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While numerous studies have demonstrated a correlation between exclusionary discipline and negative student outcomes, this relationship is likely confounded by other factors related to the underlying misbehavior or risk of disciplinary referral. Using 10 years of student-level demographic, achievement, and disciplinary data from all K-12 public schools in Arkansas, we find that exclusionary consequences are related to worse academic outcomes (e.g., test scores and grade retention) than less exclusionary consequences, controlling for type of behavioral infraction. However, despite controlling for a robust set of covariates, sensitivity checks demonstrate that the estimated relationships between consequences and academic outcomes may still be driven by selection bias into consequence type. Implications for policy and practice are discussed.

While learning analytics (LA) practices have been shown to be practical and effective, most of them require a huge amount of data and effort. This paper reports a case study which demonstrates the feasibility of practising LA at a low cost for instructors to identify at-risk students in an undergraduate business quantitative methods course. Instead of using tracking data from a learning management system as predictive variables, this study utilised clicker responses as formative assessments, together with student demographic data and summative assessments. This LA practice makes use of free cloud services, Google Forms and Google Sheets in particular for collecting and analysing clicker data. Despite a small dataset being used, the LA implementation was effective in identifying at-risk students at an early stage. A systematic proactive advising approach is proposed as an intervention strategy based on students' at-risk probability estimated by a prediction model. The result shows that the intervention success rate increases correspondingly with the number of interventions and the intervention effects on peer groups are far more successful than on individual students. Overall, the students' pass rate in the study was 7% higher than that for the whole course. Practical recommendations and concerns about using linear regression and logistic regression for classification are also discussed.

Although \"trauma-informed education\" has gained momentum across the United States in recent years, a question remains neglected by the research community: How can education research inform understandings of \"trauma-informed\" approaches when education itself is trauma-producing for many students? This article (1) explores limitations of trauma-informed educational scholarship, particularly its reliance on individualized, biomedical understandings of trauma; (2) articulates theoretical reconceptualizations for subsequent research to account for historical trauma and ways schools and research inflict harm on students; and (3) calls for expansion of relational, participatory, and humanizing methodologies. Overall, we argue for a shift from research that focuses on \"trauma-informed education\" to scholarship that enacts a sociohistorical trauma-reducing framework to more effectively interrogate the intersections of trauma, schooling, and research.

A growing body of research and popular reporting shows racial differences in school modality choices during the COVID-19 crisis, with White students more likely to attend school in person in the fall of 2020 and spring of 2021. This in-person learning gap raises serious equity concerns. We use unique panel survey data to explore possible explanations. We find that a combination of factors may explain these differences. School districts' offerings, political partisanship, perceived risk from the pandemic, and local COVID-19 outbreaks are all meaningfully associated with and plausibly explain the in-person learning racial gap. Our results illustrate how not only policy decisions but also political leanings and individuals' beliefs could contribute to inequality in access to learning and illustrate the need for a better understanding of the factors behind observed racial inequalities in education.

In the current study interaction data of students in an online learning setting was used to research whether the academic performance of students at the end of term could be predicted in the earlier weeks. The study was carried out with 76 second-year university students registered in a Computer Hardware course. The study aimed to answer two principle questions: which algorithms and features best predict the end of term academic performance of students by comparing different classification algorithms and pre-processing techniques and whether or not academic performance can be predicted in the earlier weeks using these features and the selected algorithm. The results of the study indicated that the kNN algorithm accurately predicted unsuccessful students at the end of term with a rate of 89%. When findings were examined regarding the analysis of data obtained in weeks 3, 6, 9, 12, and 14 to predict whether the end-of-term academic performance of students could be predicted in the earlier weeks, it was observed that students who were unsuccessful at the end of term could be predicted with a rate of 74% in as short as 3 weeks’ time. The findings obtained from this study are important for the determination of features for early warning systems that can be developed for online learning systems and as indicators of student success. At the same time, it will aid researchers in the selection of algorithms and pre-processing techniques in the analysis of educational data.

We know far less about the unintended social-psychological consequences of out-of-school suspensions on students than we do of the academic, behavioral, and civic consequences. Drawing on theories of socialization and deviance, I explore how suspension events influence students’ emotional engagement in school through changes in their attitudes. Using longitudinal middle school survey data connected to individual student administrative records, I find that students who receive out-of-school suspensions are psychologically vulnerable prior to their removal from school. Accounting for demographic characteristics of students, prior year disciplinary involvement, and students’beginning-of-year attitudes, I find suspensions might further harm students by negatively changing their academic identities and perceptions of adults in school. A series of robustness checks add nuance and strengthen the claims I infer from the main analyses. I close by discussing how the engagement-related consequences of suspension inform social theory and educational policy.

Classroom context and school engagement are significant predictors of academic achievement. These factors are especially important for academically at-risk students. Grounded in an ecological systems perspective, this study examined links between classroom context, school engagement, and academic achievement among early adolescents. We took a multidimensional approach to the measurement of classroom context and school engagement, incorporating both observational and self-reported assessments of various dimensions of classroom context (instruction quality, social/emotional climate, and student–teacher relationship) and school engagement (psychological and behavioral engagement). Using data from the NICHD Study of Early Child Care and Youth Development, we tested whether school engagement mediated the link between classroom context and academic achievement among 5th grade students, and whether these pathways were the same for students with previous achievement difficulties identified in 3rd grade. Participants included 1,014 children (50% female) in 5th grade (mean age = 11). The majority of the participants were white (77%) and 23% were children of color. Results indicated that psychological and behavioral engagement mediated the link between classroom context and academic achievement for students without previous achievement difficulties. However, for students with previous achievement difficulties psychological and behavioral engagement did not mediate the link between classroom context and academic achievement. These results suggest that improving classroom quality may not be sufficient to improve student engagement and achievement for students with previous achievement difficulties. Additional strategies may be needed for these students.

We conducted a randomized field trial to test an academic vocabulary intervention designed to bolster the language and literacy skills of linguistically diverse sixth-grade students (N = 2,082; n = 1,469 from a home where English is not the primary language), many demonstrating low achievement, enrolled in 14 urban middle schools. The 20-week classroom-based intervention improved students' vocabulary knowledge, morphological awareness skills, and comprehension of expository texts that included academic words taught, as well as their performance on a standardized measure of written language skills. The effects were generally larger for students whose primary home language is not English and for those students who began the intervention with underdeveloped vocabulary knowledge.

Students with disabilities (SWDs) are more likely to be suspended or expelled than their general education peers and more likely to be chronically absent. This study uses 5 years of student-level data for all Michigan special education students to examine the relationship between educational setting, absenteeism, and disciplinary outcomes. Using within-student variation in an educational setting, I find that the degree of inclusion is associated with fewer disciplinary incidents and better attendance. However, the relationship between inclusion and disciplinary outcomes only exists for certain subgroups, and primarily for students who transitioned from more to less inclusive settings experiencing more disciplinary referrals and suspensions after these moves.

This paper introduces visibility graph analysis as a supplementary approach for examining educational time series data, particularly in online learning environments. By converting temporal data into graph representations, we uncover previously hidden patterns and relationships in student interactions, enabling more effective analysis, classification, and prediction of learning outcomes. Through a rigorous case study using the Open University Learning Analytics Dataset, we demonstrate how visibility graph metrics can accurately predict at-risk online students based on their clickstream patterns, achieving classification accuracy exceeding 87% using gradient boosting algorithms. Our novel methodology outperforms several recent deep learning approaches while providing interpretable insights about student behavior through graph-theoretical features such as global efficiency, assortativity coefficient, and betweenness centrality. This research establishes visibility graph analysis as an innovative tool in educational data mining that complements traditional machine learning techniques, opening new avenues for early intervention strategies and personalized learning pathways. However, accurately modeling the problem and selecting the appropriate type of visibility graph for the educational time series data remains dependent on the researcher’s knowledge.