Research Article
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Year 2020, Volume: 2 Issue: 1, 18 - 29, 29.06.2020

Abstract

References

  • Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings 20th International Conference on Very Large Data Bases (pp. 487-499).
  • Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31, 542-550.
  • Alonso, J. M., & Casalino, G. (2019). Explainable Artificial Intelligence for Human-Centric Data Analysis in Virtual Learning Environments. In International Workshop on Higher Education Learning Methodologies and Technologies Online (pp. 125-138). Springer, Cham.
  • Bahçeci, F. (2015). Öğrenme yönetim sistemlerinde kullanılan öğrenme analitikleri araçlarının incelenmesi. Turkish Journal of Educational Studies, 2(1). 41-58. Bajracharya, B. (2019). Learning Analytics and Dashboards for Education Systems. CTE Journal, 7(2), 1-9.
  • Baker, R.S.J.D. (2010). Data mining for education. In B. McGaw, P. Peterson, & E. Baker, (Eds.), International encyclopedia of education, (3rd edition) (pp. 112–118). Amsterdam: Elsevier.
  • Bayrak, F., & Yurdugül, H. (2016). Web-tabanlı öz-değerlendirme sisteminde öğrenci uyarı indeksini temel alan öğrenme analitiği modülünün tasarlanması. Eğitim Teknolojisi Kuram ve Uygulama, 6(2), 85-99.
  • Berman, J.J. (2013). Principles of big data: Preparing, sharing, and analyzing complex information. Massachusetts: Elsevier.
  • Bernstein, A., Provost, F., & Hill, S. (2005). Toward intelligent assistance for a data mining process: An ontology-based approach for cost-sensitive classification. Knowledge and Data Engineering, IEEE Transactions on, 17(4), 503-518.
  • Berry, M. J., & Linoff, G. (1997). Data mining techniques: for marketing, sales, and customer support. New York: John Wiley & Sons, Inc..
  • Bousbia, N., & Belamri, I. (2014). Which contribution does EDM provide to computer-based learning environments?. In Educational data mining (pp. 3-28). Springer, Cham.
  • Bozkurt, A. (2016). Öğrenme analitiği: e-öğrenme, büyük veri ve bireyselleştirilmiş öğrenme. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 2(4), 55-81.
  • Calders, T., & Pechenizkiy, M. (2011). Introduction to the special section on educational data mining. ACM SIGKDD Explor. 13(2), 3–6.
  • Chang, H. H., & Huang, W. C. (2006). Application of a quantification SWOT analytical method. Mathematical and computer modelling, 43(1-2), 158-169.
  • Chatti, M.A., Dyckhoff, A.L., Schroeder, U., & Thüs, H. (2012) A reference model for learning analytics. International Journal of Technology Enhanced Learning 4(5–6), 318–331.
  • Clow, D. (2012). The learning analytics cycle: closing the loop effectively. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 134-138).
  • Dean, J. (2014). Big data, data mining, and machine learning: Value creation for business leaders and practitioners. Canada: John Wiley & Sons, Inc..
  • Dyckhoff, A. L., Lukarov, V., Muslim, A., Chatti, M. A., & Schroeder, U. (2013). Supporting action research with learning analytics. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 220-229).
  • Dyson, R. G. (2004). Strategic development and SWOT analysis at the University of Warwick. European journal of operational research, 152(3), 631-640.
  • Elias, T. (2011). Learning analytics: Definitions, processes and potential. Retrived from https://pdfs.semanticscholar.org/732e/452659685fe3950b0e515a28ce89d9c5592a.pdf at May, 07, 2020.
  • Ezen-Can, A., Boyer, K. E., Kellogg, S., & Booth, S. (2015). Unsupervised modeling for understanding MOOC discussion forums: a learning analytics approach. In Proceedings of the fifth international conference on learning analytics and knowledge (pp. 146-150).
  • Ferguson, R., & Shum, S. B. (2012). Social learning analytics: five approaches. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 23-33).
  • Fırat, M. (2015). Eğitim teknolojileri araştırmalarında yeni bir alan: Öğrenme analitikleri. Mersin Üniversitesi Eğitim Fakültesi Dergisi, 11(3). 870-882.
  • Fulantelli, G., Taibi, D., & Arrigo, M. (2013). A semantic approach to mobile learning analytics. In Proceedings of the first international conference on technological ecosystem for enhancing multiculturality (pp. 287-292).
  • Gazulla, E. D., & Leinonen, T. (2016). Why do we want data for learning? Learning analytics and the laws of media. In The Future of Ubiquitous Learning (pp. 59-72). Springer, Berlin, Heidelberg.
  • Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology & Society, 15(3), 42-57.
  • Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques. Massachusetts: Elsevier.
  • Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z., Hlosta, M., & Naydenova, G. (2017). Implementing predictive learning analytics on a large scale: the teacher's perspective. In Proceedings of the seventh international learning analytics & knowledge conference (pp. 267-271).
  • Hickey, D. T., Kelley, T. A., & Shen, X. (2014). Small to big before massive: Scaling up participatory learning analytics. In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (pp. 93-97).
  • Hurt, J. (2008). The advantages and disadvantages of teaching and learning on-line. Delta Kappa Gamma Bulletin, 74(4). 5-11.
  • Johnson, L., Smith, R., Willis, H., Levine, A., & Haywood, K. (2011). The 2011 horizon report. The New Media Consortium. Austin, Texas.
  • Kalz, M. (2014). Lifelong learning and its support with new technologies. Retrived from https://core.ac.uk/download/pdf/55538088.pdf at 08 May, 2020.
  • Karabatak, M. (2008). Özellik seçimi, sınıflama ve öngörü uygulamalarına yönelik birliktelik kuralı çıkarımı ve yazılım geliştirilmesi. Unpublished Doctoral Thesis, Fırat University, Elazığ, Turkey.
  • Khalil, M., & Ebner, M. (2017). Clustering patterns of engagement in Massive Open Online Courses (MOOCs): The use of learning analytics to reveal student categories. Journal of Computing in Higher Education, 29(1), 114-132.
  • Kilis, S., & Gülbahar, Y. (2016). Learning analytics in distance education: A systematic literature review. Proceedings of the 9th European Distance and E-Learning Network Research Workshop, Oldenburg. Pp. 310-317.
  • Kumar, A., Dabas, V., & Hooda, P. (2018). Text classification algorithms for mining unstructured data: a SWOT analysis. International Journal of Information Technology, 1-11.
  • Kurttila, M., Pesonen, M., Kangas, J., & Kajanus, M. (2000). Utilizing the analytic hierarchy process (AHP) in SWOT analysis—a hybrid method and its application to a forest-certification case. Forest policy and economics, 1(1), 41-52.
  • LAK’11 (2014). Learning analytics & knowledge: February 27-March 1, 2011 in Banff, Alberta about. Retrived form https://tekri.athabascau.ca/analytics/ at 07 May, 2020.
  • Liu, B. (2006). Web data mining. Berlin: Springer.
  • Macfadyen, L. P., & Dawson, S. (2012). Numbers are not enough. Why e-learning analytics failed to inform an institutional strategic plan. Journal of Educational Technology & Society, 15(3), 149-163.
  • Mattingly, K. D., Rice, M. C., & Berge, Z. L. (2012). Learning analytics as a tool for closing the assessment loop in higher education. Knowledge management & e-learning: An international journal, 4(3), 236-247.
  • Merrill, M. D. (1991). Constructivism and instructional design. Educational Technology, 31(5), 45-53.
  • Olmos, M., & Corrin, L. (2012). Learning analytics: A case study of the process of design of visualizations. Journal of Asynchronous Learning Networks, 16(3), 39-49.
  • Orhan Göksün, D. (2019). E-M-U öğrenme. In A. Arslan (Ed.). Eğitimde güncel konular ve yaklaşımlar. pp. 115-128. Nobel Akademi: Ankara.
  • Papamitsiou, Z. K., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Educational Technology & Society, 17(4), 49-64.
  • Pena-Ayala, A. (2014). Educational data mining: Applications and trends. Mexico City: Springer.
  • Piateski, G., & Frawley, W. (1991). Knowledge discovery in databases. Massachusetts: MIT Press.
  • Pickton, D. W., & Wright, S. (1998). What's swot in strategic analysis?. Strategic change, 7(2), 101-109.
  • Prinsloo, P., & Slade, S. (2013). An evaluation of policy frameworks for addressing ethical considerations in learning analytics. In Proceedings of the third international conference on learning analytics and knowledge (pp. 240-244).
  • Prinsloo, P., & Slade, S. (2015). Student privacy self-management: implications for learning analytics. In Proceedings of the fifth international conference on learning analytics and knowledge (pp. 83-92).
  • Reigeluth, C. M. (2013). Instructional-design theories and models: A new paradigm of instructional theory, Volume II. Routledge.
  • Reiser, R. A. (2001). A history of instructional design and technology: Part II: A history of instructional design. Educational technology research and development, 49(2), 57-67.
  • Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005, Expert Systems with Applications, 33(1), 135–146.
  • Romero, C., & Ventura, S. (2010) Educational data mining: a review of the state of the art. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40(6), 601–618.
  • Romero, C., & Ventura, S. (2013) Data mining in education. Wiley Interdisciplinary Revolutions: Data Mininig Knowledge Discovery 3(1), 12–27.
  • Romero, C., Ventura, S., Pechenizkiy, M., & Baker, R.S.J.D. (2010). Handbook of educational data mining. New York: CRC Press.
  • Sever, H., & Oğuz, B. (2002). Veri tabanlarında bilgi keşfine formel bir yaklaşım: Kısım I: Eşleştirme sorguları ve algoritmalar. Bilgi Dünyası, 3(2), 173-204.
  • Siemens, G. (2012). Learning analytics: Envisioning a research discipline and a domain of practice. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 4-8).
  • Siemens, G., & Gasevic, D. (2012). Guest editorial-learning and knowledge analytics. Educational Technology & Society, 15(3), 1-2.
  • Smith, V. C., Lange, A., & Huston, D. R. (2012). Predictive modeling to forecast student outcomes and drive effective interventions in online community college courses. Journal of Asynchronous Learning Networks, 16(3), 51-61.
  • Sweller, J. (1999). Instructional design. In Australian educational review.
  • Şahin, M., & Yurdugül, H. (2020). Educational data mining and learning analytics: past, present and future. Bartın University Journal of Faculty of Education, 9(1), 121-131.
  • Şengür, D. (2013). Öğrencilerin akademik başarılarının veri madenciliği metotları ile tahmini. Unpublished Master Thesis, Fırat University, Elazığ, Turkey.
  • Şimşek Gürsoy, U.T. (2009). Veri madenciliği ve bilgi keşfi. Ankara: Pegem Akademi.
  • Şimşek Gürsoy, U.T. (2012). Uygulamalı veri madenciliği: Sektörel analizler. (3rd Edition). Ankara: Pegem Akademi.
  • Tabaa, Y., & Medouri, A. (2013). LASyM: A learning analytics system for MOOCs. International Journal of Advanced Computer Science and Applications (IJACSA), 4(5).
  • Wilson, A., Watson, C., Thompson, T. L., Drew, V., & Doyle, S. (2017). Learning analytics: Challenges and limitations. Teaching in Higher Education, 22(8), 991-1007.

The Role of Learning Analytics in Distance Learning: A SWOT Analysis

Year 2020, Volume: 2 Issue: 1, 18 - 29, 29.06.2020

Abstract

The aim of this study was to analyze the role of learning analytics in education by discussing the phenomenon of learning analytics in detail. Thus, SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis was cınducted in the study. Initially, a literature review was conducted and the role of learning analytics in the distance learning system was detailed with the analysis of available studies in the literature. Gathered studies were analyzed by the contexts of strengths, weaknesses, opputunities and threats of learning analytics on distance learning. The strengths are “flexible and innovative design”, “rising effectiveness”, “induvidualisation of learning or system” and “understanding user expectations”, and weaknesses are “determining parameters” and “lack of experts”. On the behalf of external factors, oppurtunities are “development in artifical intelligence”, “rom globalisation to localisation change trend” and “gathering big data easily”, and threats of learning analytics on distance learning are “ethical issues (security of data, accessing data, private information etc.)” and “information consumption”. Based on the SWOT matrix, it could be suggested that strengths and opportunities of learning analytics were more dominant when compared to its weaknesses and threats in distance learning.

References

  • Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings 20th International Conference on Very Large Data Bases (pp. 487-499).
  • Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31, 542-550.
  • Alonso, J. M., & Casalino, G. (2019). Explainable Artificial Intelligence for Human-Centric Data Analysis in Virtual Learning Environments. In International Workshop on Higher Education Learning Methodologies and Technologies Online (pp. 125-138). Springer, Cham.
  • Bahçeci, F. (2015). Öğrenme yönetim sistemlerinde kullanılan öğrenme analitikleri araçlarının incelenmesi. Turkish Journal of Educational Studies, 2(1). 41-58. Bajracharya, B. (2019). Learning Analytics and Dashboards for Education Systems. CTE Journal, 7(2), 1-9.
  • Baker, R.S.J.D. (2010). Data mining for education. In B. McGaw, P. Peterson, & E. Baker, (Eds.), International encyclopedia of education, (3rd edition) (pp. 112–118). Amsterdam: Elsevier.
  • Bayrak, F., & Yurdugül, H. (2016). Web-tabanlı öz-değerlendirme sisteminde öğrenci uyarı indeksini temel alan öğrenme analitiği modülünün tasarlanması. Eğitim Teknolojisi Kuram ve Uygulama, 6(2), 85-99.
  • Berman, J.J. (2013). Principles of big data: Preparing, sharing, and analyzing complex information. Massachusetts: Elsevier.
  • Bernstein, A., Provost, F., & Hill, S. (2005). Toward intelligent assistance for a data mining process: An ontology-based approach for cost-sensitive classification. Knowledge and Data Engineering, IEEE Transactions on, 17(4), 503-518.
  • Berry, M. J., & Linoff, G. (1997). Data mining techniques: for marketing, sales, and customer support. New York: John Wiley & Sons, Inc..
  • Bousbia, N., & Belamri, I. (2014). Which contribution does EDM provide to computer-based learning environments?. In Educational data mining (pp. 3-28). Springer, Cham.
  • Bozkurt, A. (2016). Öğrenme analitiği: e-öğrenme, büyük veri ve bireyselleştirilmiş öğrenme. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 2(4), 55-81.
  • Calders, T., & Pechenizkiy, M. (2011). Introduction to the special section on educational data mining. ACM SIGKDD Explor. 13(2), 3–6.
  • Chang, H. H., & Huang, W. C. (2006). Application of a quantification SWOT analytical method. Mathematical and computer modelling, 43(1-2), 158-169.
  • Chatti, M.A., Dyckhoff, A.L., Schroeder, U., & Thüs, H. (2012) A reference model for learning analytics. International Journal of Technology Enhanced Learning 4(5–6), 318–331.
  • Clow, D. (2012). The learning analytics cycle: closing the loop effectively. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 134-138).
  • Dean, J. (2014). Big data, data mining, and machine learning: Value creation for business leaders and practitioners. Canada: John Wiley & Sons, Inc..
  • Dyckhoff, A. L., Lukarov, V., Muslim, A., Chatti, M. A., & Schroeder, U. (2013). Supporting action research with learning analytics. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 220-229).
  • Dyson, R. G. (2004). Strategic development and SWOT analysis at the University of Warwick. European journal of operational research, 152(3), 631-640.
  • Elias, T. (2011). Learning analytics: Definitions, processes and potential. Retrived from https://pdfs.semanticscholar.org/732e/452659685fe3950b0e515a28ce89d9c5592a.pdf at May, 07, 2020.
  • Ezen-Can, A., Boyer, K. E., Kellogg, S., & Booth, S. (2015). Unsupervised modeling for understanding MOOC discussion forums: a learning analytics approach. In Proceedings of the fifth international conference on learning analytics and knowledge (pp. 146-150).
  • Ferguson, R., & Shum, S. B. (2012). Social learning analytics: five approaches. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 23-33).
  • Fırat, M. (2015). Eğitim teknolojileri araştırmalarında yeni bir alan: Öğrenme analitikleri. Mersin Üniversitesi Eğitim Fakültesi Dergisi, 11(3). 870-882.
  • Fulantelli, G., Taibi, D., & Arrigo, M. (2013). A semantic approach to mobile learning analytics. In Proceedings of the first international conference on technological ecosystem for enhancing multiculturality (pp. 287-292).
  • Gazulla, E. D., & Leinonen, T. (2016). Why do we want data for learning? Learning analytics and the laws of media. In The Future of Ubiquitous Learning (pp. 59-72). Springer, Berlin, Heidelberg.
  • Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology & Society, 15(3), 42-57.
  • Han, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques. Massachusetts: Elsevier.
  • Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z., Hlosta, M., & Naydenova, G. (2017). Implementing predictive learning analytics on a large scale: the teacher's perspective. In Proceedings of the seventh international learning analytics & knowledge conference (pp. 267-271).
  • Hickey, D. T., Kelley, T. A., & Shen, X. (2014). Small to big before massive: Scaling up participatory learning analytics. In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (pp. 93-97).
  • Hurt, J. (2008). The advantages and disadvantages of teaching and learning on-line. Delta Kappa Gamma Bulletin, 74(4). 5-11.
  • Johnson, L., Smith, R., Willis, H., Levine, A., & Haywood, K. (2011). The 2011 horizon report. The New Media Consortium. Austin, Texas.
  • Kalz, M. (2014). Lifelong learning and its support with new technologies. Retrived from https://core.ac.uk/download/pdf/55538088.pdf at 08 May, 2020.
  • Karabatak, M. (2008). Özellik seçimi, sınıflama ve öngörü uygulamalarına yönelik birliktelik kuralı çıkarımı ve yazılım geliştirilmesi. Unpublished Doctoral Thesis, Fırat University, Elazığ, Turkey.
  • Khalil, M., & Ebner, M. (2017). Clustering patterns of engagement in Massive Open Online Courses (MOOCs): The use of learning analytics to reveal student categories. Journal of Computing in Higher Education, 29(1), 114-132.
  • Kilis, S., & Gülbahar, Y. (2016). Learning analytics in distance education: A systematic literature review. Proceedings of the 9th European Distance and E-Learning Network Research Workshop, Oldenburg. Pp. 310-317.
  • Kumar, A., Dabas, V., & Hooda, P. (2018). Text classification algorithms for mining unstructured data: a SWOT analysis. International Journal of Information Technology, 1-11.
  • Kurttila, M., Pesonen, M., Kangas, J., & Kajanus, M. (2000). Utilizing the analytic hierarchy process (AHP) in SWOT analysis—a hybrid method and its application to a forest-certification case. Forest policy and economics, 1(1), 41-52.
  • LAK’11 (2014). Learning analytics & knowledge: February 27-March 1, 2011 in Banff, Alberta about. Retrived form https://tekri.athabascau.ca/analytics/ at 07 May, 2020.
  • Liu, B. (2006). Web data mining. Berlin: Springer.
  • Macfadyen, L. P., & Dawson, S. (2012). Numbers are not enough. Why e-learning analytics failed to inform an institutional strategic plan. Journal of Educational Technology & Society, 15(3), 149-163.
  • Mattingly, K. D., Rice, M. C., & Berge, Z. L. (2012). Learning analytics as a tool for closing the assessment loop in higher education. Knowledge management & e-learning: An international journal, 4(3), 236-247.
  • Merrill, M. D. (1991). Constructivism and instructional design. Educational Technology, 31(5), 45-53.
  • Olmos, M., & Corrin, L. (2012). Learning analytics: A case study of the process of design of visualizations. Journal of Asynchronous Learning Networks, 16(3), 39-49.
  • Orhan Göksün, D. (2019). E-M-U öğrenme. In A. Arslan (Ed.). Eğitimde güncel konular ve yaklaşımlar. pp. 115-128. Nobel Akademi: Ankara.
  • Papamitsiou, Z. K., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Educational Technology & Society, 17(4), 49-64.
  • Pena-Ayala, A. (2014). Educational data mining: Applications and trends. Mexico City: Springer.
  • Piateski, G., & Frawley, W. (1991). Knowledge discovery in databases. Massachusetts: MIT Press.
  • Pickton, D. W., & Wright, S. (1998). What's swot in strategic analysis?. Strategic change, 7(2), 101-109.
  • Prinsloo, P., & Slade, S. (2013). An evaluation of policy frameworks for addressing ethical considerations in learning analytics. In Proceedings of the third international conference on learning analytics and knowledge (pp. 240-244).
  • Prinsloo, P., & Slade, S. (2015). Student privacy self-management: implications for learning analytics. In Proceedings of the fifth international conference on learning analytics and knowledge (pp. 83-92).
  • Reigeluth, C. M. (2013). Instructional-design theories and models: A new paradigm of instructional theory, Volume II. Routledge.
  • Reiser, R. A. (2001). A history of instructional design and technology: Part II: A history of instructional design. Educational technology research and development, 49(2), 57-67.
  • Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005, Expert Systems with Applications, 33(1), 135–146.
  • Romero, C., & Ventura, S. (2010) Educational data mining: a review of the state of the art. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40(6), 601–618.
  • Romero, C., & Ventura, S. (2013) Data mining in education. Wiley Interdisciplinary Revolutions: Data Mininig Knowledge Discovery 3(1), 12–27.
  • Romero, C., Ventura, S., Pechenizkiy, M., & Baker, R.S.J.D. (2010). Handbook of educational data mining. New York: CRC Press.
  • Sever, H., & Oğuz, B. (2002). Veri tabanlarında bilgi keşfine formel bir yaklaşım: Kısım I: Eşleştirme sorguları ve algoritmalar. Bilgi Dünyası, 3(2), 173-204.
  • Siemens, G. (2012). Learning analytics: Envisioning a research discipline and a domain of practice. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 4-8).
  • Siemens, G., & Gasevic, D. (2012). Guest editorial-learning and knowledge analytics. Educational Technology & Society, 15(3), 1-2.
  • Smith, V. C., Lange, A., & Huston, D. R. (2012). Predictive modeling to forecast student outcomes and drive effective interventions in online community college courses. Journal of Asynchronous Learning Networks, 16(3), 51-61.
  • Sweller, J. (1999). Instructional design. In Australian educational review.
  • Şahin, M., & Yurdugül, H. (2020). Educational data mining and learning analytics: past, present and future. Bartın University Journal of Faculty of Education, 9(1), 121-131.
  • Şengür, D. (2013). Öğrencilerin akademik başarılarının veri madenciliği metotları ile tahmini. Unpublished Master Thesis, Fırat University, Elazığ, Turkey.
  • Şimşek Gürsoy, U.T. (2009). Veri madenciliği ve bilgi keşfi. Ankara: Pegem Akademi.
  • Şimşek Gürsoy, U.T. (2012). Uygulamalı veri madenciliği: Sektörel analizler. (3rd Edition). Ankara: Pegem Akademi.
  • Tabaa, Y., & Medouri, A. (2013). LASyM: A learning analytics system for MOOCs. International Journal of Advanced Computer Science and Applications (IJACSA), 4(5).
  • Wilson, A., Watson, C., Thompson, T. L., Drew, V., & Doyle, S. (2017). Learning analytics: Challenges and limitations. Teaching in Higher Education, 22(8), 991-1007.
There are 66 citations in total.

Details

Primary Language English
Subjects Other Fields of Education
Journal Section Research Articles
Authors

Derya Orhan Göksün 0000-0003-0194-0451

Adile Aşkım Kurt 0000-0003-1084-5579

Publication Date June 29, 2020
Acceptance Date June 9, 2020
Published in Issue Year 2020 Volume: 2 Issue: 1

Cite

APA Orhan Göksün, D., & Kurt, A. A. (2020). The Role of Learning Analytics in Distance Learning: A SWOT Analysis. Journal of Teacher Education and Lifelong Learning, 2(1), 18-29.

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