Using Self-Assessment to Improve Student Learning synthesizes research on self-assessment and translates it into actionable guidelines and principles for pre-service and in-service teachers and for school leaders, teacher educators, and researchers. Situated beyond the simple how-to frameworks currently available for teachers and graduate students, this volume illuminates self-assessment’s complexities and substantial promise to strategically move students toward self-regulated learning and internalized goals. Addressing theory, empirical evidence, and common implementation issues, the book’s developmental approach to quality self-assessment practices will help teachers, leaders, and scholars maximize their impact on student self-regulation and learning.
The Handbook of Human and Social Conditions in Assessment is the first book to explore assessment issues and opportunities occurring due to the real world of human, cultural, historical, and societal influences upon assessment practices, policies, and statistical modeling. With chapters written by experts in the field, this book engages with numerous forms of assessment: from classroom-level formative assessment practices to national accountability and international comparative testing practices all of which are significantly influenced by social and cultural conditions. A unique and timely contribution to the field of Educational Psychology, the Handbook of Human and Social Conditions in Assessment is written for researchers, educators, and policy makers interested in how social and human complexity affect assessment at all levels of learning. Organized into four sections, this volume examines assessment in relation to teachers, students, classroom conditions, and cultural factors. Each section is comprised of a series of chapters, followed by a discussant chapter that synthesizes key ideas and offers directions for future research. Taken together, the chapters in this volume demonstrate that teachers, test creators, and policy makers must account for the human and social conditions that shape assessment if they are to implement successful assessment practices which accomplish their intended outcomes.
This open access book presents a person-centered exploration of student profiles, using variables related to motivation to do school mathematics derived from the IEA’s Trends in International Mathematics and Science Study (TIMSS) data. Statistical cluster analysis is used to identify groups of students with similar motivational profiles, across grades and over time, for multiple participating countries. While motivational variables systematically relate to school outcomes, linear relationships can obscure the diverse makeup of student subgroups, each with varying combinations of motivation, emotions, and attitudes. In this book, a person-centered analysis of distinct and meaningful motivational profiles and their differences on sociodemographic variables and mathematics performance broadens understanding about the role that motivation characteristics play in learning and achievement in mathematics. Exploiting the richness of IEA’s TIMSS data from many countries, extracted clusters reveal consistent, as well as certain nuanced patterns that are systematically linked to sociodemographic and achievement measures. Student clusters with inconsistent motivational profiles were found in all countries; mathematics self-confidence then emerged as the variable more closely associated with average achievement. The findings demonstrate that teachers, researchers, and policymakers need to take into account differential student profiles, prioritizing techniques that target skill and competence in mathematics, in educational efforts to develop student motivation.
Assessment is a concept familiar across the field of education and is inherent to the work of professors, administrators, teachers, researchers, journalists, and scholars. A multifaceted and politically charged topic, assessment ranges from informal interactions with learners in classrooms to systematic high-stakes testing and examination. Written by a leading expert on assessment, this book situates the topic within the broader context of educational psychology research and theory and brings it to a wider audience. With chapters on the fundamentals of assessment, explanations of its uses, and advice for best application, this concise volume is designed for any education course that includes assessment in the curriculum. It will be indispensable for student researchers and both pre- and in-service teachers alike.
Assessment is a concept familiar across the field of education and is inherent to the work of professors, administrators, teachers, researchers, journalists, and scholars. A multifaceted and politically charged topic, assessment ranges from informal interactions with learners in classrooms to systematic high-stakes testing and examination. Written by a leading expert on assessment, this book situates the topic within the broader context of educational psychology research and theory and brings it to a wider audience. With chapters on the fundamentals of assessment, explanations of its uses, and advice for best application, this concise volume is designed for any education course that includes assessment in the curriculum. It will be indispensable for student researchers and both pre- and in-service teachers alike.
Fully illustrated in color throughout, the text is organized into a series of examples that provides a comprehensive guide to image manipulation on Photoshop 7 -- everything from the glamour of magazine retouching, through restoration work to the nitty-gritty of color reproduction.
Assessment for learning is meant to engage, motivate, and enable students to do better in their learning. However, how students themselves perceive assessments (both high-stakes qualifications and low-stakes monitoring) is not well understood. This volume collects research studies from Europe, North and South America, Asia, and New Zealand that have deliberately focused on how students in primary, secondary, and tertiary education conceive of, experience, understand, and evaluate assessments. Assessment for learning has assumed that formative assessments and classroom practices would be an unqualified success in terms of student learning outcomes. Making use of a variety of qualitatively interpreted focus groups, observations, and interviews and factor-analytic survey methods, the studies collected in this volume raise doubts as to the validity of this formulation. We commend this volume to readers hoping to stimulate their own thinking and research in the area of student assessment. We believe the chapters will challenge researchers, policy makers, teacher educators, and instructors as to how assessment for learning can be implemented.
Using Self-Assessment to Improve Student Learning synthesizes research on self-assessment and translates it into actionable guidelines and principles for pre-service and in-service teachers and for school leaders, teacher educators, and researchers. Situated beyond the simple how-to frameworks currently available for teachers and graduate students, this volume illuminates self-assessment’s complexities and substantial promise to strategically move students toward self-regulated learning and internalized goals. Addressing theory, empirical evidence, and common implementation issues, the book’s developmental approach to quality self-assessment practices will help teachers, leaders, and scholars maximize their impact on student self-regulation and learning.
This open access book presents a person-centered exploration of student profiles, using variables related to motivation to do school mathematics derived from the IEA’s Trends in International Mathematics and Science Study (TIMSS) data. Statistical cluster analysis is used to identify groups of students with similar motivational profiles, across grades and over time, for multiple participating countries. While motivational variables systematically relate to school outcomes, linear relationships can obscure the diverse makeup of student subgroups, each with varying combinations of motivation, emotions, and attitudes. In this book, a person-centered analysis of distinct and meaningful motivational profiles and their differences on sociodemographic variables and mathematics performance broadens understanding about the role that motivation characteristics play in learning and achievement in mathematics. Exploiting the richness of IEA’s TIMSS data from many countries, extracted clusters reveal consistent, as well as certain nuanced patterns that are systematically linked to sociodemographic and achievement measures. Student clusters with inconsistent motivational profiles were found in all countries; mathematics self-confidence then emerged as the variable more closely associated with average achievement. The findings demonstrate that teachers, researchers, and policymakers need to take into account differential student profiles, prioritizing techniques that target skill and competence in mathematics, in educational efforts to develop student motivation.
This open access book presents a person-centered exploration of student profiles, using variables related to motivation to do school mathematics derived from the IEA’s Trends in International Mathematics and Science Study (TIMSS) data. Statistical cluster analysis is used to identify groups of students with similar motivational profiles, across grades and over time, for multiple participating countries. While motivational variables systematically relate to school outcomes, linear relationships can obscure the diverse makeup of student subgroups, each with varying combinations of motivation, emotions, and attitudes. In this book, a person-centered analysis of distinct and meaningful motivational profiles and their differences on sociodemographic variables and mathematics performance broadens understanding about the role that motivation characteristics play in learning and achievement in mathematics. Exploiting the richness of IEA’s TIMSS data from many countries, extracted clusters reveal consistent, as well as certain nuanced patterns that are systematically linked to sociodemographic and achievement measures. Student clusters with inconsistent motivational profiles were found in all countries; mathematics self-confidence then emerged as the variable more closely associated with average achievement. The findings demonstrate that teachers, researchers, and policymakers need to take into account differential student profiles, prioritizing techniques that target skill and competence in mathematics, in educational efforts to develop student motivation.
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