Lecturer: Sebastian Strauß, Nikol Rummel
Fields: Educational Psychology, Learning Analytics, Learning Sciences
Content
Educational technology has come a long way since the Teaching Machines from the 1920s. While some commercial educational software can arguably still be classified as Teaching Machines, research and development in the learning sciences and technology-enhanced learning have produced educational technologies that can facilitate different aspects of teaching and learning alike.
The objective of this course is to examine the concept of learning from the standpoint of educational psychology, with a particular focus on the potential of adaptive educational technology to facilitate learning and teaching processes. We will do so by conceptualizing learning and teaching contexts as a relationship between (usually) one teacher and a group of students. Educational technology can now be leveraged as a tool to facilitate learning and teaching. For example, educational technology can offer insight into learning and teaching processes (mirroring), draw inferences, offer diagnoses (alerting), or take automated actions on behalf of the learners or the teacher (guiding).
The course will examine the perspectives of both learners and their teachers. We will explore how educational technology can enhance learners’ cognitive and metacognitive abilities, beyond learning more efficiently. To this end, we first look at learning processes and learning outcomes in the context of school and higher education and discuss how they can be observed by humans (and machines).
From the perspective of the learners, we then explore educational technologies that can provide us with information about the development of our skills and our learning behavior. As an example, we will look at learning analytics dashboards. Further, we look at technologies that provide learning tasks, assess the learning progress, and utilize this information to provide us with individualized support. Examples for such a technology are intelligent tutoring systems.
Taking the perspective of the teacher, we look at educational technologies that provide us with an overview of our learners’ progress, for example teacher dashboards. Such tools may enhance our teacher vision, by providing information that is usually difficult to gather and to aggregate. This information, in turn, can provide valuable insights into the learning of the entire class and individual students which allows us to provide them with the assistance that they need. Going one step further, teacher dashboards may also process the data further and alert us of challenges that our learners face, or even suggest instructional support for the learners.
At the same time, data analytics approaches can also focus on the teachers and their teaching. For example, tools allow teachers to observe and adapt their own teaching, which offers benefits for teacher professional development.
As we look at these different perspectives, we\’ll explore the various levels of automation that educational technology can offer the classroom, the challenges that result from the need to utilize valid measurements of learning and teaching, from biased data sets, and from automation-induced complacency. Further, we will explore the consequences of (partially) offloading cognitive and metacognitive processes to automated systems. On the side of the students, this includes the question which tasks should be on-loaded or off-loaded to foster learning. With respect to teachers, we will discuss the concepts of hybrid intelligence and human-AI co-orchestration in the classroom.
Literature
- Baker, R.S., & Hawn, A. (2022) Algorithmic Bias in Education. International Journal of Artificial Intelligence in Education 32, 1052–1092. https://doi.org/10.1007/s40593-021-00285-9
- Celik, I., Dindar, M., Muukkonen, H. et al. (2022). The Promises and Challenges of Artificial Intelligence for Teachers: a Systematic Review of Research. TechTrends 66, 616–630. https://doi.org/10.1007/s11528-022-00715-y
- Doroudi, S (2023). The Intertwined Histories of Artificial Intelligence and Education. International Journal of Artificial Intelligence in Education 33, 885–928. https://doi.org/10.1007/s40593-022-00313-2
- Eberle, J., Strauß, S., Nachtigall, V., & Rummel, N. (2024). Analyse prozessbezogener Verhaltensdaten mittels Learning Analytics: Aktuelle und zukünftige Bedeutung für die Unterrichtswissenschaft. Unterrichtswissenschaft, 1-13.
- Holmes, W., Persson, J., Chounta, I. A., Wasson, B., & Dimitrova, V. (2022). Artificial intelligence and education: A critical view through the lens of human rights, democracy and the rule of law. Council of Europe.
- Holstein, K., Aleven, V., Rummel, N. (2020). A Conceptual Framework for Human–AI Hybrid Adaptivity in Education. In: Bittencourt, I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds) Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12163. Springer, Cham. https://doi.org/10.1007/978-3-030-52237-7_20
- Molenaar, I. (2022). Towards hybrid human‐AI learning technologies. European Journal of Education, 57(4), 632-645.
- Selwyn, N. (2022). The future of AI and education: Some cautionary notes. European Journal of Education, 57(4), 620-631.
- van Leeuwen, A., Strauß, S., & Rummel, N. (2023) Participatory design of teacher dashboards: navigating the tension between teacher input and theories on teacher professional vision. Frontiers in Artificial Intelligence. 6:1039739. doi: 10.3389/frai.2023.1039739
- van Leeuwen, A., Rummel, N. (2019) Orchestration tools to support the teacher during student collaboration: a review. Unterrichtswissenschaft 47, 143–158. https://doi.org/10.1007/
- Wise, A. F., & Shaffer, D. W. (2015). Why Theory Matters More than Ever in the Age of Big Data. Journal of Learning Analytics, 2(2), 5-13. https://doi.org/10.18608/jla.2015.22.2
Lecturer
Sebastian Strauß is a postdoctoral researcher at the Educational Psychology and Technology research group at the Ruhr-University Bochum (Germany). His core research focuses on collaborative learning in computer-supported settings. He is interested in how students learn and work together in small groups, how they adapt their interaction, how they acquire collaboration skills, and how we can use computer technology to facilitate collaboration. In this context, he is also interested in human-computer-collaboration. Recently, his research focus expanded to using fine-grained data about the learning process to assess and support individual learning.
Affiliation: Ruhr-University Bochum
Homepage: https://www.pe.ruhr-uni-bochum.de/erziehungswissenschaft/pp/team/strauss.html.de
Nikol Rummel is CAIS Research Professor for pedagogical psychology and educational technologies at Ruhr-University Bochum.
Affiliation: Ruhr-University Bochum
Homepage: https://www.pe.ruhr-uni-bochum.de/erziehungswissenschaft/pp/team/rummel.html.de