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1. A Programming Language for Future Interests

   Shrutarshi Basu; Nate Foster; James Grimmelmann; Shan Parikh; Ryan Richardson (October 2022, Yale journal of law technology)

   Emile Shehada (Ed.)

   Learning the system of estates in land and future interests can seem like learning a new language. Scholars and students must master unfamiliar phrases, razor-sharp rules, and arbitrarily complicated structures. Property law is this way not because future interests are a foreign language, but because they are a programming language. This Article presents Orlando, a programming language for expressing conveyances of future interests, and Littleton, a freely available online interpreter (at https://conveyanc.es) that can diagram the interests created by conveyances and model the consequences of future events. Doing so has three payoffs. First, formalizing future interests helps students and teachers of the subject by allowing them to visualize and experiment with conveyances. Second, the process of formalization is itself deeply illuminating about property doctrine and theory. And third, the computer-science subfield of programming language theory has untapped potential for legal scholarship: a programming-language approach takes advantage of the linguistic parallels between legal texts and computer programs. 

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2. Towards Understanding Community Interests With Topic Modeling

   https://doi.org/10.1109/ACCESS.2018.2815904  

   Wang, Feng; Orton, Kenneth; Wagenseller, Paul; Xu, Kuai (January 2018, IEEE Access)
3. Children use artifacts to infer others' shared interests

   Pesowski, M.L.; Kelemen, D.; Schachner, A. (July 2021, Proceedings of the Annual Conference of the Cognitive Science Society)

   Fitch, T.; Lamm, C.; Leder, H.; Teßmar-Raible, K. (Ed.)

   Artifacts – the objects we own, make, and choose – provide a source of rich social information. Adults use people’s artifacts to judge others’ traits, interests, and social affiliations. Here we show that 4-year-old children (N=32) infer others’ shared interests from their artifacts. When asked who had the same interests as a target character, children chose the character with a conceptually similar object to the target’s – an object used for the same activity – over a character with a perceptually similar object. When asked which person had the same arbitrary property (bedtime, birthday, or middle name), children did not systematically select either character, and most often reported that they did not know. Adults (N=32) made similar inferences, but differed in their tendency to use artifacts to infer friendships. Overall, by age 4, children show a sophisticated ability to make selective, warranted inferences about others’ interests based solely on their artifacts. 

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4. Quantification in Empirical Activity: Tracing Children’s Interests and Ideas

   https://doi.org/10.1007/s11191-021-00301-3  

   Manz, Eve; Beckert, Betsy (January 2021, Science and Education)
5. How Constructivist Learning Impacts Secondary Girls’ STEM Career Interests

   https://doi.org/10.11114/jets.v12i2.6793  

   Xu, Y; Yang, M; Zhang, S; Muthukumar, V (January 2024, Journal of Education and Training Studies)

   This is a quantitative study that examines how constructivist learning in a summer camp impacted middle school and high school girls’ STEM knowledge, self-efficacy, and ultimately, their interests in future STEM learning and growth. An online survey was used to collect information from thirty-one girls at the end of a five-week summer camp. The results are mostly confirmative of past studies that used student-centered project-based authentic STEM learning with significant gains in students’ understanding of STEM, self-efficacy, and interests in STEM for future development. The unique contribution of the study, though, is the finding that, when given the opportunity to engage in active learning and problem-solving, girls’ interest in STEM subjects could be substantially boosted; the constructivist learning environment along with their gains in STEM knowledge can compensate any insufficiency in self-efficacy in this regard. This study provides insight about the importance of instructional approach in STEM education. 

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