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1. Normal Reflection Subgroups

   Arreche, Carlos; Williams, Nathan (January 2020, Séminaire lotharingien de combinatoire)

   We study normal reflection subgroups of complex reflection groups. Our point of view leads to a refinement of a theorem of Orlik and Solomon to the effect that the generating function for fixed-space dimension over a reflection group is a product of linear factors involving generalized exponents. Our refinement gives a uniform proof and generalization of a recent theorem of the second author. 

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2. Voice Interaction With Conversational AI Could Facilitate Thoughtful Reflection and Substantive Revision in Writing

   https://doi.org/10.18653/v1/2025.in2writing-1.7  

   Kim, Jiho; Laban, Philippe; Chen, Xiang; Arnold, Kenneth C (May 2025, Association for Computational Linguistics)

   Writing well requires not only expressing ideas but also refining them through revision, a process facilitated by reflection. Prior research suggests that feedback delivered through dialogues, such as those in writing center tutoring sessions, can help writers reflect more thoughtfully on their work compared to static feedback. Recent advancements in multi-modal large language models (LLMs) now offer new possibilities for supporting interactive and expressive voice-based reflection in writing. In particular, we propose that LLM-generated static feedback can be repurposed as conversation starters, allowing writers to seek clarification, request examples, and ask follow-up questions, thereby fostering deeper reflection on their writing. We argue that voice-based interaction can naturally facilitate this conversational exchange, encouraging writers' engagement with higher-order concerns, facilitating iterative refinement of their reflections, and reduce cognitive load compared to text-based interactions. To investigate these effects, we propose a formative study exploring how text vs. voice input influence writers' reflection and subsequent revisions. Findings from this study will inform the design of intelligent and interactive writing tools, offering insights into how voice-based interactions with LLM-powered conversational agents can support reflection and revision. 

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3. Reflection in Engineering Education: Advancing Conversations

   Turns, J.; Mejia, K.; Atman, C. (January 2020, ASEE Conference Proceedings)

   This work, funded by the Engineering Education and Centered directorate, is motivated by a belief in the value of reflection for student learning in engineering, an appreciation of the range of activities that educators can leverage in order to support student reflection, and a sense of the need for resources to help educators make informed decisions in the design of reflection activities for specific situations. Against the backdrop of these ideas, our grant is operating on two levels. On a foundational level, we are establishing empirically informed conceptual frameworks and associated survey instruments that help educators and resources understand (a) what knowledge gains result when students engage in specific reflection activities and (b) what types of reactions students have when they engage in the activity. On a practical level, we are exploring ways to distribute our conceptual frameworks alongside relevant information. With our work, we aim to advance conversations about the potential impact of reflection and conversations about how to leverage reflection in teaching. In the paper and poster, we will focus on both the foundational insights and practical resources that are emerging from this work. 

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4. Reflection in Game-Based Learning: A Survey of Programming Games

   https://doi.org/10.1145/3402942.3403011  

   Villareale, J.; Biemer, C.; Seif El-Nasr, M.; Zhu, J. (July 2020, Foundations of Digital Games)

   Reflection is a critical aspect of the learning process. However, educational games tend to focus on supporting learning concepts rather than supporting reflection. While reflection occurs in educational games, the educational game design and research community can benefit from more knowledge of how to facilitate player reflection through game design. In this paper, we examine educational programming games and analyze how reflection is currently supported. We find that current approaches prioritize accuracy over the individual learning process and often only support reflection post-gameplay. Our analysis identifies common reflective features, and we develop a set of open areas for future work. We discuss these promising directions towards engaging the community in developing more mechanics for reflection in educational games. 

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5. Reflection-aware static regression test selection

   https://doi.org/10.1145/3360613  

   Shi, August; Hadzi-Tanovic, Milica; Zhang, Lingming; Marinov, Darko; Legunsen, Owolabi (October 2019, Proceedings of the ACM on Programming Languages)

   Regression test selection (RTS) aims to speed up regression testing by rerunning only tests that are affected by code changes. RTS can be performed using static or dynamic analysis techniques. Our prior study showed that static and dynamic RTS perform similarly for medium-sized Java projects. However, the results of that prior study also showed that static RTS can be unsafe, missing to select tests that dynamic RTS selects, and that reflection was the only cause of unsafety observed among the evaluated projects. In this paper, we investigate five techniques—three purely static techniques and two hybrid static-dynamic techniques—that aim to make static RTS safe with respect to reflection. We implement these reflection-aware (RA) techniques by extending the reflection-unaware (RU) class-level static RTS technique in a tool called STARTS. To evaluate these RA techniques, we compare their end-to-end times with RU, and with RetestAll, which reruns all tests after every code change. We also compare safety and precision of the RA techniques with Ekstazi, a state-of-the-art dynamic RTS technique; precision is a measure of unaffected tests selected. Our evaluation on 1173 versions of 24 open-source Java projects shows negative results. The RA techniques improve the safety of RU but at very high costs. The purely static techniques are safe in our experiments but decrease the precision of RU, with end-to-end time at best 85.8% of RetestAll time, versus 69.1% for RU. One hybrid static-dynamic technique improves the safety of RU but at high cost, with end-to-end time that is 91.2% of RetestAll. The other hybrid static-dynamic technique provides better precision, is safer than RU, and incurs lower end-to-end time—75.8% of RetestAll, but it can still be unsafe in the presence of test-order dependencies. Our study highlights the challenges involved in making static RTS safe with respect to reflection. 

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