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1. Rapid growth and defence evolution following multiple introductions

   https://doi.org/10.1002/ece3.5275  

   van_Boheemen, Lotte_A ; Bou‐Assi, Sarah ; Uesugi, Akane ; Hodgins, Kathryn_A ( June 2019 , Ecology and Evolution)

   Rapid adaptation can aid invasive populations in their competitive success. Resource allocation trade‐off hypotheses predict higher resource availability or the lack of natural enemies in introduced ranges allow for increased growth and reproduction, thus contributing to invasive success. Evidence for such hypotheses is however equivocal and tests among multiple ranges over productivity gradients are required to provide a better understanding of the general applicability of these theories.

   Using common gardens, we investigated the adaptive divergence of various constitutive and inducible defence‐related traits between the native North American and introduced European and Australian ranges, while controlling for divergence due to latitudinal trait clines, individual resource budgets, and population differentiation, using >11,000 SNPs.

   Rapid, repeated clinal adaptation in defence‐related traits was apparent despite distinct demographic histories. We also identified divergence among ranges in some defence‐related traits, although differences in energy budgets among ranges may explain some, but not all, defence‐related trait divergence. We do not identify a general reduction in defence in concert with an increase in growth among the multiple introduced ranges as predicted trade‐off hypotheses.

   Synthesis: The rapid spread of invasive species is affected by a multitude of factors, likely including adaptation to climate and escape from natural enemies. Unravelling the mechanisms underlying invasives' success enhances understanding of eco‐evolutionary theory and is essential to inform management strategies in the face of ongoing climate change.

   This article has been awarded Open Materials, Open Data, Preregistered Research Designs Badges. All materials and data are publicly accessible via the Open Science Framework athttps://doi.org/10.6084/m9.figshare.8028875.v1,https://github.com/lotteanna/defence_adaptation,https://doi.org/10.1101/435271.

    

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2. Using public records to support the productive use of student mathematical thinking

   Freeburn, B. ; Leatham, K. R. ; Graff, S. ; Kamlue, N. ; Stockero, S. L. ; Peterson, B. E. ; Van Zoest, L. R. ( January 2022 , Proceedings of the 44th annual meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education)

   Lischka, A. E. ; Dyer, E. B. ; Jones, R. S. ; Lovett, J. N. ; Strayer, J. ; Drown, S. (Ed.)

   The more researchers understand the subtleties of teaching practices that productively use student thinking, the better we can support teachers to develop these teaching practices. In this paper, we report the results of an exploration into how secondary mathematics teachers' use of public records appeared to support or inhibit their efforts to conduct a sense-making discussion around a particular student contribution. We use cognitive load theory to frame two broadways teachers used public records—manipulating and referencing—to support establishing and maintaining students' thinking as objects in sense-making discussions. 

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3. USING PUBLIC RECORDS TO SUPPORT THE PRODUCTIVE USE OF STUDENT MATHEMATICAL THINKING

   Freeburn, B. ; Leatham, K. R. ; Graff, S. ; Kamlue, N. ; Stockero, S. L. ; Peterson, B. E. ; Van Zoest, L. R. ( November 2022 , USING PUBLIC RECORDS TO SUPPORT THE PRODUCTIVE USE OF STUDENT MATHEMATICAL THINKING)

   Lischka, A. E. ; Dyer, E. B. ; Jones, R. S. ; Lovell, J. N. ; Strayer, J. ; Drown, S. (Ed.)

   The more researchers understand the subtleties of teaching practices that productively use student thinking, the better we can support teachers to develop these teaching practices. In this paper, we report the results of an exploration into how secondary mathematics teachers’ use of public records appeared to support or inhibit their efforts to conduct a sense-making discussion around a particular student contribution. We use cognitive load theory to frame two broad ways teachers used public records—manipulating and referencing—to support establishing and maintaining students’ thinking as objects in sense-making discussions. 

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4. Improving Automated Scoring of Student Open Responses in Mathematics

   Baral, Sami ; Botelho, Anthony ; Heffernan, Neil T. ( January 2021 , International Conference on Educational Data Mining)

   null (Ed.)

   Open-ended questions in mathematics are commonly used by teachers to monitor and assess students’ deeper conceptual understanding of content. Student answers to these types of questions often exhibit a combination of language, drawn diagrams and tables, and mathematical formulas and expressions that supply teachers with insight into the processes and strategies adopted by students in formulating their responses. While these student responses help to inform teachers on their students’ progress and understanding, the amount of variation in these responses can make it difficult and time-consuming for teachers to manually read, assess, and provide feedback to student work. For this reason, there has been a growing body of research in developing AI-powered tools to support teachers in this task. This work seeks to build upon this prior research by introducing a model that is designed to help automate the assessment of student responses to open-ended questions in mathematics through sentence-level semantic representations. We find that this model outperforms previouslypublished benchmarks across three different metrics. With this model, we conduct an error analysis to examine characteristics of student responses that may be considered to further improve the method. 

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5. Improving Automated Scoring of Student Open Responses in Mathematics

   Baral, Sami ; Botelho, Anthony ; Erickson, John ; Benachamardi, Priyanka ; Heffernan, Neil ( June 2021 , Educational Data Mining EDM 2021)

   Open-ended questions in mathematics are commonly used by teachers to monitor and assess students’ deeper conceptual understanding of content. Student answers to these types of questions often exhibit a combination of language, drawn diagrams and tables, and mathematical formulas and expressions that supply teachers with insight into the processes and strategies adopted by students in formulating their responses. While these student responses help to inform teachers on their students’ progress and understanding, the amount of variation in these responses can make it difficult and time-consuming for teachers to manually read, assess, and provide feedback to student work. For this reason, there has been a growing body of research in developing AI-powered tools to support teachers in this task. This work seeks to build upon this prior research by introducing a model that is designed to help automate the assessment of student responses to open-ended questions in mathematics through sentence-level semantic representations. We find that this model outperforms previously published benchmarks across three different metrics. With this model, we conduct an error analysis to examine characteristics of student responses that may be considered to further improve the method. 

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