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Graesser
Graesser
Professor of Psychology and Institute for Intelligent Systems, University of Memphis
Verified email at memphis.edu
Title
Cited by
Year
‒Important Considerations for Learner Models: Transfer Potential and Pedagogical Content Knowledge
A Lesgold, A Graesser
Design Recommendations for Intelligent Tutoring Systems, 15, 2013
12013
‒MATHEMATICAL MODELS TO DETERMINE
R Robson, X Hu, E Robson, AC Graesser
Design Recommendations for Intelligent Tutoring Systems: Volume 9-Competency …, 2022
2022
‒Natural Language, Discourse, and Conversational Dialogues within Intelligent Tutoring Systems: A Review
K Brawner, A Graesser
Design Recommendations for Intelligent Tutoring Systems 2, 189-204, 2014
62014
‒The DENDROGRAM Model of Instruction: On Instructional Strategies and Their Implementation in DeepTutor
V Rus, M Conley, A Graesser
Design Recommendations for Intelligent Tutoring Systems, 311, 2014
62014
–ADDING A HUMAN TO THE ADAPTIVE INSTRUCTIONAL SYSTEM LOOP: INTEGRATING GIFT AND BATTLE SPACE VISUALIZATION
B Goldberg, M Hoffman, AC Graesser
Design Recommendations for Intelligent Tutoring Systems: Volume 8-Data …, 2020
82020
–ADDINGAHUMAN TO THEADAPTIVE INSTRUCTIONALSYSTEM LOOP: INTEGRATING GIFTAND BATTLE SPACE VISUALIZATION
B Goldberg, M Hoffman, AC Graesser
Design Recommendations for Intelligent Tutoring Systems, 191, 0
–Assessment in AutoTutor
Z Cai, AC Graesser, X Hu, B Kuo
Design Recommendations for Intelligent Tutoring Systems, 309, 0
–Design and Construction of Domain Models
AM Olney, A Graesser
Design Recommendations for Intelligent Tutoring Systems, 93, 0
–Domain Modeling in AutoTutor
Z Cai, A Graesser, X Hu
Design Recommendations for Intelligent Tutoring Systems, 205, 2016
2016
–SELF-IMPROVING COMPONENTS IN CONVERSATIONAL INTELLIGENT TUTORING SYSTEMS
Z Cai, AC Graesser, X Hu, JL Cockroft
Design Recommendations for Intelligent Tutoring Systems, 119, 0
–SIMPLE HUMANS, EVOLVING COMPUTATION, SMART INTELLIGENT TUTORING SYSTEMS
AC Graesser, JA DeFalco, JL Cockroft
Design Recommendations for Intelligent Tutoring Systems, 169, 0
–VISUALIZATION IMPLICATIONS FOR THE VALIDITY OF INTELLIGENT TUTORING SYSTEMS
D Zapata-Rivera, AC Graesser, J Kay, X Hu, SJ Ososky
Design recommendations for intelligent tutoring systems, 61, 2020
132020
" Introduction to the Special Issue on Advanced Learning Technologies": Correction to Aleven, Beal, and Graesser (2013).
V Aleven, CR Beal, AC Graesser
American Psychological Association 105 (4), 931, 2013
2013
" Sustaining optimal motivation: A longitudinal analysis of interventions to broaden participation of underrepresented students in STEM": Correction to Hernandez et al.(2013).
PR Hernandez, P Schultz, M Estrada, A Woodcock, RC Chance
American Psychological Association 105 (4), 1025, 2013
82013
(1980). Advanced outlines, familiarity, and text genre on retention of prose
AC Graesser, K Hauft-Smith, AD Cohen, LI Pyles
Journal of Experimental Education 48 (28), 1-290, 0
2
12 Answering Questions About Information in
AC Graesser, PJ Byrne, ML Behrens
Questions and Information Systems, 2013
2013
13 Constructing inferences in naturalistic reading contexts
AC Graesser, H Li, S Feng
Inferences during reading, 290, 2015
362015
13 World Knowledge, Inferences, and Questions
AC Graesser, JP Magliano, PM Tidwell
REPORT NO PUB DATE, 245, 1992
21992
149 Question Asking During Tutoring and in the Design of Educational Software
AC Graesser, NK Person, J Huber
Cognitive Science Foundations of Instruction, 149-172, 0
2
25 principles of learning
AC Graesser, DF Halpern, M Hakel
Task force on lifelong learning at work and at home, 2008
92008
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Articles 1–20