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Students in the 3rd and 4th grade often encounter what has been called a reading "slump" when their class curriculums increasingly ask them to comprehend and learn from texts. Students are more likely to struggle if they have not been offered sufficient opportunities to build world and domain knowledge and engage in challenging comprehension tasks while developing their reading skills. Thus, it is essential to give young readers opportunities to build their world and domain knowledge and to teach them comprehension strategies such as asking questions, paraphrasing, and self-explaining. This paper introduces iSTART-Early, an intelligent tutoring system designed to provide instruction and practice opportunities for students to learn comprehension strategies and build knowledge about diverse topics. The theoretical foundations, history and efficacy, and design of iSTART-Early are discussed.

Modeling reading comprehension processes is a critical task for Learning Analytics, as accurate models of the reading process can be used to match students to texts, identify appropriate interventions, and predict learning outcomes. This paper introduces an improved version of the Automated Model of Comprehension, namely version 4.0. AMoC has its roots in two theoretical models of the comprehension process (i.e., the Construction-Integration model and the Landscape model), and the new version leverages state-of-the-art Large Language models, more specifically ChatGPT, to have a better contextualization of the text and a simplified construction of the underlying graph model. Besides showcasing the usage of the model, the study introduces three in-depth psychological validations that argue for the model's adequacy in modeling reading comprehension. In these studies, we demonstrated that AMoC is in line with the theoretical background proposed by the Construction-Integration and Landscape models, and it is better at replicating results from previous human psychological experiments than its predecessor. Thus, AMoC v4.0 can be further used as an educational tool to, for example, help teachers design better learning materials personalized for student profiles. Additionally, we release the code from AMoC v4.0 as open source in a Google Collab Notebook and a GitHub repository.

Past research on knowledge has differentiated between dimensions (e.g., amount, accuracy, specificity, coherence) of knowledge. This paper introduces a novel dimension of knowledge, the Motivational Utility of Knowledge (MUK), that is based on fundamental human needs (e.g., physical safety, affiliation, actualization, reproduction). Adults in the United States (N = 190) were recruited from an online survey platform and paid for participation. Participants read a set of four texts arguing different views of houselessness and were administered a comprehension test after each text. Participants were asked about their conceptions of houselessness before and after reading. Finally, they were given the MUK scale, a demographics questionnaire, including questions about their personal experience with houselessness, and were administered a general prior knowledge test and a vocabulary knowledge test. We examined MUK, the factor structure of the scale and the relationship between MUK and other measures of knowledge. The analyses showed that the subscales of MUK loaded onto a single factor—an overall value of houselessness knowledge. In addition, we found that MUK was correlated with conceptions of houselessness and comprehension of texts on houselessness, indicating that the scale was valid. Overall, the findings demonstrate that MUK is an important dimension of knowledge to consider in learning tasks.

Age of Acquisition (AoA) scores approximate the age at which a language speaker fully understands a word's semantic meaning and represent a quantitative measure of the relative difficulty of words in a language. AoA word lists exist across various languages, with English having the most complete lists that capture the largest percentage of the vocabulary. In contrast, other languages have smaller lists making large-scale analyses difficult. Given the usefulness of AoA scores, methods have been developed to leverage the use of Machine Learning models to estimate AoA scores automatically through Age of Exposure (AoE) scores for the entire vocabulary of a language. These generated AoE scores use simulated learning trajectories to evaluate properties similar to AoA. In this work, we propose a method that leverages the greater size of existing English AoA lists to improve the performance of AoE prediction models for other languages. Our main contributions are threefold. First, we introduce a novel AoE regression architecture that uses a Recurrent Neural Network applied to the simulated word exposure trajectories. Second, we consider word embeddings projected into a unified multilingual space. Third, we apply transfer learning on the English AoE regressor to improve the performance of non-English AoE regressors. We show that AoA lists across languages share inherent similarities that enable Machine Learning models to transfer insights from one language to another, thus diminishing the effect of the smaller sample sizes for non-English languages. [ABSTRACT FROM AUTHOR]

An introduction is presented in which editor discusses various articles within the issue on topics including teachers' knowledge around reading of texts with a focus on comprehension; how children engage in disciplinary literacies and scale for teachers to measure children's reading engagement.

Students in the 3rd and 4th grade often encounter what has been called a reading "slump" when their class curriculums increasingly ask them to comprehend and learn from texts. Students are more likely to struggle if they have not been offered sufficient opportunities to build world and domain knowledge and engage in challenging comprehension tasks while developing their reading skills. Thus, it is essential to give young readers opportunities to build their world and domain knowledge and to teach them comprehension strategies such as asking questions, paraphrasing, and self‐explaining. This paper introduces iSTART‐Early, an intelligent tutoring system designed to provide instruction and practice opportunities for students to learn comprehension strategies and build knowledge about diverse topics. The theoretical foundations, history and efficacy, and design of iSTART‐Early are discussed. [ABSTRACT FROM AUTHOR]

Past research on knowledge has differentiated between dimensions (e.g., amount, accuracy, specificity, coherence) of knowledge. This paper introduces a novel dimension of knowledge, the Motivational Utility of Knowledge (MUK), that is based on fundamental human needs (e.g., physical safety, affiliation, actualization, reproduction). Adults in the United States (N = 190) were recruited from an online survey platform and paid for participation. Participants read a set of four texts arguing different views of houselessness and were administered a comprehension test after each text. Participants were asked about their conceptions of houselessness before and after reading. Finally, they were given the MUK scale, a demographics questionnaire, including questions about their personal experience with houselessness, and were administered a general prior knowledge test and a vocabulary knowledge test. We examined MUK, the factor structure of the scale and the relationship between MUK and other measures of knowledge. The analyses showed that the subscales of MUK loaded onto a single factor—an overall value of houselessness knowledge. In addition, we found that MUK was correlated with conceptions of houselessness and comprehension of texts on houselessness, indicating that the scale was valid. Overall, the findings demonstrate that MUK is an important dimension of knowledge to consider in learning tasks. [ABSTRACT FROM AUTHOR]

The Interactive Strategy Training for Active Reading and Thinking (iSTART) game-based intelligent tutoring system (ITS) was developed with a foundation of comprehension theory and principles of learning science to improve students' comprehension of complex scientific texts. iSTART has been shown to improve reading comprehension for learners from middle school through adulthood, particularly lower knowledge readers, through strategy instruction and game-based practice. This paper describes iSTART, the theoretical foundations that have guided iSTART development, and evidence for the feasibility of game-based practice to improve learning outcomes. This paper also introduces a novel method of assessing students' reading comprehension through game-based literacy assessments that have been incorporated in iSTART. The development of these stealth assessments was guided by recent work emphasizing the need for rapid, dynamic, and low stakes assessments that evaluate students' reading skills in the context of brief, dynamic games. Stealth assessments can generate estimates of multiple aspects of students' reading comprehension quickly and within a motivating environment. The work described in this paper is a promising method to assess students' literacy in an unobtrusive and authentic way that may lead to improved learning outcomes for students. [ABSTRACT FROM AUTHOR]

Age of acquisition (AoA) is a measure of word complexity which refers to the age at which a word is typically learned. AoA measures have shown strong correlations with reading comprehension, lexical decision times, and writing quality. AoA scores based on both adult and child data have limitations that allow for error in measurement, and increase the cost and effort to produce. In this paper, we introduce Age of Exposure (AoE) version 2, a proxy for human exposure to new vocabulary terms that expands AoA word lists through training regressors to predict AoA scores. Word2vec word embeddings are trained on cumulatively increasing corpora of texts, word exposure trajectories are generated by aligning the word2vec vector spaces, and features of words are derived for modeling AoA scores. Our prediction models achieve low errors (from 13% with a corresponding R2 of.35 up to 7% with an R2 of.74), can be uniformly applied to different AoA word lists, and generalize to the entire vocabulary of a language. Our method benefits from using existing readability indices to define the order of texts in the corpora, while the performed analyses confirm that the generated AoA scores accurately predicted the difficulty of texts (R2 of.84, surpassing related previous work). Further, we provide evidence of the internal reliability of our word trajectory features, demonstrate the effectiveness of the word trajectory features when contrasted with simple lexical features, and show that the exclusion of features that rely on external resources does not significantly impact performance. [ABSTRACT FROM AUTHOR]

The editorial team greatly appreciates the reviewers who have dedicated their considerable time and expertise to the journal's rigorous editorial process over the past 12 months, regardless of whether the papers are finally published or not. In 2019, a total of 105 papers were published in the journal, with a median time to first decision of 32.1 days and a median time from submission to publication of 56 days. [Extracted from the article]