Technological advances have transformed the landscape of educational assessments, particularly in how we collect and analyze data on individuals' cognitive processes and interactions with assessment items. The rich data recorded in log files during human-machine interactions are often referred to as process data. This study uses sequential process data from the 2012 Program for the International Assessment of Adult Competencies (PIAAC), focusing specifically on how respondents navigate an interactive numeracy item ("Map") that involves spatial cognition. The objectives of this study are trifold: (1) to explore factors that contribute to success or failure on a spatial numeracy item, (2) to identify spatial cognitive process features across high and low numeracy performance levels, and (3) to introduce a novel time-embedded n-grams model to incorporate elapsed time with sequential actions in process data analysis. Using a sample of 596 U.S. adult respondents, we employed the time-embedded n-grams model and two machine learning methods, random forest and XGBoost, to predict respondents' numeracy performance and to identify robust classifiers. Results indicate that time-related features and understanding directions in the spatial dimensions are the most predictive of adults' numeracy skills. The findings highlight the potential of process data to support analyses of latent numeracy skills and cognitive processes in educational contexts.

We experience narratives in different media, such as text and picture stories. While some research suggests that inference generation operates similarly across media, few studies have directly compared inference generation across media. This study investigated whether there are differences in the generation of bridging inferences for narratives presented in either texts or pictures. Participants saw wordless pictures or text versions of the same stories, and the presence of a coherence gap was manipulated within them. When there was a coherence gap, a bridging inference was required. Experiment 1 involved participants doing think aloud protocols and Experiments 2a and 2b measured processing time. The results showed an advantage of pictures for generating bridging inferences in the think-aloud task, but no difference between media in the context of processing time data. These findings suggest that differences in comprehension may be influenced by contextual factors, such as the activity that one engages (e.g., think aloud or reading silently) in when processing narratives.

Background/Objectives: AI voice interpretation applications like Google Translate in speech/voice mode are increasingly used in clinical settings to address verbal language access challenges, yet evidence comparing their performance to qualified in-person medical interpreters in authentic clinical encounters remains limited. The aim of this study is to compare the linguistic and clinical accuracy of AI-based Google Translate in speech/voice mode and qualified in-person medical interpretation using spoken real-world speech segments from clinical encounters. Methods: Outpatient clinical encounters involving patients with limited English proficiency were audio-recorded. Fourteen physician speech segments (mean length 78.5 words) representing common diagnostic, treatment, and counseling content were extracted, spoken in English into Google Translate speech/voice mode, and translated into six languages. Audio recordings of the same sentence segments were provided to qualified in-person medical interpreters. All non-English translations were back-translated into English by professional interpreters. Researchers and a clinician evaluated the back-translated English speech segments for linguistic accuracy, completeness, and clinical significance. Qualitative analyses examined error patterns and contextual loss; quantitative comparisons assessed error rate differences across languages and interpretation conditions. Results: Google Translate in speech/voice mode exhibited significantly higher linguistic errors (χ2[1] = 19.78, p < 0.001) and clinical accuracy errors (χ2[1] = 45.07, p < 0.001) than qualified medical interpreter translations. Clinically significant error rates were 33.3% for Google Translate in speech/voice mode versus 4.8% for interpreter-generated translations. Error rates were also higher for less commonly spoken languages compared to commonly spoken languages when using Google Translate in speech/voice mode (42.9% vs. 14.3%). Conclusions: Qualified in-person interpreters provided translated speech segments that had fewer errors that were either linguistically or clinically significant, and remain essential for safe, accurate clinical communication.

Background/Objectives: AI voice interpretation applications are increasingly used in clinical settings to address language access challenges, yet evidence comparing their performance to qualified in-person medical interpreters in authentic clinical encounters remains limited. This study compares the linguistic and clinical accuracy of AI-based voice interpretation and certified in-person medical interpretation using recorded real-world clinical encounters. Methods: Outpatient clinical encounters involving patients with limited English proficiency were audio-recorded. Fourteen physician speech segments (mean length 78.5 words) representing common diagnostic, treatment, and counseling content were extracted and translated into seven languages using both an AI voice interpretation application and certified in-person medical interpreters. Bilingual reviewers and a clinician evaluated translations for accuracy, completeness, and clinical fidelity. Qualitative analyses examined error patterns and contextual loss; quantitative comparisons assessed error rate differences across languages and interpretation conditions. Results: AI voice app translations exhibited significantly higher linguistic errors (χ2[1] = 19.78, p < .001) and clinical accuracy errors (χ2[1] = 45.07, p < .001) than qualified medical interpreter translations. Clinical error rates were 33.3% for AI-generated versus 4.8% for interpreter-generated translations. Error rates were also higher for less commonly spoken languages compared to commonly spoken languages when using the AI voice app (42.9% vs. 14.3%). Conclusions: Qualified in-person interpreters remain essential for safe, accurate clinical communication. Hybrid models integrating professional interpretation with appropriately deployed AI technology may offer a balanced approach to expanding language access while maintaining communication safety and equity.

This study used archival data from 30,453 students enrolled in 289 adult education programs in the state of Georgia, United States during the 2018–2019 school year. We explored how student-, teacher-, and classroom-level factors predict student attendance and academic performance (reading, language, and math) over time. Results indicated several student-level factors predict attendance and educational performance, including age, gender, race/ethnicity, previous educational attainment, low-income status, special needs status, reported barriers to education, and dual enrollment. Depending on the academic subject, age, race, previous education, special needs, and dual enrollment predicted academic growth over time. Teacher-level factors such as teacher experience predicted educational performance; classroom factors such as whether the course was an Integrated Education and Training (IET) class predicted attendance, and distance classes predicted attendance and performance. Certification predicted growth over time for language, and employment status and designation as a distance class predicted growth over time for math. These findings will help state officials improve the delivery of services to further enhance and improve student outcomes.

Morphemes are the smallest meaningful unit of language (e.g., affixes, base words) that express grammatical and semantic information. Additionally, morphological knowledge is significantly related to children's word reading and reading comprehension skills. Researchers have broadly assessed morphological knowledge by using a wide range of tasks and stimuli, which has influenced the interpretation of the relations between morphological knowledge and reading outcomes. This review of 103 studies used meta-analytic structural equation modeling (MASEM) to investigate the relations between commonly occurring morphological knowledge assessment features (e.g., written versus oral, spelling versus no spelling) in the literature to reading outcomes, including word reading and reading comprehension. Meta-regression techniques were used to examine moderators of age and reading ability. Morphological assessments that used a written modality (e.g., reading, writing) were more predictive of word reading outcomes than those administered orally. Assessments of morphological spelling were more predictive of both word reading and reading comprehension outcomes than those that did not examine spelling accuracy. Age was a significant moderator of the relation between morphology and word reading, such that the relation was stronger for the younger than the older children. Younger children also demonstrated higher relations between multiple task dimensions and reading comprehension, including oral tasks, tasks without decoding, and tasks that provided context clues. These findings have important implications for future morphological intervention studies aimed to improve children's reading outcomes, in particular the use of orthography and spelling within the context of teaching morphology.

Abstract This study uses Program for the International Assessment of Adult Competencies (PIAAC) data to examine latent profiles of engagement in reading, numeracy, writing, and computer skills-use for U.S. adults with low literacy skills. Additionally, we examine how these profiles relate to overall literacy performance and demographic heterogeneity. Results indicate five engagement profiles: High (8%), Average-High (44%), Average-Low (40%), and two generally Low profiles that are most differentiated on numeracy skills-use engagement (5.5%, 2%). High and Average-High engagement profiles had higher probabilities of adults scoring at a higher literacy level than the Average-Low profile and to a greater degree than the lowest engagement profile. Covariates of learning disability status, English speaker status, race, ethnicity, age, and educational attainment were also related to differences in literacy performance and skills-use engagement. Employment status was unrelated to differences in literacy performance and skills-use engagement. Generally, native English speakers, non-learning disabled adults, younger adults, those identifying as non-Hispanic and White, and adults with higher educational attainment (high school/some college and college degree or higher) had higher probabilities of higher literacy performance, particularly in the High and Average-High engagement profiles. Findings have implications for increasing skills-use engagement in daily life to increase literacy performance and suggest differences by demographic characteristics that may be of interest to adult foundational education programs.

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.

Objective: It has been theorized that pediatric brain tumor survivors may have reduced insight into their executive functioning. Agreement between informants and survivors has been used to probe this theory, but findings have been inconsistent. This study sought to expand on prior work by examining the relationship between participant role and ratings on the Frontal Systems Behavior Scale (FrSBe) among 73 adult survivors and their informants. This study also sought to test whether agreement on scores varied as a function of tumor treatment. Method: Dyadic mixed effects models examined the relationship between participant ratings on FrSBe subscales and the role of a participant (survivor or informant). Intraclass correlations (ICC) were used to calculate reliable change indices to evaluate significant divergence in self and informant agreement. Results: Dyadic mixed effects models showed an insignificant relationship between participant role and ratings on the FrSBe apathy and executive dysfunction subscales. Participant role was related to ratings on the disinhibition subscale of the FrSBe. The ICC for apathy was ICC = .583, for disinhibition ICC = .420, and for executive dysfunction ICC = .373. Significant divergence in scores did not vary by history of chemoradiation. Conclusions: Results demonstrate an effect of role on one FrSBe subscale and weak to moderate agreement between survivor and informant scores, which suggests that agreement between informants and survivors varies by FrSBe domain. The strongest relationship between survivors and informants was seen on apathy, which suggests that apathy is a shared concern for survivors and their families.

Background: Research suggests that younger adult African American people (age 18–35 years) have more than double the risk of having a stroke than White people. Stroke risk education is lacking for this cohort; there is a dearth of materials that are targeted and focused for young adult African Americans. There is also little research on developing and testing age and culturally appropriate health literate materials that may help this population better understand personal risk factors for stroke. Objective: The aim of this study was to understand factors to guide creating and disseminating plain language health messages about stroke risk awareness among young adult African Americans. Methods: African American participants age 18 years and older completed an online survey (N = 413). Descriptive statistics, one-way analysis of variance, and two-step cluster analyses were used to evaluate stroke risk awareness, perceived risk of stroke, message creation factors, and online health information seeking behavior. Open-ended survey items described modifiable and non-modifiable reasons for perceived risk of stroke. Key Results: Participants reported differences on overall stroke risk factor awareness by perceived risk of stroke was significant (F[2, 409] = 4.91, p = .008) with the very low/low group (M = 1.66, p < .01), showing significantly lower overall stroke risk factor awareness compared to the moderate and high/very high groups. Both respondents who thought their stroke risk was very low/low and moderate/high/very high commented about family history (54.1% and 45.9%, respectively) as the reason and 88.2% of very low/low commented that they did not have risk factors for stroke because they were young. Cluster analysis indicated the Mostly Clear Preferences cluster was more likely to select mostly/very on positive, informational, and long-term messages and medical authority sources. The largest of three clusters reported medical sources as the highest rated source for both finding and trusting health information (47.2%, n = 195). Conclusion: Young adult African Americans have a scarce understanding of modifiable stroke risk factors; health education materials should focus on positive information messaging that shows a long-term result and is presented by a medical authority. We did not observe any age or sex differences among the data, which suggests different message modalities may not be needed. [HLRP: Health Literacy Research and Practice. 2024;8(1):e38–e46.]

Abstract Childhood education affects how individuals adapt to the challenges of adulthood. Although various generalizations are made relating childhood educational experiences to characteristics of adults, there is scant evidence to support those assertions for adult literacy learners in the United States. This study investigates the relationship of childhood educational attainment to other characteristics of adult learners. In this study, 201 native English-speaking adult learners in the United States who read at the 3.0–7.9 grade equivalency levels were administered surveys and tests to better understand the relationships between childhood educational attainment and the following characteristics: childhood school disability status and grade repetition; as well as adult characteristics including current reading-related skills, reading avoidance behaviors, reading practices for informational and digital texts, employment status, and Readiness-to-Learn. Results indicated that only school disability status was correlated with educational attainment (Cramer’s V test, V = 0.279, p = 0.004). The results contribute to the body of knowledge about adult learners who want to develop literacy skills and the nuances of childhood schooling experiences in this population. Based on these results, caution should be exercised when treating educational attainment as a signal of other characteristics, including reading skills, among adult literacy students. These findings align with other international research findings.

Comprehension monitoring is a meta-cognitive skill that is defined as the ability to self-evaluate one’s comprehension of text. Although it is known that struggling adult readers are poor at monitoring their comprehension, additional research is needed to understand the mechanisms underlying comprehension monitoring and their role in reading comprehension in this population. This study used a comprehension monitoring task with struggling adult readers, which included online eye movements (reread and regression path durations) and an offline verbal protocol (oral explanations of key information). We examined whether eye movements predicted accuracy on the passages’ reading comprehension questions, a norm-referenced reading assessment, and an offline verbal protocol after controlling for age and traditional component skills (i.e., decoding, oral language, working memory). Regression path duration uniquely predicted accuracy on the questions; however, decoding and oral vocabulary were the most salient predictors of the norm-referenced reading comprehension measure. Regression path duration also predicted the offline verbal protocol, such that those who exhibited longer regression path duration were also better at explaining key information. These results contribute to the literature regarding struggling adults’ reading component skills, eye movement behaviors involved in processing connected text, and future considerations in assessing comprehension monitoring.

Inferencing skills uniquely contribute to the reading comprehension skills of older grade-school and college students. Evidence also suggests that children's reading component skills, such as decoding and language comprehension, differentially contribute to various reading comprehension assessments. However, additional research is needed to investigate the complex relations of foundational reading skills and inferencing skills to sentence-level and passage-level reading comprehension assessments with struggling adult readers. This study examined the relations between struggling adult readers' (N = 125) text-based (decoding, fluency), language-based (morphological awareness, vocabulary, language comprehension), and inferencing skills. The indirect effects of language-based reading component skills to sentence-level and passage-level reading comprehension measures through inferencing were also examined. Vocabulary and morphological awareness indirectly predicted passage-level reading comprehension through inferencing. Word reading fluency and vocabulary knowledge uniquely predicted sentence-level comprehension whereas inferencing skills predicted passage-level comprehension. These results suggest that inferencing is an important contributor to the reading comprehension skills of struggling adult readers. These findings also emphasize a need for multiple measures of comprehension to understand the complexity and underlying component processes involved in struggling adults' reading comprehension skills. Educational implications for adult literacy programs are discussed.

In response to Isaac Sasson and Sergio DellaPergola’s commentaries on our assessment of the validity of the Pew Research Center's 2020 estimate of 7.5 million US Jewish adults and children (Tighe et al. 2022), we address key points of agreement and contention in the validity of the estimate; in particular, how the Jewish population is identified and defined. We argue that Pew’s definition of the Jewish population is consistent with major studies of American Jewry, from NJPS 1990 to recent local Jewish community studies. Applying a consistent definition that includes the growing group of “Jews of no religion” with one Jewish parent, as Pew Research Center does, allows for a faithful comparison across national and local studies and a more accurate understanding of levels of Jewish engagement and expressions of Jewish identity.

The purpose of this study was to (a) examine the underlying assessment structure of the Derivational Morphology Task (DMORPH) and (b) investigate the relation of the DMORPH to vocabulary and reading comprehension outcomes with a sample of struggling adult readers. Specifically, participants included 218 struggling adult readers enrolled in adult literacy classes. We used item-level analyses to evaluate the underlying structure of the DMORPH. Items with phonological (e.g., “music” to “musician”) and non-phonological transformations (“teach” to “teacher”) were examined in relation to adult literacy students’ vocabulary and reading comprehension skills. A bifactor model was the best fit to the data, suggesting that the DMORPH measured a single factor of derivational morphological awareness with some variation due to phonological and non-phonological change items. Follow-up analyses revealed that the DMORPH can essentially be considered unidimensional, which justified the use of a single scoring system for the DMORPH with adult literacy students. However, after controlling for word reading and phonological awareness, the phonological change items uniquely predicted vocabulary knowledge and reading comprehension, whereas the non-phonological change items were not significant. The results support the structural validity of the DMORPH and the need to use both phonological and non-phonological change items with adult literacy students. The present findings also provide insight into potential intervention targets for instructors in adult literacy programs who are interested in improving students’ vocabulary and reading comprehension skills.