Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Schmucker, Robin"'
Knowledge Components (KCs) linked to assessments enhance the measurement of student learning, enrich analytics, and facilitate adaptivity. However, generating and linking KCs to assessment items requires significant effort and domain-specific knowled
Externí odkaz:
http://arxiv.org/abs/2405.20526
Curriculum Analytics (CA) studies curriculum structure and student data to ensure the quality of educational programs. One desirable property of courses within curricula is that they are not unexpectedly more difficult for students of different backg
Externí odkaz:
http://arxiv.org/abs/2406.04348
Conversational tutoring systems (CTSs) offer learning experiences through interactions based on natural language. They are recognized for promoting cognitive engagement and improving learning outcomes, especially in reasoning tasks. Nonetheless, the
Externí odkaz:
http://arxiv.org/abs/2404.17460
Conversational tutoring systems (CTSs) offer learning experiences driven by natural language interaction. They are known to promote high levels of cognitive engagement and benefit learning outcomes, particularly in reasoning tasks. Nonetheless, the t
Externí odkaz:
http://arxiv.org/abs/2310.01420
Autor:
Tong, Richard Jiarui, Cao, Cassie Chen, Lee, Timothy Xueqian, Zhao, Guodong, Wan, Ray, Wang, Feiyue, Hu, Xiangen, Schmucker, Robin, Pan, Jinsheng, Quevedo, Julian, Lu, Yu
This paper presents the Never Ending Open Learning Adaptive Framework (NEOLAF), an integrated neural-symbolic cognitive architecture that models and constructs intelligent agents. The NEOLAF framework is a superior approach to constructing intelligen
Externí odkaz:
http://arxiv.org/abs/2308.03990
Autor:
Schmucker, Robin, Mitchell, Tom M.
Millions of learners worldwide are now using intelligent tutoring systems (ITSs). At their core, ITSs rely on machine learning algorithms to track each user's changing performance level over time to provide personalized instruction. Crucially, studen
Externí odkaz:
http://arxiv.org/abs/2202.03980
We consider the problem of assessing the changing performance levels of individual students as they go through online courses. This student performance (SP) modeling problem is a critical step for building adaptive online teaching systems. Specifical
Externí odkaz:
http://arxiv.org/abs/2109.01753
Hyperparameter optimization (HPO) is increasingly used to automatically tune the predictive performance (e.g., accuracy) of machine learning models. However, in a plethora of real-world applications, accuracy is only one of the multiple -- often conf
Externí odkaz:
http://arxiv.org/abs/2106.12639
Tree-form sequential decision making (TFSDM) extends classical one-shot decision making by modeling tree-form interactions between an agent and a potentially adversarial environment. It captures the online decision-making problems that each player fa
Externí odkaz:
http://arxiv.org/abs/2103.04546
Autor:
Perrone, Valerio, Donini, Michele, Zafar, Muhammad Bilal, Schmucker, Robin, Kenthapadi, Krishnaram, Archambeau, Cédric
Given the increasing importance of machine learning (ML) in our lives, several algorithmic fairness techniques have been proposed to mitigate biases in the outcomes of the ML models. However, most of these techniques are specialized to cater to a sin
Externí odkaz:
http://arxiv.org/abs/2006.05109