Datafication in Education: My experience with “Learning Analytics" as a public secondary school teacher in Nigeria

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The field of education is undergoing a significant change toward the integration of data and digital technology (Jarke & Breiter, 2019; Williamson, 2017). Furthermore, data has become increasingly important in influencing and organizing educational practices (Fenwick et al., 2014; Ozga, 2016). The practice of measuring, recording, and displaying many parts of our educational environment in numerical form, known as datafication, has left an indelible effect on the world of learning (Mascheroni, G. 2018). As a teacher, I've been a part of this transforming journey, witnessing firsthand the dramatic influence of datafication, notably via the use of learning analytics. In this blog, I will share a personal reflection that  highlights the effects of datafication on teaching and learning.


During my teaching career in Nigeria, I was introduced to the realm of datafication in education. The educational institution I worked for implemented an innovative system based on the collection and analysis of student data using learning analytics. Each academic term began with a baseline assessment exam in which students were asked pre-set subject-specific questions to judge their beginning knowledge prior to the term's formal start (Cope & Kalantzis, 2015; Lang et al., 2017; Piety, Hickey, & Bishop). The pupils responded to these questions, which were then thoroughly reviewed, and the resulting scores were documented in a sophisticated database.


As the academic term progressed, another key assessment, known as the “endline test”, was conducted. This test consisted of exactly the same set of questions as the baseline test, to evaluate my students' progress. Once again, I followed the same procedure of marking the scripts and recording the scores in the database. 


In addition to these assessments, the organization offered a teacher evaluation form. The questionnaire was designed to assess many aspects of my teaching talents, including classroom management, subject matter competence, teacher-student relationships, diversity and inclusion, emotional intelligence, and communication skills. This technique of acquiring and analyzing data to enhance teaching methods is consistent with the idea that "evaluation should consider not only what teachers do but also the relationship between teachers and students" (Braskamp, 2000; Biesta, 2012).


I believe that an in-person interview with the student may have also supported this, potentially serving as a source for qualitative data collection.  Ben, Bayne, and Shay (2020) highlight that data and metrics play a pivotal role in shaping our understanding of various practices and behaviors within the educational context. This emphasizes how these metrics determine what is visible and what remains hidden, consequently influencing the perceived value of different aspects of education.


My leadership development manager collected all the data, including assessment scores and my teaching and leadership outcomes, and subsequently submitted it to our organization's monitoring and evaluation department. As we neared the end of each term or session, I was provided with a detailed and comprehensive report by my organization through a user-friendly interface that is used to present students' academic achievements alongside related reports. 


Personally, from the academic progress report, I was able to identify that some of my students performed well, while about others performed below the baseline outcome. I could not give an instant judgement of why that could have been, bit I was able to my personal review and understanding to reevaluate myself and the students. Feedback were also given on the general student outcome, and I realised that I had some impressive commendation, but a few comments on student engagement in the classroom. 


Generally, these reports became the cornerstone for invaluable discussions among us teacher-leaders, offering a holistic view of our roles as educational leaders, the influence we had in our classrooms, and the impact we made in our wider community. Navigating the entire process of collecting, analyzing, and interpreting data often proved to be a substantial task, frequently leading to an increased workload and elevated stress levels for me and my fellow educators (Ben, Bayne, & Shay, 2020).


References:

  1. Ben Williamson, Sian Bayne and Suellen Shay (2020), The datafication of teaching in Higher Education: critical issues and perspectives, Teaching in Higher Education, 25:4, 351-365, DOI: 10.1080/13562517.2020.1748811B

  2. Braskamp, L. A. (2000). Toward a more holistic approach to assessing faculty as teachers. New Directions for Teaching and Learning, 83, 19–34. https://doi.org/10.1002/tl.8303.

  3. Cope, B., & Kalantzis, M. (2015). Interpreting Evidence-of-Learning: Educational research in the era of big data. Open Review of Educational Research, 2(1), 218-239.

  4. Fenwick, T. J., Mangez, E., & Ozga, J. (2014). Governing knowledge: Comparison, knowledge-based technologies, education, and expertise in the regulation of education. In T. J. Fenwick, E. Mangez, & J. Ozga (Eds.), World Yearbook of Education 2014. Routledge.

  5. Jarke, J., & Breiter, A. (2016). Datafying education: How digital assessment practices reconfigure the organization of learning (Research Network “Communicative Figurations” No. 11). https://doi.org/10.13140/RG.2.1.2866.5686

  6. Lang, C., Siemens, G., Wise, A., & Gasevic, D. (Eds.) (2017). Handbook of Learning Analytics (1st ed.). Society of Learning Analytics Research.

  7. Mascheroni, G. (2018). Datafied childhoods: Contextualizing datafication in everyday life. Current Sociology, 68(6), 798-813. https://doi.org/10.1177/0011392118807534

  8. Piety, P. J., Hickey, D. T., & Bishop, M. J. (2014). Educational data sciences—framing emergent practices for analytics of learning, organizations, and systems. LAK '14, March 24 - 28, 2014, Indianapolis.

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