Using Prior Knowledge and Student Engagement to Understand Student Performance in an Undergraduate Learning-to-Learn Course
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This study examined prior knowledge and student engagement in student performance. Log data were used to explore the distribution of final grades (i.e., weak, good, excellent final grades) occurring in an elective under-graduate course. Previous research has established behavioral and agentic engagement factors contribute to academic achievement (Reeve, 2013). Hierarchical logistic regression using both prior knowledge and log data from the course revealed: (a) the weak-grades group demonstrated less behavioral engagement than the good-grades group, (b) the good-grades group demonstrated less agentic engagement than the excellent-grades group, and (c) models composed of both prior knowledge and engagement measures were more accurate than models composed of only engagement measures. Findings demonstrate students performing at different grade-levels may experience different challenges in their course engagement. This study informs our own instructional strategies and interventions to increase student success in the course and provides recommendations for other instructors to support student success.Keywords:
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This seminar introduces the Mplus multilevel latent variable modeling framework and describes multilevel variance/effect decomposition, random/fixed effects (including random slopes), and then proceeds to explore multilevel path analysis, multilevel CFA including multilevel bi-factor models, and multilevel SEM. Topics include ways to handle strong correlations at the between-group level, indirect effects (multilevel mediation), interaction effects (multilevel moderation), and conditional indirect effects (multilevel moderated mediation). An official Instats certificate of completion and 2 ECTS Equivalent points are provided at the conclusion of the seminar.
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1. Introduction 2. Getting Started with Multilevel Analysis 3. Multilevel Regression Models 4. Extending the Two-Level Regression Model 5. Defining Multilevel Latent Variables 6. Multilevel Structural Equation Models 7. Methods for Examining Individual and Organizational Change 8. Multilevel Models with Categorical Variables 9. Multilevel Mixture Models 10. Data Consideration in Examining Multilevel Models
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70% of medication errors occurring in the hospitals are preventable. The study was aimed to document, classify and examine interventions and examine reasons as to why pharmacists initiate changes in drug therapy and the outcomes of interventions, also examine the acceptability of interventions to analyze if intervention study can be a reliable learning process and to identify the areas of weakness in case of ineffective interventions. Interventions were broadly classified into Reactive interventions and Passive interventions. The study was conducted for six months. A total of 470 interventions were recorded in this study. Out of these 470 interventions, 104 were reactive interventions and 366 were passive interventions. Out of 92 outcome assessed interventions, the outcomes were beneficial in (91.30%) and had no effect in (8.70%). Active involvement of clinical pharmacists in the wards helps physicians in taking better therapeutic decisions which highlights areas where clinical pharmacists could prove their skill and knowledge to achieve better patient outcomes.
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Abstract Achievement in the academic settings has long lasting positive outcomes. In this study, hope, self‐efficacy, and engagement are linked to academic success. The aims of this study were to test a model which predicts academic success in the Dominican Republic while testing for the mediator role of engagement. The sample was composed of 614 middle‐school students. Scales of Dispositional Hope, Academic Self‐Efficacy, Academic Self‐concept, and Engagement were used. Academic performance was measured with students' grades in Spanish and Mathematics. Three structural equation models were tested. The retained model stated hope and self‐efficacy as antecedents, engagement dimensions as mediators and grades and academic self‐concept as final outcomes. There were significant effects of hope and self‐efficacy on engagement, and behavioral engagement was the best predictor of academic success. These results point out that interventions should target variables, such as hope or engagement to increase academic success.
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Hierarchical linear regression models using cross-sectional survey data from over 750 students at a single large public institution were used to assess relationships between TA support, TA-student interactions, and three forms of student behavioral engagement. <br>
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This study explores the impact of classroom relationships on student engagement. To determine whether improved classroom relations lead to higher levels of student engagement, surveys were distributed to 2,340 students from 117 fourth- and fifth-grade classrooms. Respondents reported the degree to which they felt support from teachers, collegiality with classmates, and engagement in classroom activities. Hierarchical linear modeling analyses were employed, the results of which indicate that supportive teacher behavior and collegial support positively affect the level of student engagement for the classrooms within this study. Implications for classroom and school relations are discussed.
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Multi-level structures are common in social work administration research, where individuals are hierarchically nested within organizations. Hierarchical linear modeling (HLM) is a statistical method developed to address issues associated with the unique nature of multilevel data. We introduce the basic notion of HLM, highlighting the need for using this technique in multilevel data. We also present analytical procedures, illustrating the major issues involved in the application of HLM to social work administration research. Currently, there has been an increasing interest in promoting a multilevel approach in social work administration research. To comprehend and benefit from the results of research dealing with the multilevel approach, administrators as well as researchers are expected to understand the basic logic of HLM. This paper delivers sufficient practical knowledge for research consumers to appreciate the results of research that has employed HLM, especially for those consumers who are not familiar with advanced statistics.
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One area in which medical students can add significant value is medical education, and involving them as key stakeholders in their education can have a profound impact on students and the institutions that serve them. However, detailed descriptions of the structure, implementation and quality of programs facilitating student engagement are lacking. We describe the structure of a novel student engagement program at the University of Illinois College of Medicine-Chicago (UICOM-Chicago) known as the Student Curricular Board (SCB). We surveyed 563 medical students across all levels of training at our institution in order to examine the impact of this program, including its strengths and potential areas of improvement. The SCB serves as a highly structured and collaborative student group that has far-reaching involvement from course-level program evaluation to longitudinal curriculum design. Medical students overwhelmingly valued opportunities to be involved in their curriculum. Students with the greatest exposure to the SCB were more aware of specific program initiatives and expressed increased interest in academic medicine as a career. By highlighting this innovative student engagement program, we aim to share best practices for a highly structured, value-added approach to medical student engagement in medical education that is applicable to other medical schools and student leaders.
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The performance of Computer Science (CS) on a range of international student engagement benchmarks, including the North American National Survey of Student Engagement (NSSE) in the USA and Canada, Student Experience Survey (SES) in Australia, and the Student Engagement Survey (SES) in the UK, has generally been poor over a number of years and unfortunately shows little sign of improvement. In this ITiCSE Working Group we propose to carry out an in-depth analysis of student perspectives and experiences regarding student engagement in their CS courses and to contrast these with the perspectives and experiences of CS academics. We hope this will allow us to better understand the alignment between CS student and CS academic perspectives on student engagement and obtain insight into possible reasons for the reported poor engagement performance.
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