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Abstract This paper provides an experience report on a co‐design approach with teachers to co‐create learning analytics‐based technology to support problem‐based learning in middle school science classrooms. We have mapped out a workflow for such applications and developed design narratives to investigate the implementation, modifications and temporal roles of the participants in the design process. Our results provide precedent knowledge on co‐designing with experienced and novice teachers and co‐constructing actionable insight that can help teachers engage more effectively with their students' learning and problem‐solving processes during classroom PBL implementations. Practitioner notesWhat is already known about this topicSuccess of educational technology depends in large part on the technology's alignment with teachers' goals for their students, teaching strategies and classroom context.Teacher and researcher co‐design of educational technology and supporting curricula has proven to be an effective way for integrating teacher insight and supporting their implementation needs.Co‐designing learning analytics and support technologies with teachers is difficult due to differences in design and development goals, workplace norms, and AI‐literacy and learning analytics background of teachers.What this paper addsWe provide a co‐design workflow for middle school teachers that centres on co‐designing and developing actionable insights to support problem‐based learning (PBL) by systematic development of responsive teaching practices using AI‐generated learning analytics.We adapt established human‐computer interaction (HCI) methods to tackle the complex task of classroom PBL implementation, working with experienced and novice teachers to create a learning analytics dashboard for a PBL curriculum.We demonstrate researcher and teacher roles and needs in ensuring co‐design collaboration and the co‐construction of actionable insight to support middle school PBL.Implications for practice and/or policyLearning analytics researchers will be able to use the workflow as a tool to support their PBL co‐design processes.Learning analytics researchers will be able to apply adapted HCI methods for effective co‐design processes.Co‐design teams will be able to pre‐emptively prepare for the difficulties and needs of teachers when integrating middle school teacher feedback during the co‐design process in support of PBL technologies.
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Exploring AI intervention points in high-school engineering education: a research through co-design approach
PurposeChallenges in teaching the engineering design process (EDP) at the high-school level, such as promoting good documentation practices, are well-documented. While developments in educational artificial intelligence (AI) systems have the potential to assist in addressing these challenges, the open-ended nature of the EDP leads to challenges that often lack the specificity required for actionable AI development. In addition, conventional educational AI systems (e.g. intelligent tutoring systems) primarily target procedural domain tasks with well-defined outcomes and problem-solving strategies, while the EDP involves open-ended problems and multiple correct solutions, making AI intervention timing and appropriateness complex. Design/methodology/approachAuthors conducted a six-week-long Research through Co-Design (RtCD) process (i.e. a co-design process rooted in Research through Design) with two experienced high-school engineering teachers to co-construct actionable insight in the form of AI intervention points (AI-IPs) in engineering education where an AI system can effectively intervene to support them while highlighting their pedagogical practices. FindingsThis paper leveraged the design of task models to iteratively refine our prior understanding of teachers’ experiences with teaching the EDP into three AI-IPs related to documentation, ephemeral interactions between teachers and students and disruptive failures that can serve as a focus for intelligent educational system designs. Originality/valueThis paper discusses the implications of these AI-IPs for designing educational AI systems to support engineering education as well as the importance of leveraging RtCD methodologies to engage teachers in developing intelligent educational systems that align with their needs and afford them control over computational interventions in their classrooms.
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- Award ID(s):
- 2119135
- PAR ID:
- 10652153
- Publisher / Repository:
- Emerald Insight
- Date Published:
- Journal Name:
- Information and Learning Sciences
- Volume:
- 126
- Issue:
- 7-8
- ISSN:
- 2398-5348
- Page Range / eLocation ID:
- 472 to 490
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1108/ILS-04-2024-0042
