J
John Hollander Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Assistant Professor
faculty
15 h-index
142 pubs
1,690 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
John Hollander's research investigates the intersection of cognitive processes, artificial intelligence, and educational applications, with a particular focus on adult literacy. His work explores how technology, including large language models and intelligent tutoring systems, can be leveraged to enhance learning performance and develop literacy skills in digital environments. Hollander has published on topics such as predicting learning outcomes using AI, the importance of learner characteristics in educational systems, and integrating assessment with reading comprehension training. He also studies the extraction of data from eye-tracking technology within cognitive paradigms, such as mind wandering. Hollander's scholarly contributions are reflected in an h-index of 15 and over 1,600 citations across 142 publications.
Metrics
- h-index: 15
- Publications: 142
- Citations: 1,690
Selected Publications
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The role of parental psychological control in domain-specific self-esteem among Mexican adolescents. (2025)
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Exploring the Potential of Large Language Models for Estimating the Reading Comprehension Question Difficulty (2025)
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Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI (2025)
Collaboration Network
Top Collaborators
John Sabatini
13 shared publications
- How Item and Learner Characteristics Matter in Intelligent Tutoring Systems Data
- Predicting Learning Performance with Large Language Models: A Study in Adult Literacy
- A framework of literacy development and how <scp>AI</scp> can transform theory and practice
- Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
- Integrating SARA Assessment with Reading Comprehension Training in AutoTutor
Showing 5 of 13 shared publications
Xiangen Hu
6 shared publications
- Predicting Learning Performance with Large Language Models: A Study in Adult Literacy
- Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
- Integrating SARA Assessment with Reading Comprehension Training in AutoTutor
- Predicting Learning Performance with Large Language Models: A Study in Adult Literacy
- Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
Showing 5 of 6 shared publications
Liang Zhang
6 shared publications
- Predicting Learning Performance with Large Language Models: A Study in Adult Literacy
- Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
- Exploring the Potential of Large Language Models for Estimating the Reading Comprehension Question Difficulty
- Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
- Generative Data Imputation for Sparse Learner Performance Data Using Generative Adversarial Imputation Networks
Showing 5 of 6 shared publications
Jionghao Lin
5 shared publications
- Predicting Learning Performance with Large Language Models: A Study in Adult Literacy
- Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
- Predicting Learning Performance with Large Language Models: A Study in Adult Literacy
- Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
- Generative Data Imputation for Sparse Learner Performance Data Using Generative Adversarial Imputation Networks
Art Graesser
University of Memphis (US)
4 shared publications
- How Item and Learner Characteristics Matter in Intelligent Tutoring Systems Data
- Integrating SARA Assessment with Reading Comprehension Training in AutoTutor
- An intelligent tutoring system for improving adult literacy skills in digital environments
- Importance of Learner Characteristics in Intelligent Tutoring for Adult Literacy
Arthur C. Graesser
4 shared publications
- A framework of literacy development and how <scp>AI</scp> can transform theory and practice
- Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
- Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
- Generative Data Imputation for Sparse Learner Performance Data Using Generative Adversarial Imputation Networks
Conrad Borchers
Carnegie Mellon University (US)
4 shared publications
- Predicting Learning Performance with Large Language Models: A Study in Adult Literacy
- Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
- Predicting Learning Performance with Large Language Models: A Study in Adult Literacy
- Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
Andrew M. Olney
University of Memphis (US)
2 shared publications
- Raising the Roof: Situating Verbs in Symbolic and Embodied Language Processing
- Vertical directionality ratings as lexical norms for English verbs
Stephanie Huette
University of Memphis (US)
2 shared publications
- Extracting blinks from continuous eye-tracking data in a mind wandering paradigm
- Extracting blinks from continuous eye-tracking data in a mind wandering paradigm
Tenaha O’Reilly
Educational Testing Service (US)
2 shared publications
- A framework of literacy development and how <scp>AI</scp> can transform theory and practice
- Importance of Learner Characteristics in Intelligent Tutoring for Adult Literacy
Meng Cao
2 shared publications
- Predicting Learning Performance with Large Language Models: A Study in Adult Literacy
- Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
D. P. Weitekamp
Carnegie Mellon University (US)
2 shared publications
- Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
- Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
Y. K. Jain
2 shared publications
- Exploring the Potential of Large Language Models for Estimating the Reading Comprehension Question Difficulty
- Exploring the Potential of Large Language Models for Estimating the Reading Comprehension Question Difficulty
Ang He
2 shared publications
- Exploring the Potential of Large Language Models for Estimating the Reading Comprehension Question Difficulty
- Exploring the Potential of Large Language Models for Estimating the Reading Comprehension Question Difficulty
Sunny X. Tang
2 shared publications
- Exploring the Potential of Large Language Models for Estimating the Reading Comprehension Question Difficulty
- Exploring the Potential of Large Language Models for Estimating the Reading Comprehension Question Difficulty
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