Purpose: The Center for the Study of Adult Literacy (CSAL) sought to improve our understanding of ways to advance the reading skills of struggling adult learners reading at the 3rd to 8th grade levels. CSAL’s main work focused on developing and pilot testing a multi-component reading intervention for this population. CSAL also conducted exploratory work on underlying cognitive and motivational processes that contribute to or impede reading development and on the adequacy of measurement instruments for this population. Additionally, CSAL provided national leadership and dissemination.

CSAL was staffed by researchers with expertise in adult and child literacy, education technology, statistics, and psychometrics. It was a collaborative effort across four research sites: Atlanta, Georgia; Memphis, Tennessee; and Toronto and St. Catharines, Ontario, Canada. Adults who attend adult basic education programs in both the United States and Canada participated.

Key Personnel: Maureen Lovett (University of Toronto and The Hospital for Sick Children), Art Graesser (University of Memphis), Jan Frijters (Brock University), Lee Branum-Martin (Georgia State University), Chris Oshima (Georgia State University), Hongli Li (Georgia State University), Robin Morris (Georgia State University), Xiangen Hu (University of Memphis), Zhiqiang Cai (University of Memphis), Mark Conley (University of Memphis), Andrew Olney (University of Memphis)

Project Website: https://sites.gsu.edu/csal/

Archived CSAL Fact Sheet: View, Download, and Print PDF version of the CSAL Fact Sheet (927 KB)

IES Program Contact: Meredith Larson (Meredith.Larson@ed.gov)

R&D CENTER ACTIVITIES DESCRIPTION

The following summary describes the three major projects of CSAL with associated findings from publications, its possible future, and a list of publications.

Development and Piloting of Web-based Reading Instruction for Struggling Adult Readers

Using an iterative design process and feedback from students and practitioners, the researchers developed, modified, and refined a hybrid reading curriculum for adults who struggle with reading. They conducted usability and feasibility tests, before running a pilot study.

Key Features of the Curriculum:

• • • 100 hours of hybrid strategy- and skill-based reading instruction, with different components focusing on word reading and spelling, vocabulary development, reading comprehension, and independent· reading for adults reading at 3rd-8th grade equivalency levels (see Einarson et al. (2021) for description full text available).
    • Animated, interactive, and adaptive web-based comprehension intelligent tutoring program called AutoTutor (YouTube video)
    • Web-based text repository

Key Findings from Usability and Feasibility Studies:

• • • Profiles of participants’ performance on AutoTutor could be categorized into 4 groups: (1) Fast and accurate (AutoTutor was probably too easy for them), (2) Slow and accurate (AutoTutor was probably appropriate for them), (3) Fast with medium accuracy (Participants would benefit from slower performance), and (4) Slow and inaccurate (AutoTutor was probably too hard for them) (see Chen et al., 2021 and Fang et al. 2021))

Key Findings from Teachers (see Einarson et al., 2021):

• • The professional development and ongoing coaching they received throughout the intervention was essential to their ability to administer the intervention with efficacy.
  • The students benefited from the highly sequenced and scripted instructional approach.
  • The students appreciated the basic foundational reading instruction and wanted more.

Exploration of Underlying Cognitive and Motivational Factors and the Appropriateness of Common Assessments

CSAL collected data on 37 different measures on 544 participants to clarify the underlying cognitive and motivational profiles of the target population and the appropriateness of commonly used assessments.

Findings from Exploration of Underlying Cognitive Factors:

• • Uncovered four distinct adult learner profiles: (1) Globally Impaired Readers (i.e., adult learners who were relatively weak in all reading-related competencies), (2) Globally Better Readers (i.e., adult learners who were relatively strong in all competencies), (3) Weak Decoders (i.e., adult learners who were weak in lower-level competencies and relatively strong in higher-level competencies), and (4) Weak Language Comprehenders (i.e., adult learners who were relatively strong in lower-level competencies and weak in higher-level competencies). These different profiles may imply different instructional needs (Talwar et al., 2020).
  • After the analysis controlled for educational attainment, background knowledge made a significant unique contribution to reading comprehension beyond decoding, listening comprehension and educational attainment (Talwar et al., 2018).

Findings on the Appropriateness of Common Assessments:

• • Analysis of five fluency assessments suggests that though these assessments tap into similar skills, not all of them showed convergent validity with one another as expected. The researchers also examined the correlations between the fluency assessments and the assessments of other reading skills and did not find clear evidence of discriminant validity for all measures: some of the fluency measures had weak relationships with other measures, others showed strong relationships (Nightingale et al. 2016).

Leadership and Dissemination Activities

In addition to common national leadership activities, such as presentations and publications, CSAL hosted many additional activities and distributed resources for adult education stakeholders.

CSAL Website: CSAL maintained a website and newsflash service for those interested in the center’s work and adult literacy more broadly and contained our web-based text repository for adult learners, resources for practitioners and policymakers, information about CSAL presentations and archived professional development workshops, and information on the various aspects of our Center.

Practitioner-friendly Documents: Findings from 22 of CSAL’s studies were described in practitioner-friendly documents found at https://sites.gsu.edu/csal/additional-studies/.

Culminating Convening on Technology and Adult Learning: In July 2021, CSAL held a virtual convening attended by researchers, instructors, professional development providers, state directors, federal staff, EdTech developers, and foundation staff. Panelists and attendees discussed the approaches and challenges of using digital instruction with adult learners, focusing on the methods and priorities for building knowledge and evidence in the field. Documents from this convening include a handout on AutoTutor studies and written summaries of the sessions. Audio recordings from the convening are also available:

• • Opening remarks and Auto Tutor – https://vimeo.com/583403131/6b587fbf95
  • Session 1 – https://vimeo.com/583405959/a03c90f640
  • Session 2 – https://vimeo.com/583411024/e4027ad085
  • Closing remarks – https://vimeo.com/583471529/8bdd8da0b5

The Possible Future of the CSAL Curriculum

As of Spring 2022, CSAL had completed its project objectives. The researchers, however, continue to collaborate and work on the instructional materials and AutoTutor.

The curriculum designers aim to have the instructional materials ready for distribution to adult literacy programs by approximately 2023 under the possible names “EmpowerTM Reading for Adults: Decoding, Spelling, and Vocabulary” and “EmpowerTM Reading for Adults: Comprehension and Vocabulary Program.” The purchase of the program would include a full set of instructional materials (books, binders, 10 workbooks, and support materials). For the Decoding, Spelling, and

Vocabulary series, the researchers envision the cost also including professional development and training.

The AutoTutor team also continues to innovate. AutoTutor, which is the web-based interactive and adaptive reading comprehension practice component of the intervention, received additional funding from IES (grant R305A200413) to develop it into a stand-alone comprehension practice tool. This development work aims to create engaging materials for adult learners to practice reading comprehension strategies, modules to address and strengthen learners’ digital literacy needs, and components focused on instructors’ professional development needs.

Related IES Projects

Projects that led to CSAL: Coh-Metrix: Automated Cohesion and Coherence Scores to Predict Text Readability and Facilitate Comprehension (R305G020018), An Implementation of Vicarious Learning with Deep-Level Reasoning Questions in Middle School and High School Classrooms (R305H050169), Multiple-Component Remediation for Struggling Middle School Readers (R324G060005), Acquiring Research Investigative and Evaluative Skills (ARIES) for Scientific Inquiry (R305B070349), The Writing Pal: An Intelligent Tutoring System that Provides Interactive Writing Strategy Training (R305A080589), Guru: A Computer Tutor that Models Expert Human Tutors (R305A080594), Applications of Intelligent Tutoring Systems (ITS) to Improve the Skill Levels of Students with Deficiencies in Mathematics (R305A090528), DeepTutor: An Intelligent Tutoring System Based on Deep Language and Discourse Processing and Advanced Tutoring Strategies (R305A100875)

Projects that CSAL led to: Developing and Validating Web-administered, Reading for Understanding Assessments for Adult Education (R305A190522), Developing and Implementing a Technology-Based Reading Comprehension Instruction System for Adult Literacy Students (R305A200413), GSU Postdoctoral Training on Adult Literacy: G-PAL (R305B200007)

PRODUCTS

ERIC Citations: Find available citations in ERIC for this award here (ERIC – Search Results (ed.gov).

Selected Publication List:

Book

Sottilare, R., Graesser, A., Hu, X., Holden, H. (Eds.) (2013). Design Recommendations for Intelligent Tutoring Systems: Learner Modeling (Vol.1). Orlando, FL: Army Research Laboratory.

Sottilare, R., Graesser, A.C., Hu, X., & Goldberg, B. (Eds.) (2014), Design Recommendations for Intelligent Tutoring Systems: Instructional Management (Vol.2). Orlando, FL: Army Research Laboratory.

Sottilare, R., Graesser, A.C., Hu, X., & Brawner, K. (Eds.) (2015), Design Recommendations for Intelligent Tutoring Systems: Authoring Tools (Vol.3). Orlando, FL: Army Research Laboratory.

Sottilare, R., Graesser, A.C., Hu, X., Olney, A., Nye, B., & Sinatra, A. (Eds.) (2016). Design Recommendations for Intelligent Tutoring Systems: Domain Modeling (Vol. 4). Orlando, FL: U.S. Army Research Laboratory. ISBN 978-0-9893923-9-6.

Sottilare, R., Graesser, A.C., Hu, X., & Goodwin, G. (Eds.) (2017). Design Recommendations for Intelligent Tutoring Systems: Assessment (Vol. 5). Orlando, FL: U.S. Army Research Laboratory.

Sottilare, R., Graesser, A.C., Hu, X., & Sinatra, A. (Eds.) (2018). Design Recommendations for Intelligent Tutoring Systems: Team Tutoring (Vol. 6). Orlando, FL: U.S. Army Research Laboratory.

Sinatra, A., Graesser, A.C., Hu,X., Brawner. K., & Rus, V. (Eds.) (2019) Design Recommendations for Intelligent Tutoring Systems: Self-improving systems (Vol.7). Orlando, FL: Army Research Laboratory.

Sinatra, A., Graesser, A.C., Hu, X., Goldberg, B., & Hampton, A. (2020). Design Recommendations for Intelligent Tutoring Systems: Data Visualization (Vol.8). Orlando, FL: Army Research Laboratory.

Book chapters

Brawner, K., and Graesser, A. (2014). Natural language, discourse, and conversational dialogues within intelligent tutoring systems: A review. In R. Sottilare, A.C. Graesser, X. Hu, and B. Goldberg (Eds.), Design Recommendations for Intelligent Tutoring Systems: Instructional Management, Volume 2 (pp. 189–204). Orlando, FL: Army Research Laboratory.

Cai, Z., Feng, S., Baer, W., and Graesser, A. (2014). Instructional strategies in trialogue-based intelligent tutoring systems. In R. Sottilare, A.C. Graesser, X. Hu, and B. Goldberg (Eds.), Design Recommendations for Intelligent Tutoring Systems: Instructional Management, Volume 2 (pp. 225–235). Orlando, FL: Army Research Laboratory.

Cai, Z., Graesser, A.C., Hu, X., and Cockroft, J. L. (2019). Self-improving components in conversational intelligent tutoring systems. In A. Sinatra, A.C. Graesser, X. Hu, K.

Brawner and V. Rus (Eds.), Design Recommendations for Intelligent Tutoring Systems: Self-improving systems (Vol.7) (pp. 119-126). Orlando, FL: Army Research Laboratory.

Cai, Z., Hampton, A.J., Graesser, A.C., Hu, X., Cockroft, J.L., Shaffer, D.W., and Dorneich, M.C. (2018). Roles of talking agents in online collaborative learning environments. In R.A. Sottilare, A.C. Graesser, X. Hu, and A.M. Sinatra (Eds.). Design Recommendations for Intelligent Tutoring Systems: Team Tutoring (Vol. 6) (pp. 169-178). Orlando, FL: U.S. Army Research Laboratory.

D’Mello, S.K., and Graesser, A.C. (2015). Feeling, thinking, and computing with affect-aware learning technologies. In R. Calvo, S. D’Mello, J. Gratch, and A. Kappas (Eds.), The Oxford Handbook of Affective Computing (pp. 419–434). New York: Oxford University Press.

Frijters, J. C., Brown, E., and Greenberg, D. (2019). Gender Differences in the Reading Motivation of Adults with Low Literacy Skills. In D. Perin (Ed). The Wiley Handbook of Adult Literacy, (pp. 63-87.). New York: Wiley.

Graesser, A. C., Cai, Z., Baer, W. O., Olney, A. M., Hu, X., Reed, M., and Greenberg, D. (2016). Reading Comprehension Lessons in AutoTutor for the Center for the Study of Adult Literacy. In S. A. Crossley and D. S. McNamara (Eds.), Adaptive Educational Technologies for Literacy Instruction. (pp. 288–293). New York: Taylor and Francis Routledge.

Graesser, A. C., DeFalco, J. A., and Cockroft, J. L. (2019). Simple humans, evolving computation, smart intelligent tutoring systems. In A. Sinatra, A.C. Graesser, X. Hu, K. Brawner and V. Rus (Eds.), Design Recommendations for Intelligent Tutoring Systems: Self-improving systems (Vol.7) (pp.169-174) Orlando, FL: Army Research Laboratory.

Graesser, A. C., Lippert, A. M., and Hampton, A. J. (2017). Successes and failures in building learning environments to promote deep learning: The value of conversational agents. In J. Buder and F. W. Hesse (Eds.), Informational Environments: Effects of Use, Effective Designs (pp. 273–298). Springer International Publishing. doi.org/10.1007/978-3-319-64274-1_12

Graesser, A.C. (2014). Guided instruction and scaffolding. In R.A. Sottilare, A.C. Graesser, X. Hu, and B.S. Goldberg (Eds.), Design recommendations for intelligent tutoring systems, Volume 2: Instructional management (pp. 261–264). Orlando, FL: Army Research Laboratory.

Graesser, A.C. (2017). Reflections on serious games. In H. van Oostendorp and P. Wouters (Eds.) Instructional techniques to facilitate learning and motivation of serious games (pp. 199-212). AG, Switzerland, Springer.

Graesser, A.C. (2020). Learning science principles and technologies with agents that promote deep learning. In R.S. Feldman (ed.), Learning science: Theory, research, and practice (pp. 2-33). New York: McGraw-Hill.

Graesser, A.C., and Li, H. (2013). How Might Comprehension Deficits be Explained by the Constraints of Text and Multilevel Discourse Processes? In B. Miller, L.E. Cutting, and P. McCardle (Eds.), Unraveling Reading Comprehension: Behavioral, Neurobiological, and Genetic Components (pp. 33–42). Baltimore: Paul Brookes Publishing.

Graesser, A.C., Baer, W., Feng, S., Walker, B., Clewley, D., Hays, D.P., Greenberg, D. (2015). Emotions in adaptive computer technologies for adults improving reading. In S. Tettegah and M. Gartmeier (Eds.), Emotions, Technology, Design, and Learning (pp. 3-25). New York: Elsevier.

Graesser, A.C., Cai, Z., Baer, W.O., Olney, A.M., Hu, X., Reed, M., and Greenberg, D. (2016). Reading comprehension lessons in AutoTutor for the Center for the Study of Adult Literacy. In S.A. Crossley and D.S. McNamara (Eds.). Adaptive educational technologies for literacy instruction (pp. 288-293). New York: Taylor and Francis Routledge.

Graesser, A.C., Cai, Z., Hu, X., Foltz, P.W., Greiff, S, Kuo, B. C. Liao, C. H., and Shaffer, D.W. (2017). Assessment of collaborative problem solving. In R. Sottilare, A. Graesser, X. Hu, and G. Goodwin (Eds.), Design Recommendations for Intelligent Tutoring Systems: Volume 5 – Assessment. Orlando, FL: U.S. Army Research Laboratory.

Graesser, A.C., Dowell, N., and Clewley, D. (2017). Assessing collaborative problem solving through conversational agents. In A. Von Davier, M. Zhu, and P,C. Kyllonen (Eds)., Innovative assessment of collaboration (pp. 65-80). New York: Springer.

Graesser, A.C., Dowell, N., Hampton, A.J., Lippert, A.M., Li, H., and Shaffer, D.W. (2018). Building intelligent conversational tutors and mentors for team collaborative problem solving: Guidance from the 2015 Program for International Student Assessment. In J. J. Johnston, R. Sottilare, A. Sinatra, and C. S. Burke (Ed.). RMGT 19: Building intelligent tutoring systems for teams: What matters (pp. 173-211). Emerald Publishing: West Yorkshire, UK.

Graesser, A.C., Feng, S., and Cai, Z. (2017). Two technologies to help adults with reading difficulties improve their comprehension. In E. Segers and P. Van den Broek (Eds.), Developmental perspectives in written language and literacy. In honor of Ludo Verhoeven (pp. 295-313). John Benjamin Publishing Company.

Graesser, A.C., Greenberg, D., Olney, A.M., and Lovett, M.W. (2019). Educational technologies that support reading comprehension for adults who have low literacy skills. In D. Perin (Ed). Wiley adult literacy handbook (pp. 471-493). New York: Wiley.

Graesser, A.C., Hu, X., Nye, B., and Sottilare, R. (2016). Intelligent tutoring systems, serious games, and the generalized intelligent framework for tutoring (GIFT). In H.F. O’Neil, E.L. Baker, and R.S. Perez (Eds.), Using Games and Simulation for Teaching and Assessment (pp. 58–79). New York: Routledge.

Graesser, A.C., Hu, X., Rus, V., and Cai, Z. (2020). Conversation-based learning and assessment environments. In D. Yan, A. Rupp, and P. Foltz (Eds.)., Handbook of automated scoring: Theory into practice (pp. 383-402). New York: CRC Press/Taylor and Francis.

Graesser, A.C., Keshtkar, F., and Li, H. (2014). The role of natural language and discourse processing in advanced tutoring systems. In T. Holtgraves (Ed.), The Oxford Handbook of Language and Social Psychology (pp. 491–509). New York: Oxford Handbooks Online.

Graesser, A.C., Li, H., and Feng, S. (2015). Constructing Inferences in Naturalistic Reading Contexts. In E. O’Brien, A. Cook, and R. Lorch (Eds.), Inferences During Reading (pp. 290–320). New York: Cambridge University Press.

Graesser, A.C., Millis, K., D’Mello, S.K., and Hu, X. (2014). Conversational Agents can Help Humans Identify Flaws in the Science Reported in Digital Media. In D. Rapp, and J. Braasch (Eds.), Processing Inaccurate Information: Theoretical and Applied Perspectives from Applied Perspectives from Cognitive Science and the Educational Sciences (pp. 139–159). Cambridge, MA: MIT Press.

Graesser, A.C., Rus, V., and Hu, X. (2017). Instruction Based on Tutoring. In R.E. Mayer and P.A. Alexander (Eds.), Handbook of Research on Learning and Instruction (pp. 460-482). New York.

Graesser, A.C., Rus, V., Hu, X. (2017). Instruction based on tutoring. In R.E. Mayer and P.A. Alexander (Eds.), Handbook of Research on Learning and Instruction (pp. 460-482). New York: Routledge Press.

Li., H., and Baer, W. (2019). Scaffolding adult learners’ reading strategies in the intelligent tutoring system. In K. Millis, D. Long, J. P. Magliano, and K. Wiemer (Eds.), Deep Comprehension: Multi-disciplinary approaches to understanding, enhancing, and measuring comprehension (pp. 166–179). Abingdon, UK: Taylor and Francis.

Li, H., Shubeck, K., and Graesser, A. C. (2016). Using technology in language assessment. In D. Tsagari, and J. V. Banerjee (Eds.), Contemporary second language assessment: Contemporary applied linguistics (Vol. 4, pp. 281-298). London, UK: Bloomsbury Academic.

Morgan, B.A., Hogan, A.M., Hampton, D., Lippert, A.and Graesser, A.C. (2020). The need for personalized learning and the potential of intelligent tutoring systems. In P. van Meter, A. List, and D. Lombardin and P. Kendeou (Eds), Handbook of learning from multiple representations and perspectives (pp. 495-512). New York: Routledge.

Olney, A. M. (2014). Scaffolding Made Visible. In R. Sottilare, A.C. Graesser, X. Hu, and B. Goldberg (Eds.), Design Recommendations for Intelligent Tutoring Systems: Instructional Management.  (Vol. 2) (pp. 327-340). Orlando, FL: U.S. Army Research Laboratory. Full text

Olney, A. M., and Cade, W. L. (2015). Authoring Intelligent Tutoring Systems Using Human Computation: Designing for Intrinsic Motivation. In D. D. Schmorrow and C. M. Fidopiastis (Eds.), Foundations of Augmented Cognition (Vol. 9183, pp. 628–639). Springer International Publishing.

Olney, A. M., Risko, E. F., D’Mello, S. K., and Graesser, A. C. (2015). Attention in Educational Contexts: The Role of the Learning Task in Guiding Attention. In J. Fawcett, E. F. Risko, and A. Kingstone (Eds.), The Handbook of Attention (pp. 623–642). MIT Press.

Shaffer, D.W., Ruis, A.R., and Graesser, A.C. (2015). Authoring networked learner models in complex domains. In. R. Sottilare, A.C. Graesser, X. Hu, and K. Brawner (Eds.), Design Recommendations for Intelligent Tutoring Systems: Authoring Tools (Vol.3)(pp.179-192). Orlando, FL: Army Research Laboratory.

Journal articles

Chen, S, Fang, Y., Shi, G., Sabatini, J., Greenberg, D., Frijters, J., and Graesser, A.C. (2021). Automated disengagement tracking within an intelligent tutoring system. Frontiers in Artificial Intelligence, 3, 1-16.

Choo, A. L., Greenberg, D., Li, H., & Talwar, A. (in press) Prevalence of stuttering and factors associated with oral language fluency characteristics in adult struggling readers. Journal of Learning Disabilities.

Einarson, I., Miller, C., Rodgerson, D., Lacerenza, L., Lovett, M. W., and Greenberg, D. (2021). Reflections from Teaching Basic Adult Literacy. Adult Literacy Education, 3(2), 37-42.

Graesser, A. C., Cai, Z., Morgan, B., and Wang, L. (2017). Assessment with computer agents that engage in conversational dialogues and trialogues with learners. Computers in Human Behavior, 76, 607-616. Full text

Graesser, A. C., Forsyth, C. M., and Lehman, B. A. (2017). Two heads may be better than one: Learning from agents in conversational trialogues.  Teacher College Record, 119, 1-20. Full text

Graesser, A.C. (2016). Conversations with AutoTutor help students learn. International Journal of Artificial Intelligence in Education, 26, 124–132. Full text

Graesser, A.C., Li, H., and Forsyth, C. (2014). Learning by Communicating in Natural Language With Conversational Agents. Current Directions in Psychological Science, 23(5): 374–380. Full text

Graesser, A.C., McNamara, D.S., Cai, Z., Conley, M., Li, H., and Pennebaker, J. (2014). Coh-Metrix Measures Text Characteristics at Multiple Levels of Language and Discourse. Elementary School Journal, 115(2): 210–229.Full text

Greenberg, D., Miller, C., and Graesser, A. C. (in press). An intelligent tutoring system for adult literacy learners: Lessons for practitioners. Adult Literacy Education.

Hollander, J., Sabatini, J., & Graesser, A. (2022). An intelligent tutoring system for improving adult literacy skills in digital environments. COABE Journal: The Resource for Adult Education

Li, H., and Graesser, A.C. (2021). The impact of conversational agents’ language on summary writing.  Journal of Research on Technology in Education, 53:1, 44–66. doi: 10.1080/15391523.2020.1826022 Full text

Li, H., Cai, Z., and Graesser, A.C. (2018).  Computerized summary scoring: Crowdsourcing-based latent semantic analysis. Behavioral Research Method, 50, 2144–2161. doi: 10.3758/s13428-017-0982-7 Full text

Li, H., Graesser, A.C., and Gobert, J. (2017). Where is embodiment hidden in the intelligent tutoring system? Journal of South China Normal University, 3, 79-91.

Lippert, A., Shubeck, K., Morgan, B., Hampton, A, and Graesser, A.C. (2020) Multiple Agent Designs in Conversational Intelligent Tutoring Systems. Technology, Knowledge, and Learning, 25, 443-463. Full text

Nightingale, E., Greenberg, D., Branum-Martin, L., and Bakhtiari, D. (2016). Selecting fluency assessments for adult learners. Journal of Research and Practice for Adult Literacy, Secondary, and Basic Education, 5, 18-29. Full text

Nye, B.D., Graesser, A.C., and Hu, X. (2014). AutoTutor and Family: A Review of 17 Years of Natural Language Tutoring. International Journal of Artificial Intelligence in Education, 24(4): 427–469. Full text

Shi, G., Lippert, A. M., Shubeck, K., Fang, Y., Chen, S., Pavlik, P., Greenberg, D., and Graesser, A. C. (2018). Exploring an intelligent tutoring system as a conversation-based assessment tool for reading comprehension. Behaviormetrika, 45(2), 615–633. https://doi.org/10.1007/s41237-018-0065-9 Full text

Talwar, A., Greenberg, D., and Li, H. (2018). Does memory contribute to reading comprehension in adults who struggle with reading? Journal of Research in Reading, 41(S1), S163-S182. https://doi.org/10.1111/1467-9817.12258 Full Text

Talwar, A., Greenberg, D., and Li, H. (2020). Identifying profiles of struggling adult readers: relative strengths and weaknesses in lower-level and higher-level competencies. Reading and Writing, 1-17. Full Text

Talwar, A., Greenberg, D., Tighe, E. L., and Li, H. (2020). Unpacking the Simple View of Reading for Struggling Adult Readers. Journal of Learning Disabilities, 0022219420979964. Full text

Talwar, A., Greenberg, D., Tighe, E. L., and Li, H. (2021). Examining the reading-related competencies of struggling adult readers: nuances across reading comprehension assessments and performance levels. Reading and Writing, 34(6), 1569-1592.

Talwar, A., L. Tighe, E., and Greenberg, D. (2018). Augmenting the simple view of reading for struggling adult readers: A unique role for background knowledge. Scientific Studies of Reading, 22(5), 351-366. https://doi.org/10.1080/10888438.2018.1450410 Full text

Peer-Reviewed Conference Proceedings

Baer, W. O., Cheng, Q., McGlown, C., Gong, Y., Cai, Z., and Graesser, A. C. (2016). Using virtual agents to deliver lessons in reading comprehension to struggling adult learners. In International Conference on  Intelligent Virtual Agents (pp. 516–518). Springer, Cham. Full text

Cai, Z., Gong, Y., Qiu, Q., Hu, X., and Graesser, A. (2016). Making AutoTutor agents smarter: AutoTutor answer clustering and iterative script authoring. In International Conference on Intelligent Virtual Agents (pp. 438–441). Springer, Cham. Full text

Cai, Z., Graesser, A.C., Windsor, L., Cheng, Q., Shaffer, D.W., and Hu, X. (2018). Impact of corpus size and dimensionality of LSA spaces from Wikipedia articles on AutoTutor answer evaluation. In Proceedings of the International Conference on Educational Data Mining (pp.127-136). EDM Society, Buffalo, NY. Full Text

Cai, Z., Hu, X., and Graesser, A.C. (2019). Authoring conversational Intelligent Tutoring Systems. In International Conference on Human-Computer Interaction  (pp.593-603). Springer, Cham.  Full text

Cai, Z., Li, H., Hu, X., and Graesser A. C. (2016). Can word probabilities from LDA be simply added up to represent documents? In  Proceedings of the 9^th International Conference on Educational Data Mining (pp. 577-578). EDM Society, Raleigh, North Carolina. Full Text

Cai, Z., Pennebaker, J. W., Eagan, B., Shaffer, D. W., Dowell, N. M., and Graesser, A. C. (2017). Epistemic network analysis and topic modeling for chat data from a collaborative learning environment. In Proceedings of the International Conference on Educational Data Mining (pp.104-111). EDM Society, Wuhan, China. Full Text

Cai, Z., Siebert-Evernston, A., Eagan, B., Shaffer, D.W., Hu, X., and Graesser, A.C. (2019). nCoder+: A Semantic Tool for Improving Recall of nCoder Coding. In Proceedings of the First International Conference on Quantitative Ethnography (pp.41-54). Springer, Cham. 

Chen, S., Lippert, A., Shi, G., Fang, Y., and Graesser, A. C. (2018). Disengagement Detection within an intelligent tutoring system. In Intelligent Tutoring Systems 2018 Proceedings (127-134). Springer, Cham. Full Text

Fang, Y., Lippert, A., Cai, Z., Chen, S., Frijters, J., Greenberg, D., & Graesser, A. (2021). Patterns of adults with low literacy skills interacting with an intelligent tutoring system. International Journal of Artificial Intelligence in Education, 32, 1-26.

Fang, Y., Lippert, A., Cai, Z., Hu, X., and Graesser, A. C. (2019). A conversation-based intelligent tutoring system benefits adult readers with low literacy skills. In International Conference on Human-Computer Interaction (pp. 604–614). Springer, Cham.

Fang, Y., Shubeck, K.T., Lippert, A., Cheng,Q., Shi, G., Feng, S., Gatewood, J., Chen, S., Cai, Z., Pavlik, P. I., Frijters, J.C., Greenberg, D., Graesser, A. C. (2018). Clustering the Learning Patterns of Adults with Low Literacy Interacting with an Intelligent Tutoring System. In Proceedings of the 11th International Conference on Educational Data Mining (pp.348-354). EDM Society, Buffalo, NY. Full Text

Feng, S., Stewart, J., Clewley, D., and Graesser, A. C.(2015). Emotional, epistemic, and neutral feedback in AutoTutor trialogues to improve reading comprehension. In  Proceedings of the International Conference on Artificial Intelligence in Education (pp. 570-573). Springer, Cham. Full Text

Goedecke, P., Dong, D., Shi, G., Feng, S., Risko, E., Olney, A., D’Mello, S., and Graesser, A. (2015). Breaking off engagement: Readers’ cognitive decoupling as a function of reader and text characteristics. In  Proceedings of the 8th International Conference on Educational Data Mining (pp. 448-451). EDM Society, London. Full Text

Hu, X., Cai, Z., Hampton, A. J., Cockroft, J. L., Graesser, A. C., Copeland, C., and Folsom-Kovarik, J. T. (2019). Capturing AIS behavior using xAPI-like statements. In International Conference on Human-Computer Interaction (pp. 204–216). Springer, Cham. Full text  

Li, H., Cai, Z., and Graesser A. C. (2016). How good is popularity? Summary grading in crowdsourcing. In Proceedings of the 9^th International Conference on Educational Data Mining (pp. 430-435). EDM Society, Raleigh, North Carolina. Full Text

Li, H., Cheng, C., Yu, Q., and Graesser A. C. (2015). The role of peer agent’s learning competency in trialogue-based reading intelligent systems. In Proceedings of International Conference on Artificial Intelligence in Education (pp. 694–697). Cham: Springer. Full Text

Li, H., and Graesser, A.C. (2017). Impact of pedagogical agents’ conversational formality on learning and engagement. In E. André, R. Baker, X. Hu, M. Rodrigo, and B. du Boulay (Eds.), Artificial Intelligence in Education. AIED 2017. Lecture Notes in Computer Science (Vol. 10331, pp. 188–200). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-61425-0_16 Full Text

Li, H., and Graesser, A.C. (2020). Impact of conversational formality on the quality and formality of written summaries. In I.I. Bittencourt, M. Cukurova, K. Muldner, R. Luckin, and E. Millan (Eds.), Artificial Intelligence in Education. 21^st International Coherence, AIED 2020. Lecture Notes in Computer Science (Vol. 12163, pp. 321–332). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-52237-7_26 Full text

Lippert, A., Gatewood, J., Cai, Z., and Graesser, A. C. (2019). Using an adaptive intelligent tutoring system to promote learning affordances for adults with low literacy skills. In International Conference on Human-Computer Interaction (pp. 327–339). Springer, Cham.  Full text

Olney, A. M., and Cade, W. L. (2015). Authoring intelligent tutoring systems using human computation: Designing for intrinsic motivation. In D. D. Schmorrow and C. M. Fidopiastis (Eds.), Foundations of Augmented Cognition (pp. 628–639). Springer International Publishing. doi.org/10.1007/978-3-319-20816-9_60 Full text

Olney, A. M., Bakhtiari, D., Greenberg, D., and Graesser, A. (2017). Assessing computer literacy of adults with low literacy skills. In Proceedings of the 10th International Conference on Educational Data Mining (pp.128-134). EDM Society, Wuhan, China. Full Text

Olney, A. M., Pavlik, P. I., and Maass, J. K. (2017). Improving reading comprehension with automatically generated cloze item practice. In International Conference on Artificial Intelligence in Education (pp. 262–273). Springer, Cham. Full text

Olney, A., Hosman, E., Graesser, A., D’Mello, S. (2017). Tracking online reading of college students. In Proceedings of the 10th International Conference on Educational Data Mining (pp.406-407). EDM Society, Wuhan, China.  Full text

Olney, A. M., Walker, B., Davis, R. N., and Graesser, A. (2017). The Reading Ability of College Freshmen. In Proceedings of the 10th International Conference on Educational Data Mining (pp. 396-397). Full text

Shi, G., Lippert, A. M., Hampton, A. J., Chen, S., Fang, Y., and Graesser, A. C. (2018). Diagnosing reading deficiencies of adults with low literacy skills in an intelligent tutoring system. In International Tutoring Systems 2018 Workshops (pp. 105–112). Springer, Cham Full text

Shi, G., Pavlik Jr, P., and Graesser, A. (2017). Using an additive factor model and performance factor analysis to assess learning gains in a tutoring system to help adults with reading difficulties. In International Conference on Educational Data Mining (pp.376-377). EDM Society, Wuhan, China. Full Text