Towards Value-Sensitive Learning Analytics Design

To support ethical considerations and system integrity in learning analytics, this paper introduces two cases of applying the Value Sensitive Design methodology to learning analytics design. The first study applied two methods of Value Sensitive Design, namely stakeholder analysis and value analysis, to a conceptual investigation of an existing learning analytics tool. This investigation uncovered a number of values and value tensions, leading to design trade-offs to be considered in future tool refinements. The second study holistically applied Value Sensitive Design to the design of a recommendation system for the Wikipedia WikiProjects. To proactively consider values among stakeholders, we derived a multi-stage design process that included literature analysis, empirical investigations, prototype development, community engagement, iterative testing and refinement, and continuous evaluation. By reporting on these two cases, this paper responds to a need of practical means to support ethical considerations and human values in learning analytics systems. These two cases demonstrate that Value Sensitive Design could be a viable approach for balancing a wide range of human values, which tend to encompass and surpass ethical issues, in learning analytics design.Comment: The 9th International Learning Analytics & Knowledge Conference (LAK19

Innovating Pedagogy 2024

In this series of annual reports, we continue to explore new forms of teaching, learning, and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This twelfth report proposes another ten innovations which are already in currency but have the potential to exert a greater influence on education. To produce the report, a group of academics at the Institute of Educational Technology at The Open University (UK) collaborated with researchers and practitioners from the LIVE Initiative at Vanderbilt University in the US. A wide range of pedagogical innovations were proposed by the authors and then, in a process of collective discussion of major themes and associated research, ten ideas were developed through multiple drafts and peer review, with reference to published studies and other sources from research and practice. This twelfth report covers: 1. Speculative worlds 2. Pedagogies of peace 3. Climate action pedagogy 4. Learning in conversation with generative AI  5. Talking AI ethics with young people 6. AI enhanced multimodal writing 7. Intelligent textbooks 8. Assessments through extended reality 9. Immersive language and culture 10. Exploring scientific models from the insid

Designing Pedagogical Interventions to Support Student Use of Learning Analytics

ABSTRACT This article addresses a relatively unexplored area in the emerging field of learning analytics, the design of learning analytics interventions. A learning analytics intervention is defined as the surrounding frame of activity through which analytic tools, data, and reports are taken up and used. It is a soft technology that involves the orchestration of the human process of engaging with the analytics as part of the larger teaching and learning activity. This paper first makes the case for the overall importance of intervention design, situating it within the larger landscape of the learning analytics field, and then considers the specific issues of intervention design for student use of learning analytics. Four principles of pedagogical learning analytics intervention design that can be used by teachers and course developers to support the productive use of learning analytics by students are introduced: Integration, Agency, Reference Frame and Dialogue. In addition three core processes in which to engage students are described: Grounding, Goal-Setting and Reflection. These principles and processes are united in a preliminary model of pedagogical learning analytics intervention design for students, presented as a starting point for further inquiry

Microanalytic case studies of individual participation patterns in an asynchronous online discussion in an undergraduate blended course.

Abstract This study presents three case studies of students' participation patterns in an online discussion to address the gap in our current understanding of how individuals experience asynchronous learning environments. Cases were constructed via microanalysis of log-file data, post contents, and the evolving discussion structure. The first student was Thorough, reading all the posts in the forum in sequence, revisiting different posts multiple times, and creating posts outside of the discussion tool. The second student was Self-Monitoring, revisiting his own posts multiple times, checking the discussion frequently for replies, and replying to or editing his posts in response. Finally, the third student was Independent, using the forum as a tool for her own individual reflection. The behaviors found for these cases are aligned with a theoretical taxonomy for participation proposed b

Data literacies and social justice: Exploring critical data literacies through sociocultural perspectives

The ability to interpret, evaluate, and make data-based decisions is critical in the age of big data. Normative scripts around the use of data position them as a privileged epistemic form conferring authority through objectivity that can serve as a lever for effecting change. However, humans and materials shape how data are created and used which can reinscribe existing power relations in society at large (Van Wart, Lanouette & Parikh, 2020). Thus, research is needed on how learners can be supported to engage in critical data literacies through sociocultural perspectives. As a field intimately concerned with data-based reasoning, social justice, and design, the learning sciences is well-positioned to contribute to such an effort. This symposium brings together scholars to present theoretical frameworks and empirical studies on the design of learning spaces for critical data literacies. This collection supports a larger discussion around existing tensions, additional design considerations, and new methodologies