Zobrazeno 1 - 10
of 101
pro vyhledávání: '"Rafferty, Anna"'
A central goal of both knowledge tracing and traditional assessment is to quantify student knowledge and skills at a given point in time. Deep knowledge tracing flexibly considers a student's response history but does not quantify epistemic uncertain
Externí odkaz:
http://arxiv.org/abs/2407.17427
Autor:
Kumar, Harsh, Xiao, Ruiwei, Lawson, Benjamin, Musabirov, Ilya, Shi, Jiakai, Wang, Xinyuan, Luo, Huayin, Williams, Joseph Jay, Rafferty, Anna, Stamper, John, Liut, Michael
Self-reflection on learning experiences constitutes a fundamental cognitive process, essential for the consolidation of knowledge and the enhancement of learning efficacy. However, traditional methods to facilitate reflection often face challenges in
Externí odkaz:
http://arxiv.org/abs/2406.07571
Autor:
Denny, Paul, Gulwani, Sumit, Heffernan, Neil T., Käser, Tanja, Moore, Steven, Rafferty, Anna N., Singla, Adish
This survey article has grown out of the GAIED (pronounced "guide") workshop organized by the authors at the NeurIPS 2023 conference. We organized the GAIED workshop as part of a community-building effort to bring together researchers, educators, and
Externí odkaz:
http://arxiv.org/abs/2402.01580
Autor:
Kumar, Harsh, Li, Tong, Shi, Jiakai, Musabirov, Ilya, Kornfield, Rachel, Meyerhoff, Jonah, Bhattacharjee, Ananya, Karr, Chris, Nguyen, Theresa, Mohr, David, Rafferty, Anna, Villar, Sofia, Deliu, Nina, Williams, Joseph Jay
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence (IAAI) 2024
Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support. While these interventions can be effective, real-world experimental testing can further enhanc
Externí odkaz:
http://arxiv.org/abs/2310.18326
Autor:
Li, ZhaoBin, Yee, Luna, Sauerberg, Nathaniel, Sakson, Irene, Williams, Joseph Jay, Rafferty, Anna N.
Digital educational technologies offer the potential to customize students' experiences and learn what works for which students, enhancing the technology as more students interact with it. We consider whether and when attempting to discover how to pe
Externí odkaz:
http://arxiv.org/abs/2309.02856
Autor:
Zavaleta-Bernuy, Angela, Zheng, Qi Yin, Shaikh, Hammad, Nogas, Jacob, Rafferty, Anna, Petersen, Andrew, Williams, Joseph Jay
Adaptive experiments can increase the chance that current students obtain better outcomes from a field experiment of an instructional intervention. In such experiments, the probability of assigning students to conditions changes while more data is be
Externí odkaz:
http://arxiv.org/abs/2208.05092
Autor:
Yanez, Fernando J., Zavaleta-Bernuy, Angela, Han, Ziwen, Liut, Michael, Rafferty, Anna, Williams, Joseph Jay
Conducting randomized experiments in education settings raises the question of how we can use machine learning techniques to improve educational interventions. Using Multi-Armed Bandits (MAB) algorithms like Thompson Sampling (TS) in adaptive experim
Externí odkaz:
http://arxiv.org/abs/2208.05090
Autor:
Zavaleta-Bernuy, Angela, Han, Ziwen, Shaikh, Hammad, Zheng, Qi Yin, Lim, Lisa-Angelique, Rafferty, Anna, Petersen, Andrew, Williams, Joseph Jay
Email communication between instructors and students is ubiquitous, and it could be valuable to explore ways of testing out how to make email messages more impactful. This paper explores the design space of using emails to get students to plan and re
Externí odkaz:
http://arxiv.org/abs/2208.05087
Autor:
Li, Tong, Nogas, Jacob, Song, Haochen, Kumar, Harsh, Durand, Audrey, Rafferty, Anna, Deliu, Nina, Villar, Sofia S., Williams, Joseph J.
Multi-armed bandit algorithms like Thompson Sampling (TS) can be used to conduct adaptive experiments, in which maximizing reward means that data is used to progressively assign participants to more effective arms. Such assignment strategies increase
Externí odkaz:
http://arxiv.org/abs/2112.08507
This survey article has grown out of the RL4ED workshop organized by the authors at the Educational Data Mining (EDM) 2021 conference. We organized this workshop as part of a community-building effort to bring together researchers and practitioners i
Externí odkaz:
http://arxiv.org/abs/2107.08828