Theories of student engagement for dropout and school completion are proposed to begin as early as age five. Student engagement in elementary-age students has been linked to school completion, post-secondary education, and achievement. However, there is little research examining patterns and profiles of student engagement in this population. In an effort to provide more individualized support and intervention, work in the field of student engagement has begun to examine profiles of student engagement and disengagement or disaffection. This study explored the identification of distinguishable groups based on student engagement in a sample of third-grade students and those groups' associations with demographic and outcome variables. Consistent with findings of research with older students, four distinct profiles of student engagement were found for males and females: Engaged, Cognitively/Affectively Disengaged, Behaviorally Disengaged, Disaffected. An additional class characterized by high absences was found for males. Demographic characteristics were associated with cluster attributes for males (Free or Reduced Lunch, special education, English Learner, and race-ethnicity) and female (El status). Cluster affiliation was significantly associated with seventh grade Math and Language Arts grades for both males and females and absences for males. Implications and future directions for research are discussed. The findings from the current study indicate there are discernible classes of student engagement and disengagement that can be identified as early as the third grade. These results may aid in the early identification of those at risk for dropping out of school and more effectively link students to targeted early interventions.

Research suggests the need to assess both positive and negative forms of student engagement. The purpose of this study was to pilot disaffection items with the Student Engagement Instrument (SEI) with a sample of middle school students from a rural area in the Southeastern U.S. This study explored the factor structure of the piloted items alongside the SEI, measurement invariance, and associations between student engagement and disaffection with educational outcomes such as mathematics and reading test scores, discipline referrals, and absences. Results hold implications for our theoretical understanding of engagement, suggesting that engagement and disaffection dimensions are theoretically and psychometrically distinct.

The purpose of this study was to examine students’ self-reported cognitive and affective engagement before and during the COVID-19 pandemic. Student engagement was examined in three ways: Fall 2020 survey responses for digital and in-person instruction were compared to those from Fall 2018 and Fall 2019; the engagement of students who remained in the same school ( n = 49,161) from fall 2019 to fall 2020 was examined; and among those students in the same schools with prior year responses, we examined predictors of a change in their self-reported engagement. Students were also asked to rate their perceptions of learning and support compared to before their district transitioned to remote learning in March of 2020. All in-person student responses showed slight to large increases across grades for Teacher-Student Relationships and some slight declines on other factors. For students responding for the same schools across years, overall engagement decreased, with in-person students reporting consistent increases in Teacher-Student Relationships but varied changes across other factors. Statistical models indicated prior engagement predicted nearly all of the variance in fall 2020 engagement. Student reflections on the spring 2020 transition to online learning found the continued digital learners (into fall 2020) reporting worse engagement and support. In general, changes in student engagement were more positive for students receiving in-person instruction, and greater initial student engagement was related to greater subsequent engagement.

As the economic landscape changes, a college degree has become increasingly necessary for securing employment in an information-based society. Student engagement is an important factor in predicting and preventing high school dropout, and improving student outcomes. Although the relationship between secondary school engagement and high school completion is well supported in existing research, the relationship between secondary school engagement and postsecondary enrollment and persistence is unclear. In this study, we examined whether students' engagement in high school predicts postsecondary matriculation and persistence in the first year after accounting for demographic and school-level variables. Results indicated secondary student engagement does predict postsecondary enrollment and persistence beyond academic and behavioral variables. Consistent with research among secondary students, the Future Goals and Aspirations Scale of the Student Engagement Instrument was the strongest engagement predictor. Results have implications for early warning systems and college retention efforts.

This chapter describes a shared focus on the student engagement assessment-to-intervention link across the school staff supporting students. We illustrate theory in practice using two students with engagement risk and appropriate team intervention responses along with systems-level responses for more universal engagement concerns. We detail the use of student engagement data in two districts that differ from each other in location, number and demographic characteristics of students enrolled, annual budget amount, and student populations served. Though many engagement measures exist, we focus on combining existing district student data with student perspective data obtained using the student engagement instrument. The main requirement of a measure under our approaches, beyond sufficient reliability and validity for monitoring engagement, is that the results of the measure are linked to individual students and that link is maintained over time.

This study evaluated the psychometric properties of the Student Engagement Instrument-College version (SEI-C) with college students in the southeastern United States. Participants self-selected paper-and-pencil or online administration. Confirmatory factor analysis revealed a modified 5-factor structure. Measurement invariance of the modified 5-factor structure of the SEI-C was assessed across the paper-and-pencil and online samples. Full configural, full metric, partial scalar, and full residual variance invariance were established. The paper-and-pencil and online data were aggregated, and correlational analyses between the 5 SEI-C factors and the 4 higher order factors of the Motivation and Engagement Scale-University/College (MES-UC) provided evidence of convergent and divergent validity. All but 1 of the 20 correlations were statistically significant, and all correlations were in the expected direction. Overall, there is evidence to suggest the appropriateness of extending the SEI upward for use with college students and for collecting data via online or paper-and-pencil administration.

Early warning systems use school record data— such as attendance rate, behavior records, and course performance—to identify students at risk of dropping out. These are useful predictors of graduation-related outcomes, in large part because they indicate a student's level of engagement with school. However, these data do not indicate how invested students are in education—information that could help school counselors and other staff understand and intervene when students are falling off the path to graduation. To examine whether student engagement surveys have additional predictive value beyond data readily available in school databases, we followed a cohort of students, who completed a survey of cognitive/affective engagement as ninth graders, to one year beyond their expected high school graduation. Some engagement factors measured by the survey met rigorous tests of predictive value in terms of identifying which students were falling off the graduation path, even when controlling for other powerful predictors of the outcome.

The chapter “Engaging Adolescents in Secondary Schools,” from the volume School Mental Health Services for Adolescents, covers the topics of student engagement, connections between engagement/disengagement and mental health, and interventions to enhance engagement. Student engagement is a construct with wide appeal to scholars and practitioners who study and work with school-age youth. Engagement interventions have the potential to enhance students’ functioning across academic, behavioral, and social-emotional domains. Tables of student engagement interventions, organized by type (academic, behavioral, cognitive, affective) and level of intensity (universal, targeted), in accordance with response-to-intervention (RTI)/multi-tiered system of support (MTSS) service-delivery models, are included, along with general implementation recommendations.

Digitization of educational data and processes has enabled widespread development of technologies to support personalized learning. A key requirement in any personalized learning setup is to be able to accurately estimate students' weaknesses so they can be addressed appropriately during personalization. In this paper, we describe our work toward identifying K-12 students at risk of poor academic performance, with a special focus on 1) identifying specific components of varying granularity in the curriculum (such as subjects, topics, and concepts) that a student is finding difficult and 2) determining how early we can accurately estimate the risks. Such predictions could help teachers in planning effective personalized interventions for at-risk students and hence could help in achieving a long-term goal of minimal grade-level retentions and school dropouts. To predict performance risks, we use statistical models that utilize historical student data to learn patterns in their longitudinal journeys that correspond to performance risks. We describe in detail the risk prediction system we developed and its evaluation using data from one of the largest school districts in the United States. The experimental results demonstrate the ability of our system to make accurate risk predictions for subject-specific outcomes of varying granularity across different grade levels, early in a student's K-12 journey.

With the concurrent emphasis on accountability, prevention, and early intervention, curriculum-based measurement of reading (R-CBM) is playing an increasingly important role in the educational process. This study investigated the differences in diagnostic accuracy and utility between commercial norms and local norms when making high-stakes, local decisions. Scores on Dynamic Indicators of Early Literacy Skills Oral Reading Fluency for 1,374 students in Grades 2 to 5 were used to predict outcomes the Georgia reading achievement test, the Criterion Referenced Competency Tests. Local norms were generated using logistic regression and receiver operator characteristic curve analysis. The generated cut scores were compared to the commercial norms for differences in diagnostic efficiency. The generated cut scores were lower than the commercial norms and had improved diagnostic efficiency. Implications related to educational policy and the use of R-CBM are discussed.

This study evaluated the psychometric properties of two measures of student engagement, the Student Engagement Instrument (SEI) and the Motivation-Engagement Scale (MES), with adolescents in the southeastern United States. Confirmatory factor analyses revealed an acceptable fit of the SEI and a relatively poor fit of the MES in this sample. Correlational analyses provided evidence of convergent and divergent validity of SEI and MES factors. In addition, SEI and MES factors were correlated as expected with external measures of academic functioning and school behavior.

This article is a product of the project PTDC/CPE-CED/114362/2009 - Envolvimento dos Alunos na escola: Diferenciacao e Promocao/Students Engagment in School: Differentiation and Promotion, financed by National funding, through the Fundacao para a Ciencia e Tecnologia (FCT).

The Student Engagement Instrument (SEI) is a self-report measure of cognitive and affective engagement with school. Prior SEI validation studies have focused primarily on construct validity through analyses of internal consistency, factor analysis, and measurement invariance. Results are presented here from a two-pronged study of the criterion validity of SEI scores. Using a middle school sample ( N = 35,900), concurrent validity was assessed through analyses of group differences in SEI scores across student subgroups expected to differ in cognitive and affective engagement levels: behaviorally disengaged versus non-disengaged, high-risk versus low-risk disability status, and high versus low academic achievement. Next, through multiple logistic regression analyses, the 4-year predictive validity of SEI scores for on-time graduation and dropout was assessed in a cohort of first-time ninth graders ( N = 11,588). Nearly all SEI factors demonstrated directionally consistent associations with each criterion, including considerable long-term predictive associations with both dropout and on-time graduation.

Poor academic performance in K-12 is often a precursor to unsatisfactory educational outcomes such as dropout, which are associated with significant personal and social costs. Hence, it is important to be able to predict students at risk of poor performance, so that the right personalized intervention plans can be initiated. In this paper, we report on a large-scale study to identify students at risk of not meeting acceptable levels of performance in one state-level and one national standardized assessment in Grade 8 of a major US school district. An important highlight of our study is its scale - both in terms of the number of students included, the number of years and the number of features, which provide a very solid grounding to the research. We report on our experience with handling the scale and complexity of data, and on the relative performance of various machine learning techniques we used for building predictive models. Our results demonstrate that it is possible to predict students at-risk of poor assessment performance with a high degree of accuracy, and to do so well in advance. These insights can be used to pro-actively initiate personalized intervention programs and improve the chances of student success.

The Student Engagement Instrument (SEI) is a relatively new inventory designed to measure cognitive and affective engagement in school for middle and high school students. We explored the reliability and validity of the SEI for 122 college students. Results provided evidence for adequate to good reliability and validity--indicating a good fit between the data and a 4-factor structure based on Teacher-Student Relationships, Peer Support at School, Future Aspirations and Goals, and Family Support for Learning. Two factors representing affective engagement (Peer Support at School and Teacher-Student Relationships) emerged as important predictors of career perceptions in our college student sample. Peer Support at School also predicted college GPA. Facilitating continuity in the operationalization and measurement of student engagement across secondary and post-secondary settings, findings also highlight the potential importance of student engagement to career development.

Early school withdrawal, commonly referred to as dropout, is associated with a plethora of negative outcomes for students, schools, and society. Student engagement, however, presents as a promising theoretical model and cornerstone of school completion interventions. The purpose of the present study was to validate the Student Engagement Instrument-Elementary Version (SEI-E). The psychometric properties of this measure were assessed based on the responses of an ethnically diverse sample of 1,943 students from an urban locale. Exploratory and confirmatory factor analyses indicated that the 4-factor model of student engagement provided the best fit for the current data, which is divergent from previous SEI studies suggesting 5- and 6-factor models. Discussion and implications of these findings are presented in the context of student engagement and dropout prevention.

Schools and districts encounter many challenges when attempting to support systems-level use of engagement data. These challenges range from integrating disparate sources of data to collecting (and ensuring the meaningfulness of) data on the less observable cognitive and affective subtypes of engagement, to increasing the frequency with which data can be updated and disseminated, and to implementing appropriate interventions based upon assessed engagement. Acknowledging the need for further study on school systems' empirically guided efforts to effectively use engagement data, this chapter details one, large, urban-fringe district's effort to use these types of data. The delineation is intended as a tangible example with sufficient detail to support commentary, suggestions for improvements, and calls for relevant further research. The example should also provide guidance sufficient for other school systems to consider and select types of information and methods of dissemination useful within their efforts to promote student engagement and outcomes related to it. Suggestions for further research and visions of future use of engagement data are also provided.