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Articles

Analysing user reviews of interactive educational apps: a sentiment analysis approach

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Pages 355-372 | Received 25 Jun 2021, Accepted 30 May 2022, Published online: 13 Jun 2022

References

  • Akçayır, M., Akçayır, G., Pektaş, H. M., & Ocak, M. A. (2016). Augmented reality in science laboratories: The effects of augmented reality on university students’ laboratory skills and attitudes toward science laboratories. Computers in Human Behavior, 57, 334–342. https://doi.org/10.1016/j.chb.2015.12.054
  • Argüello, J. M., & Dempski, R. E. (2020). Fast, simple, student generated augmented reality approach for protein visualization in the classroom and home study. Journal of Chemical Education, 97(8), 2327–2331. https://doi.org/10.1021/acs.jchemed.0c00323
  • Arici, F., Yildirim, P., Caliklar, Ş, & Yilmaz, R. M. (2019). Research trends in the use of augmented reality in science education: Content and bibliometric mapping analysis. Computers & Education, 142, 103647. https://doi.org/10.1016/j.compedu.2019.103647
  • Arth, C., & Schmalstieg, D. (2011). Challenges of large-scale augmented reality on smartphones. Graz University of Technology, Graz, 1–4.
  • Azuma, R., Baillot, Y., Behringer, R., Feiner, S., Julier, S., & MacIntyre, B. (2001). Recent advances in augmented reality. IEEE Computer Graphics and Applications, 21(6), 34–47. https://doi.org/10.1109/38.963459
  • Azuma, R. T. (1997). A survey of augmented reality. Presence: Teleoperators & Virtual Environments, 6(4), 355–385. https://doi.org/10.1162/pres.1997.6.4.355
  • Barsom, E. Z., Graafland, M., & Schijven, M. P. (2016). Systematic review on the effectiveness of augmented reality applications in medical training. Surgical Endoscopy, 30(10), 4174–4183. https://doi.org/10.1007/s00464-016-4800-6
  • Bhagat, K. K., Mishra, S., Dixit, A., & Chang, C.-Y. (2021). Public opinions about online learning during COVID-19: A sentiment analysis approach. Sustainability, 13(6), 3346. https://doi.org/10.3390/su13063346
  • Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
  • Brooks, F. P. (1999). What's real about virtual reality? IEEE Computer Graphics and Applications, 19(6), 16–27. https://doi.org/10.1109/38.799723
  • Buckley, P., & Lardinois, F. (2014). Virtual reality beginner's guide + Google Cardboard inspired VR viewer. Regan Arts.
  • Burdea, G. C., & Coiffet, P. (2003). Virtual reality technology. John Wiley & Sons.
  • Cheng, K.-H., & Tsai, C.-C. (2013). Affordances of augmented reality in science learning: Suggestions for future research. Journal of Science Education and Technology, 22(4), 449–462. https://doi.org/10.1007/s10956-012-9405-9
  • Chiang, T. H., Yang, S. J., & Hwang, G.-J. (2014). An augmented reality-based mobile learning system to improve students’ learning achievements and motivations in natural science inquiry activities. Journal of Educational Technology & Society, 17(4), 352–365.
  • Conley, Q., Atkinson, R. K., Nguyen, F., & Nelson, B. C. (2020). MantarayAR: Leveraging augmented reality to teach probability and sampling. Computers & Education, 153, 1–22. https://doi.org/10.1016/j.compedu.2020.103895
  • Cooper, D. M. (2011). User and design perspectives of mobile augmented reality. http://cardinalscholar.bsu.edu/handle/123456789/194739
  • Daniel, M., Neves, R. F., & Horta, N. (2017). Company event popularity for financial markets using Twitter and sentiment analysis. Expert Systems with Applications, 71, 111–124. https://doi.org/10.1016/j.eswa.2016.11.022
  • Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K.(2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 1, 4171–4186.
  • di Lanzo, J. A., Valentine, A., Sohel, F., Yapp, A. Y. T., Muparadzi, K. C., & Abdelmalek, M. (2020). A review of the uses of virtual reality in engineering education. Computer Applications in Engineering Education, 28(3), 748–763. https://doi.org/10.1002/cae.22243
  • Duan, W., Yu, Y., Cao, Q., & Levy, S. (2015). Exploring the impact of social media on hotel service performance: A sentimental analysis approach. Cornell Hospitality Quarterly, 57(3), 282–296. https://doi.org/10.1177/1938965515620483
  • Dutt, R., Sinha, S., Joshi, R., Chakraborty, S. S., Riggs, M., Yan, X., Bao, H., & Rosé, C. P. (2021). RESPER: Computationally Modelling Resisting Strategies in Persuasive Conversations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), (pp. 78–90), Online, 8 November 2020 to 12 November 2020. Association for Computational Linguistics. URL https://www.aclweb.org/anthology/2020.emnlp-main.605 https://doi.org/10.18653/v1/2020.emnlp-main.605
  • Fassi, F., Mandelli, A., Teruggi, S., Rechichi, F., Fiorillo, F., & Achille, C. (2016). VR for cultural heritage. In Lucio Tommaso De Paolis, & Antonio Mongelli (Eds.), International Conference on augmented reality, virtual reality and Computer graphics (pp. 139–157). Springer.
  • Fonseca, D., Martí, N., Redondo, E., Navarro, I., & Riera, A. (2014). Relationship between student profile, tool use, participation, and academic performance with the use of augmented reality technology for visualized architecture models. Computers in Human Behavior, 31, 434–445. https://doi.org/10.1016/j.chb.2013.03.006
  • Grießhaber, D., Maucher, J., & Vu, N. T. (2020). Fine-tuning BERT for low-resource natural language understanding via active learning. arXiv preprint arXiv:2012.02462
  • Grajewski, D., & Hamrol, A. (2020). Low-cost VR system for interactive education of manual assembly procedure. Interactive Learning Environments, 1–19. https://doi.org/10.1080/10494820.2020.1761836
  • Hoang, M., Bihorac, O. A., & Rouces, J. (2019). Aspect-based sentiment analysis using BERT. In Proceedings of the 22nd nordic conference on computational linguistics, September 30–October 2, 2019 (pp. 187–196).
  • Hwang, G.-J., Wu, P.-H., Chen, C.-C., & Tu, N.-T. (2016). Effects of an augmented reality-based educational game on students’ learning achievements and attitudes in real-world observations. Interactive Learning Environments, 24(8), 1895–1906. https://doi.org/10.1080/10494820.2015.1057747
  • 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
  • Kastrati, Z., Imran, A. S., & Kurti, A. (2020). Weakly supervised framework for aspect-based sentiment analysis on students’ reviews of MOOCs. IEEE Access, 8, 106799–106810. https://doi.org/10.1109/ACCESS.2020.3000739
  • Klopfer, E., & Squire, K. (2008). Environmental detectives—the development of an augmented reality platform for environmental simulations. Educational Technology Research and Development, 56(2), 203–228. https://doi.org/10.1007/s11423-007-9037-6
  • Lamb, R., & Etopio, E. A. (2020). Virtual reality: A tool for preservice science teachers to Put Theory into practice. Journal of Science Education and Technology, 29(4), 573–585. https://doi.org/10.1007/s10956-020-09837-5
  • Liu, B. (2015). Sentiment analysis: Mining opinions, sentiments, and emotions. Cambridge University Press.
  • Liu, R., Shi, Y., Ji, C., & Jia, M. (2019). A survey of sentiment analysis based on transfer learning. IEEE Access, 7, 85401–85412. https://doi.org/10.1109/ACCESS.2019.2925059
  • Lovreglio, R., Duan, X., Rahouti, A., Phipps, R., & Nilsson, D. (2020). Comparing the effectiveness of fire extinguisher virtual reality and video training. Virtual Reality, 25(1), 133–145. https://doi.org/10.1007/s10055-020-00447-5
  • Maas, M. J., & Hughes, J. M. (2020). Virtual, augmented and mixed reality in K–12 education: A review of the literature. Technology, Pedagogy and Education, 29(2), 231–249. https://doi.org/10.1080/1475939X.2020.1737210
  • Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011
  • Mitchell, R. (2011). Alien contact!: Exploring teacher implementation of an augmented reality curricular unit. Journal of Computers in Mathematics and Science Teaching, 30(3), 271–302.
  • Mite-Baidal, K., Delgado-Vera, C., Solís-Avilés, E., Espinoza, A. H., Ortiz-Zambrano, J., & Varela-Tapia, E. (2018). Sentiment Analysis in Education Domain: A Systematic Literature Review. Paper presented at the Technologies and Innovation, Cham.
  • Nazri, N. I. A. M., & Rambli, D. R. A. (2014, June). Current limitations and opportunities in mobile augmented reality applications. In 2014 International Conference on Computer and Information Sciences (ICCOINS), 03-05 June 2014 (pp. 1–4). IEEE.
  • Nóbrega, R., Cabral, D., Jacucci, G., & Coelho, A. (2015, March). Nari: Natural augmented reality interface. In Proceedings of the International Conference on Computer Graphics Theory and Applications, GRAPP, March 11–14, 2015 (pp. 504–510).
  • Nunnally, J. C. (1967). Psychometric theory. McGraw-Hill.
  • Onan, A. (2021). Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach. Computer Applications in Engineering Education, 29(3), 572–589. https://doi.org/10.1002/cae.22253
  • Pandey, A. C., Singh, D. R., & Saraswat, M. (2017). Twitter sentiment analysis using hybrid cuckoo search method. Information Processing & Management, 53(4), 764–779. https://doi.org/10.1016/j.ipm.2017.02.004
  • Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of empirical methods of natural language processing (EMNLP’02), 6 July 2002 (pp. 79–86).
  • Papanastasiou, G., Drigas, A., Skianis, C., Lytras, M., & Papanastasiou, E. (2019). Virtual and augmented reality effects on K-12, higher and tertiary education students’ twenty-first century skills. Virtual Reality, 23(4), 425–436. https://doi.org/10.1007/s10055-018-0363-2
  • Parekh, P., Patel, S., Patel, N., & Shah, M. (2020). Systematic review and meta-analysis of augmented reality in medicine, retail, and games. Visual Computing for Industry, Biomedicine, and Art, 3(1), 21. https://doi.org/10.1186/s42492-020-00057-7
  • Parmaxi, A. (2020). Virtual reality in language learning: A systematic review and implications for research and practice. Interactive Learning Environments, 1–13. https://doi.org/10.1080/10494820.2020.1765392
  • Petersen, G. B., Klingenberg, S., Mayer, R. E., & Makransky, G. (2020). The virtual field trip: Investigating how to optimize immersive virtual learning in climate change education. British Journal of Educational Technology, 51(6), 2099–2115. https://doi.org/10.1111/bjet.12991
  • Radu, I. (2014). Augmented reality in education: A meta-review and cross-media analysis. Personal and Ubiquitous Computing, 18(6), 1533–1543. https://doi.org/10.1007/s00779-013-0747-y
  • Rosa, R. L., Schwartz, G. M., Ruggiero, W. V., & Rodríguez, D. Z. (2019). A knowledge-based recommendation system that includes sentiment analysis and deep learning. IEEE Transactions on Industrial Informatics, 15(4), 2124–2135. https://doi.org/10.1109/TII.2018.2867174
  • Sänger, M., Leser, U., Kemmerer, S., Adolphs, P., & Klinger, R. (2016, May). SCARE―the Sentiment Corpus of App Reviews with Fine-grained Annotations in German. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), May 23–28, 2016 (pp. 1114–1121).
  • Siriborvornratanakul, T. (2016). A study of virtual reality headsets and physiological extension possibilities. In International Conference on Computational Science and Its Applications, July 4–7, 2016 (pp. 497–508). Cham, Springer.
  • Sung, C., Dhamecha, T., Saha, S., Ma, T., Reddy, V., & Arora, R. (2019). Pre-training BERT on domain resources for short answer grading. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), November 3–7, 2019 (pp. 6073–6077).
  • Tarng, W., Ou, K.-L., Yu, C.-S., Liou, F.-L., & Liou, H.-H. (2015). Development of a virtual butterfly ecological system based on augmented reality and mobile learning technologies. Virtual Reality, 19(3–4), 253–266. https://doi.org/10.1007/s10055-015-0265-5
  • Vieira, S. M., Kaymak, U., & Sousa, J. M. (2010, July). Cohen's kappa coefficient as a performance measure for feature selection. In International Conference on Fuzzy Systems, 18–23 July, 2010 (pp. 1–8). IEEE.
  • Wang, H.-Y., Duh, H., Li, N., Lin, T.-J., & Tsai, C.-C. (2014). An investigation of University students’ collaborative inquiry learning behaviors in an augmented reality simulation and a traditional simulation. Journal of Science Education and Technology, 23(5), 682–691. https://doi.org/10.1007/s10956-014-9494-8
  • Watkins, J., Kitner, K. R., & Mehta, D. (2012). Mobile and smartphone use in urban and rural India. Continuum, 26(5), 685–697. https://doi.org/10.1080/10304312.2012.706458
  • Westphal, C. (2017). Challenges in networking to support augmented reality and virtual reality. IEEE ICNC.
  • Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., & Davison, J. (2019). HuggingFace's Transformers: State-of-the-art Natural Language Processing. ArXiv, arXiv: 1910.03771.
  • Wu, H.-K., Lee, S. W.-Y., Chang, H.-Y., & Liang, J.-C. (2013). Current status, opportunities and challenges of augmented reality in education. Computers & Education, 62, 41–49. https://doi.org/10.1016/j.compedu.2012.10.024
  • Wu, Y., Xu, J., Jiang, M., Zhang, Y., & Xu, H. (2015). A study of neural word embeddings for Named entity recognition in clinical text. AMIA … Annual Symposium Proceedings. AMIA Symposium, 2015, 1326–1333. https://doi.org/10.1016/j.compedu.2012.10.024
  • Yuen, S. C.-Y., Yaoyuneyong, G., & Johnson, E. (2011). Augmented reality: An overview and five directions for AR in education. Journal on Educational Technology Development and Exchange, 4(1), 119–140. https://doi.org/10.18785/jetde.0401.10
  • Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1253. https://doi.org/10.1002/widm.1253

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