Responsible AI Lab’s Post

Theory-building and research on statistical learning offers new perspectives on how more students can become proficient readers (Elleman, Steacy, & Compton, 2019). According to this model, students acquire and use statistical regularities in language to become more proficient readers (Seidenberg, 2017). Readers build connections between phonemes and graphemes through encounters with patterns in the words that they read. The more that students read, the more frequently they will encounter common and consistent patterns. This frequency, in turn, leads to increased automaticity in recognizing words with patterns when they encounter them in words. Presenting children with decodable texts gives them exposure to recognizing and formulating these connections. https://lnkd.in/g5cwKJ7R

Review: Everything is Predictable - Tom Chivers ***** I can't remember when I last enjoyed a maths book as much as this celebration of Bayesian statistics, and how scientific studies would benefit from using them. Great! https://lnkd.in/gFcyHCFR #bookreview #popularscience #bayes

Are you conducting research related to mathematics and machine learning? Then you are invited to submit to our Special Issue "Mathematical Methods in Machine Learning and Data Science" guest edited with my colleagues Dr. Hana Sulieman and Dr. Ayman Alzaatreh.

Closing the gap between Social researchers and Data scientists. Sometimes it's difficult for professionals with a background in Social Sciences to fully understand the work, logic, tools, and methods of Data Scientists (statistics, math, programming, machine learning 🤯). But understanding the data analysis field is essential in daily work in market research agencies to formulate the right hypotheses and design -or help data experts in- effective investigations. 🤩 Good news: The complete course *Data Analysis For Social Scientists* of MIT (2023) is now available for free! The seminar (25 h.) introduces methods for harnessing data to answer questions of cultural, social, economic, and political interest. Bonus: contents are taught by Prof. Esther Duflo (2019 Nobel Prize for Economics!) and Dr. Sara Ellison through very clear presentations, exercises, and examples. What a pleasure! Check it out: https://lnkd.in/dVyd_PjX #dataanalysis #research #consumerinsights #methodology #socialsciences

If your research lies at the intersection of Mathematics and Machine Learning & Data Science, then this special issue of Mathematics, a highly recognized Scopus-indexed journal, is for you. Consider submitting your work before the deadline. 🙏

In the social sciences, #CausalAnalysis focuses on evaluating the effect of a predefined treatment, see e.g. https://lnkd.in/eGs5PB5q. In contrast, #CausalDiscovery (more prominent in #computerscience) aims to learn causal relations in an entire system of variables - see here for a gentle introduction with examples in the statistical software R: https://lnkd.in/emVpPXZt

Good refresh of applications of PDF and CDF in research.

CRCT Members Andrew Ruis, Yuanru Tan, and David Williamson Shaffer, along with many International Society for Quantitative Ethnography members contributed to this book.This open access comprehensive methodological book offers a much-needed answer to the lack of resources and methodological guidance in learning analytics, which has been a problem ever since the field started. The book covers all important quantitative topics in education at large as well as the latest in learning analytics and education data mining. The book also goes deeper into advanced methods that are at the forefront of novel methodological innovations. Authors of the book include world-renowned learning analytics researchers, R package developers, and methodological experts from diverse fields offering an unprecedented interdisciplinary reference on novel topics that is hard to find elsewhere. https://lamethods.org/