do you want to be good or to be optimized?

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This Saturday Morning Breakfast Cereal comic from yesterday spoke to me at a deep level: My first thoughts went to generative AI, an area in which I feel like a fetishization of optimization is crowding out really important questions of what is good. As I put it in a university survey earlier today, there are undeniable benefits to the use of AI tools, but there are important questions as to who benefits.

new publication: an autoethnography on French, data science, and paradigm change

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I’m pleased to share the publication of a new chapter of an edited volume. The chapter in question is “I’m a French teacher, not a data scientist”: Culture and languages across my professions, and it’s part of a volume called Cultures and languages across the curriculum in higher education. According to the CLAC Consortium, Culture and Languages Across the Curriculum (CLAC) is a: a curricular framework that provides opportunities to develop and apply language and intercultural competence within all academic disciplines through the use of multilingual resources and the inclusion of multiple cultural perspectives.

why 'open access' isn't enough

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I just barely microblogged something about what I want to say here, but over the past hour, it’s been nagging at me more and more, and I want to write some more about it. I was introduced to academia through educational technology, and I was introduced to educational technology through a class at BYU taught by David Wiley. This class was not about educational technology, but David’s passion for Web 2.

🔗 linkblog: my thoughts on 'Coverage in EdWeek of a recent article on uncertainty in science | Joshua M. Rosenberg, Ph.D.'

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Really enjoyed this coverage of Josh’s work! I haven’t ever done Bayesian work, so it surprised me how closely the ideas in the article resembled thoughts I’ve been having about positivism and other research paradigms. link to ‘Coverage in EdWeek of a recent article on uncertainty in science | Joshua M. Rosenberg, Ph.D.’

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We should promote open science practices in social science projects where they make sense but also stop normalizing it in a way that ignores non-positivist paradigms.

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I am increasingly of the opinion that the distinction between “qualitative” and “quantitative” isn’t all that useful and that what we actually mean is usually better expressed in other terms.

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I feel subversive (but absolutely justified) whenever I argue for interpreting “quantitative” data through an interpretivist lens.