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1. Programming as Language and Manipulative for Second-Grade Mathematics

   https://doi.org/10.1007/s40751-020-00083-3  

   Goldenberg, E. Paul; Carter, Cynthia J.; Mark, June; Reed, Kristen; Spencer, Deborah; Coleman, Kate (April 2021, Digital Experiences in Mathematics Education)

   Abstract This article reports on an exploration of how second-graders can learn mathematics through programming. We started from the theory that a suitably designed programming language can serve children as a language for expressing and experimenting with mathematical ideas and processes in order to do mathematics and thereby, with appropriate tasks and teaching, learn and enjoy the subject. This is very different from using the computer as a teaching app or a digital medium for exploration. Children tackled genuine puzzles – problems for which they did not already have a pre-learned solution. So far, we have built four microworlds for second-graders and tested them with a diverse population of well over three hundred children. The microworlds focus on the most critical second-grade mathematical content (as mandated in state standards), let children pick up all key programming ideas in contexts that make them ‘obvious’ (to maintain focus on the mathematics) and suppress all other distractions to minimize overhead for teachers or students using the microworlds. Because children see the results of the actions they articulate (in the computer language, Snap ! ), they can evaluate their methods and solutions themselves. The feedback is purely the outcome, not happy or sad sounds from the computer. Notably, nearly all children showed intense engagement, some choosing microworlds even outside of mathematics time. Teachers spontaneously reported this as well, with special mention of children whom they found hard to engage in regular lessons. We report our experiments and observations in the spirit of sharing the ideas and promoting more research. 

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2. Music, memory, and second language grammar: An EEG study.

   Tkacikova, Victoria A; Terrazas, Gabriela; Slevc, Lloyd R; Tokowicz, Natasha (November 2024, Psychonomic Society)
3. Lexically-specific syntactic restrictions in second-language speakers

   https://doi.org/10.1016/j.jml.2023.104470  

   Vega-Mendoza, Mariana; Ivanova, Iva; McLean, Janet F; Pickering, Martin J; Branigan, Holly P (February 2024, Journal of Memory and Language)
4. Modeling Second-Language Learning from a Psychological Perspective

   Rich, A.S.; Popp, P.J.O.; Halpern, D.; Rothe, A.; Gureckis, T.M. (July 2018, Proceedings of the NAACL-HLT Workshop on Innovative Use of NLP for Building Educational Applications (BEA).)

   Psychological research on learning and memory has tended to emphasize small-scale laboratory studies. However, large datasets of people using educational software provides opportunities to explore these issues from a new perspective. In this paper we describe our approach to the Duolingo Second Language Acquisition Modeling (SLAM) competition which was run in early 2018. We used a well-known class of algorithms (gradient boosted decision trees), with features partially informed by theories from the psychological literature. After detailing our modeling approach and a number of supplementary simulations, we reflect on the degree to which psychological theory aided the model, and the potential for cognitive science and predictive modeling competitions to gain from each other. 

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5. Automatic recognition of second language speech-in-noise

   https://doi.org/10.1121/10.0024877  

   Kim, Seung-Eun; Chernyak, Bronya_R; Seleznova, Olga; Keshet, Joseph; Goldrick, Matthew; Bradlow, Ann_R (February 2024, JASA Express Letters)

   Measuring how well human listeners recognize speech under varying environmental conditions (speech intelligibility) is a challenge for theoretical, technological, and clinical approaches to speech communication. The current gold standard—human transcription—is time- and resource-intensive. Recent advances in automatic speech recognition (ASR) systems raise the possibility of automating intelligibility measurement. This study tested 4 state-of-the-art ASR systems with second language speech-in-noise and found that one, whisper, performed at or above human listener accuracy. However, the content of whisper's responses diverged substantially from human responses, especially at lower signal-to-noise ratios, suggesting both opportunities and limitations for ASR--based speech intelligibility modeling. 

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