/14975
  • Kanoksilapatham, B. (2022). Digital technology in English education: linguistic gain and pain points. International Journal of Information and Education Technology, 12(4), 346-351. https://doi.org/10.18178/ijiet.2022.12.4.1625
  • Kapočiūtė-Dzikienė, J., Damaševičius, R., & Woźniak, M. (2019). Sentiment analysis of Lithuanian texts using traditional and deep learning approaches. Computers, 8(1), 4. https://doi.org/10.3390/computers8010004
  • Kastrati, Z., Dalipi, F., Imran, A. S., Pireva Nuci, K., & Wani, M. A. (2021). Sentiment analysis of students’ feedback with NLP and deep learning: a systematic mapping study. Applied Sciences, 11(9), 3986. https://doi.org/10.3390/app11093986
  • Kemaloğlu, N., Küçüksille, E., & Özgünsür, M. (2021). Turkish sentiment analysis on social media. Sakarya University Journal of Science, 25(3), 629-638. https://doi.org/10.16984/saufenbilder.872227
  • Kiesler, N., & Pfülb, B. (2023). Higher education programming competencies: A novel dataset. In L. Iliadis, A. Papaleonidas, P. Angelov, & C. Jayne (Eds), Lecture notes in computer science: Vol 14261. Artificial Neural Networks and Machine Learning – ICANN 2023. (pp. 319-330). Springer, Cham. https://doi.org/10.1007/978-3-031-44198-1_27
  • Kim, S.-M., & Hovy, E. (2006). Identifying and analyzing judgment opinions. Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT-NAACL ‘06), 200-207. https://doi.org/10.3115/1220835.1220861
  • Kokkinogenis, Z., Filguieras, J., Carvalho, S., Sarmento, L., & Rossetti, R. J. (2015). Mobility network evaluation in the user perspective: real-time sensing of traffic information in twitter messages. In R.J.F. Rossetti, & R. Liu (Eds.), Advances in artificial transportation systems and simulation (pp. 219-234). Academic Press. https://doi.org/10.1016/B978-0-12-397041-1.00012-1
  • Konate, A., & Du, R. (2018). Sentiment analysis of code-mixed Bambara-French social media text using deep learning techniques. Wuhan University Journal of Natural Sciences, 23, 237-243. https://doi.org/10.1007/s11859-018-1316-z
  • Kumar, S., Nabeem, M., Manoj, C. K., & Jeyachandran, K. (2020). Sentimental analysis (opinion mining) in social network by using Svm algorithm. Proceedings of the 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC). India, 859-865. https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000159
  • Lehmann, T., Blumschein, P., & Seel, N. M. (2023). Accept it or forget it: mandatory digital learning and technology acceptance in higher education. Journal of Computers in Education, 10, 797-817. https://doi.org/10.1007/s40692-022-00244-w
  • Li, X., Zhang, J., Du, Y., Zhu, J., Fan, Y., & Chen, X. (2023). A novel deep learning-based sentiment analysis method enhanced with emojis in microblog social networks. Enterprise Information Systems, 17(5). https://doi.org/10.1080/17517575.2022.2037160
  • Mancillas, L. K., & Brusoe, P. W. (2016). Born digital: integrating media technology in the political science classroom. Journal of Political Science Education, 12(4), 375-386. https://doi.org/10.1080/15512169.2015.1096792
  • Mäntylä, M., Graziotin, D., & Kuutila, M. (2018). The evolution of sentiment analysis – a review of research topics, venues, and top cited papers. Computer Science Review, 27, 16-32. https://doi.org/10.1016/j.cosrev.2017.10.002
  • McQuistan, A. (2019, July 15). Building a text analytics app in Python with Flask, Requests, BeautifulSoup, and TextBlob. theCodingInterface. https://thecodinginterface.com/blog/text-analytics-app-with-flask-and-textblob/
  • Mertz, D. (2004, June). Charming Python #b18: the natural language toolkit. Using Python in computational linguistics. Gnosis. https://gnosis.cx/publish/programming/charming_python_b18.html
  • Mihalcea, R., Banea, C., & Wiebe, J. (2007). Learning multilingual subjective language via cross-lingual projections. Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics Czech Republic, 976–983.
  • Mohamed Hashim, M., Tlemsani, I., & Matthews, R. (2022). Higher education strategy in digital transformation. Education and Information Technologies, 27, 3171–3195. https://doi.org/10.1007/s10639-021-10739-1
  • Nandwani, P., & Verma, R. (2021). A review on sentiment analysis and emotion detection from text. Social Network Analysis and Mining, 11(81). https://doi.org/10.1007/s13278-021-00776-6
  • Özmen, B., & Altun, A. (2014). Undergraduate students’ experiences in programming: Difficulties and obstacles. Turkish Online Journal of Qualitative Inquiry, 5(3), 1-27. https://doi.org/10.17569/tojqi.20328.
  • Pagolu, V. S., Reddy, K. N., Panda, G., & Majhi, B. (2016). Sentiment analysis of Twitter data for predicting stock market movements. Proceedings of the International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), 1345-1350. https://doi.org/10.1109/SCOPES.2016.7955659
  • Pérez, J. M., Rajngewerc, M., Giudici, J. C., Furman, D. A., Luque, F., Alemany, L. A., & Martínez, M. V. (2023). pysentimiento: A Python toolkit for opinion mining and social NLP tasks. arXiv preprint arXiv:2106.09462. https://doi.org/10.48550/arXiv.2106.09462
  • Pinto, M., & Leite, C. (2020). Digital technologies in support of students learning in higher education: literature review. Digital Education Review, 37, 343-360. https://doi.org/10.1344/der.2020.37.343-360
  • Piryani, R., Madhavi, M., & Singh, V. K. (2017). Analytical mapping of opinion mining and sentiment analysis research during 2000-2015. Information Processing & Management, 53(1), 122-150. https://doi.org/10.1016/j.ipm.2016.07.001
  • Pooja, & Bhalla, R. A. (2022). Review paper on the role of sentiment analysis in quality education. SN Computer Science, 3(6), 469. https://doi.org/10.1007/s42979-022-01366-9
  • Pozzi, F. A., Fersini, E., Messina, E., & Liu, B. (2017). Challenges of sentiment analysis in social networks: an overview. In F.A. Pozzi, E. Fersini, E. Messina, & B. Liu (Eds.), Sentiment Analysis in Social Networks (pp. 1-11). Morgan Kaufmann. https://doi.org/10.1016/B978-0-12-804412-4.00001-2
  • Raffaghelli, J. E., Rodríguez, M., Guerrero, A.-E., & Baneres, D. (2022). Applying the UTAUT model to explain the students’ acceptance of an early warning system in Higher Education. Computers & Education, 182, 104468. https://doi.org/10.1016/j.compedu.2022.104468
  • Rathee, N., Joshi, N., & Kaur, J. (2018). Sentiment analysis using machine learning techniques on Python. Proceedings of the Second International Conference on Intelligent Computing and Control Systems (ICICCS), 779-785. https://doi.org/10.1109/ICCONS.2018.8663224
  • Romero, M., Lepage, A., & Lille, B. (2017). Computational thinking development through creative programming in higher education. International Journal of Educational Technology in Higher Education, 14(42). https://doi.org/10.1186/s41239-017-0080-z
  • Romero-Rodríguez, J. M., Alonso-García, S., Marín-Marín, J.-A., & Gómez-García, G. (2020). Considerations on the implications of the internet of things in Spanish universities: The usefulness perceived by professors. Future Internet, 12(8), 123. https://doi.org/10.3390/fi12080123
  • Sailer, M., Schultz-Pernice, F., & Fischer, F. (2021). Contextual facilitators for learning activities involving technology in higher education: The C♭-model. Computers in Human Behavior, 121, 106794. https://doi.org/10.1016/j.chb.2021.106794
  • Salloum, S. A., & Shaalan, K. (2019). Factors affecting students’ acceptance of E-Learning system in higher education using UTAUT and structural equation modeling approaches. In A. Hassanien, M. Tolba, K. Shaalan, & A. Azar (Eds.), Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2018. AISI 2018: Vol. 845. Advances in Intelligent Systems and Computing (pp. 469-480). Springer. https://doi.org/10.1007/978-3-319-99010-1_43
  • Sarker, A., & Gonzalez, G. (2015). Portable automatic text classification for adverse drug reaction detection via multi-corpus training. Journal of biomedical informatics, 53, 196-207. https://doi.org/10.1016/j.jbi.2014.11.002
  • Saura, J. R., Palos-Sánchez, P. R., & Grilo, A. M. (2019). Detecting indicators for startup business success: sentiment analysis using text data mining. Sustainability, 11(3), 917. https://doi.org/10.3390/su11030917
  • Schneider, M., & Preckel, F. (2017). Variables associated with achievement in higher education: A systematic review of meta-analyses. Psychological Bulletin, 143(6), 565-600. https://doi.org/10.1037/bul0000098
  • Sinnott, R., Duan, H., & Sun, Y. (2016). A case study in big data analytics: exploring Twitter sentiment analysis and the weather. In R. Buyya, R. Calheiros, & A. Vahid Dastjerdi (Eds.), Big Data: Principles and Paradigms (pp. 357-388). Morgan Kaufmann. https://doi.org/10.1016/B978-0-12-805394-2.00015-5
  • Sobral, S. R. (2021). Teaching and learning to program: Umbrella review of introductory programming in higher education. Mathematics, 9(15), 1737. https://doi.org/10.3390/math9151737
  • Sufi, F. K., & Khalil, I. (2022). Automated disaster monitoring from social media posts using AI based location intelligence and sentiment analysis. IEEE Transactions on Computational Social Systems, 11(4), 4614-4624. https://doi.org/10.1109/TCSS.2022.3157142
  • Tikva, C., & Tambouris, E. (2021). Mapping computational thinking through programming in K-12 education: A conceptual model based on a systematic literature review. Computers & Education, 162, 104083. https://doi.org/10.1016/j.compedu.2020.104083
  • Tiwari, P., Yadav, P., Kumar, S., Mishra, B. K., Nguyen, G. N., Gochhayat, S. P., Singh, J., & Prasad, M. (2019). Sentiment analysis for airlines services based on Twitter dataset. In N. Dey, S. Borah, R. Babo, & A. S. Ashour (Eds.), Social Network Analytics. (pp. 149-162). Academic Press. https://doi.org/10.1016/B978-0-12-815458-8.00008-6
  • Troussas, C., Krouska, A., & Sgouropoulou, C. (2020). Collaboration and fuzzy-modeled personalization for mobile game-based learning in higher education. Computers & Education, 144, 103698. https://doi.org/10.1016/j.compedu.2019.103698
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
  • Vuorikari, R., Kluzer, S., & Punie, Y. (2022). DigComp 2.2: The digital competence framework for citizens - with new examples of knowledge, skills and attitudes. EUR 31006 EN, Publications Office of the European Union, Luxembourg. https://dx.doi.org/10.2760/115376
  • Wang, L. (2023). Adoption of the PICRAT model to guide the integration of innovative technologies in the teaching of a linguistics course. Sustainability, 15, 3886. https://doi.org/10.3390/su15053886
  • Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55, 5731-5780. https://doi.org/10.1007/s10462-022-10144-1
  • Wolff, R. (2020, December 11). Top 8 no-code machine learning tools & How to use them. MonkeyLearn. https://monkeylearn.com/blog/no-code-machine-learning/
  • Yadav, A., & Vishwakarma, D. K. (2020). Sentiment analysis using deep learning architectures: a review. Artificial Intelligence Review, 53, 4335-4385. https://doi.org/10.1007/s10462-019-09794-5
  • Yılmaz, A. (2021). The effect of technology integration in education on prospective teachers’ critical and creative thinking, multidimensional 21st century skills and academic achievements. Participatory Educational Research, 8(2), 163-199. https://doi.org/10.17275/per.21.35.8.2
  • Zahidi, Y., Younoussi, Y. E., & Al-Amrani, Y. (2021). Different valuable tools for Arabic sentiment analysis: a comparative evaluation. International Journal of Electrical and Computer Engineering, 11, 753-762. http://doi.org/10.11591/ijece.v11i1.pp753-762
  • Zhang, Y. Applying digital technology to linguistic education: a connectivism-based intelligent learning system (2021). Proceedings of the 3rd International Conference on Internet Technology and Educational Informization (ITEI), China, 111-115. https://doi.org/10.1109/ITEI55021.2021.00034
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Details

Primary Language English
Subjects Higher Education Studies (Other)
Journal Section Research Articles
Authors

Olga Riezina 0000-0001-6077-9413

Larysa Yarova 0000-0001-6817-1787

Publication Date October 31, 2024
Submission Date February 29, 2024
Acceptance Date August 19, 2024
Published in Issue Year 2024

Cite

APA Riezina, O., & Yarova, L. (2024). Digital technologies in linguistic education: Experience of development and implementation. Turkish Journal of Education, 13(4), 308-331. https://doi.org/10.19128/turje.1444808

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