Explore the vast range of titles available.

Relationships between students’ perceptions and their non-cognitive outcomes (epistemological beliefs, self-efficacy and attitudes to science) were investigated through secondary analysis of data from 14,167 United Arab Emirates students who participated in the Programme for International Student Assessment (PISA). Structural equation modeling (SEM) suggested that students’ perceptions of the learning environment were related to the non-cognitive outcomes of epistemological beliefs, self-efficacy and attitudes. Also, epistemological beliefs were found to have a statistically-significant and positive relationship with self-efficacy and attitudes, and self-efficacy was significantly related to attitudes.

Structural Equation Modeling (SEM) is a statistical approach to testing hypothesis about the relationships among observed and latent variables. The use of SEM in research has increased in psychology, sociology, and economics in recent years. In particular educational researchers try to obtain the complete image of the process of education through the measurement of personality differences, learning environment, motivation levels and host of other variables that affect the teaching and learning process. With the use of survey instruments and interviews with students, teachers and other stakeholders as a lens, educators can assess and gain valuable information about the social ecology of the classrooms that could help in improving the instructional approach, classroom management and the learning organizations. A considerable number of research have been conducted to identify the factors and interactions between students characteristics, personal preferences, affective traits, study skills, and various other factors that could help in better educational performance. In recent years, educational researchers use Structural Equation Modeling (SEM) as a statistical technique to explore the complex and dynamic nature of interactions in educational research and practice. SEM is becoming a powerful analytical tool and making methodological advances in multivariate analysis. This book presents the collective works on concepts, methodologies and applications of SEM in educational research and practice. The anthology of current research described in this book will be a valuable resource for the next generation educational practitioners.

This study investigates the impact of family engagement on student reading achievement in the United Arab Emirates (UAE) using counterfactual explainable artificial intelligence (CXAI) analysis. Drawing data from 24,600 students in the UAE PISA dataset, the analysis employed Gradient Boosting, SHAP (SHapley Additive exPlanations), and counterfactual simulations to model and interpret the influence of ten parental involvement variables. The results identified time spent talking with parents, frequency of family meals, and encouragement to achieve good marks as the strongest predictors of reading performance. Counterfactual analysis revealed that increasing the time spent talking with parents and frequency of family meals from their minimum (1) to maximum (5) levels, while holding other variables constant at their medians, could increase the predicted reading score from the baseline of 358.93 to as high as 448.68, marking an improvement of nearly 90 points. These findings emphasize the educational value of culturally compatible parental behaviors. The study also contributes to methodological advancement by integrating interpretable machine learning with prescriptive insights, demonstrating the potential of XAI for educational policy and intervention design. Implications for educators, policymakers, and families highlight the importance of promoting high-impact family practices to support literacy development. The approach offers a replicable model for leveraging AI to understand and enhance student learning outcomes across diverse contexts.

\"Assessment and evaluation have always been an integral part of the educational process. Quality and purposeful assessment can assist in students' learning and their achievement. In recent years, considerable attention has been given to the roles of educational measurement, evaluation, and assessment with a view to improving the education systems throughout the world. Educators are interested in how to adequately prepare the young generation to meet the ever-growing demands of the 21st century utilizing robust assessment methods. There has also been increased demand in accountability and outcomes assessment in schools to bridge the gap between classroom practices and measurement and assessment of learners' performance. This volume contains selected and invited papers from the First International Conference on Educational Measurement, Evaluation and Assessment (ICEMEA). Contributors are: Peter Adams, Derin Atay, Nafisa Awwal, Helen Barefoot, Patrick Griffin, Bahar Hasirci, Didem Karakuzular, Don Klinger, Leigh Powell, Vicente Reyes, Mark Russell, Charlene Tan, Bryan Taylor, and Zhang Quan\"-- Provided by publisher.

Structural Equation Modeling (SEM) is a statistical approach to testing hypothesis about the relationships among observed and latent variables. The use of SEM in research has increased in psychology, sociology, and economics in recent years. This book presents the collective works on concepts, methodologies and applications of SEM in educational research and practice. The anthology of current research described in this book will be a valuable resource for the next generation educational practitioners.

The term 'learning analytics' is defined as the measurement, collection, analysis, and reporting of information about learners and their contexts for the purposes of understanding and optimizing learning. In recent years learning analytics has emerged as a promising area of research that trails the digital footprint of the learners and extracts useful knowledge from educational databases to understand students' progress and success. With the availability of an increased amount of data, potential benefits of learning analytics can be far-reaching to all stakeholders in education including students, teachers, leaders, and policymakers. Educators firmly believe that, if properly harnessed, learning analytics will be an indispensable tool to enhance the teaching-learning process, narrow the achievement gap, and improve the quality of education. [...] This book documents recent attempts to conduct systematic, prodigious and multidisciplinary research in learning analytics and present their findings and identify areas for further research and development. The book also unveils the distinguished and exemplary works by educators and researchers in the field highlighting the current trends, privacy and ethical issues, creative and unique approaches, innovative methods, frameworks, and theoretical and practical aspects of learning analytics. (Orig.). Contents: Big data analytics in education for dynamic personalised learning design / Myint Swe Khine -- Post-traditional learning analytics : how data and information technology transform learning environment / Alexander Amigud -- The use of analytics for educational purposes : a review of literature, from 2015 to present / Min Liu, Zilong Pan, Xin Pan, Dongwook An, Wenting Zou, Chenglu Li and Yi Shi -- A snapshot of research on learning analytics : a systematic review / Selcan Kilis and Yasemin Gulbahar -- The benefits of learning analytics in open and distance education : a review of the evidence / Billy Tak-Ming Wong -- The new smarts in teaching and learning / Jon Mason, Stefan Popenici, Leigh Blackall and Peter Shaw -- Following the learners' traces : profiling learners and visualizing the learning process for building better learning experiences / Arif Altun and Mehmet Kokoc -- Analytical indicators for profiling and improving engagement and success of vulnerable participants / Mirella Atherton -- Triangulating student engagement with \"built & bought\" learning analytics / John Fritz and Robert Carpenter -- Implementation of a learning analytics system in a productive higher education environment / Clara Schumacher, Daniel Schon and Dirk Ifenthaler -- Discourse analysis visualization based on community of inquiry framework / Masanori Yamada, Yoshiko Goda, Kosuke Kaneko, Junko Handa and Yumi Ishige -- Learning support systems based on cohesive learning analytics / Fumiya Okubo, Masanori Yamada, Misato Oi, Atsushi Shimada, Yuta Taniguchi and Shin'ichi Konomi -- The learning analytics and flipped p b l for tool-design learning / Il-Hyun Jo -- Learning analytics cockpit for MOOC platforms / Karin Maier, Philipp Leitner and Martin Ebner.

This book will provide teachers, school administrators, policy makers, teacher trainers and educational researchers with an up-to-date information about and current trends in assessment theories, methods and application to facilitate informed-practices in educational settings.