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Uzaktan eğitimde kavramsal dönüşüm: 2006-2023 döneminde yaşanan değişimler ve geleceğe yönelik eğilimler

Yıl 2025, Cilt: 15 Sayı: 1, 733 - 764, 23.03.2025
https://doi.org/10.48146/odusobiad.1613383

Öz

Uzaktan eğitim, artan teknolojik yenilikler ve değişen toplumsal gereksinimlerle birlikte, eğitim alanının en fazla ve hızlı değişen alt alanlarından bir olarak öne çıkmaktadır. Bu bağlamda, bu çalışmanın amacı uzaktan eğitim alanının 2006-2023 yılları arasındaki kavramsal dönüşümünün incelenmesidir.Bu amaçla alanın önde gelen dergileri olan American Journal of Distance Education ve Distance Education dergilerinde yayımlanan 853 araştırma ve derleme makalesinin tam metinleri anlamsal değişim tespiti ve anlamsal ağ analizi yöntemleri ile incelenmiştir. SciBERT modeli kullanılarak gerçekleştirilen anlamsal değişim tespiti analizleri, "öğrenci", "andragoji", "bilgi", "sanal" ve "teknoloji" kavramlarının en fazla anlam değişimine uğrayan terimler olduğunu ortaya koymuştur. Alanın makro düzeydeki yapısını ortaya koymak için gerçekleştirilen anlamsal ağ analizi sonuçları, uzaktan eğitim alanının 2010 öncesinde web-tabanlı öğrenme ve senkron iletişim kavramlarının etrafında yapılandığını, 2010'lu yıllarda MOOC ve öğrenme analitiğinin, son yıllarda ise yapay zeka ve dijital dönüşüm kavramlarının merkezi konuma geçtiğini göstermiştir. Bulgular, uzaktan eğitimin basit bir erişim modelinden çok boyutlu bir öğrenme ekosistemine dönüştüğünü; özellikle COVID-19 sonrası dönemde yapay zeka destekli kişiselleştirilmiş öğrenme, uyarlanabilir öğretim ve esnek programları kapsayan bir yapıya evrildiğini ortaya koymaktadır.

Kaynakça

  • Agayon, A. J., R. Agayon, A. K., & T. Pentang, J. (2022). Teachers in the new normal: Challenges and coping mechanisms in secondary schools. Journal of Humanities and Education Development, 4(1), 67-75. https://doi.
  • Akkaya, B. (2021). The analysis of metaphorical perceptions of teachers related to teachers in terms of teaching approaches they adopt. Journal of Education and Learning, 10(5), 109. https://doi.org/10.5539/jel.v10n5p109
  • Al-Hasnawi, A. (2007). A cognitive approach to translating metaphor. Translation Journal, 11(3). Retrieved from https://translationjournal.net/journal/41metaphor.htm
  • Allen, I. E., & Seaman, J. (2007). Online Nation: Five Years of Growth in Online Learning. Sloan Consortium.
  • Anderson, T., & Rivera Vargas, P. (2020). A critical look at educational technology from a distance education perspective. Education Review, 37, 1-20. https://diposit.ub.edu/dspace/handle/2445/172738
  • Anderson, T., & Dron, J. (2011). Three generations of distance education pedagogy. The International Review of Research in Open and Distributed Learning, 12(3), 80-97.
  • Anderson, T., & Kanuka, H. (1997). On-line forums: New platforms for professional development and group collaboration. Journal of Computer-Mediated Communication, 3(3).
  • Beltagy, I., Lo, K., & Cohan, A. (2019). SciBERT: A pretrained language model for scientific text. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (pp. 3615-3620). Association for Computational Linguistics.
  • Borgatti, S. P., & Everett, M. G. (2016). Analyzing social networks (2nd ed.). SAGE Publications.
  • Bozkurt, A., Akgün-Özbek, E., & Zawacki-Richter, O. (2017). Trends and patterns in distance education (2009-2013): A bibliometric mapping analysis. Open Learning: The Journal of Open, Distance and e-Learning, 32(3), 221-235.
  • Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W.W. Norton & Company.
  • Cleveland-Innes, M., & Garrison, D. R. (2010). An introduction to distance education: Understanding teaching and learning in a new era. Routledge.
  • Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382-1402.
  • Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT) (pp. 4171-4186.)
  • Ethayarajh, K. (2019). How contextual are contextualized word representations? Comparing the geometry of BERT, ELMo, and GPT-2 embeddings. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 55-65). https://doi.org/10.18653/v1/D19-1015
  • Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215-239. Garrison, D. R., & Anderson, T. (2003). E-learning in the 21st century: A framework for research and practice. Routledge.
  • Gupta, S. K., Basu, A., Nievas, M., & Thomas, J. (2024). PRISM: Patient Records Interpretation for Semantic Clinical Trial Matching using Large Language Models. arXiv preprint. https://arxiv.org/abs/2404.15549
  • Hale, M. (2007). Historical linguistics: Theory and method. Blackwell.
  • Hamilton, W. L., Leskovec, J., & Jurafsky, D. (2016). Diachronic word embeddings reveal statistical laws of semantic change. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (pp. 1489-1501).
  • Harasim, L. (2000). Shift happens: Online education as a new paradigm in learning. The Internet and Higher Education, 3(1-2), 41-61.
  • He, C., Ding, Y., & Yang, L. (2020). Mining patterns of word usage to explore intellectual structure of scholarly fields. Journal of Informetrics, 14(3), 101029.
  • Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020). The difference between emergency remote teaching and online learning. Educause Review, 27, 1-12.
  • Hood, N., & Littlejohn, A. (2018). Hacking history: Redressing gender inequalities on Wikipedia through an editathon. The Internet and Higher Education, 38, 28-35.
  • Liu, X., Yu, A. W., Gao, J., Deng, L., & Wang, D. (2018). A comparative study of word co-occurrence for term clustering in language model-based sentence retrieval. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics (pp. 657-662).
  • Lockee, B. B. (2021). Online education in the post-COVID era. Nature Electronics, 4(1), 5-6. Mahanty, B., Boons, E., Handl, J., & Batista-Navarro, R. (2021). Detecting semantic change in economics-related concepts due to COVID-19: A computational approach. In Proceedings of the 1st Workshop on Computational Approaches to Historical Language Change.
  • Martin, F., Sun, T., & Westine, C. D. (2020). A systematic review of research on online teaching and learning from 2009 to 2018. Computers & Education, 159, 104009.
  • Means, B., Neisler, J., & Langer Research Associates. (2023). The post-pandemic future of learning: Leveraging technology to support quality education. Online Learning Journal, 27(1), 1-18.
  • Moore, M. G. (2022). The Theory of Transactional Distance: New Developments. The American Journal of Distance Education, 36(3), 182-193.
  • Moore, M. G. (2023). From correspondence education to online distance education. In M. G. Moore (Ed.), Handbook of Open, Distance, and Digital Education. Springer. https://doi.org/10.1007/978-981-19-2080-6_2
  • Nieto-Taborda, M. L., & Luppicini, R. (2024). Accelerated Digital Transformation of Higher Education in the Wake of COVID-19: A Systematic Literature Review. International Journal of Changes in Education.
  • Perifanou, M., & Economides, A. A. (2022). The landscape of MOOC platforms worldwide. International Review of Research in Open and Distributed Learning, 23(3), 104-133.
  • Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). Deep contextualized word representations. In Proceedings of NAACL-HLT 2018 (pp. 2227-2237).
  • Pottenger, W. M., & Yang, J. (2001). Detecting emerging concepts in textual data mining. In Proceedings 2001 IEEE International Conference on Data Mining (pp. 652-655).
  • Reeves, T. C., & Herrington, J. (2010). Evaluating e-learning. In J. M. Spector et al. (Eds.), Handbook of Research on Educational Communications and Technology (3rd ed., pp. 93-104). Lawrence Erlbaum Associates.
  • Røe, Y., Wojniusz, S., & Bjerke, A. H. (2022). The digital transformation of higher education teaching: Four pedagogical prescriptions to move active learning pedagogy forward. Frontiers in Education, 6, 784701. https://doi.org/10.3389/feduc.2021.784701
  • Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach.Pearson.
  • Schlechtweg, D., Schulte im Walde, S., & Eckmann, S. (2019). Diachronic usage relatedness (DURel): A framework for the annotation of lexical semantic change. In Proceedings of NAACL-HLT 2019 (pp. 169-174).
  • Schönemann, P. H. (1966). A generalized solution of the orthogonal procrustes problem. Psychometrika, 31(1), 1-10. https://doi.org/10.1007/BF02289451
  • Selwyn, N. (2021). Education and Technology: Key Issues and Debates (4th Edition). Bloomsbury Academic.
  • Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3-10.
  • Siemens, G., & Tittenberger, P. (2019). Handbook of Emerging Technologies for Learning. University of Manitoba.
  • Simonson, M., Smaldino, S., Albright, M., & Zvacek, S. (2019). Teaching and Learning at a Distance: Foundations of Distance Education (7th Edition). Pearson.
  • Singh, V., & Thurman, A. (2019). How many ways can we define online learning? A systematic literature review of definitions of online learning (1988-2018). American Journal of Distance Education, 33(4), 289-306. https://doi.org/10.1080/08923647.2019.1663082
  • Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510-1529. https://doi.org/10.1177/0002764213479366
  • Tahmasebi, N., Borin, L., & Jatowt, A. (2021). Survey of computational approaches to lexical semantic change detection. Computational approaches to semantic change, 6(1).
  • Valverde-Berrocoso, J., Garrido-Arroyo, M. D. C., Burgos-Videla, C., & Morales-Cevallos, M. B. (2020). Trends in educational research about e-learning: A systematic literature review (2009-2018). Sustainability, 12(12), 5153. https://doi.org/10.3390/su12125153
  • Varadarajan, S., Koh, J. H. L., & Daniel, B. K. (2023). A systematic review of the opportunities and challenges of micro-credentials for multiple stakeholders: learners, employers, higher education institutions and government. International Journal of Educational Technology in Higher Education, 20(1), 13. https://doi.org/10.1186/s41239-023-00381-x
  • Veletsianos, G. (2012). Higher education scholars' participation and practices on Twitter. Journal of Computer Assisted Learning, 28(4), 336-349.
  • Wan, H. P., Zhu, Y. K., & Luo, Y. (2024). Unsupervised deep learning approach for structural anomaly detection using probabilistic features. Structural Health Monitoring. https://doi.org/10.1177/14759217241226804
  • Wang, Y., Hou, Y., Che, W., & Liu, T. (2020). From static to dynamic word representations: a survey. International Journal of Machine Learning and Cybernetics. https://doi.org/10.1007/s13042-020-01069-8
  • Wang, X., Wu, J., Zhang, D., Yu, Y., & Qi, G. (2020). Towards understanding semantic change in natural language. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (pp. 8815-8825). Association for Computational Linguistics.
  • Whang, J. C., Cheng, R. Y., & Lu, L. (2014). Co-word analysis for topic evolution of digital library research. Scientometrics, 98(3), 2353-2374.
  • Wiley, D. A. (2002). The Instructional Use of Learning Objects. Agency for Instructional Technology.
  • Zawacki-Richter, O. (2021). The current state and impact of Covid-19 on digital higher education in Germany. Human Behavior and Emerging Technologies, 3(1), 218-226.
  • Zawacki-Richter, O., Alturki, U., & Aldraiweesh, A. (2017). Review and content analysis of the International Review of Research in Open and Distance/Distributed Learning (2000-2015). The International Review of Research in Open and Distributed Learning, 18(2).
  • Zawacki-Richter, O., & Anderson, T. (2014). Online distance education: Towards a research agenda. Athabasca University Press.
  • Zhu, M., Sari, A. R., & Lee, M. M. (2022). Trends and Issues in MOOC Learning Analytics Empirical Research: A Systematic Literature Review (2011-2021). Education and Information Technologies, 27, 10135-10160.
  • Zichert, J., & Wüthrich, C. (in press). Tracing conceptual change in physics: A computational analysis. Studies in History and Philosophy of Science.
  • Zimmerman, B. J., & Schunk, D. H. (2011). Handbook of self-regulation of learning and performance. Routledge.

Conceptual transformation in distance education: Changes in the period 2006-2023 and future trends

Yıl 2025, Cilt: 15 Sayı: 1, 733 - 764, 23.03.2025
https://doi.org/10.48146/odusobiad.1613383

Öz

Distance education stands out as one of the most rapidly changing sub-fields in the field of education with increasing technological innovations and changing social needs. In this context, the aim of this study is to examine the conceptual transformation of the field of distance education between 2006 and 2023. For this purpose, the full texts of 853 research and review articles published in the leading journals of the field, American Journal of Distance Education and Distance Education, were analyzed by semantic change detection and semantic network analysis methods. The semantic change detection analysis using the SciBERT model revealed that the terms “student”, “andragogy”, “knowledge”, “virtual” and “technology” were the most frequently changed terms. The results of the semantic network analysis conducted to reveal the macro-level structure of the field showed that the field of distance education was structured around the concepts of web-based learning and synchronous communication before 2010, MOOCs and learning analytics in the 2010s, and artificial intelligence and digital transformation in recent years. The findings reveal that distance education has evolved from a simple access model to a multidimensional learning ecosystem; especially in the post-COVID-19 period, it has evolved into a structure that includes AI-supported personalized learning, adaptive teaching and flexible programs.

Kaynakça

  • Agayon, A. J., R. Agayon, A. K., & T. Pentang, J. (2022). Teachers in the new normal: Challenges and coping mechanisms in secondary schools. Journal of Humanities and Education Development, 4(1), 67-75. https://doi.
  • Akkaya, B. (2021). The analysis of metaphorical perceptions of teachers related to teachers in terms of teaching approaches they adopt. Journal of Education and Learning, 10(5), 109. https://doi.org/10.5539/jel.v10n5p109
  • Al-Hasnawi, A. (2007). A cognitive approach to translating metaphor. Translation Journal, 11(3). Retrieved from https://translationjournal.net/journal/41metaphor.htm
  • Allen, I. E., & Seaman, J. (2007). Online Nation: Five Years of Growth in Online Learning. Sloan Consortium.
  • Anderson, T., & Rivera Vargas, P. (2020). A critical look at educational technology from a distance education perspective. Education Review, 37, 1-20. https://diposit.ub.edu/dspace/handle/2445/172738
  • Anderson, T., & Dron, J. (2011). Three generations of distance education pedagogy. The International Review of Research in Open and Distributed Learning, 12(3), 80-97.
  • Anderson, T., & Kanuka, H. (1997). On-line forums: New platforms for professional development and group collaboration. Journal of Computer-Mediated Communication, 3(3).
  • Beltagy, I., Lo, K., & Cohan, A. (2019). SciBERT: A pretrained language model for scientific text. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (pp. 3615-3620). Association for Computational Linguistics.
  • Borgatti, S. P., & Everett, M. G. (2016). Analyzing social networks (2nd ed.). SAGE Publications.
  • Bozkurt, A., Akgün-Özbek, E., & Zawacki-Richter, O. (2017). Trends and patterns in distance education (2009-2013): A bibliometric mapping analysis. Open Learning: The Journal of Open, Distance and e-Learning, 32(3), 221-235.
  • Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W.W. Norton & Company.
  • Cleveland-Innes, M., & Garrison, D. R. (2010). An introduction to distance education: Understanding teaching and learning in a new era. Routledge.
  • Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382-1402.
  • Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT) (pp. 4171-4186.)
  • Ethayarajh, K. (2019). How contextual are contextualized word representations? Comparing the geometry of BERT, ELMo, and GPT-2 embeddings. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 55-65). https://doi.org/10.18653/v1/D19-1015
  • Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215-239. Garrison, D. R., & Anderson, T. (2003). E-learning in the 21st century: A framework for research and practice. Routledge.
  • Gupta, S. K., Basu, A., Nievas, M., & Thomas, J. (2024). PRISM: Patient Records Interpretation for Semantic Clinical Trial Matching using Large Language Models. arXiv preprint. https://arxiv.org/abs/2404.15549
  • Hale, M. (2007). Historical linguistics: Theory and method. Blackwell.
  • Hamilton, W. L., Leskovec, J., & Jurafsky, D. (2016). Diachronic word embeddings reveal statistical laws of semantic change. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (pp. 1489-1501).
  • Harasim, L. (2000). Shift happens: Online education as a new paradigm in learning. The Internet and Higher Education, 3(1-2), 41-61.
  • He, C., Ding, Y., & Yang, L. (2020). Mining patterns of word usage to explore intellectual structure of scholarly fields. Journal of Informetrics, 14(3), 101029.
  • Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020). The difference between emergency remote teaching and online learning. Educause Review, 27, 1-12.
  • Hood, N., & Littlejohn, A. (2018). Hacking history: Redressing gender inequalities on Wikipedia through an editathon. The Internet and Higher Education, 38, 28-35.
  • Liu, X., Yu, A. W., Gao, J., Deng, L., & Wang, D. (2018). A comparative study of word co-occurrence for term clustering in language model-based sentence retrieval. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics (pp. 657-662).
  • Lockee, B. B. (2021). Online education in the post-COVID era. Nature Electronics, 4(1), 5-6. Mahanty, B., Boons, E., Handl, J., & Batista-Navarro, R. (2021). Detecting semantic change in economics-related concepts due to COVID-19: A computational approach. In Proceedings of the 1st Workshop on Computational Approaches to Historical Language Change.
  • Martin, F., Sun, T., & Westine, C. D. (2020). A systematic review of research on online teaching and learning from 2009 to 2018. Computers & Education, 159, 104009.
  • Means, B., Neisler, J., & Langer Research Associates. (2023). The post-pandemic future of learning: Leveraging technology to support quality education. Online Learning Journal, 27(1), 1-18.
  • Moore, M. G. (2022). The Theory of Transactional Distance: New Developments. The American Journal of Distance Education, 36(3), 182-193.
  • Moore, M. G. (2023). From correspondence education to online distance education. In M. G. Moore (Ed.), Handbook of Open, Distance, and Digital Education. Springer. https://doi.org/10.1007/978-981-19-2080-6_2
  • Nieto-Taborda, M. L., & Luppicini, R. (2024). Accelerated Digital Transformation of Higher Education in the Wake of COVID-19: A Systematic Literature Review. International Journal of Changes in Education.
  • Perifanou, M., & Economides, A. A. (2022). The landscape of MOOC platforms worldwide. International Review of Research in Open and Distributed Learning, 23(3), 104-133.
  • Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). Deep contextualized word representations. In Proceedings of NAACL-HLT 2018 (pp. 2227-2237).
  • Pottenger, W. M., & Yang, J. (2001). Detecting emerging concepts in textual data mining. In Proceedings 2001 IEEE International Conference on Data Mining (pp. 652-655).
  • Reeves, T. C., & Herrington, J. (2010). Evaluating e-learning. In J. M. Spector et al. (Eds.), Handbook of Research on Educational Communications and Technology (3rd ed., pp. 93-104). Lawrence Erlbaum Associates.
  • Røe, Y., Wojniusz, S., & Bjerke, A. H. (2022). The digital transformation of higher education teaching: Four pedagogical prescriptions to move active learning pedagogy forward. Frontiers in Education, 6, 784701. https://doi.org/10.3389/feduc.2021.784701
  • Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach.Pearson.
  • Schlechtweg, D., Schulte im Walde, S., & Eckmann, S. (2019). Diachronic usage relatedness (DURel): A framework for the annotation of lexical semantic change. In Proceedings of NAACL-HLT 2019 (pp. 169-174).
  • Schönemann, P. H. (1966). A generalized solution of the orthogonal procrustes problem. Psychometrika, 31(1), 1-10. https://doi.org/10.1007/BF02289451
  • Selwyn, N. (2021). Education and Technology: Key Issues and Debates (4th Edition). Bloomsbury Academic.
  • Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3-10.
  • Siemens, G., & Tittenberger, P. (2019). Handbook of Emerging Technologies for Learning. University of Manitoba.
  • Simonson, M., Smaldino, S., Albright, M., & Zvacek, S. (2019). Teaching and Learning at a Distance: Foundations of Distance Education (7th Edition). Pearson.
  • Singh, V., & Thurman, A. (2019). How many ways can we define online learning? A systematic literature review of definitions of online learning (1988-2018). American Journal of Distance Education, 33(4), 289-306. https://doi.org/10.1080/08923647.2019.1663082
  • Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510-1529. https://doi.org/10.1177/0002764213479366
  • Tahmasebi, N., Borin, L., & Jatowt, A. (2021). Survey of computational approaches to lexical semantic change detection. Computational approaches to semantic change, 6(1).
  • Valverde-Berrocoso, J., Garrido-Arroyo, M. D. C., Burgos-Videla, C., & Morales-Cevallos, M. B. (2020). Trends in educational research about e-learning: A systematic literature review (2009-2018). Sustainability, 12(12), 5153. https://doi.org/10.3390/su12125153
  • Varadarajan, S., Koh, J. H. L., & Daniel, B. K. (2023). A systematic review of the opportunities and challenges of micro-credentials for multiple stakeholders: learners, employers, higher education institutions and government. International Journal of Educational Technology in Higher Education, 20(1), 13. https://doi.org/10.1186/s41239-023-00381-x
  • Veletsianos, G. (2012). Higher education scholars' participation and practices on Twitter. Journal of Computer Assisted Learning, 28(4), 336-349.
  • Wan, H. P., Zhu, Y. K., & Luo, Y. (2024). Unsupervised deep learning approach for structural anomaly detection using probabilistic features. Structural Health Monitoring. https://doi.org/10.1177/14759217241226804
  • Wang, Y., Hou, Y., Che, W., & Liu, T. (2020). From static to dynamic word representations: a survey. International Journal of Machine Learning and Cybernetics. https://doi.org/10.1007/s13042-020-01069-8
  • Wang, X., Wu, J., Zhang, D., Yu, Y., & Qi, G. (2020). Towards understanding semantic change in natural language. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (pp. 8815-8825). Association for Computational Linguistics.
  • Whang, J. C., Cheng, R. Y., & Lu, L. (2014). Co-word analysis for topic evolution of digital library research. Scientometrics, 98(3), 2353-2374.
  • Wiley, D. A. (2002). The Instructional Use of Learning Objects. Agency for Instructional Technology.
  • Zawacki-Richter, O. (2021). The current state and impact of Covid-19 on digital higher education in Germany. Human Behavior and Emerging Technologies, 3(1), 218-226.
  • Zawacki-Richter, O., Alturki, U., & Aldraiweesh, A. (2017). Review and content analysis of the International Review of Research in Open and Distance/Distributed Learning (2000-2015). The International Review of Research in Open and Distributed Learning, 18(2).
  • Zawacki-Richter, O., & Anderson, T. (2014). Online distance education: Towards a research agenda. Athabasca University Press.
  • Zhu, M., Sari, A. R., & Lee, M. M. (2022). Trends and Issues in MOOC Learning Analytics Empirical Research: A Systematic Literature Review (2011-2021). Education and Information Technologies, 27, 10135-10160.
  • Zichert, J., & Wüthrich, C. (in press). Tracing conceptual change in physics: A computational analysis. Studies in History and Philosophy of Science.
  • Zimmerman, B. J., & Schunk, D. H. (2011). Handbook of self-regulation of learning and performance. Routledge.
Toplam 59 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Alan Eğitimleri (Diğer)
Bölüm ARAŞTIRMA MAKALESİ
Yazarlar

Hüseyin Özçınar 0000-0001-8715-2653

Yayımlanma Tarihi 23 Mart 2025
Gönderilme Tarihi 4 Ocak 2025
Kabul Tarihi 2 Mart 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 15 Sayı: 1

Kaynak Göster

APA Özçınar, H. (2025). Conceptual transformation in distance education: Changes in the period 2006-2023 and future trends. Ordu Üniversitesi Sosyal Bilimler Enstitüsü Sosyal Bilimler Araştırmaları Dergisi, 15(1), 733-764. https://doi.org/10.48146/odusobiad.1613383

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