“Sitting at the Stern and Holding the Rudder”: Teachers’ Reflections on Action in Higher Education Based on Student Agency Analytics

Heilala, Ville; Jääskelä, Päivikki; Saarela, Mirka; Kuula, Anna-Stina; Eskola, Anne; Kärkkäinen, Tommi

doi:10.1007/978-3-031-00801-6_4

Cited by 3 publications

(1 citation statement)

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“…Importantly, the information gained from using aptitude and event measures complements each other: student self-evaluation can, for example, explain and self-efficacy (Jääskelä et al, 2017), which are also present in selfregulated learning and, as such, may be measured similarly. Consolidating and visualising measures benefits not only students, but also teachers, as they are able to better reflect on their actions and the learning environment (Heilala et al, 2022). Further, the work can be expanded by considering specific learning resources more closely.…”

Section: Discussionmentioning

confidence: 99%

Measuring self‐regulated learning in a junior high school mathematics classroom: Combining aptitude and event measures in digital learning materials

Zhidkikh

Saarela

Kärkkäinen

2023

Computer Assisted Learning

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BackgroundMeasurement of students' self‐regulation skills is an active topic in education research, as effective assessment helps devising support interventions to foster academic achievement. Measures based on event tracing usually require large amounts of data (e.g., MOOCs and large courses), while aptitude measures are often qualitative and need careful interpretation. Precise and interpretable evaluation of self‐regulation skills in a normal K‐12 classroom thus poses a challenge.ObjectivesThe present study proposes and explores a learning analytics method of combining aptitude and event measures to evaluate student's self‐regulation skills.MethodsAn explorative learning analytics study was conducted in a junior high school mathematics class ( students), using a three‐lesson intervention with digital learning materials. Students first assessed their self‐regulation skills with a self‐report questionnaire, after which trace logs and observations of student behaviour were collected. Learning sessions were extracted from trace logs, clustered, and linked to learning strategies. Students were clustered by the self‐report results and learning behaviour profiles. Session clusters, student behaviour clusters and assignment grades were also tested for association.Results and ConclusionsThe detected session and student behaviour types were linked to learning tactics and strategies found in prior studies. Additionally, association was found between self‐reported self‐regulation skills and the student behaviour obtained from trace logs.ImplicationsThe results demonstrate the feasibility of concurrently using aptitude and event measures on a classroom scale, providing teachers with a tool to evaluate and support self‐regulated learning. Combined with further measures like predictive learning analytics, teachers can obtain an early and highly interpretable picture of at‐risk students in their classes.

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Section: Discussionmentioning

confidence: 99%

Measuring self‐regulated learning in a junior high school mathematics classroom: Combining aptitude and event measures in digital learning materials

Zhidkikh

Saarela

Kärkkäinen

2023

Computer Assisted Learning

Self Cite

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Toward Scalable and Transparent Multimodal Analytics to Study Standard Medical Procedures: Linking Hand Movement, Proximity, and Gaze Data

Heilala,

Lehesvuori,

Hämäläinen

et al. 2024

Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing

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Adapting Teaching and Learning in Higher Education Using Explainable Student Agency Analytics

Heilala,

Jääskelä,

Saarela

et al. 2023

Principles and Applications of Adaptive Artificial Intelligence

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This chapter deals with the learning analytics technique called student agency analytics and explores its foundational technologies and their potential implications for adaptive teaching and learning. Student agency is vital to consider as it can empower students to take control of their learning, fostering autonomy, meaningful experiences, and improved educational outcomes. Beginning with an overview of the technique, its underlying educational foundations, and analytical approaches, the chapter demonstrates the synergy between computational psychometrics, learning analytics, and educational sciences. Considering adaptive artificial intelligence in the context of adaptive learning and teaching, the chapter underscores the potential of these approaches in education. The chapter serves as a brief guide for educators, researchers, and stakeholders interested in the convergence of AI and education.

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“Sitting at the Stern and Holding the Rudder”: Teachers’ Reflections on Action in Higher Education Based on Student Agency Analytics

Cited by 3 publications

References 37 publications

Measuring self‐regulated learning in a junior high school mathematics classroom: Combining aptitude and event measures in digital learning materials

Measuring self‐regulated learning in a junior high school mathematics classroom: Combining aptitude and event measures in digital learning materials

Toward Scalable and Transparent Multimodal Analytics to Study Standard Medical Procedures: Linking Hand Movement, Proximity, and Gaze Data

Adapting Teaching and Learning in Higher Education Using Explainable Student Agency Analytics

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