A Bibliometric Analysis of Datafication in Education: Trends, Impact and Future Implications
Keywords:
AI in Education, Datafication, Data Literacy, Educational Data, Ethical Consideration
Abstract
The massive use of digital learning platforms and AI in education has generated large quantities of data
on teacher and student information, online interactions, and teaching and learning practices. This data can be collected, analyzed, and interpreted to improve educational outcomes. Following the interest and attention in this topic, numerous research studies have been conducted to gain a better understanding in this area. Thus, the purpose of this study is to summarize the literature on datafication in education from 2020 to 2024 and to explore the key terms related to its influence on educational practices, and its potential future implications. The method used in this study is a bibliometric analysis. A total of 200 articles were found in Google Scholar through a search using the keywords "datafication in education."The study found that research on datafication in education has grown significantly in recent years. Initially focused on technological aspects, the research has shifted towards practical applications and critical perspectives. Key themes identified include data literacy, AI, and the ethical implications of data use in education. As an implication of this study, this overview aims to assist future research by enriching and expanding the scope of research and encouraging progress in related disciplines. This will contribute to a fuller understanding of the potential benefits and risks of datafication in transforming education.
References
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Camacho, V. L., Guia, E. D. L., Olivares, T., Julia Flores, M., and Orozco-Barbosa, L. (2020). Data capture and
multimodal learning analytics focused on engagement with a new wearable IoT approach. IEEE Transactions on
Learning Technologies, 13(4). https://doi.org/10.1109/TLT.2020.2999787.
Crompton, H., Jones, M. V., and Burke, D. (2024). Affordances and challenges of artificial intelligence in
K-12 education: A systematic review. Journal of Research on Technology in Education, 56(3). https:
//doi.org/10.1080/15391523.2022.2121344
Dewantara, K. T. W. (2024). Mobile learning dalam endidikan: Analisis bibliometrik. UPGRADE: Jurnal
Pendidikan Teknologi Informasi, 2(1):23–32. https://doi.org/10.30812/upgrade.v2i1.4153.
Erstad, O., Guðmundsdóttir, G. B., Viberg, O., Williamson, B., Gilje, Ø., Gudmundsdottir, G. B., Rebekka, B.,
Wagstaffe, K., Kumpulainen, O., Viberg, B., Williamson, J. O., and Howard, S. (2023). Datafication in and of
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Eynon, R. (2022). Datafication and the role of schooling: Challenging the status quo. In Learning to Live with
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Finandhita, A., Mega, R. U., Jumansyah, R., Rafdhi, A. A., and Oktafiani, D. (2022). VOSviewer application
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Indriyanti, F., Fauziah, T. N., and Nuryadin, A. (2023). Analisis bibliometrik penggunaan video pembelajaran
di sekolah dasar tahun 2013-2022 menggunakan aplikasi vosviewer. Jurnal Educatio, 9(1):23–31. https:
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Education: Practices, Challenges, and Debates. https://doi.org/10.4324/9780429329067-10.
Kordzadeh, N. and Ghasemaghaei, M. (2022). Algorithmic bias: Review, synthesis, and future research directions.
https://doi.org/10.1080/0960085X.2021.1927212.
Lesmana, R. and Rifaldi, M. I. (2023). A computational bibliometric analysis of e-groceries analysis using
VOSviewer. International Journal of Informatics, Information System and Computer Engineering (INJIISCOM),
4(1). https://doi.org/10.34010/injiiscom.v4i1.9586.
Mills, M., Mockler, N., Stacey, M., and Taylor, B. (2021). Teachers’ orientations to educational research
and data in England and Australia: implications for teacher professionalism. Teaching Education, 32(1).
https://doi.org/10.1080/10476210.2020.1843617.
Muhammad, U. A., Fuad, M., Ariyani, F., and Suyanto, E. (2022). Bibliometric analysis of local wisdom-based
learning: Direction for future history education research. International Journal of Evaluation and Research in
Education, 11(4). https://doi.org/10.11591/ijere.v11i4.23547.
Nottingham, E., Stockman, C., and Burke, M. (2022). Education in a datafied world: Balancing children’s
rights and school’s responsibilities in the age of Covid 19. Computer Law and Security Review, 45. https:
//doi.org/10.1016/j.clsr.2022.105664.
Oyewola, D. O. and Dada, E. G. (2022). Exploring machine learning: A scientometrics approach using bibliometrix
and VOSviewer. SN Applied Sciences, 4(5). https://doi.org/10.1007/s42452-022-05027-7.
Pangrazio, L., Selwyn, N., and Cumbo, B. (2023). A patchwork of platforms: Mapping data infrastructures in
schools. Learning, Media and Technology, 48(1). https://doi.org/10.1080/17439884.2022.2035395.
Perrotta, C. and Selwyn, N. (2020). Deep learning goes to school: Toward a relational understanding of AI in
education. Learning, Media and Technology, 45(3). https://doi.org/10.1080/17439884.2020.1686017.
Ria, R. R. P. and Susilowati, D. (2023). Local wisdom-based computational thinking diagnostic Test: A bibliometric
analysis mapping atate-of-the-art and research gaps. Nusantara: Jurnal Pendidikan Indonesia, 3(3):371–394.
https://doi.org/10.14421/njpi.2023.v3i3-2.
Robinson, S. C. (2020). Trust, transparency, and openness: How inclusion of cultural values shapes Nordic national
public policy strategies for artificial intelligence (AI). Technology in Society, 63. https://doi.org/10.1016/
j.techsoc.2020.101421.
Roslan, M. B. and Chen, C. (2022). Educational data mining for student performance prediction: A systematic
literature review (2015-2021). International Journal of Emerging Technologies in Learning (iJET), 17(5):147–
179.
Sefton-Green, J. and Pangrazio, L. (2022). The death of the educative subject? The limits of criticality under
datafication. Educational Philosophy and Theory, 54(12). https://doi.org/10.1080/00131857.2021.
1978072.
Selwyn, N., Hillman, T., Bergviken-Rensfeldt, A., and Perrotta, C. (2023). Making sense of the digital automation
of education. https://doi.org/10.1007/s42438-022-00362-9.
Soegoto, E. S., Luckyardi, S., Rafdhi, A. A., and Oktafiani, D. (2022). Computational analysis of waste management
and entrepreneur using VosViewer application. Moroccan Journal of Chemistry, 10(3):542–552. https:
//doi.org/10.48317/IMIST.PRSM/morjchem-v10i3.33142.
Stoilova, M., Nandagiri, R., and Livingstone, S. (2021). Children’s understanding of personal data and privacy
online–a systematic evidence mapping. Information Communication and Society, 24(4). https://doi.org/
10.1080/1369118X.2019.1657164.
Susilowati, D., Saputri, D. S. C., Ria, R. R. P., and Hidjah, K. (2024). Bibliometric analysis of artificial intelligent
studies in education and pedagogy. Nusantara: Jurnal Pendidikan Indonesia, 4(3):621–634. https://doi.
org/10.14421/njpi.2024.v4i3-1.
Vezzoli, Y., Mavrikis, M., and Vasalou, A. (2020). Inspiration cards workshops with primary teachers in the
early co-design stages of learning analytics. In ACM International Conference Proceeding Series. https:
//doi.org/10.1145/3375462.3375537.
Williamson, B. (2023). Governing through infrastructural control: Artificial intelligence and cloud computing
in the data-intensive state. In The SAGE Handbook of Digital Society. https://doi.org/10.4135/9781529783193.n29.
Williamson, B., Bayne, S., and Shay, S. (2020). The datafication of teaching in Higher Education: critical issues
and perspectives. https://doi.org/10.1080/13562517.2020.1748811.
Williamson, B., Macgilchrist, F., and Potter, J. (2023). Re-examining AI, automation and datafication in education.
https://doi.org/10.1080/17439884.2023.2167830.
Witzenberger, K. and Gulson, K. N. (2021). Why edtech is always right: students, data and machines in preemptive
configurations. Learning, Media and Technology, 46(4). https://doi.org/10.1080/17439884.2021.1913181.
Yunita, A., Santoso, H. B., and Hasibuan, Z. A. (2021). Research review on big data usage for learning analytics
and educational data mining: A way forward to develop an intelligent automation system. In Journal of Physics:
Conference Series, volume 1898. IOP Publishing Ltd. https://doi.org/10.1088/1742-6596/1898/1/
012044.
computer-based assessment data with classroom instruction? Technology, Pedagogy and Education, 29(5).
https://doi.org/10.1080/1475939X.2020.1825992.
Al Husaeni, D. F. and Nandiyanto, A. B. D. (2022). Bibliometric using Vosviewer with Publish or Perish (using
google scholar data): From step-by-step processing for users to the practical examples in the analysis of digital
learning articles in pre and post Covid-19 pandemic. ASEAN Journal of Science and Engineering, 2(1):19–46.
Baker, R. S. and Hawn, A. (2022). Algorithmic bias in education. International Journal of Artificial Intelligence in
Education, 32(4):1–41. https://doi.org/10.1007/s40593-021-00285-9.
Camacho, V. L., Guia, E. D. L., Olivares, T., Julia Flores, M., and Orozco-Barbosa, L. (2020). Data capture and
multimodal learning analytics focused on engagement with a new wearable IoT approach. IEEE Transactions on
Learning Technologies, 13(4). https://doi.org/10.1109/TLT.2020.2999787.
Crompton, H., Jones, M. V., and Burke, D. (2024). Affordances and challenges of artificial intelligence in
K-12 education: A systematic review. Journal of Research on Technology in Education, 56(3). https:
//doi.org/10.1080/15391523.2022.2121344
Dewantara, K. T. W. (2024). Mobile learning dalam endidikan: Analisis bibliometrik. UPGRADE: Jurnal
Pendidikan Teknologi Informasi, 2(1):23–32. https://doi.org/10.30812/upgrade.v2i1.4153.
Erstad, O., Guðmundsdóttir, G. B., Viberg, O., Williamson, B., Gilje, Ø., Gudmundsdottir, G. B., Rebekka, B.,
Wagstaffe, K., Kumpulainen, O., Viberg, B., Williamson, J. O., and Howard, S. (2023). Datafication in and of
education. Agile Edu. https://doi.org/10.13140/RG.2.2.35661.00483.
Eynon, R. (2022). Datafication and the role of schooling: Challenging the status quo. In Learning to Live with
Datafication: Educational Case Studies and Initiatives from Across the World. https://doi.org/10.4324/
9781003136842-2.
Finandhita, A., Mega, R. U., Jumansyah, R., Rafdhi, A. A., and Oktafiani, D. (2022). VOSviewer application
analysis: Computational physical chemistry case study. Moroccan Journal of Chemistry, 10(1):091–101.
https://doi.org/10.48317/IMIST.PRSM/morjchem-v10i1.31756.
Ginting, S. L. B. (2023). A computational bibliometric analysis of esport management using VOSviewer. International
Journal of Informatics, Information System and Computer Engineering (INJIISCOM), 4(1):31–48.
https://doi.org/10.34010/injiiscom.v4i1.9570.
Hansen, M. and Komljenovic, J. (2023). Automating learning situations in edtech: Techno-commercial logic of
assetisation. Postdigital Science and Education, 5(1). https://doi.org/10.1007/s42438-022-00359-4.
Helsper, E. (2022). The digital disconnect: The social causes and consequences of digital inequalities. https:
//doi.org/10.4135/9781526492982.
Hillman, V. (2023). Bringing in the technological, ethical, educational and social-structural for a new education
data governance. Learning, Media and Technology, 48(1). https://doi.org/10.1080/17439884.2022.
2052313.
Hooper, L., Livingstone, S., and Pothong, K. (2022). Problem with data gorvenance in UK school: The cases of
Google Clasroom and Classdojo d. Technical report, London.
Howard, S. K., Swist, T., Gasevic, D., Bartimote, K., Knight, S., Gulson, K., Apps, T., Peloche, J., Hutchinson, N.,
and Selwyn, N. (2022). Educational data journeys: Where are we going, what are we taking and making for AI?
Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100073.
Huang, Y. J., Cheng, S., Yang, F. Q., and Chen, C. (2022). Analysis and visualization of research on resilient cities
and communities based on VOSviewer. https://doi.org/10.3390/ijerph19127068.
Indriyanti, F., Fauziah, T. N., and Nuryadin, A. (2023). Analisis bibliometrik penggunaan video pembelajaran
di sekolah dasar tahun 2013-2022 menggunakan aplikasi vosviewer. Jurnal Educatio, 9(1):23–31. https:
//doi.org/10.31949/educatio.v9i1.3906.
Kizilcec, R. F. and Lee, H. (2022). Algorithmic fairness in education. In The Ethics of Artificial Intelligence in
Education: Practices, Challenges, and Debates. https://doi.org/10.4324/9780429329067-10.
Kordzadeh, N. and Ghasemaghaei, M. (2022). Algorithmic bias: Review, synthesis, and future research directions.
https://doi.org/10.1080/0960085X.2021.1927212.
Lesmana, R. and Rifaldi, M. I. (2023). A computational bibliometric analysis of e-groceries analysis using
VOSviewer. International Journal of Informatics, Information System and Computer Engineering (INJIISCOM),
4(1). https://doi.org/10.34010/injiiscom.v4i1.9586.
Mills, M., Mockler, N., Stacey, M., and Taylor, B. (2021). Teachers’ orientations to educational research
and data in England and Australia: implications for teacher professionalism. Teaching Education, 32(1).
https://doi.org/10.1080/10476210.2020.1843617.
Muhammad, U. A., Fuad, M., Ariyani, F., and Suyanto, E. (2022). Bibliometric analysis of local wisdom-based
learning: Direction for future history education research. International Journal of Evaluation and Research in
Education, 11(4). https://doi.org/10.11591/ijere.v11i4.23547.
Nottingham, E., Stockman, C., and Burke, M. (2022). Education in a datafied world: Balancing children’s
rights and school’s responsibilities in the age of Covid 19. Computer Law and Security Review, 45. https:
//doi.org/10.1016/j.clsr.2022.105664.
Oyewola, D. O. and Dada, E. G. (2022). Exploring machine learning: A scientometrics approach using bibliometrix
and VOSviewer. SN Applied Sciences, 4(5). https://doi.org/10.1007/s42452-022-05027-7.
Pangrazio, L., Selwyn, N., and Cumbo, B. (2023). A patchwork of platforms: Mapping data infrastructures in
schools. Learning, Media and Technology, 48(1). https://doi.org/10.1080/17439884.2022.2035395.
Perrotta, C. and Selwyn, N. (2020). Deep learning goes to school: Toward a relational understanding of AI in
education. Learning, Media and Technology, 45(3). https://doi.org/10.1080/17439884.2020.1686017.
Ria, R. R. P. and Susilowati, D. (2023). Local wisdom-based computational thinking diagnostic Test: A bibliometric
analysis mapping atate-of-the-art and research gaps. Nusantara: Jurnal Pendidikan Indonesia, 3(3):371–394.
https://doi.org/10.14421/njpi.2023.v3i3-2.
Robinson, S. C. (2020). Trust, transparency, and openness: How inclusion of cultural values shapes Nordic national
public policy strategies for artificial intelligence (AI). Technology in Society, 63. https://doi.org/10.1016/
j.techsoc.2020.101421.
Roslan, M. B. and Chen, C. (2022). Educational data mining for student performance prediction: A systematic
literature review (2015-2021). International Journal of Emerging Technologies in Learning (iJET), 17(5):147–
179.
Sefton-Green, J. and Pangrazio, L. (2022). The death of the educative subject? The limits of criticality under
datafication. Educational Philosophy and Theory, 54(12). https://doi.org/10.1080/00131857.2021.
1978072.
Selwyn, N., Hillman, T., Bergviken-Rensfeldt, A., and Perrotta, C. (2023). Making sense of the digital automation
of education. https://doi.org/10.1007/s42438-022-00362-9.
Soegoto, E. S., Luckyardi, S., Rafdhi, A. A., and Oktafiani, D. (2022). Computational analysis of waste management
and entrepreneur using VosViewer application. Moroccan Journal of Chemistry, 10(3):542–552. https:
//doi.org/10.48317/IMIST.PRSM/morjchem-v10i3.33142.
Stoilova, M., Nandagiri, R., and Livingstone, S. (2021). Children’s understanding of personal data and privacy
online–a systematic evidence mapping. Information Communication and Society, 24(4). https://doi.org/
10.1080/1369118X.2019.1657164.
Susilowati, D., Saputri, D. S. C., Ria, R. R. P., and Hidjah, K. (2024). Bibliometric analysis of artificial intelligent
studies in education and pedagogy. Nusantara: Jurnal Pendidikan Indonesia, 4(3):621–634. https://doi.
org/10.14421/njpi.2024.v4i3-1.
Vezzoli, Y., Mavrikis, M., and Vasalou, A. (2020). Inspiration cards workshops with primary teachers in the
early co-design stages of learning analytics. In ACM International Conference Proceeding Series. https:
//doi.org/10.1145/3375462.3375537.
Williamson, B. (2023). Governing through infrastructural control: Artificial intelligence and cloud computing
in the data-intensive state. In The SAGE Handbook of Digital Society. https://doi.org/10.4135/9781529783193.n29.
Williamson, B., Bayne, S., and Shay, S. (2020). The datafication of teaching in Higher Education: critical issues
and perspectives. https://doi.org/10.1080/13562517.2020.1748811.
Williamson, B., Macgilchrist, F., and Potter, J. (2023). Re-examining AI, automation and datafication in education.
https://doi.org/10.1080/17439884.2023.2167830.
Witzenberger, K. and Gulson, K. N. (2021). Why edtech is always right: students, data and machines in preemptive
configurations. Learning, Media and Technology, 46(4). https://doi.org/10.1080/17439884.2021.1913181.
Yunita, A., Santoso, H. B., and Hasibuan, Z. A. (2021). Research review on big data usage for learning analytics
and educational data mining: A way forward to develop an intelligent automation system. In Journal of Physics:
Conference Series, volume 1898. IOP Publishing Ltd. https://doi.org/10.1088/1742-6596/1898/1/
012044.
Published
2025-01-25
Section
New Submission
