no code implementations • 10 Apr 2024 • Ryan S. Baker, Nigel Bosch, Stephen Hutt, Andres F. Zambrano, Alex J. Bowers
Recently, ACM FAccT published an article by Kwegyir-Aggrey and colleagues (2023), critiquing the use of AUC ROC in predictive analytics in several domains.
1 code implementation • 9 Dec 2023 • Conrad Borchers, Jiayi Zhang, Ryan S. Baker, Vincent Aleven
We discuss system re-design opportunities to add SRL support during stages of processing and paths forward for using machine learning to speed research depending on the assessment of SRL based on transcription of think-aloud data.
no code implementations • 23 Nov 2023 • Yan Tao, Olga Viberg, Ryan S. Baker, Rene F. Kizilcec
Culture fundamentally shapes people's reasoning, behavior, and communication.
1 code implementation • 14 Jul 2023 • Valdemar Švábenský, Ryan S. Baker, Andrés Zambrano, Yishan Zou, Stefan Slater
First, we train and cross-validate several models on an original data set of 3, 503 posts from MOOCs at University of Pennsylvania.
no code implementations • 30 Jun 2023 • Maciej Pankiewicz, Ryan S. Baker
Furthermore, when GPT hints were unavailable, students in the experimental condition were initially less likely to solve the assignment correctly.
no code implementations • 14 Oct 2019 • Richard Scruggs, Ryan S. Baker, Bruce M. McLaren
We apply this extension to DKT and DKVMN, resulting in knowledge estimates that correlate better with a posttest than knowledge estimates from Bayesian Knowledge Tracing (BKT), an algorithm designed to infer knowledge, and another classic algorithm, Performance Factors Analysis (PFA).
no code implementations • WS 2019 • Srecko Joksimovic, Ryan S. Baker, Jaclyn Ocumpaugh, Juan Miguel L. Andres, Ivan Tot, Elle Yuan Wang, Shane Dawson
Discussion forum participation represents one of the crucial factors for learning and often the only way of supporting social interactions in online settings.
no code implementations • 12 Jan 2019 • Eda Okur, Sinem Aslan, Nese Alyuz, Asli Arslan Esme, Ryan S. Baker
One open question in annotating affective data for affect detection is whether the labelers (i. e., human experts) need to be socio-culturally similar to the students being labeled, as this impacts the cost feasibility of obtaining the labels.