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One barrier to participating in clinical research is that patients with low literacy skills (1 in 5 US adults) may struggle to understand the informed consent document (ICD). Writing consents using health literacy and plain language guidelines including simplified syntax and semantics can increase understandability and facilitate inclusivity of research populations with literacy challenges. Our study aim was to evaluate a simplified ICD for understandability while considering factors known to relate to comprehension (reading skills and working memory). We performed an on-line survey of 192 adults ages 18–77 in Georgia. Participants performed significantly better on the simplified ICD test. We built an additional model with all version x measure interactions (i.e., age, sex, race, urbanicity, GMVT, WM). This model did not significantly improve model fit, F  < 1.00, suggesting that individual differences did not moderate the effect of simplification. Our findings suggest that using plain language and simplified syntax and semantics in ICD as a universal precaution may reduce cognitive reading burden for adults regardless of differences in reading skill or working memory. Increasing understandability in ICD may help improve targets for clinical trial enrollment.

Literacy skills are critical for future success, yet over 60% of high school seniors lack proficient reading skills according to standardized tests. The focus on high stakes, standardized test performance may lead educators to “teach-to-the-test” rather than supporting transferable comprehension strategies that students need. StairStepper can fill this gap by blending necessary test prep and reading comprehension strategy practice in a fun, game-based environment. StairStepper is an adaptive literacy skill training game within Interactive Strategy Training for Active Reading and Thinking (iSTART) intelligent tutoring system. StairStepper is unique in that it models text passages and multiple-choice questions of high-stakes assessments, iteratively supporting skill acquisition through self-explanation prompts and scaffolded, adaptive feedback based on performance and self-explanations. This paper describes an experimental study employing a delayed-treatment control design to evaluate users’ perceptions of the StairStepper game and its influence on reading comprehension scores. Results indicate that participants enjoyed the visual aspects of the game environment, wanted to perform well, and considered the game feedback helpful. Reading comprehension scores of students in the treatment condition did not increase. However, the comprehension scores of the control group decreased. Collectively, these results indicate that the StairStepper game may fill the intended gap in instruction by providing enjoyable practice of essential reading comprehension skills and test preparation, potentially increasing students’ practice persistence while decreasing teacher workload.

Scene Perception and Event Comprehension Theory (SPECT) posits that understanding picture stories depends upon a coordination of two processes: (1) integrating new information into the current event model that is coherent with it (i.e., mapping) and (2) segmenting experiences into distinct event models (i.e., shifting). In two experiments, we investigated competing hypotheses regarding how viewers coordinate the mapping process of bridging inference generation and the shifting process of event segmentation by manipulating the presence/absence of Bridging Action pictures (i.e., creating coherence gaps) in wordless picture stories. The Computational Effort Hypothesis says that experiencing a coherence gap prompts event segmentation and the additional computational effort to generate bridging inferences. Thus, it predicted a positive relationship between event segmentation and explanations when Bridging Actions were absent. Alternatively, the Coherence Gap Resolution Hypothesis says that experiencing a coherence gap prompt generating a bridging inference to close the gap, which obviates segmentation. Thus, it predicted a negative relationship between event segmentation and the production of explanations. Replicating prior work, viewers were more likely to segment and generate explanations when Bridging Action pictures were absent than when they were present. Crucially, the relationship between explanations and segmentation was negative when Bridging Action pictures were absent, consistent with the Coherence Gap Resolution Hypothesis. Unexpectedly, the relationship was positive when Bridging Actions were present. The results are consistent with SPECT’s assumption that mapping and shifting processes are coordinated, but how they are coordinated depends upon the experience of a coherence gap.

During reading, students construct mental models of what they read. Summaries can be used to evaluate the latent knowledge structure of these mental models. We used indices from Student Mental Model Analyzer for Research and Teaching (SMART) to explore the potential of a global index, Graph Centrality (GC), as a measure to describe mental model structure and its relation to the quality of student summaries (e.g., the amount of contentcoverage). Students (n = 73) in an online graduate-level course wrote and revised summaries of their course readings. Data preview left the total count of 32 cases to evaluate how students’ mental representations changed from initial to final version. These summaries were analyzed using indices derived from the 3S model (surface, structure, semantic) as well as a measure of GC. The results of this initial investigation are promising, demonstrating that Graph Centrality captures important differences in students’ summaries, including revision behaviors to the wholistic structure of mental models, modification trajectories toward a cohesive and solid mental representation that is semantically similar to the expert model.

For decades, educators have relied on readability metrics that tend to oversimplify dimensions of text difficulty. This study examines the potential of applying advanced artificial intelligence methods to the educational problem of assessing text difficulty. The combination of hierarchical machine learning and natural language processing (NLP) is leveraged to predict the difficulty of practice texts used in a reading comprehension intelligent tutoring system, iSTART. Human raters estimated the text difficulty level of 262 texts across two text sets (Set A and Set B) in the iSTART library. NLP tools were used to identify linguistic features predictive of text difficulty and these indices were submitted to both flat and hierarchical machine learning algorithms. Results indicated that including NLP indices and machine learning increased accuracy by more than 10% as compared to classic readability metrics (e.g., Flesch-Kincaid Grade Level). Further, hierarchical outperformed non-hierarchical (flat) machine learning classification for Set B (72%) and the combined set A + B (65%), whereas the non-hierarchical approach performed slightly better than the hierarchical approach for Set A (79%). These findings demonstrate the importance of considering deeper features of language related to text difficulty as well as the potential utility of hierarchical machine learning approaches in the development of meaningful text difficulty classification.

Readers often struggle to identify the main ideas in expository texts. Existing research and instruction provide some guidance on how to encourage readers to identify main ideas. However, there is substantial variability in how main ideas are operationalized and how readers are prompted to identify main ideas. This variability hinders identification of best practices for instruction and intervention. The goal of the current series of experiments was to systematically examine the extent to which different tasks (e.g., selecting main ideas vs. deleting details) and different operationalizations of main ideas (e.g., “important ideas” vs. “main ideas”) influenced adult readers’ identification of sentences containing main ideas as they read 11 expository texts. Across experiments, the results showed that readers were generally unreliable in identifying main idea sentences; however, they were more reliable when they were instructed to select main idea sentences compared to when they were instructed to delete sentences comprised of details, and more skilled readers were more reliable than less skilled readers. The findings from the current experiments challenge existing instructional approaches and call for additional research to better understand readers’ main idea selection.

Purpose: Complex features of science texts present idiosyncratic challenges for middle grade readers, especially in a post-Common Core educational world where students' learning is dependent on understanding informational text. The primary aim of this study was to explore how middle school readers process science texts and whether such comprehension processes differed due to features of complexity in two science texts. Method: Thirty 7th grade students read two science texts with different profiles of text complexity in a think-aloud task. Think-aloud protocols were coded for six comprehension processes: connecting inferences, elaborative inferences, evaluative comments, metacognitive comments, and associations. We analyzed the quantity and type of comprehension processes generated across both texts in order to explore how features of text complexity contributed to the comprehension processes students produced while reading. Results: Students made significantly more elaborative and connecting inferences when reading a text with deep cohesion, simple syntax, and concrete words, while students made more evaluative comments, paraphrases, and metacognitive comments when reading a text with referential cohesion, complex syntax, and abstract words. Conclusions: The current study provides exploratory evidence for features of text complexity affecting the type of comprehension processes middle school readers generate while reading science texts. Accordingly, science classroom texts and materials can be evaluated for word, sentence, and passage features of text complexity in order to encourage deep level comprehension of middle school readers.

The goal of the present study was to explore the impact of cinematic features on the comprehension of fiction film. Professional filmmakers filmed an event (a dance performance) to create three versions of a film (one objective long shot version, two versions edited to focus on one of the characters). Participants “thought aloud” as they watched one of the versions of the film. Think-aloud responses were coded on the cognitive framing (e.g., description, narrative) and the ways the characters were conveyed (e.g., internal states, actions). Analysis of the think-aloud responses revealed that cinematic features affected the cognitive framing of the film but had little effect on how the characters were conveyed.

Visual media (pictures, photographs) are often used in adult literacy instruction, presumably because they are easy for adult literacy learners to process. However, relatively little research has been conducted on how adult literacy learners comprehend visual media, such as picture stories. Some have argued that picture stories could be used as a scaffold for training higher-level skills, such as inference strategies. Before such interventions can be developed, an understanding of how adult literacy learners process picture stories is warranted. The present study consisted of a contrastive case study in which adult literacy learners, less proficient college readers, and proficient college readers (three individuals per group) thought aloud while viewing picture stories. This comparison was chosen because less proficient and proficient college readers provide an aspirational comparison for many adult literacy learners who want to eventually pursue college. A qualitative analysis of participants’ think-aloud protocols demonstrated that there were strengths and challenges for the adult literacy learners, less proficient college readers, and proficient college readers in comprehending the picture stories. All three groups of participants demonstrated that they were able to establish explanatory coherence, which involves inferring the meaning of story events. However, there were differences in the extent to which efforts to achieve coherence were directed at the context of individual pictures, the local story context, or global story context. These differences will be discussed in terms of exploring the merits of using visual media in adult literacy instruction.

Research suggests that promoting metacognitive awareness can increase performance in, and learning from, intelligent tutoring systems (ITSs). The current work examines the effects of two metacognitive prompts within iSTART, a reading comprehension strategy ITS in which students practice writing quality self-explanations. In addition to comparing iSTART practice to a no-training control, those in the iSTART condition ( n  = 116) were randomly assigned to a 2 (performance threshold: off, on) × 2(self-assessment: off, on) design. The performance threshold notified students when their average self-explanation score was below an experimenter-set threshold and the self-assessment prompted students to estimate their self-explanation score on the current trial. Students who practiced with iSTART had higher posttest self-explanation scores and inference comprehension scores on a transfer test than students in the no training control, replicating previous benefits for iSTART. However, there were no effects of either metacognitive prompt on these learning outcomes. In-system self-explanation scores indicated that the metacognitive prompts were detrimental to performance relative to standard iSTART practice. This study did not find benefits of metacognitive prompts in enhancing performance during practice or after the completion of training. Such findings support the idea that improving reading comprehension strategies comes from deliberate practice with actionable feedback rather than explicit metacognitive supports.