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1. Empirical Re-Conceptualization: Bridging from Empirical Patterns to Insight and Understanding

   Ellis, A.B. (July 2020, Proceedings of the twenty-third Annual Conference on Research in Undergraduate Mathematics Education)

   Identifying patterns is an important part of mathematical investigation, but many students struggle to explain or justify their pattern-based generalizations or conjectures. These findings have led some researchers to argue for a de-emphasis on pattern-based activities, but others argue that empirical investigation can support the discovery of insight into a problem’s structure. We introduce a phenomenon we call empirical re-conceptualization, in which learners identify a conjecture based on an empirical pattern, and then re-interpret that conjecture from a structural perspective. We elaborate this construct by drawing on interview data from undergraduate calculus students and research mathematicians, providing a representative example of empirical re-conceptualization from each participant group. Our findings indicate that developing empirical results can foster subsequent insights, which can in turn lead to justification and proof. 

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2. Towards reproducible research: automatic classification of empirical requirements engineering papers

   https://doi.org/10.1145/3190645.3190689  

   Woodson, Clinton; Hayes, Jane Huffman; Griffioen, Sarah (January 2018, ACMSE '18 Proceedings of the ACMSE 2018 Conference)

   Research must be reproducible in order to make an impact on science and to contribute to the body of knowledge in our field. Yet studies have shown that 70% of research from academic labs cannot be reproduced. In software engineering, and more specifically requirements engineering (RE), reproducible research is rare, with datasets not always available or methods not fully described. This lack of reproducible research hinders progress, with researchers having to replicate an experiment from scratch. A researcher starting out in RE has to sift through conference papers, finding ones that are empirical, then must look through the data available from the empirical paper (if any) to make a preliminary determination if the paper can be reproduced. This paper addresses two parts of that problem, identifying RE papers and identifying empirical papers within the RE papers. Recent RE and empirical conference papers were used to learn features and to build an automatic classifier to identify RE and empirical papers. We introduce the Empirical Requirements Research Classifier (ERRC) method, which uses natural language processing and machine learning to perform supervised classification of conference papers. We compare our method to a baseline keyword-based approach. To evaluate our approach, we examine sets of papers from the IEEE Requirements Engineering conference and the IEEE International Symposium on Software Testing and Analysis. We found that the ERRC method performed better than the baseline method in all but a few cases. 

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3. Macroeconomic dynamics and reallocation in an epidemic: evaluating the ‘Swedish solution’

   https://doi.org/10.1093/epolic/eiac010  

   Krueger, Dirk; Uhlig, Harald; Xie, Taojun (January 2022, Economic Policy)

   Abstract In this paper, we argue that endogenous shifts in private consumption behaviour across sectors of the economy can act as a potent mitigation mechanism during an epidemic or when the economy is re-opened after a temporary lockdown. We introduce a susceptible-infected-recovered epidemiological model into a neoclassical production economy in which goods are distinguished by the degree to which they can be consumed at home rather than in a social, possibly contagious context. We demonstrate within the model, that the ‘Swedish solution’ of letting the epidemic play out without much government intervention and allowing agents to reduce their overall consumption as well as shift their consumption behaviour towards relatively safe sectors can lead to substantial mitigation of the economic and human costs of the COVID-19 crisis. We argue that significant seasonal variation in the infection risk is needed to account for the two-wave nature of the pandemic. We estimate the model on Swedish health data and show that it predicts the dynamics of weekly deaths, aggregate as well as sectoral consumption, that accord well with the empirical record and the two-waves for Sweden for 2020 and early 2021. We also characterize the allocation a social planner would choose and how it would dictate sectoral consumption patterns. In so doing, we demonstrate that the laissez-faire outcome with sectoral reallocation mitigates the economic and health crisis but possibly at the expense of unnecessary deaths and too massive a decline in economic activity. 

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4. Deconstructing Conceptions of Rigor in Computer Science Education

   https://doi.org/10.1145/3776542  

   Everson, Jayne; Kivuva, F Megumi; Sherif, Eman; Oleson, Alannah; Ko, Amy (November 2025, ACM Transactions on Computing Education)

   ObjectivesIn calls for excellent and equitable Computer Science (CS) education, the wordrigoroften appears, but it often goes undefined. The goal of this work is to understand how CS teachers, instructors, and students conceive of rigor. Research Questions:1) What do CS instructors think rigor is? and 2) What do students think rigor is? Methods:Using the principles of phenomenological research, we conducted a semi-structured interview study with 10 post-secondary CS students, 10 secondary CS teachers, and 9 post-secondary CS instructors, to understand their conceptions of rigor. Results:Analysis showed that no participants had the same understanding of rigor. We found that participants had abstractPrinciples of Rigorwhich included: Precision, Systematic Thought Process, Depth of Understanding, and Challenge. They also had concreteObservations of Rigorthat included Time and Effort, Intrinsic Drive, Productive Failure, Struggle, Outcomes, and Gatekeeping. Participants also sharedConditions for Rigorwhich included Expectations, Standards, Community Support, and Resources. Implications:Our data supports prior work that educators are using different definitions of rigor. This implies that each educator holds different expectations for students, without necessarily communicating these expectations to their students. In the best case, this might confuse students; in the worst case, it reinforces hegemonic norms which can lead to gatekeeping which prevents students from fully participating in the CS field. Based on these insights, we argue that to commit to the idea of quality CS learning, the community must discard the use of this concept of rigor to justify student learning and re-imagine alternate benchmarks. 

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5. Fostering Students’ Epistemic Agency For Rigorous Science Instruction

   Akcil-Okan, O.; Tekkumru-Kisa, M. (January 2022, Annual meeting program American Educational Research Association)

   Reform-based rigorous instruction which fosters all students’ thinking and sensemaking is possible; however, it is not yet prevalent in science classrooms. This study explored promoting rigorous instruction by enhancing students’ intellectual work through cognitively demanding tasks. We examined instruction during the five lessons in a science classroom. We found variations in students’ intellectual work across the lessons. Our analysis revealed that the instructional practices associated with promoting students’ engagement in rigorous thinking were consequential for promoting students’ epistemic agency. Thus, we argue that maintaining and enhancing demand on students’ intellectual engagement in cognitively demanding tasks requires the work of providing opportunities for students to learn science-as-practice by acting as epistemic agents. These findings can inform professional efforts regarding rigorous instruction. 

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