Fetching the paper…
Reading the bibliography…
Intelligent Tutoring Systems (ITSs) have significantly enhanced adult literacy training, a key factor for societal participation, employment opportunities, and lifelong learning.
“Knowledge tracing: Modeling the acquisition of procedural knowledge”
Albert Corbett and John Anderson · 1994
Earlier work this paper cites.
“Intelligent tutoring systems”
Albert Corbett, Kenneth Koedinger and John Anderson · 1997
Earlier work this paper cites.
“AutoTutor: A tutor with dialogue in natural language”
Arthur Graesser et al · 2004
Earlier work this paper cites.
“Computerized Learning Environments That Incorporate Research in Discourse Psychology, Cognitive Science, and Computational Linguistics.”
Arthur Graesser, Xiangen Hu and Danielle McNamara · 2005
Earlier work this paper cites.
“Another look at measures of forecast accuracy”
Rob Hyndman and Anne Koehler · 2006
Earlier work this paper cites.
“Performance Factors Analysis–A New Alternative to Knowledge Tracing.”
Philip Pavlik, Hao Cen and Kenneth Koedinger · 2009
Earlier work this paper cites.
“Modeling individualization in a bayesian networks implementation of knowledge tracing”
Zachary Pardos and Neil Heffernan · 2010
Earlier work this paper cites.
“Comparing knowledge tracing and performance factor analysis by using multiple model fitting procedures”
Yue Gong, Joseph Beck and Neil Heffernan · 2010
Earlier work this paper cites.
“Reading comprehension”
Gary Woolley and Gary Woolley · 2011
Earlier work this paper cites.
“Instructional Factors Analysis: A Cognitive Model For Multiple Instructional Interventions.”
Min Chi et al · 2011
Earlier work this paper cites.
“KT-IDEM: Introducing item difficulty to the knowledge tracing model”
Zachary Pardos and Neil Heffernan · 2011
Earlier work this paper cites.
“User modeling–a notoriously black art”
Michael Yudelson, Philip Pavlik and Kenneth Koedinger · 2011
Earlier work this paper cites.
“Improving adult literacy instruction: Options for practice and research”
National Council · 2012
Earlier work this paper cites.
“Literacy, lives and learning”
David Barton et al · 2012
Earlier work this paper cites.
“Intelligent tutoring systems.”
Arthur Graesser, Mark Conley and Andrew Olney · 2012
Earlier work this paper cites.
“A review of recent advances in learner and skill modeling in intelligent learning environments”
Michel Desmarais and Ryan Baker · 2012
Earlier work this paper cites.
“The Knowledge-Learning-Instruction framework: Bridging the science-practice chasm to enhance robust student learning”
Kenneth Koedinger, Albert Corbett and Charles Perfetti · 2012
Earlier work this paper cites.
“Individualized bayesian knowledge tracing models”
Michael Yudelson, Kenneth Koedinger and Geoffrey Gordon · 2013
Earlier work this paper cites.
“Wheel-spinning: Students who fail to master a skill”
Joseph Beck and Yue Gong · 2013
Earlier work this paper cites.
“A Review of Learner Models Used in Intelligent Tutoring Systems”
Philip Pavlik, Keith Brawner, Andrew Olney and Antonija Mitrovic · 2013
Earlier work this paper cites.
“Sparse factor analysis for learning and content analytics”
Andrew Lan, Andrew Waters, Christoph Studer and Richard Baraniuk · 2013
Earlier work this paper cites.
“AutoTutor and family: A review of 17 years of natural language tutoring”
Benjamin Nye, Arthur Graesser and Xiangen Hu · 2014
Earlier work this paper cites.
“Quantized matrix completion for personalized learning”
Andrew Lan, Christoph Studer and Richard Baraniuk · 2014
Earlier work this paper cites.
“Student modeling in intelligent tutoring systems”, 2014
Yue Gong · 2014
Earlier work this paper cites.
“Xgboost: extreme gradient boosting”
Tianqi Chen et al · 2015
Cited alongside, same era.
“Deep knowledge tracing”
Chris Piech et al · 2015
Cited alongside, same era.
“Reading comprehension lessons in AutoTutor for the Center for the Study of Adult Literacy”
Arthur Graesser et al · 2016
Cited alongside, same era.
“Xgboost: A scalable tree boosting system”
Tianqi Chen and Carlos Guestrin · 2016
Cited alongside, same era.
“Going deeper with deep knowledge tracing.”
Xiaolu Xiong, Siyuan Zhao, Eric Van and Joseph Beck · 2016
Cited alongside, same era.
“Two heads may be better than one: Learning from computer agents in conversational trialogues”
Arthur Graesser, Carol Forsyth and Blair Lehman · 2017
Cited alongside, same era.
“Patterns of adults with low literacy skills interacting with an intelligent tutoring system”
Ying Fang et al · 2022
Later among the works it cites.
“Towards reasoning in large language models: A survey”
Jie Huang and Kevin-Chuan Chang · 2022
Later among the works it cites.
“Chain-of-thought prompting elicits reasoning in large language models”
Jason Wei et al · 2022
Later among the works it cites.
“How to optimize student learning using student models that adapt rapidly to individual differences”
Luke Eglington and Philip Pavlik · 2022
Later among the works it cites.
“Mathprompter: Mathematical reasoning using large language models”
Shima Imani, Liang Du and Harsh Shrivastava · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“Assessment with computer agents that engage in conversational dialogues and trialogues with learners”
Arthur Graesser, Zhiqiang Cai, Brent Morgan and Lijia Wang · 2017
Cited alongside, same era.
“Bayesian knowledge tracing, logistic models, and beyond: an overview of learner modeling techniques”
Radek Pelánek · 2017
Cited alongside, same era.
“Dynamic key-value memory networks for knowledge tracing”
Jiani Zhang, Xingjian Shi, Irwin King and Dit-Yan Yeung · 2017
Cited alongside, same era.
“Diagnostic Assessment of Adults’ Reading Deficiencies in an Intelligent Tutoring System.”
Genghu Shi et al · 2018
Cited alongside, same era.
“Clustering the Learning Patterns of Adults with Low Literacy Skills Interacting with an Intelligent Tutoring System.”
Ying Fang et al · 2018
Cited alongside, same era.
“Exploring an intelligent tutoring system as a conversation-based assessment tool for reading comprehension”
Genghu Shi et al · 2018
Cited alongside, same era.
Ming Jin et al · 2023
Later among the works it cites.
“Large language models are zero-shot time series forecasters”
Nate Gruver, Marc Finzi, Shikai Qiu and Andrew Wilson · 2023
Later among the works it cites.
“Beyond Predictive Learning Analytics Modelling and onto Explainable Artificial Intelligence with Prescriptive Analytics and ChatGPT”
Teo Susnjak · 2023
Later among the works it cites.
Josh Achiam et al · 2023
Later among the works it cites.
“Enhancing the prediction of student performance based on the machine learning XGBoost algorithm”
Amal Asselman, Mohamed Khaldi and Souhaib Aammou · 2023
Later among the works it cites.
“Exploring the Individual Differences in Multidimensional Evolution of Knowledge States of Learners”
Liang Zhang et al · 2023
Later among the works it cites.
“Automated Search Improves Logistic Knowledge Tracing, Surpassing Deep Learning in Accuracy and Explainability”
Philip Pavlik and Luke Eglington · 2023
Later among the works it cites.
“Can large language models provide feedback to students? A case study on ChatGPT”
Wei Dai et al · 2023
Later among the works it cites.
“Ruffle&Riley: Towards the Automated Induction of Conversational Tutoring Systems”
Robin Schmucker, Meng Xia, Amos Azaria and Tom Mitchell · 2023
Later among the works it cites.
Chee Tan · 2023
Later among the works it cites.
“Evaluating reading comprehension exercises generated by LLMs: A showcase of ChatGPT in education applications”
Changrong Xiao et al · 2023
Later among the works it cites.
“Evaluating the logical reasoning ability of chatgpt and gpt-4”
Hanmeng Liu et al · 2023
Later among the works it cites.
“Knowledge tracing: A survey”
Ghodai Abdelrahman, Qing Wang and Bernardo Nunes · 2023
Later among the works it cites.
“An XGBoost-Based Knowledge Tracing Modelf”
Wei Su et al · 2023
Later among the works it cites.
“Chatgpt in the generalized intelligent framework for tutoring”
Faruk Ahmed, Keith Shubeck and Xiangen Hu · 2023
Later among the works it cites.
“Large Language Models for Mathematical Reasoning: Progresses and Challenges”
Janice Ahn et al · 2024
Closest in time.
“Large Language Models for Time Series: A Survey”
Xiyuan Zhang, Ranak Chowdhury, Rajesh Gupta and Jingbo Shang · 2024
Closest in time.
“Improving Assessment of Tutoring Practices using Retrieval-Augmented Generation”
Jionghao Lin et al · 2024
Closest in time.
Liang Zhang et al · 2024
Closest in time.