This study integrates an intersectional framework with data on 15,000 U.S. ninth graders from the High School Longitudinal Study of 2009 to investigate differences in ninth-grade math course placement at the intersection of adolescents' learning disability status, race, and socioeconomic status (SES). Descriptive results support an increased liability perspective, with the negative relationship between a learning disability and math course placement larger for adolescents more privileged in terms of their race and/or SES. Adjusted results suggest that the lower math course placements of youth with learning disabilities are due to cumulative disadvantage rather than disability-related inequities in the transition to high school for youth of diverse racial and socioeconomic backgrounds. In addition to demonstrating the importance of intersectional perspectives, this study provides a roadmap for future studies by introducing the new perspective of increased liability to be used in conjunction with the widely employed perspective of multiple marginalization.

This study examined how book features influence talk during shared book reading. We used data from a study in which parent-child dyads (n = 157; child's M-age = 43.99 months; 88 girls, 69 boys; 91.72% of parents self-reported as white) were randomly assigned to read two number books. The focus was comparison talk (i.e., talk in which dyads count a set and also label its total), as this type of talk has been shown to promote children's understanding of cardinality. Replicating previous findings, dyads produced relatively low levels of comparison talk. However, book features influenced the talk. Books containing a greater number of numerical representations (e.g., number word, numeral, and non-symbolic set) and a greater word count elicited more comparison talk.

Children's ethnicity‐status associations are often studied in societies where one ethnic group possesses status across multiple dimensions, such as political influence and wealth. This study examined children's (6–12 years) and adults' representations of more complex hierarchies in Indonesia ( N = 341; 38% Native Indonesian, 33% Chinese Indonesian, and 27% other ethnicities; 55% female, 36% male; 2021–2022), a society where ethnic groups hold distinct forms of status (on average, Native Indonesians have political influence; Chinese Indonesians have wealth). By 6.5 years, children associated Native Indonesians with political influence and Chinese Indonesians with wealth. Intersectional analyses indicated that ethnicity‐status associations were stronger for male than female targets. Children of all ethnicities preferred Chinese Indonesians and preferences were predicted by wealth judgments.

Racial disparities in school discipline may have collateral consequences on the larger non-suspended student population. The present study leveraged two longitudinal datasets with 1201 non-suspended adolescents (48% Black, 52% White; 55% females, 45% males; M-age: 12-13) enrolled in 84 classrooms in an urban mid-Atlantic city of the United States during the 2016-2017 and 2017-2018 academic years. Classmates' minor infraction suspensions predicted greater next year's defiant infractions among non-suspended Black adolescents, and this longitudinal relation was worse for Black youth enrolled in predominantly Black classrooms. For White youth, classmates' minor infraction suspensions predicted greater defiant infractions specifically when they were enrolled in predominantly non-White classrooms. Racial inequities in school discipline may have repercussions that disadvantage all adolescents regardless of race.

Games are frequently used to promote math learning, yet the competitive and collaborative contexts introduced by games may exacerbate gender differences. In this study, 1st and 2nd grade children in the U.S. (ages 5-8; N = 274; 70% White, 15% Asian, 2% Black, 1% Native American, 14% mixed or other race; 17% Hispanic) played either a competitive, collaborative, or solo game to learn about a challenging novel math concept: proportion. Overall, both social contexts boosted perseverance and task attitudes. However, analyses revealed the competitive condition yielded gender differences in attention to proportion in the presence of competing cues, with older boys underperforming in the competition condition. Potential explanations for these findings, as well as implications for classroom math learning, are discussed.

Evidence-based learning strategies, such as the testing effect, might help address the achievement gap. However, exploiting the testing effect depends on having a set of instructional activities with fine-grained tagging. While instructors might find questions in textbooks, they often lack fine-grained tagging, and data labeling is laborious. Despite much research on text classification, to our best knowledge, state-of-the-art question classifiers are mostly based on extensive models (i.e., BERT) and English text. Respectively, those are incompatible with the resource-constrained devices (e.g., mobile) and languages (e.g., Portuguese) of many underprivileged countries in the global south. Therefore, we developed a question classifier on top of DistilBERT, a version of BERT compatible with resource-constrained applications, using grid search and hold-out. Based on a corpus of 1045 coding questions written in Brazilian Portuguese, we found a model that achieved a near-perfect performance on unseen data, similar to last-generation results using BERT for English text. Thus, we present a step towards equitable education by i) providing underprivileged Portuguese-speaking countries with the support that enables opportunities already available for first-world countries and ii) demonstrating the feasibility of creating resource-constrained applications compatible with state-of-the-art AIED systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Question Generation (QG) is a task within Natural Language Processing (NLP) that involves automatically generating questions given an input, typically composed of a text and a target answer. Recent work on QG aims to control the type of generated questions so that they meet educational needs. A remarkable example of controllability in educational QG is the generation of questions underlying certain narrative elements, e.g., causal relationship, outcome resolution, or prediction. This study aims to enrich controllability in QG by introducing a new guidance attribute: question explicitness. We propose to control the generation of explicit and implicit (wh)-questions from children-friendly stories. We show preliminary evidence of controlling QG via question explicitness alone and simultaneously with another target attribute: the question’s narrative element. The code is publicly available at https://github.com/bernardoleite/question-generation-control. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Background: Teachers often rely on the use of open-ended questions to assess students' conceptual understanding of assigned content. Particularly in the context of mathematics; teachers use these types of questions to gain insight into the processes and strategies adopted by students in solving mathematical problems beyond what is possible through more close-ended problem types. While these types of problems are valuable to teachers, the variation in student responses to these questions makes it difficult, and time-consuming, to evaluate and provide directed feedback. It is a well-studied concept that feedback, both in terms of a numeric score but more importantly in the form of teacher-authored comments, can help guide students as to how to improve, leading to increased learning. It is for this reason that teachers need better support not only for assessing students' work but also in providing meaningful and directed feedback to students. Objectives: In this paper, we seek to develop, evaluate, and examine machine learning models that support automated open response assessment and feedback. Methods: We build upon the prior research in the automatic assessment of student responses to open-ended problems and introduce a novel approach that leverages student log data combined with machine learning and natural language processing methods. Utilizing sentence-level semantic representations of student responses to open-ended questions, we propose a collaborative filtering-based approach to both predict student scores as well as recommend appropriate feedback messages for teachers to send to their students. Results and Conclusion: We find that our method outperforms previously published benchmarks across three different metrics for the task of predicting student performance. Through an error analysis, we identify several areas where future works may be able to improve upon our approach. © 2023 John Wiley & Sons Ltd.

Recently, researchers have advocated for using complex systems methodologies including agent-based modeling in education. This study proposes using agent-based models to simulate teaching and learning environments. Specifically, we present ABICAP, an agent-based model that simulates learning in accordance with the ICAP framework, which defines four levels of cognitive engagement: Interactive, Constructive, Active, and Passive. The ICAP hypothesis suggests a higher level of engagement results in improved learning outcomes. To show how ABICAP can support running hypothetical studies in a risk-free and inexpensive environment, we present two simulations examining different pedagogical scenarios. We show how our model can surface counterintuitive results which may lead to a more nuanced understanding of ICAP. More generally, this paper provides a concrete example of how agent-based modeling can be used as a methodology for advancing education research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

BackgroundProviding adaptive scaffolds to help learners develop effective self-regulated learning (SRL) behaviours has been an important goal for intelligent learning environments. Adaptive scaffolding is especially important in open-ended learning environments (OELE), where novice learners often face difficulties in completing their learning tasks. ObjectivesThis paper presents a systematic framework for adaptive scaffolding in Betty's Brain, a learning-by-teaching OELE for middle school science, where students construct a causal model to teach a virtual agent, generically named Betty. We evaluate the adaptive scaffolding framework and discuss its implications on the development of more effective scaffolds for SRL in OELEs. MethodsWe detect key cognitive/metacognitive inflection points, that is, moments where students' behaviours and performance change during learning, often suggesting an inability to apply effective learning strategies. At inflection points, Mr. Davis (a mentor agent in Betty's Brain) or Betty (the teachable agent) provides context-specific conversational feedback, focusing on strategies to help the student become a more productive learner, or encouragement to support positive emotions. We conduct a classroom study with 98 middle schoolers to analyse the impact of adaptive scaffolds on students' learning behaviours and performance. We analyse how students with differential pre-to-post learning outcomes receive and use the scaffolds to support their subsequent learning process in Betty's Brain. Results and ConclusionsAdaptive scaffolding produced mixed results, with some scaffolds (viz., strategic hints that supported debugging and assessment of causal models) being generally more useful to students than others (viz., encouragement prompts). Additionally, there were differences in how students with high versus low learning outcomes responded to some hints, as suggested by the differences in their learning behaviours and performance in the intervals after scaffolding. Overall, our findings suggest how adaptive scaffolding in OELEs like Betty's Brain can be further improved to better support SRL behaviours and narrow the learning outcomes gap between high and low performing students. ImplicationsThis paper contributes to our understanding and impact of adaptive scaffolding in OELEs. The results of our study indicate that successful scaffolding has to combine context-sensitive inflection points with conversational feedback that is tailored to the students' current proficiency levels and needs. Also, our conceptual framework can be used to design adaptive scaffolds that help students develop and apply SRL behaviours in other computer-based learning environments.