{"results":[{"id":"ss_0a18c461af8db796659e79090616a7114c2dd81b","title":"Foundations of Bilingual Education and Bilingualism","authors":[{"name":"Wayne E. Wright"},{"name":"C. Baker"}],"abstract":"The seventh edition of this bestselling textbook has been extensively revised and updated to provide a comprehensive and accessible introduction to bilingualism and bilingual education in an everchanging world. Written in a compact and clear style, the book covers all the crucial issues in bilingualism at individual, group and societal levels.","source":"Semantic Scholar","year":2025,"language":"en","subjects":null,"doi":"10.2307/330123","url":"https://www.semanticscholar.org/paper/0a18c461af8db796659e79090616a7114c2dd81b","is_open_access":true,"citations":2615,"published_at":"","score":99},{"id":"ss_a98862ffe4c18634a67a3df8a965a35e5e0d7ec8","title":"ChatGPT for good? On opportunities and challenges of large language models for education","authors":[{"name":"Enkelejda Kasneci"},{"name":"Kathrin Seßler"},{"name":"S. Küchemann"},{"name":"M. Bannert"},{"name":"Daryna Dementieva"},{"name":"F. Fischer"},{"name":"Urs Gasser"},{"name":"G. Groh"},{"name":"Stephan Günnemann"},{"name":"Eyke Hüllermeier"},{"name":"Stephan Krusche"},{"name":"Gitta Kutyniok"},{"name":"Tilman Michaeli"},{"name":"Claudia Nerdel"},{"name":"J. Pfeffer"},{"name":"Oleksandra Poquet"},{"name":"Michael Sailer"},{"name":"Albrecht Schmidt"},{"name":"T. Seidel"},{"name":"Matthias Stadler"},{"name":"J. Weller"},{"name":"Jochen Kuhn"},{"name":"Gjergji Kasneci"}],"abstract":"Large language models represent a significant advancement in the field of AI. The underlying technology is key to further innovations and, despite critical views and even bans within communities and regions, large language models are here to stay. This position paper presents the potential benefits and challenges of educational applications of large language models, from student and teacher perspectives. We briefly discuss the current state of large language models and their applications. We then highlight how these models can be used to create educational content, improve student engagement and interaction, and personalize learning experiences. With regard to challenges, we argue that large language models in education require teachers and learners to develop sets of competencies and literacies necessary to both understand the technology as well as their limitations and unexpected brittleness of such systems. In addition, a clear strategy within educational systems and a clear pedagogical approach with a strong focus on critical thinking and strategies for fact checking are required to integrate and take full advantage of large language models in learning settings and teaching curricula. Other challenges such as the potential bias in the output, the need for continuous human oversight, and the potential for misuse are not unique to the application of AI in education. But we believe that, if handled sensibly, these challenges can offer insights and opportunities in education scenarios to acquaint students early on with potential societal biases, criticalities, and risks of AI applications. We conclude with recommendations for how to address these challenges and ensure that such models are used in a responsible and ethical manner in education.","source":"Semantic Scholar","year":2023,"language":"en","subjects":null,"doi":"10.1016/j.lindif.2023.102274","url":"https://www.semanticscholar.org/paper/a98862ffe4c18634a67a3df8a965a35e5e0d7ec8","pdf_url":"https://doi.org/10.1016/j.lindif.2023.102274","is_open_access":true,"citations":4767,"published_at":"","score":97},{"id":"ss_dfdf7ff01aa6f691831e663fd29bc71890be39e2","title":"ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns","authors":[{"name":"Malik Sallam"}],"abstract":"ChatGPT is an artificial intelligence (AI)-based conversational large language model (LLM). The potential applications of LLMs in health care education, research, and practice could be promising if the associated valid concerns are proactively examined and addressed. The current systematic review aimed to investigate the utility of ChatGPT in health care education, research, and practice and to highlight its potential limitations. Using the PRIMSA guidelines, a systematic search was conducted to retrieve English records in PubMed/MEDLINE and Google Scholar (published research or preprints) that examined ChatGPT in the context of health care education, research, or practice. A total of 60 records were eligible for inclusion. Benefits of ChatGPT were cited in 51/60 (85.0%) records and included: (1) improved scientific writing and enhancing research equity and versatility; (2) utility in health care research (efficient analysis of datasets, code generation, literature reviews, saving time to focus on experimental design, and drug discovery and development); (3) benefits in health care practice (streamlining the workflow, cost saving, documentation, personalized medicine, and improved health literacy); and (4) benefits in health care education including improved personalized learning and the focus on critical thinking and problem-based learning. Concerns regarding ChatGPT use were stated in 58/60 (96.7%) records including ethical, copyright, transparency, and legal issues, the risk of bias, plagiarism, lack of originality, inaccurate content with risk of hallucination, limited knowledge, incorrect citations, cybersecurity issues, and risk of infodemics. The promising applications of ChatGPT can induce paradigm shifts in health care education, research, and practice. However, the embrace of this AI chatbot should be conducted with extreme caution considering its potential limitations. As it currently stands, ChatGPT does not qualify to be listed as an author in scientific articles unless the ICMJE/COPE guidelines are revised or amended. An initiative involving all stakeholders in health care education, research, and practice is urgently needed. This will help to set a code of ethics to guide the responsible use of ChatGPT among other LLMs in health care and academia.","source":"Semantic Scholar","year":2023,"language":"en","subjects":["Medicine"],"doi":"10.3390/healthcare11060887","url":"https://www.semanticscholar.org/paper/dfdf7ff01aa6f691831e663fd29bc71890be39e2","pdf_url":"https://www.mdpi.com/2227-9032/11/6/887/pdf?version=1679359384","is_open_access":true,"citations":2358,"published_at":"","score":97},{"id":"ss_49b66b980c91f989637b089c2e8284af443aaa25","title":"Students’ voices on generative AI: perceptions, benefits, and challenges in higher education","authors":[{"name":"C. Chan"},{"name":"Wenjie Hu"}],"abstract":"This study explores university students’ perceptions of generative AI (GenAI) technologies, such as ChatGPT, in higher education, focusing on familiarity, their willingness to engage, potential benefits and challenges, and effective integration. A survey of 399 undergraduate and postgraduate students from various disciplines in Hong Kong revealed a generally positive attitude towards GenAI in teaching and learning. Students recognized the potential for personalized learning support, writing and brainstorming assistance, and research and analysis capabilities. However, concerns about accuracy, privacy, ethical issues, and the impact on personal development, career prospects, and societal values were also expressed. According to John Biggs’ 3P model, student perceptions significantly influence learning approaches and outcomes. By understanding students’ perceptions, educators and policymakers can tailor GenAI technologies to address needs and concerns while promoting effective learning outcomes. Insights from this study can inform policy development around the integration of GenAI technologies into higher education. By understanding students’ perceptions and addressing their concerns, policymakers can create well-informed guidelines and strategies for the responsible and effective implementation of GenAI tools, ultimately enhancing teaching and learning experiences in higher education. This study focuses on the integration of generative AI (GenAI) technologies, like ChatGPT, into higher education settings. University students’ perceptions of generative AI technologies in higher education were explored, including familiarity, potential benefits, and challenges. A survey of 399 undergraduate and postgraduate students from various disciplines in Hong Kong revealed a generally positive attitude towards GenAI in teaching and learning. Insights from this study can inform policy development around the integration of GenAI technologies into higher education, helping to create well-informed guidelines and strategies for responsible and effective implementation. This study focuses on the integration of generative AI (GenAI) technologies, like ChatGPT, into higher education settings. University students’ perceptions of generative AI technologies in higher education were explored, including familiarity, potential benefits, and challenges. A survey of 399 undergraduate and postgraduate students from various disciplines in Hong Kong revealed a generally positive attitude towards GenAI in teaching and learning. Insights from this study can inform policy development around the integration of GenAI technologies into higher education, helping to create well-informed guidelines and strategies for responsible and effective implementation.","source":"Semantic Scholar","year":2023,"language":"en","subjects":["Computer Science"],"doi":"10.1186/s41239-023-00411-8","url":"https://www.semanticscholar.org/paper/49b66b980c91f989637b089c2e8284af443aaa25","pdf_url":"https://educationaltechnologyjournal.springeropen.com/counter/pdf/10.1186/s41239-023-00411-8","is_open_access":true,"citations":1515,"published_at":"","score":97},{"id":"ss_2c1d7e6f485da3b7189012227fed0a1839af353e","title":"What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature","authors":[{"name":"C. Lo"}],"abstract":"An artificial intelligence-based chatbot, ChatGPT, was launched in November 2022 and is capable of generating cohesive and informative human-like responses to user input. This rapid review of the literature aims to enrich our understanding of ChatGPT’s capabilities across subject domains, how it can be used in education, and potential issues raised by researchers during the first three months of its release (i.e., December 2022 to February 2023). A search of the relevant databases and Google Scholar yielded 50 articles for content analysis (i.e., open coding, axial coding, and selective coding). The findings of this review suggest that ChatGPT’s performance varied across subject domains, ranging from outstanding (e.g., economics) and satisfactory (e.g., programming) to unsatisfactory (e.g., mathematics). Although ChatGPT has the potential to serve as an assistant for instructors (e.g., to generate course materials and provide suggestions) and a virtual tutor for students (e.g., to answer questions and facilitate collaboration), there were challenges associated with its use (e.g., generating incorrect or fake information and bypassing plagiarism detectors). Immediate action should be taken to update the assessment methods and institutional policies in schools and universities. Instructor training and student education are also essential to respond to the impact of ChatGPT on the educational environment.","source":"Semantic Scholar","year":2023,"language":"en","subjects":null,"doi":"10.3390/educsci13040410","url":"https://www.semanticscholar.org/paper/2c1d7e6f485da3b7189012227fed0a1839af353e","pdf_url":"https://www.mdpi.com/2227-7102/13/4/410/pdf?version=1681825504","is_open_access":true,"citations":1424,"published_at":"","score":97},{"id":"ss_632a4ad39813444ebe48233680e701281de4b2c9","title":"Handbook of Research on Science Education","authors":[{"name":"Sandra K. Abell"},{"name":"Norman G. Lederman"}],"abstract":"Contents: S.K. Abell, N.G. Lederman, Preface. Part I: Science Learning. C.W. Anderson, Perspectives on Science Learning. P. Scott, H. Asoko, J. Leach, Student Conceptions and Conceptual Learning in Science. W.S. Carlsen, Language and Science Learning. T.R. Koballa, Jr., S.M. Glynn, Attitudinal and Motivational Constructs in Science Learning. B.J. Fraser, Classroom Learning Environments. L.J. Rennie, Learning Science Outside of School. Part II: Culture, Gender, and Society and Science Learning. O. Lee, A. Luykx, Science Education and Student Diversity: Race/Ethnicity, Language, Culture, and Socioeconomic Status. E. McKinley, Postcolonialism, Indigenous Students, and Science. C-J. Guo, Issues in Science Learning: An International Perspective. K. Scantlebury, D. Baker, Gender Issues in Science Education Research: Remembering Where the Difference Lies. J.R. McGinnis, G.P. Stefanich, Special Needs and Talents in Science Learning. A.C. Barton, Science Learning in Urban Settings. J.S. Oliver, Rural Science Education. Part III: Science Teaching. D. Treagust, General Instructional Methods and Strategies. V.N. Lunetta, A. Hoftein, M.P. Clough, Learning and Teaching in the School Science Laboratory: An Analysis of Research, Theory, and Practice. G.J. Kelly, Discourse in Science Classrooms. N.B. Songer, Digital Resources Versus Cognitive Tools: A Discussion of Learning Science With Technology. K. Appleton, Elementary Science Teaching. C.M. Czerniak, Interdisciplinary Science Teaching. R. Lazarowitz, High School Biology Curricula Development: Implementation, Teaching, and Evaluation From the 20th to the 21st Century. R. Duit, H. Neidderer, H. Schecke, Teaching Physics. O. De Jong, K.S. Taber, Teaching and Learning the Many Faces of Chemistry. N. Orion, C.R. Ault, Jr., Learning Earth Sciences. P. Hart, Environmental Education. Part IV: Curriculum and Assessment in Science. D.A. Roberts, Scientific Literacy/Science Literacy. J.M. Atkin, P. Black, History of Curriculum Reform in Science Education in the United States and United Kingdom. R.D. Anderson, Inquiry as an Organizing Theme for Science Curricula. N.G. Lederman, Nature of Science: Past, Present, and Future. G.S. Aikenhead, Humanistic Perspectives in the Science Curriculum. J.B. Kahle, Systemic Reform: Research, Vision, and Politics. F. Lawrenz, Review of Science Education Program Evaluation. B. Bell, Classroom Assessment of Science Learning. E. Britton, S. Schneider, Large-Scale Assessments in Science Education. Part V: Science Teacher Education. J.J. Loughran, Science Teacher as Learner. M.G. Jones, G. Carter, Science Teacher Attitudes and Beliefs. S.K. Abell, Research on Science Teacher Knowledge. T. Russell, A.K. Martin, Learning to Teach Science. P.W. Hewson, Teacher Professional Development in Science. K.J. Roth, Science Teachers as Researchers.","source":"Semantic Scholar","year":2023,"language":"en","subjects":["Psychology"],"doi":"10.4324/9780203824696","url":"https://www.semanticscholar.org/paper/632a4ad39813444ebe48233680e701281de4b2c9","is_open_access":true,"citations":1528,"published_at":"","score":97},{"id":"ss_6d2943c52b9650b8245a69f7eaf780198756ff8a","title":"Understanding the Role of Digital Technologies in Education: A review","authors":[{"name":"Prof. Abid Haleem"},{"name":"Dr Mohd Javaid"},{"name":"Prof Mohd Asim Qadri"},{"name":"Dr Rajiv Suman"}],"abstract":"One of the fundamental components of the United Nations’ sustainable development 2030 agenda is quality education. It aims to ensure inclusive and equitable quality education for all. Digital technologies have emerged as an essential tool to achieve this goal. These technologies are simple to detect emissions sources, prevent additional damage through improved energy efficiency and lower-carbon alternatives to fossil fuels, and even remove surplus greenhouse gases from the environment. Digital technologies strive to decrease or eliminate pollution and waste while increasing production and efficiency. These technologies have shown a powerful impact on the education system. The recent COVID-19 Pandemic has further institutionalised the applications of digital technologies in education. These digital technologies have made a paradigm shift in the entire education system. It is not only a knowledge provider but also a co-creator of information, a mentor, and an assessor. Technological improvements in education have made life easier for students. Instead of using pen and paper, students nowadays use various software and tools to create presentations and projects. When compared to a stack of notebooks, an iPad is relatively light. When opposed to a weighty book, surfing an E-book is easier. These methods aid in increasing interest in research. This paper is brief about the need for digital technologies in education and discusses major applications and challenges in education.","source":"Semantic Scholar","year":2022,"language":"en","subjects":["Medicine"],"doi":"10.1016/j.susoc.2022.05.004","url":"https://www.semanticscholar.org/paper/6d2943c52b9650b8245a69f7eaf780198756ff8a","pdf_url":"https://doi.org/10.1016/j.susoc.2022.05.004","is_open_access":true,"citations":2358,"published_at":"","score":96},{"id":"ss_a3a498fc5f5227f8c3ad23ef402adfe5acace418","title":"A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda","authors":[{"name":"Jaziar Radianti"},{"name":"Tim A. Majchrzak"},{"name":"Jennifer Fromm"},{"name":"Isabell Wohlgenannt"}],"abstract":"Abstract Researchers have explored the benefits and applications of virtual reality (VR) in different scenarios. VR possesses much potential and its application in education has seen much research interest lately. However, little systematic work currently exists on how researchers have applied immersive VR for higher education purposes that considers the usage of both high-end and budget head-mounted displays (HMDs). Hence, we propose using systematic mapping to identify design elements of existing research dedicated to the application of VR in higher education. The reviewed articles were acquired by extracting key information from documents indexed in four scientific digital libraries, which were filtered systematically using exclusion, inclusion, semi-automatic, and manual methods. Our review emphasizes three key points: the current domain structure in terms of the learning contents, the VR design elements, and the learning theories, as a foundation for successful VR-based learning. The mapping was conducted between application domains and learning contents and between design elements and learning contents. Our analysis has uncovered several gaps in the application of VR in the higher education sphere—for instance, learning theories were not often considered in VR application development to assist and guide toward learning outcomes. Furthermore, the evaluation of educational VR applications has primarily focused on usability of the VR apps instead of learning outcomes and immersive VR has mostly been a part of experimental and development work rather than being applied regularly in actual teaching. Nevertheless, VR seems to be a promising sphere as this study identifies 18 application domains, indicating a better reception of this technology in many disciplines. The identified gaps point toward unexplored regions of VR design for education, which could motivate future work in the field.","source":"Semantic Scholar","year":2020,"language":"en","subjects":["Computer Science"],"doi":"10.1016/j.compedu.2019.103778","url":"https://www.semanticscholar.org/paper/a3a498fc5f5227f8c3ad23ef402adfe5acace418","pdf_url":"https://doi.org/10.1016/j.compedu.2019.103778","is_open_access":true,"citations":2444,"published_at":"","score":94},{"id":"ss_a7a407968c13ced804a063259d72315a43b84f29","title":"Artificial Intelligence in Education: A Review","authors":[{"name":"Lijia Chen"},{"name":"Pingping Chen"},{"name":"Zhijian Lin"}],"abstract":"The purpose of this study was to assess the impact of Artificial Intelligence (AI) on education. Premised on a narrative and framework for assessing AI identified from a preliminary analysis, the scope of the study was limited to the application and effects of AI in administration, instruction, and learning. A qualitative research approach, leveraging the use of literature review as a research design and approach was used and effectively facilitated the realization of the study purpose. Artificial intelligence is a field of study and the resulting innovations and developments that have culminated in computers, machines, and other artifacts having human-like intelligence characterized by cognitive abilities, learning, adaptability, and decision-making capabilities. The study ascertained that AI has extensively been adopted and used in education, particularly by education institutions, in different forms. AI initially took the form of computer and computer related technologies, transitioning to web-based and online intelligent education systems, and ultimately with the use of embedded computer systems, together with other technologies, the use of humanoid robots and web-based chatbots to perform instructors’ duties and functions independently or with instructors. Using these platforms, instructors have been able to perform different administrative functions, such as reviewing and grading students’ assignments more effectively and efficiently, and achieve higher quality in their teaching activities. On the other hand, because the systems leverage machine learning and adaptability, curriculum and content has been customized and personalized in line with students’ needs, which has fostered uptake and retention, thereby improving learners experience and overall quality of learning.","source":"Semantic Scholar","year":2020,"language":"en","subjects":["Computer Science"],"doi":"10.1109/ACCESS.2020.2988510","url":"https://www.semanticscholar.org/paper/a7a407968c13ced804a063259d72315a43b84f29","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/8948470/09069875.pdf","is_open_access":true,"citations":2361,"published_at":"","score":94},{"id":"ss_2941fc3d3e786b9ec0751487ad429b0f0b1af973","title":"Education at a Glance","authors":null,"abstract":"• Further analysis of results of the 2003 survey of the OECD’s Programme for International Student Assessment (PISA) , including student access to and use of ICT, analysis of the lowest performing students and the effects on students performance of family background and the way classes are organised in schools. • Trend data on tertiary qualifications, including projections for the year 2014. • Trend data on survival rates in tertiary education. • The impact of demographic trends on education systems, as well as projections on expenditure for the year 2015. • Trend data on expected years of education. • Instruction time per subject for 9-to-14-year-olds. • A picture of student mobility and the significance of internationalisation of higher education.","source":"Semantic Scholar","year":2020,"language":"en","subjects":null,"doi":"10.1787/eag-data-en","url":"https://www.semanticscholar.org/paper/2941fc3d3e786b9ec0751487ad429b0f0b1af973","pdf_url":"https://www.oecd-ilibrary.org/deliver/e13bef63-en.pdf?itemId=%2Fcontent%2Fpublication%2Fe13bef63-en\u0026mimeType=pdf","is_open_access":true,"citations":2291,"published_at":"","score":94},{"id":"ss_04de4d9eb0d53025c8ab6c99d1e743f4c1bc1eb6","title":"Systematic review of research on artificial intelligence applications in higher education – where are the educators?","authors":[{"name":"Olaf Zawacki-Richter"},{"name":"Victoria I. Marín"},{"name":"Melissa Bond"},{"name":"Franziska Gouverneur"}],"abstract":"According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education.","source":"Semantic Scholar","year":2019,"language":"en","subjects":["Psychology"],"doi":"10.1186/s41239-019-0171-0","url":"https://www.semanticscholar.org/paper/04de4d9eb0d53025c8ab6c99d1e743f4c1bc1eb6","pdf_url":"https://educationaltechnologyjournal.springeropen.com/track/pdf/10.1186/s41239-019-0171-0","is_open_access":true,"citations":4001,"published_at":"","score":93},{"id":"ss_1d14acd52b03a472eea04bbd1f9d1552bf6c5399","title":"Experience and education","authors":[{"name":"K. John-Alder"}],"abstract":"Objective To learn more about Biomedical Engineering and potentially obtain a lab position at the Tan Laboratory. I finished core classes for engineering and biology I took Engineering Calculus, General Chemistry and the Introductory Biology series. I learned a bit about synthetic and metabolic pathways in Bis2A. I also took Statistics and Upper-Division Economics. My professors were focused on using Excel to compute various statistics and spreadsheet calculations in Excel because that was what professionals were doing using the knowledge we learned in class. Thus, I am quite skilled in Excel. I continued on with classes, but my more relevant classes were organic chemistry, biochemistry, linear algebra, and econometrics. In organic chemistry, I learned about synthesis, NMR, IR, and I gained an understanding of how reaction mechanisms and protecting groups. In the last course of the series, the professor focused on bio-organic chemistry, which enabled me to better understand the reactions occurring in the human body. In biochemistry, I learned about the properties of the 20 amino acids, about protein purification, and about catalysts, enzymes, and how proteins function. In econometrics and linear algebra, I learned more about data analysis and how to compute regressions and other basic data analysis. My coursework was supplemented by learning a bit about Matlab and Stata. Over 300 hours of experience and community service from High School at UCI Med Center. I was a distinguished volunteer because I was the first volunteer to receive a thank you letter in all of the volunteer department's history. At UCD Med Center, I volunteered in the SICU. I am learning more about patient interactions, patient care, as well as secretarial work like answering phones and keeping track of multiple calls at once. With the College of Biology, I am currently enrolled in the Peer-Mentorship Program, where I offer advice to a freshman mentee to ease the transition from high school to college. I learned about what it takes to be a good role model and inspiration, and I intend to continue inspiring others in my life.\\","source":"Semantic Scholar","year":2019,"language":"en","subjects":["Psychology"],"doi":"10.4324/9781315545639-2","url":"https://www.semanticscholar.org/paper/1d14acd52b03a472eea04bbd1f9d1552bf6c5399","is_open_access":true,"citations":5811,"published_at":"","score":93},{"id":"ss_d122e198a2f6f50642e0bc9541e1675715429a65","title":"Experience and Education","authors":[{"name":"J. Dewey"}],"abstract":"","source":"Semantic Scholar","year":2018,"language":"en","subjects":["Sociology"],"doi":"10.1080/00131728609335764","url":"https://www.semanticscholar.org/paper/d122e198a2f6f50642e0bc9541e1675715429a65","is_open_access":true,"citations":9327,"published_at":"","score":92},{"id":"ss_ccc38dd46fc6ed6d2c6c6e8b1613d6f74a0f7df1","title":"The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education","authors":[{"name":"K. Taber"}],"abstract":"Cronbach’s alpha is a statistic commonly quoted by authors to demonstrate that tests and scales that have been constructed or adopted for research projects are fit for purpose. Cronbach’s alpha is regularly adopted in studies in science education: it was referred to in 69 different papers published in 4 leading science education journals in a single year (2015)—usually as a measure of reliability. This article explores how this statistic is used in reporting science education research and what it represents. Authors often cite alpha values with little commentary to explain why they feel this statistic is relevant and seldom interpret the result for readers beyond citing an arbitrary threshold for an acceptable value. Those authors who do offer readers qualitative descriptors interpreting alpha values adopt a diverse and seemingly arbitrary terminology. More seriously, illustrative examples from the science education literature demonstrate that alpha may be acceptable even when there are recognised problems with the scales concerned. Alpha is also sometimes inappropriately used to claim an instrument is unidimensional. It is argued that a high value of alpha offers limited evidence of the reliability of a research instrument, and that indeed a very high value may actually be undesirable when developing a test of scientific knowledge or understanding. Guidance is offered to authors reporting, and readers evaluating, studies that present Cronbach’s alpha statistic as evidence of instrument quality.","source":"Semantic Scholar","year":2017,"language":"en","subjects":["Psychology"],"doi":"10.1007/s11165-016-9602-2","url":"https://www.semanticscholar.org/paper/ccc38dd46fc6ed6d2c6c6e8b1613d6f74a0f7df1","pdf_url":"https://link.springer.com/content/pdf/10.1007%2Fs11165-016-9602-2.pdf","is_open_access":true,"citations":8827,"published_at":"","score":91},{"id":"ss_cbad715ec2e0cca4ff74d0751f3f0424535a7cca","title":"PHILOSOPHY OF EDUCATION","authors":[{"name":"як В. Андреєв"},{"name":"Д. Богоявленська"},{"name":"Наталья Анатольевна Вишнякова"},{"name":"А.В. Деркач"},{"name":"Ю. Орлов"},{"name":"Д. Чернілевський"},{"name":"Віталій Зінченко"},{"name":"В. Кан-Калика"},{"name":"Б. Мастєрова"},{"name":"Н. Нікітіної"},{"name":"М. Поташника"},{"name":"С. Сисоєвої"},{"name":"В. Сластьоніна"}],"abstract":"Philosophy of education is a branch of philosophy that determines the nature and purpose of education through thought and reasoning. It is a practical or practical concept that deals with the conditions and goals of education and the philosophical problems arising from educational theory and practice. Since this practice has many aspects in human life, its social and personal manifestations are diverse and its impact on the current situation touches on many issues such as the general context, ethics, culture and ethics, epistemology, metaphysics, philosophy and other areas of thought","source":"Semantic Scholar","year":2025,"language":"en","subjects":null,"doi":"10.1111/1468-0149.t01-1-00094","url":"https://www.semanticscholar.org/paper/cbad715ec2e0cca4ff74d0751f3f0424535a7cca","is_open_access":true,"citations":695,"published_at":"","score":89.85},{"id":"ss_cee6c4973596fb320af3d871bf1920c8bc376adc","title":"Research Methods in Education","authors":[{"name":"Joseph Check"},{"name":"R. 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