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Doctoral defence of Ismaila Temitayo Sanusi, MEd, 19.12.2023: Machine Learning Education in the K–12 Context

The doctoral dissertation in the field of Computer Science will be examined at the Faculty of Science, Forestry and Technology, Joensuu Campus and online.

What is the topic of your doctoral research? Why is it important to study the topic?

The younger generation is growing up interacting with machine learning (ML) algorithms which influences their everyday experiences. This has increased the need for everyone to acquire a basic understanding of these new and emerging technologies. Recently, introducing ML education at an early age is regarded as a strategic action for the digital transformation of societies and economies by international organisation’s such as the European Commission. Thus, a need exists for the design and implementation of ML curriculum activities and innovative approaches for promoting learning across learning settings and also considering the cross-cultural experiences of learners. As initiatives regarding learning ML within the compulsory level of education continue to increase, significant efforts are required on what and how to effectively teach and learn ML. This research attempts to democratize the benefits of ML systems for all children across contexts and regions. The result offers insights into how to broaden participation and address equity issues in ML education.

What are the key findings or observations of your doctoral research?

The findings highlighted the importance of participatory learning approaches, including design-oriented pedagogy, for engaging students in learning ML. They also suggested that incorporating ethics into ML curriculum activities assists in amplifying students’ interest since they are prepared to understand the social consequences of the technology. An analysis of the curriculum evaluation with the students indicated that practical materials that are relatable and customizable to learners’ interests can foster learning of ML. This doctoral research further provided insights into how ML can be promoted to young learners across learning contexts and settings using computer-based applications and tangible pedagogical objects.

How can the results of your doctoral research be utilised in practice?

In my doctoral research, I developed curriculum activities and identified different pedagogical approaches to support young learners to learn ML in different learning settings. With this, in practice, my study provides educators and practitioners pedagogical strategies and curriculum materials to effectively engage diverse young learners with learning ML. In addition to supporting learners with engaging activities to understand the basic concepts of ML, including its social consequences, my research proposed design principles for implementing ML education across learning settings.

What are the key research methods and materials used in your doctoral research?

This study employed Educational Design Research (EDR) as the main design approach. The EDR is supported by case studies to understand how and what to teach young learners considering various learning contexts and settings. The EDR constituted the process, while the case studies represented the steps that formed the dissertation. This research employed several qualitative research methods, including participatory collaborative design. For the data collection, interviews, open-ended questionnaires, note-taking, and observation techniques were employed. These methods were deployed during the various stages of the EDR process and case-study implementation.

The doctoral dissertation of Ismaila Sanusi, M.Ed., entitled Machine Learning Education in the K-12 Context will be examined at the Faculty of Science, Forestry and Technology, Joensuu Campus. The opponent will be Director, Principal Research Associate Sue Sentance, University of Cambridge, UK, and the custos will be Staff Scientist Jarkko Suhonen, University of Eastern Finland. Language of the public defence is English.

For more information, please contact: 

Ismaila Temitayo Sanusi,, tel. +358 44 952 6250, X (Twitter): @ishmaelsanusi1