Applying Data Science to Analyze and Improve Student Learning Outcomes in Educational Environments

Authors

  • Nizirwan Anwar Esa Unggul University
  • Juanda Institute of Economic Science Putra Perdana Indonesia
  • James Anderson Pandawan Incorporation
  • Tane Williams Pandawan Incorporation

DOI:

https://doi.org/10.33050/itee.v3i1.679

Keywords:

Educational Data Science, Learning Outcomes, Predictive Analytics, Personalized Learning, Longitudinal Study

Abstract

This study explores the application of data science to analyze and improve student learning outcomes within educational environments, responding to the increasing demand for data-driven approaches in education. The objective is to identify key performance indicators that influence learning success and to develop predictive models that support personalized academic interventions. The research applies a mixed-method approach, combining quantitative data analysis from student records and qualitative insights gathered from educational stakeholders. Machine learning algorithms and statistical models are employed to identify patterns and relationships within large datasets, helping to pinpoint factors such as attendance, engagement levels, and assessment performance that most strongly correlate with learning outcomes. Results indicate that predictive models can effectively forecast student performance, allowing educators to proactively support at risk students and tailor learning experiences to individual needs. Furthermore, the findings demonstrate that integrating data science tools into educational decision-making can improve not only academic outcomes but also institutional strategies for student success. This study concludes that data science offers substantial potential for enhancing learning environments, enabling a more responsive and personalized education system that supports each student’s unique journey towards academic achievement

References

M. Annas and S. N. Wahab, “Data mining methods: K-means clustering algorithms,” International Journal of Cyber and IT Service Management, vol. 3, no. 1, pp. 40–47, 2023.

K. Arora, M. Faisal et al., “The use of data science in digital marketing techniques: Work programs, performance sequences and methods.” Startupreneur Business Digital (SABDA Journal), vol. 1, no. 2, pp. 143–155, 2022.

S. Purnama, M. Kamal, and A. B. Yadila, “Application of restful method with jwt security and haversine algorithm on web service-based teacher attendance system,” International Transactions on Artificial Intelligence, vol. 2, no. 1, pp. 33–39, 2023.

S. Watini and W. Setyowati, “Using gamification to increase e-learning engagement,” International Transactions on Education Technology, vol. 1, no. 2, pp. 84–94, 2023.

N. Lutfiani, D. Apriani, E. A. Nabila, and H. L. Juniar, “Academic certificate fraud detection system framework using blockchain technology,” Blockchain Frontier Technology, vol. 1, no. 2, pp. 55–64, 2022.

A. Singh and A. S. Bist, “Ai and healthcare: Praiseworthy aspects and shortcomings,” in Data Driven Decision Making using Analytics. CRC Press, 2021, pp. 124–135.

J. A. Ruipe´rez-Valiente, S. Martin, J. Reich, and M. Castro, “The unmoocing process: Extending the impact of mooc educational resources as oers,” Sustainability, vol. 12, no. 18, p. 7346, 2020.

C. Lukita, G. A. Pangilinan, M. H. R. Chakim, D. B. Saputra et al., “Examining the impact of artificial intelligence and internet of things on smart tourism destinations: A comprehensive study,” Aptisi Transactions on Technopreneurship (ATT), vol. 5, no. 2sp, pp. 135–145, 2023.

N. K. A. Dwijendra, U. Rahardja, N. B. Kumar, I. Patra, M. M. A. Zahra, Y. Finogenova, J. W. G. Guerrero, S. E. Izzat, and T. Alawsi, “An analysis of urban block initiatives influencing energy consumption and solar energy absorption,” Sustainability, vol. 14, no. 21, p. 14273, 2022.

A. Ruangkanjanases, A. Khan, O. Sivarak, U. Rahardja, S.-W. Chien, and S.-C. Chen, “The magic of brand experience: A value co-creation perspective of brand equity on short-form video platforms,” Emerg. Sci. J, vol. 7, no. 5, pp. 1588–1601, 2023.

J. van der Merwe, S. M. Wahid, G. P. Cesna, D. A. Prabowo et al., “Improving natural resource management through ai: Quantitative analysis using smartpls,” International Transactions on Artificial Intelligence, vol. 2, no. 2, pp. 135–142, 2024.

H. Nusantoro, P. A. Sunarya, N. P. L. Santoso, and S. Maulana, “Generation smart education learning process of blockchain-based in universities,” Blockchain Frontier Technology, vol. 1, no. 01, pp. 21–34, 2021.

Q. Aini, W. Febriani, C. Lukita, S. Kosasi, and U. Rahardja, “New normal regulation with face recognition technology using attendx for student attendance algorithm,” in 2022 International Conference on Science and Technology (ICOSTECH). IEEE, 2022, pp. 1–7.

S. Sudaryono, Q. Aini, N. Lutfiani, F. Hanafi, and U. Rahardja, “Application of blockchain technology for ilearning student assessment,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 14, no. 2, pp. 209–218, 2020.

C. Guan, J. Mou, and Z. Jiang, “Artificial intelligence innovation in education: A twenty-year data-driven historical analysis,” International Journal of Innovation Studies, vol. 4, no. 4, pp. 134–147, 2020.

J. Williams, A. G. Prawiyogi, M. Rodriguez, and I. Kovac, “Enhancing circular economy with digital technologies: A pls-sem approach,” International Transactions on Education Technology (ITEE), vol. 2, no. 2, pp. 140–151, 2024.

A. S. Anwar, U. Rahardja, A. G. Prawiyogi, N. P. L. Santoso, and S. Maulana, “ilearning model approach in creating blockchain based higher education trust,” Int. J. Artif. Intell. Res, vol. 6, no. 1, 2022.

S. Mehta and L. Magdalena, “Education 4.0: Online learning management using education smart courses,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 4, no. 1, pp. 70–76, 2022.

S. Wijaya, A. Husain, M. Laurens, and A. Birgithri, “ilearning education challenge: Combining the power of blockchain with gamification concepts,” CORISINTA, vol. 1, no. 1, pp. 8–15, 2024.

M. A. Mohamed Hashim, I. Tlemsani, and R. Matthews, “Higher education strategy in digital transformation,” Education and Information Technologies, vol. 27, no. 3, pp. 3171–3195, 2022.

J. Fan, “A big data and neural networks driven approach to design students management system,” Soft Computing, vol. 28, no. 2, pp. 1255–1276, 2024.

P. Bhardwaj, P. Gupta, H. Panwar, M. K. Siddiqui, R. Morales-Menendez, and A. Bhaik, “Application of deep learning on student engagement in e-learning environments,” Computers & Electrical Engineering, vol. 93, p. 107277, 2021.

C. Troussas, A. Krouska, C. Sgouropoulou, and I. Voyiatzis, “Ensemble learning using fuzzy weights to improve learning style identification for adapted instructional routines,” Entropy, vol. 22, no. 7, p. 735, 2020.

L. K. Choi, K. B. Rii, and H. W. Park, “K-means and j48 algorithms to categorize student research abstracts,” International Journal of Cyber and IT Service Management, vol. 3, no. 1, pp. 61–64, 2023.

S. Liu, S. Liu, Z. Liu, X. Peng, and Z. Yang, “Automated detection of emotional and cognitive engagement in mooc discussions to predict learning achievement,” Computers & Education, vol. 181, p. 104461, 2022.

T. Shaik, X. Tao, Y. Li, C. Dann, J. McDonald, P. Redmond, and L. Galligan, “A review of the trends and challenges in adopting natural language processing methods for education feedback analysis,” Ieee Access, vol. 10, pp. 56 720–56 739, 2022.

Y. Rohali, Y. Z. Basri, R. Ismail, and R. A. D. Septian, “Factors affecting the decision-making of indonesian sharia banking companies,” ADI Journal on Recent Innovation, vol. 4, no. 1, pp. 13–25, 2022.

V. B. Munagandla, S. S. V. Dandyala, and B. C. Vadde, “Improving educational outcomes through data driven decision-making,” International Journal of Advanced Engineering Technologies and Innovations, vol. 1, no. 3, pp. 698–718, 2024.

A. Pambudi, N. Lutfiani, M. Hardini, A. R. A. Zahra, and U. Rahardja, “The digital revolution of startup matchmaking: Ai and computer science synergies,” in 2023 Eighth International Conference on Informatics and Computing (ICIC). IEEE, 2023, pp. 1–6.

M. Adnan, A. Habib, J. Ashraf, S. Mussadiq, A. A. Raza, M. Abid, M. Bashir, and S. U. Khan, “Predicting at-risk students at different percentages of course length for early intervention using machine learning models,” Ieee Access, vol. 9, pp. 7519–7539, 2021.

D. N. Dwivedi, G. Mahanty, and V. nath Dwivedi, “The role of predictive analytics in personalizing education: Tailoring learning paths for individual student success,” in Enhancing Education With Intelligent Systems and Data-Driven Instruction. IGI Global, 2024, pp. 44–59.

F. G. Karaoglan Yilmaz and R. Yilmaz, “Student opinions about personalized recommendation and feedback based on learning analytics,” Technology, knowledge and learning, vol. 25, pp. 753–768, 2020.

K. Diantoro, D. Supriyanti, Y. P. A. Sanjaya, S. Watini et al., “Implications of distributed energy development in blockchain-based institutional environment,” Aptisi Transactions on Technopreneurship (ATT), vol. 5, no. 2sp, pp. 209–220, 2023.

A. Goslen, M. Taub, D. Carpenter, R. Azevedo, J. Rowe, and J. Lester, “Leveraging student planning in game-based learning environments for self-regulated learning analytics.” Journal of Educational Psychology, 2024.

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Published

2024-11-26
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How to Cite

Anwar, N., Juanda, Anderson, J., & Williams, T. (2024). Applying Data Science to Analyze and Improve Student Learning Outcomes in Educational Environments. International Transactions on Education Technology (ITEE), 3(1), 72–83. https://doi.org/10.33050/itee.v3i1.679