XXVII.5 September - October 2020
Page: 6
Digital Citation

Learning and education in HCI: A reflection on the SIG at CHI 2019

Viktoria Pammer-Schindler, Erik Harpstead, Benjamin Xie, Betsy DiSalvo, Ahmed Kharrufa, Petr Slovak, Amy Ogan, Joseph Williams, Michael Lee

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The field of human-computer interaction (HCI) has always been interested in aspects of learning. Recently, that interest has risen to new heights, leading to the creation of the Learning, Education, and Families subcommittee. The inaugural year of the subcommittee at CHI 2019 attracted 191 paper submissions, while a similarly large 179 papers were submitted for CHI 2020.

In addition, at CHI 2019, a Special Interest Group (SIG) on Learning, Education, and HCI took place. The goal was to bring together researchers across the CHI community and discuss the position of learning within the field [1]. The workshop was set up to gather a broad set of perspectives from across the field and better understand what community members mean when they talk about learning. Multiple roundtable discussions were organized, such that at any time there were at least eight different discussions going on in the room. Each roundtable explored one of the following three guiding questions: What does learning mean to us? What are valid ways of evaluating a learning contribution? How can we foster the relationship between learning and HCI?


Our analysis of the discussions led to three distinct themes. First, it highlighted that HCI researchers are interested in learning and education design for a vastly heterogeneous set of learning and educational settings. It is therefore desirable that in order to enable communication and cross-fertilization, the community needs to be open to a range of learning and instructional theoretical backgrounds. At the same time, we need to ensure that discussions of learning remain accessible to the breadth of HCI researchers. The face-to-face discussion indicated that in order to meet both goals—inclusivity and accessibility—every learning-focused contribution in HCI would need to present a solid explanation of its learning and instructional sciences background. While this may feel redundant, grounding the work in learning theory helps communicate the assumptions underlying the work to other HCI researchers interested in learning and education. This also addresses one issue of interdisciplinarity, namely to integrate knowledge from different fields.

The community needs to be open to a range of learning and instructional theoretical backgrounds.

Second, a materialization of this interdisciplinarity is the challenge of integrating different methodological foci. In learning sciences, a suitable evaluation typically hinges on the predefined goals of an intervention, as well as explicit assumptions about what factors are important to learning and how they are evaluated. In contrast, HCI contributions on learning and education may also be exploratory, qualitative, and longitudinal rather than quantitative and experimentally comparative, especially if the contributions involve novel interventions to promote learning. This acknowledges that HCI research is ultimately interventionist. So, while deep understanding of given contexts and the validation of theory-derived hypotheses are relevant to design, the ultimate goal of HCI is to construct design-relevant knowledge.

It is thus critical in evaluations to assess both the formative learning process, including learners' assessment of the qualities of an intervention as well as the summative outcomes of the intervention, while remaining open to novel, bottom-up insights that stem from exploratory and qualitative research.

Third, in thinking about how we might build more connections between these communities and HCI more broadly, we asked what learning work might be able to contribute to HCI and what HCI work can contribute to learning sciences. For example, HCI can bring to learning and instructional sciences significant knowledge about the design of interventions, such as methodological knowledge about participatory design and similar design and research methods. HCI can also bring design knowledge, such as affordances of different types of technologies, as well as usability and user experience best practices. At the same time, HCI research could look more at how learning theories could be used to shape participatory design activities as collaborative knowledge-construction activities. Finally, the relationship between research and (educational) practice seemed to SIG participants to be particularly relevant for this HCI sub-community. To understand learning and education, and to observe expected impacts, significant interactions with stakeholders outside of labs are required. Of course, laboratory studies have their place in HCI and also in this subfield. But though we can build technologies, test them in labs, and prove that learning happens if they are used, we also need HCI research to observe longer-term processes of technology appropriation and interactions between users and technology. In addition, HCI research needs to design for teacher, administrator, and parent use so they are motivated and able to provide access to students from all walks of life.

Overall, discussions at any time at the SIG workshop were highly heterogeneous and occurred at multiple levels. This suggests that there is, as yet, no shared, systematic way of answering the foundational questions of what learning means in an HCI community, how to foster learning through technology design, and what the specific characteristics are of a high-quality contribution that sits at the intersection between learning and HCI. We see this as an important call for ourselves, as well as others researching and practicing in this space: to continue interdisciplinary discussion, and at the same time try to identify particular knowledge and perspectives that come out of work at the intersection of HCI and learning.

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1. Xie, B., Harpstead, E., DiSalvo, B., Slovak, P., Kharrufa, A., Lee, M.J., Pammer-Schindler, V., Ogan, A., and Williams, J.J. Learning, education, and HCI. Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, Paper SIG09. ACM, New York, 2019.

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Viktoria Pammer-Schindler is an associate professor at Graz University of Technology. She researches, and develops sociotechnical interventions for, digital transformation, with a focus on workplace learning and knowledge construction in different workplace contexts, such as modern manufacturing, or in strategic development of data-centric business models.

Erik Harpstead is a systems scientist in the HCI Institute at Carnegie Mellon University. His work focuses on leveraging computational theories of human learning to develop smarter tools and processes for designers of educational technologies and games to interrogate their products and consider how well they manifest designers' intentions.

Benjamin Xie is a Ph.D. candidate at the University of Washington Information School. His focus is designing interactive intelligent tools for equitable computing education.

Betsy DiSalvo is an associate professor in the School of Interactive Computing at Georgia Institute of Technology. At Georgia Tech she leads the Culture and Technology (CAT) Lab, where researchers study cultural values and how those values impact technology use, learning, and production.

Ahmed Kharrufa is a lecturer in interaction design at Newcastle University, where he leads educational technology research at Open Lab. His research focuses on the design, development, implementation, and evaluation of processes and technologies than can bridge the gap between schools and their communities as well as enhance learning and the learning experience.

Petr Slovak is an assistant professor in human-computer interaction at King's College London. He also holds an Honorary Research Fellow position at Evidence-Based Practice Unit at UCL and a visiting position at the Human-Centred Computing group at Oxford University. His research is focused on envisioning, designing, and evaluating new technology-enabled mental health interventions.

Amy Ogan is the Thomas and Lydia Moran Assistant Professor of Learning Science in the Human-Computer Interaction Institute at Carnegie Mellon University. She is an educational technologist focusing on ways to make learning experiences more engaging, effective, and enjoyable.

Joseph Jay Williams is an assistant professor in HCI and computer science, designing intelligent adaptive educational and health technology by using randomized A/B comparisons to bridge statistical machine learning with crowdsourcing and psychology.

Michael J. Lee is the Dorman-Bloom Assistant Professor of Informatics at the New Jersey Institute of Technology (NJIT). There, he directs the Gidget Lab, which focuses on designing, creating, and testing technology-focused educational tools for all. His research is funded by the National Science Foundation and Oculus Research, and has received several best paper awards.

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