Richard Lee Davis

2025

1. SIGCSE

   

   Jupyter Analytics: A Toolkit for Collecting, Analyzing, and Visualizing Distributed Student Activity in Jupyter Notebooks

   Zhenyu Cai, Richard Lee Davis, Raphaël Mariétan, and 2 more authors

   In Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1, Feb 2025

   Abs DOI PDF Code

   Jupyter is a web-based, interactive computing environment that supports many commonly-used programming languages. It has been widely adopted in the CS education community and is now rapidly expanding to other STEM disciplines due to the growing integration of programming in STEM education. However, unlike other educational platforms, there is currently no integrated way to capture, analyze, and visualize student interaction data in Jupyter notebooks. This means that teachers have limited to no visibility into student activity, preventing them from drawing insights from these data and providing timely interventions on the fly. In this paper, we present Jupyter Analytics, an end-to-end solution for teachers to collect, analyze, and visualize both synchronous and asynchronous learning activities in Jupyter. The Jupyter Analytics system consists of two JupyterLab extensions connected via a cloud-based backend. On the student side, we introduce the Jupyter Analytics Telemetry extension to anonymously capture students’ interaction activity with more structure and higher granularity than log data. On the teacher side, we introduce the Jupyter Analytics Dashboard extension, which visualizes real-time student data directly in the notebook interface. The Jupyter Analytics system was developed through an iterative co-design process with university instructors and teaching assistants, and has been implemented and tested in several university STEM courses. We report two use cases where Jupyter Analytics impacted teaching and learning in the context of exercise sessions, and discuss the potential value of our tools for CS education.

2. CSCL

   

   “Jupyter-Notebook-as-Script”: Investigating the Nature and Impact of Implicit Collaboration Scripts in Computational Notebooks

   Zhenyu Cai, Richard Lee Davis, Roland Tormey, and 1 more author

   In Proceedings of the 18th International Conference on Computer-Supported Collaborative Learning-CSCL 2025, Pp. 223-231, Feb 2025

   Naomi Miyake Outstanding Student Paper Award PDF

   Won the Naomi Miyake Outstanding Student Paper Award, Nominated for the Best Full Paper Award

3. CHI

   

   SketchAI: A "Sketch-First" Approach to Incorporating Generative AI into Fashion Design

   Richard Lee Davis, Kevin Fred Mwaita, Livia Müller, and 4 more authors

   In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, Apr 2025

   Abs DOI PDF

   Generative AI technologies are increasingly being incorporated into creativity support tools. However, most generative AI tools rely on text-based prompting, requiring users to translate visual ideas into linguistic descriptions. This approach is misaligned with the sketch-driven workflows of creative professionals. To address this gap, we introduce SketchAI, a novel sketch-first interface for diffusion models that allows practitioners to use real-time sketching on a tablet computer to guide model outputs. Through a qualitative study with 29 fashion design apprentices, we explored the interface’s potential impacts on creative workflows. While some participants identified use cases where SketchAI streamlined routine tasks, others expressed concerns about its potential to undermine creative agency and exploration. These findings unearthed hidden complexity: while generative AI can support some aspects of creativity, its core capabilities may challenge the central identity of creative practitioners. While SketchAI does not resolve this problem, it does take a meaningful step towards reconciliation.

4. CHI

   

   SpatiaLearn: Exploring XR Learning Environments for Reflective Writing

   Jinqiao Li, Seyed Parsa Neshaei, Livia Müller, and 3 more authors

   In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, Apr 2025

   Abs DOI PDF

   Reflective writing promotes deeper learning by enhancing metacognitive awareness and critical thinking, but learners often struggle with structuring their reflections and maintaining focus. Generative AI and advances in spatial computing offer promising solutions. Extended reality (XR) environments create immersive, distraction-free settings, while conversational agents use dialog-based scaffolding guides to structure learners’ thoughts. However, research on combining dialog-based scaffolding with XR for reflective writing remains limited. To address this, we introduce SpatiaLearn, an adaptive XR tool that enhances reflective writing through conversational guidance in both traditional and immersive environments. A within-subjects study (N = 19) compared participants’ performance in traditional laptop and XR environments. Qualitative analysis shows the spatial interface enhances engagement but raises challenges like unfamiliar interactions and health concerns, requiring task adaptation for XR. This study advances the design of immersive tools for reflective writing, highlighting both the opportunities and challenges of spatial interfaces.

5. BJET

   

   Metacognition Meets AI: Empowering Reflective Writing with Large Language Models

   Seyed Parsa Neshaei, Paola Mejia-Domenzain, Richard Lee Davis, and 1 more author

   British Journal of Educational Technology, Apr 2025

   Abs DOI PDF

   Reflective writing is known as a useful method in learning sciences to improve the metacognitive skills of students. However, students struggle to structure their reflections properly, limiting the possible learning gains. Previous works in educational technologies literature have explored the paradigms of learning from worked and modelling examples, but (a) their application to the domain of reflective writing is rare, (b) such methods might not scale properly to large-scale classrooms, and (c) they do not necessarily take the learning needs of each student into account. In this work, we suggest two approaches of integrating AI-enabled support in digital systems designed around learning from worked and modelling examples paradigms, to provide personalized learning and feedback to students using large language models (LLMs). We evaluate Reflectium, our reflective writing assistant, show benefits of integrating AI support into the learning from examples modalities and compare the perception of the users and their interaction behaviour when using each version of our tool. Our work sheds light on the applicability of generative LLMs to different types of providing support using the learning from examples paradigm, in the domain of reflective writing. Practitioner notes What is already known about this topic Reflective writing fosters metacognitive skills and improves learning gains and personal growth. The learning from worked and modelling examples paradigms is effective for skill acquisition and applying the acquired knowledge. Existing reflective writing assistants usually lack dynamic, AI-driven feedback or interactivity, limiting personalization and adaptability to each user’s own needs in the learning process. What this paper adds It introduces Reflectium, an AI-enabled reflective writing assistant, integrating intelligent and interactive writing support for both the learning from worked and modelling examples paradigms. It demonstrates the use of a fine-tuned large language model (LLM) for providing feedback in the learning from worked examples version, and an LLM-powered conversational agent simulating instructor interactions for the learning from modelling examples version. It reports findings from a user study comparing the positive impact of artificial intelligence (AI) support on learners’ performance, interaction behaviour and learning experience. Implications for practice and/or policy Digital tutoring systems for teaching reflective writing using the learning from worked examples paradigm should incorporate adaptive AI feedback to enhance learning gains. Conversational agents simulating peers/instructors and powered by LLMs can provide scalable, interactive support for learning from modelling examples, notably in large-scale educational settings. Reflective writing tools should be evaluated for their impact on different aspects of the learning process, such as task performance, interaction behaviour and user experience, to guide future improvements. Educators and policymakers should consider the integration of AI-driven reflective writing tools into teaching curricula to enhance reflective practices and metacognitive skill development.

6. AIED

   

   One Code to Predict Them All: Universal Encoding for Inquiry Modeling

   Jade Mai Cock, Valentine Delevaux, Ido Roll, and 2 more authors

   In International Conference on Artificial Intelligence in Education, Apr 2025
7. EDM

   

   Using Large Multimodal Models to Extract Knowledge Components for Knowledge Tracing from Multimedia Question Information

   Hyeongdon Moon, Richard Lee Davis, Seyed Parsa Neshaei, and 1 more author

   In Proceedings of the 18th International Conference on Educational Data Mining, Apr 2025

   DOI
8. EDM

   

   Bridging the Data Gap: Using LLMs to Augment Datasets for Text Classification

   Seyed Parsa Neshaei, Richard Lee Davis, Paola Mejia-Domenzain, and 2 more authors

   In Proceedings of the 18th International Conference on Educational Data Mining, Apr 2025

   DOI

2024

1. SIGCSE

   

   A Comparative Analysis of Tools & Task Types for Measuring Computational Problem-Solving

   Engin Bumbacher, Jérôme Brender, and Richard Lee Davis

   In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 2, Mar 2024

   DOI PDF
2. ECTEL

   

   Learning Analytics Beyond Traditional Classrooms: Addressing the Tensions of Cognitive and Meta-Cognitive Goals in Exercise Sessions

   Zhenyu Cai, Richard Davis, Roland Tormey, and 1 more author

   In Technology Enhanced Learning for Inclusive and Equitable Quality Education, Mar 2024

   Abs DOI PDF

   A range of learning analytics (LA) tools have been designed and integrated into university classes to facilitate teaching and learning. However, exercise sessions, the educational setting that complements lectures with practical activities, are commonly overlooked by LA researchers and designers. Little work has focused on involving the key stakeholders, teaching assistants (TAs), and incorporating human-centered design approaches in this context. To address this gap, we conducted a qualitative study to understand TAs’ common approaches and challenges of teaching in exercise sessions, and to explore their visions for LA dashboards that could be adapted into their current practices. Our results indicated that TAs in exercise sessions held two sets of goals in supporting students’ cognitive and meta-cognitive activities, and while LA tools were seen as offering numerous potential benefits, they were also seen as introducing tensions threatened to disrupt the delicate balance of both goals.

3. EDM

   

   Investigation of Behavioral Differences: Uncovering Behavioral Sources of Demographic Bias in Educational Algorithms

   Jade Mai Cock, Hugues Saltini, Haoyu Sheng, and 3 more authors

   In Proceedings of the 17th International Conference on Educational Data Mining, Mar 2024

   DOI PDF
4. CHI

   

   Fashioning Creative Expertise with Generative AI: Graphical Interfaces for Design Space Exploration Better Support Ideation Than Text Prompts

   Richard Lee Davis, Thiemo Wambsganss, Wei Jiang, and 3 more authors

   In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, May 2024

   Abs DOI PDF

   This paper investigates the potential impact of deep generative models on the work of creative professionals. We argue that current generative modeling tools lack critical features that would make them useful creativity support tools, and introduce our own tool, generative.fashion1, which was designed with theoretical principles of design space exploration in mind. Through qualitative studies with fashion design apprentices, we demonstrate how generative.fashion supported both divergent and convergent thinking, and compare it with a state-of-the-art text-based interface using Stable Diffusion. In general, the apprentices preferred generative.fashion, citing the features explicitly designed to support ideation. In two follow-up studies, we provide quantitative results that support and expand on these insights. We conclude that text-only prompts in existing models restrict creative exploration, especially for novices. Our work demonstrates that interfaces which are theoretically aligned with principles of design space exploration are essential for unlocking the full creative potential of generative AI.

5. ETRD

   

   Hands-on Tasks Make Learning Visible: A Learning Analytics Lens on the Development of Mechanistic Problem-Solving Expertise in Makerspaces

   Richard Lee Davis, Bertrand Schneider, Leah F. Rosenbaum, and 1 more author

   Education Tech Research Dev, Feb 2024

   Abs DOI PDF

   This study investigated the impact of participating in a year-long digital-fabrication course on high-school seniors’ problem-solving skills, with a focus on problems involving mechanistic systems. The research questions centered on whether working in a makerspace impacted students’ abilities to solve such problems and whether the process data generated during problem-solving activities could be used to identify the different problem-solving approaches taken by the participants. A novel set of hands-on, mechanistic problems were created to answer these questions, and the results showed that after taking part in the course students performed significantly better on these problems, with the post-course students making more progress towards the solutions than the pre-course students. The process data revealed two distinct problem-solving approaches for each problem, one adopted primarily by experts (the expert approach) and one by pre-course students (the novice approach). The post-course students were more likely to adopt the expert approaches, which were strongly associated with better performance on each problem. The study found that participation in the course made the high-school students better able to “see” the various components and their ways of interacting, making them more like expert engineers.

6. EDM

   

   Towards Modeling Learner Performance with Large Language Models

   Seyed Parsa Neshaei, Richard Lee Davis, Adam Hazimeh, and 3 more authors

   In Proceedings of the 17th International Conference on Educational Data Mining, Feb 2024

   DOI PDF