This article covers recent research activities in educational psychology that have an interdisciplinary emphasis and that accommodate twenty-first-century skills in addition to the traditional foundations of literacy, numeracy, science, reasoning (problem-solving), and academic subject matter. We emphasize digital technologies because they are capable of tracking learning data in rich detail and reliably delivering interventions that are tailored to individual learners in particular sociocultural contexts. This is a departure from inflexible pedagogical approaches that previously have been routinely adopted in most classrooms and other contexts of instruction with no precise record of learning and instructional activities. A good design of educational technology embraces the principles of learning science, identifies the basic types of learning that are needed, implements relevant technological affordances, and accommodates feedback from different stakeholders. This article covers research in literacy, collaborative problem-solving, motivation, emotion, and science, technology, engineering, and mathematics (STEM) areas.

More knowledge is needed regarding the student factors that impact academic reading and success in college beyond traditional measures of college readiness. This study examined the contributions of student factors specified by the Proficient Academic Reader framework—reading literacy skills, metacognitive reading strategies, and reading motivation—to performance on a complex academic reading task and cumulative GPA across three semesters within the first two years of college. Hierarchical regression models were estimated using a sample of 166 students at a four-year public institution. Results indicated the importance of vocabulary, reading comprehension, and problem-solving reading strategies to academic reading performance and early college GPA. However, some of these effects diminished after controlling for students’ standardized test scores and high school GPA. The findings imply that institutions should implement sustained support for reading skills and strategies across disciplines.

In this research report, we describe the conceptual foundation and measurement properties of the Reading Inventory and Scholastic Evaluation (RISE). The RISE is a 6‐subtest, Web‐administered reading skills components battery. We review the theoretical and empirical foundations of each subtest in the battery, as well as item designs. The results included in this report feature a calibrated item pool based on a national sample of students, an extension of the vertical scale to span Grades 3–12, psychometric analyses of the data for each subtest, an item response theory scaling study for each of the subtests across the entire grade span, an evaluation of multidimensionality, an evaluation of differential item functioning for gender and race/ethnicity, and an expanded review of validity evidence.

In this study, we investigate the effect of reading purpose on students’ processing behavior during a reading comprehension test. In a repeated measures design, sixty undergraduates answered multiple-choice (MC) reading comprehension questions in a condition with no overarching goal for reading and in an alternate condition where the same students were first provided with the goal of summarizing the text before answering MC questions. Results from eye tracking analysis showed that when students read and answered questions without an overarching goal, they spent much less time reading the passages before answering the questions, more time re-reading the texts while answering the questions, and more time on parts of the text that were not necessary to answer the questions. We conclude that providing examinees without an explicit goal for reading may inadvertently encourage a “search for the answer” reading process, rather than on building a coherent mental model of text content.

Learning performance data, such as correct or incorrect answers and problem-solving attempts in Intelligent Tutoring Systems (ITSs), facilitate the assessment of knowledge mastery and the delivery of effective instructions. However, these data tend to be highly sparse (80% $\sim$ 90% missing observations) in most real-world applications. This data sparsity presents challenges to using learner models to effectively predict learners' future performance and explore new hypotheses about learning. This article proposes a systematic framework for augmenting learning performance data to address data sparsity. First, learning performance data can be represented as a 3-Dimensional (3D) tensor with dimensions corresponding to learners, questions, and attempts, effectively capturing longitudinal knowledge states during learning. Second, a tensor factorization method is used to impute missing values in sparse tensors of collected learner data, thereby grounding the imputation on knowledge tracing tasks that predict missing performance values based on real observations. Third, data augmentation using Generative Artificial Intelligence (GenAI) models, including Generative Adversarial Network, specifically Vanilla Generative Adversarial Networks (GAN), and Generative Pretrained Transformers (GPT, specifically GPT-4o), generate data tailored to individual clusters of learning performance. We tested this systemic framework on adult literacy datasets from AutoTutor lessons developed for Adult Reading Comprehension (ARC). We found that: (1) tensor factorization outperformed baseline knowledge tracing techniques in tracing and predicting learning performance, demonstrating higher fidelity in data imputation, and 2) the Vanilla GAN-based augmentation demonstrated greater overall stability across varying sample sizes, whereas GPT-4o based augmentation exhibited higher variability, with occasional cases showing closer fidelity to the original data distribution. This framework facilitates the effective augmentation of learning performance data, enabling controlled, cost-effective approach for the evaluation and optimization of ITS instructional designs in both online and offline environments prior to deployment, and supporting advanced educational data mining and learning analytics.