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Dergiler / Journal of Teacher Education and Lifelong Learning / 2020 / Cilt: 2 - Sayı: 1

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

Derya ORHAN GÖKSÜN
Adile Aşkım KURT
Sayfa
18–29
Yayın Tarihi
2020-06-29
DOI
—
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:The aim of this study was to analyze the role of learning analytics in education by discussing the phenomenon of learning analytics in detail.
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Temel Bulgular & Veri:Thus, SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis was cınducted in the study.
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Sonuç & Katkı: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.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Distance learning · Learning analytics · SWOT

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.

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Bu Makaleye Atıf Yap

GÖKSÜN, D. O., KURT, A. A. (2020). The Role of Learning Analytics in Distance Learning: A SWOT Analysis. Journal of Teacher Education and Lifelong Learning, Cilt: 2 - Sayı: 1, 18–29. https://ajindex.com/articles/836705

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Kaynakça (10)

  1. 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.
  2. 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).
  3. Springer, Cham. Bahçeci, F. (2015). Öğrenme yönetim sistemlerinde kullanılan öğrenme analitikleri araçlarının incelenmesi. Turkish Journal of Educational Studies, 2(1). 41-58.
  4. Bajracharya, B. (2019). Learning Analytics and Dashboards for Education Systems. CTE Journal, 7(2), 1-9.
  5. 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.
  6. 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.
  7. 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).
  8. Springer, Cham. Bozkurt, A. (2016). Öğrenme analitiği: e-öğrenme, büyük veri ve bireyselleştirilmiş öğrenme. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 2(4), 55-81.
  9. Calders, T., & Pechenizkiy, M. (2011). Introduction to the special section on educational data mining. ACM SIGKDD Explor. 13(2), 3–6.
  10. Chang, H. H., & Huang, W. C. (2006). Application of a quantification SWOT analytical method. Mathematical and computer modelling, 43(1-2)