An intelligent tutoring system for improving adult literacy skills in digital environments

Hollander, John; Sabatini, John; Graesser, Art

doi:10.31234/osf.io/f9xva

Cited by 3 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More recently, intervention work has explored a digital learning tool called AutoTutor to teach comprehension strategies to adults with lower literacy skills ( Graesser et al, 2021 ; for an example, see www.arcweb.us ). Recent evidence suggests that this tool may be a valuable supplement to traditional instruction ( Hollander et al, 2021 ), potentially leading to improvements in the foundational reading skills needed (e.g., decoding, vocabulary) to perform better on literacy assessments, particularly digital literacy assessments.…”

Section: Educational Implicationsmentioning

confidence: 99%

It’s about time! Exploring time allocation patterns of adults with lower literacy skills on a digital assessment

Kaldes,

Tighe,

2024

Front. Psychol.

View full text Add to dashboard Cite

IntroductionDespite the necessity for adults with lower literacy skills to undergo and succeed in high-stakes computer-administered assessments (e.g., GED, HiSET), there remains a gap in understanding their engagement with digital literacy assessments.MethodsThis study analyzed process data, specifically time allocation data, from the Program for the International Assessment of Adult Competencies (PIAAC), to investigate adult respondents’ patterns of engagement across all proficiency levels on nine digital literacy items. We used cluster analysis to identify distinct groups with similar time allocation patterns among adults scoring lower on the digital literacy assessment. Finally, we employed logistic regression to examine whether the groups varied by demographic factors, in particular individual (e.g., race/ethnicity, age) and contextual factors (e.g., skills-use at home).ResultsAdults with lower literacy skills spent significantly less time on many of the items than adults with higher literacy skills. Among adults with lower literacy skills, two groups of time allocation patterns emerged: one group (Cluster 1) exhibited significantly longer engagement times, whereas the other group (Cluster 2) demonstrated comparatively shorter durations. Finally, we found that adults who had a higher probability of Cluster 1 membership (spending more time) exhibited relatively higher literacy scores, higher self-reported engagement in writing skills at home, were older, unemployed, and self-identified as Black.DiscussionThese findings emphasize differences in digital literacy engagement among adults with varying proficiency levels. Additionally, this study provides insights for the development of targeted interventions aimed at improving digital literacy assessment outcomes for adults with lower literacy skills.

show abstract

Section: Educational Implicationsmentioning

confidence: 99%

It’s about time! Exploring time allocation patterns of adults with lower literacy skills on a digital assessment

Kaldes,

Tighe,

2024

Front. Psychol.

View full text Add to dashboard Cite

show abstract

“…Another project, targeting English Language learners or any struggling middle or early secondary level reader, the Language Muse system, used NLP to analyse text features, then automated item generation to develop probes that spanned the foundational and text and discourse systems understanding of curriculum based text content (Burstein et al, 2017). The AutoTutor for Adult Reading Comprehension system also targets a range of foundational and text and discourse reading strategies (Graesser et al, 2019; Hollander et al, 2021).…”

Section: A Framework For Organizing Learning Engineering Efforts Arou...mentioning

confidence: 99%

A framework of literacy development and how AI can transform theory and practice

Sabatini

Graesser

Hollander

et al. 2023

Brit J Educational Tech

View full text Add to dashboard Cite

We argue in this paper that there is currently no adequate theoretical framework or model that spans the twelve odd year trajectory from non‐reader to proficient reader, nor addresses fine‐grain skill acquisition, mastery and integration. The target construct itself, reading proficiency, as often operationalized as an endpoint of formal secondary schooling, is defined and measured in imprecise, fragmented terms. Consequently, schools (and empirical research) fall back on heuristics like the Simple View of Reading, or a few stages (learn to read, read to learn, read to do) to describe reading development. Those models, however, are too general to guide instructional decisions for adaptive learning systems. Progress in engineering an adequate learning system has been inhibited by a mismatch with curriculum standards and school organization that impose good‐faith but not fully optimized developmental targets on the educational system. We propose the development of a learning and assessment framework to scaffold reading proficiency development while accounting for the diverse learning trajectories of groups or individuals across development. We then identify some key problems, challenges and opportunities that AI technologies are poised to help us address in conceiving individualized, adaptive learning systems for reading proficiency across the developmental spectrum. We close with a selective review of examples of AI‐enhanced research or products. Practitioner notesWhat is already known about this topic Reading proficiency is a vital component of education systems design. Theoretical and empirical studies across multiple disciplines have been published, but much of this research is framed in fragmented theories that do not seamlessly span the trajectory of life‐long reading development. Current assessments are time‐consuming, coarse‐grained and fail to provide a roadmap for educators and designers of adaptive learning environments. There is a significant lack of knowledge of potentially non‐linear growth in reading skills within or between years and how to design adaptive instruction for diverse subpopulations. What this paper adds We describe the initial steps towards a literacy learning and assessment framework that spans the trajectory from non‐reader to proficient reader. We provide a landscape of exemplars of artificial intelligence and computational linguistics that reflect the possibilities of a more comprehensive, cohesive literacy development system. We reflect upon key problems, challenges and opportunities that AI technologies can help address in conceiving individualized, adaptive learning systems for reading proficiency across the developmental spectrum. Implications for practice and/or policy AI and computational linguistics can help fill in the gaps in understanding and enacting a longitudinal vision of reading development. Educators would ideally know what to expect of their students at particular points of development, identify deviations and have additional tools to intervene effectively to maximize progress. There is a need to develop adaptive instruction that spans the development of proficiency from preschool to college/career levels and adapts to address common barriers among diverse subpopulations.

show abstract

Intelligent Tutoring System and Learning: Complexity and Resilience

Della Ventura

2023

Advances in Sustainability Science and Technology

View full text Add to dashboard Cite

An intelligent tutoring system for improving adult literacy skills in digital environments

Cited by 3 publications

References 4 publications

It’s about time! Exploring time allocation patterns of adults with lower literacy skills on a digital assessment

It’s about time! Exploring time allocation patterns of adults with lower literacy skills on a digital assessment

A framework of literacy development and how AI can transform theory and practice

Intelligent Tutoring System and Learning: Complexity and Resilience

Contact Info

Product

Resources

About