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Digital expectations and experiences in education
For more than three decades, researchers, policy makers and educationalists have all harboured great expectations towards the use of technology in schools. This belief has received a hard knock after an OECD 2015 report has shown that computers do not improve pupil results: Investing heavily in school computers and classroom technology does not improve pupils' performance, and frequent use of computers in schools is more likely to be associated with lower results. Educational technology has raised false expectations! The prevailing view of educational technology has shifted. This book is an attempt to raise questions and start a debate. It presents new research relevant to a better understanding of the challenges and opportunities inherent in educational technology and strategies are discussed in relation to handling these challenges. Rather than presenting ready solutions, the book attempts to provoke debate and to contribute to a firmer grasp on reality.
Book
The data collection toolkit
\"This book provides quick and easy tips for data collection within the classroom. Behavioral Quik-Graphs provides quick and easy tips for data collection within the classroom. Special educators, administrators, and other paraprofessionals often view data collection as time-consuming and complex, but collecting data on behaviors, academic abilities, and Individualized Education Plans (IEPs) is a crucial procedure that shows the progress of individual students. A variety of reproducible forms and tools are available for immediate use in recording and analyzing classroom data. Data collection is a critical piece of an educator's job (not just in special education) and it can be very intimidating to a teacher. This accessible book will make data collection easy with realistic vignettes, diagrams and sample forms, and explanations written in clear language.\" -- Provided by publisher.
eBook
Data for learning : building a smart education data system
Data are a crucial ingredient in any successful education system, but building and sustaining a data system are challenging tasks. Many countries around the world have spent significant resources but still struggle to accomplish a functioning Education Management Information System (EMIS). On the other hand, countries that have created successful systems are harnessing the power of data to improve education outcomes. Increasingly, EMISs are moving away from using data narrowly for counting students and schools. Instead, they use data to drive system-wide innovations, accountability, professionalization, and, most important, quality and learning. This broader use of data also benefits classroom instruction and support at schools. An effective data system ensures that education cycles, from preschool to tertiary, are aligned and that the education system is monitored so it can achieve its ultimate goal-- producing graduates able to successfully transition into the labor market and contribute to the overall national economy. This publication sheds light on challenges in building a data system and provide actionable direction on how to navigate the complex issues associated with education data for better learning outcomes and beyond. It details the key ingredients of successful data systems, including tangible examples, common pitfalls, and good practices. It is a resource for policy makers working to craft the vision and strategic road map of an EMIS, as well as a handbook to assist teams and decision makers in avoiding common mistakes.
Book
Data Science in Education Using R
Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like?
If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a “learn by doing” approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job.
This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development.
eBook
Multi-dimensional education
\"This comprehensive guide to school improvement outlines the steps for identifying, collecting, analyzing, and using data as a basis for making instructional and schoolwide decisions\"-- Provided by publisher.
eBook
Innovative learning analytics for evaluating instruction : a big data roadmap to effective online learning
Innovative Learning Analytics for Evaluating Instruction covers the application of a forward-thinking research methodology that uses big data to evaluate the effectiveness of online instruction. Analysis of Patterns in Time (APT) is a practical analytic approach that finds meaningful patterns in massive data sets, capturing temporal maps of students' learning journeys by combining qualitative and quantitative methods. Offering conceptual and research overviews, design principles, historical examples, and more, this book demonstrates how APT can yield strong, easily generalizable empirical evidence through big data; help students succeed in their learning journeys; and document the extraordinary effectiveness of First Principles of Instruction. It is an ideal resource for faculty and professionals in instructional design, learning engineering, online learning, program evaluation, and research methods.
Book
A systematic review of the impact of artificial intelligence on educational outcomes in health professions education
Background
Artificial intelligence (AI) has a variety of potential applications in health professions education and assessment; however, measurable educational impacts of AI-based educational strategies on learning outcomes have not been systematically evaluated.
Methods
A systematic literature search was conducted using electronic databases (CINAHL Plus, EMBASE, Proquest, Pubmed, Cochrane Library, and Web of Science) to identify studies published until October 1st 2024, analyzing the impact of AI-based tools/interventions in health profession assessment and/or training on educational outcomes. The present analysis follows the PRISMA 2020 statement for systematic reviews and the structured approach to reporting in health care education for evidence synthesis.
Results
The final analysis included twelve studies. All were single centers with sample sizes ranging from 4 to 180 participants. Three studies were randomized controlled trials, and seven had a quasi-experimental design. Two studies were observational. The studies had a heterogenous design. Confounding variables were not controlled. None of the studies provided learning objectives or descriptions of the competencies to be achieved. Three studies applied learning theories in the development of AI-powered educational strategies. One study reported the analysis of the authenticity of the learning environment. No study provided information on the impact of feedback activities on learning outcomes. All studies corresponded to Kirkpatrick’s second level evaluating technical skills or quantifiable knowledge. No study evaluated more complex tasks, such as the behavior of learners in the workplace. There was insufficient information on training datasets and copyright issues.
Conclusions
The results of the analysis show that the current evidence regarding measurable educational outcomes of AI-powered interventions in health professions education is poor. Further studies with a rigorous methodological approach are needed. The present work also highlights that there is no straightforward guide for evaluating the quality of research in AI-based education and suggests a series of criteria that should be considered.
Trial registration
Methods and inclusion criteria were defined in advance, specified in a protocol and registered in the OSF registries (
https://osf.io/v5cgp/
). Clinical Trial number: not applicable.
Journal Article