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Ana Stojanov; Ben Kei Daniel – Education and Information Technologies, 2024
The need for data-driven decision-making primarily motivates interest in analysing Big Data in higher education. Although there has been considerable research on the value of Big Data in higher education, its application to address critical issues within the sector is still limited. This systematic review, conducted in December 2021 and…
Descriptors: Higher Education, Learning Analytics, Well Being, Decision Making
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Li, Yuyao; Akram, Aftab; Tang, Yong – International Journal of Information and Communication Technology Education, 2022
The application of information technology (IT) in education has opened new scenarios for this ancient process. With the rapidly changing field of IT, the adoption of IT in education has been changed drastically. It is quite difficult for researchers to keep pace with changing research trends. An analysis based on the keywords could provide a…
Descriptors: Comparative Analysis, Information Retrieval, Information Technology, Trend Analysis
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Talan, Tarik; Kalinkara, Yusuf – International Society for Technology, Education, and Science, 2022
With the rapid development of science and technology in recent years, the application areas of fuzzy logic have also gained speed. Fuzzy logic is a frequently preferred approach in the educational process, and it can be said that scientific publications on this topic have recently gained momentum in the literature. In this context, the present…
Descriptors: Databases, Research Reports, Foreign Countries, Universities
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Morakinyo Akintolu; Akinpelu A. Oyekunle – Journal of Educators Online, 2025
This paper provides a comprehensive overview of the research on the application of artificial intelligence (AI) in primary education to explore its potential to enhance teaching and learning processes. Through a systematic review of the relevant literature, this study identifies key areas in which AI can significantly impact primary education and…
Descriptors: Data Analysis, Learning Analytics, Artificial Intelligence, Computer Software
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Bozkurt, Aras; Sharma, Ramesh C. – Asian Journal of Distance Education, 2022
Humans have always been lured by the idea that they can use data to understand a phenomenon and make predictions about it. Learning analytics, in this sense, promise to understand and optimize learning and the environments in which it occurs by collecting data from learners and learning contexts. In this regard, this study systematically examines…
Descriptors: Learning Analytics, Teaching Methods, Learning Processes, Prediction
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Sarafoglou, Alexandra; van der Heijden, Anna; Draws, Tim; Cornelisse, Joran; Wagenmakers, Eric-Jan; Marsman, Maarten – Psychology Learning and Teaching, 2022
Current developments in the statistics community suggest that modern statistics education should be structured holistically, that is, by allowing students to work with real data and to answer concrete statistical questions, but also by educating them about alternative frameworks, such as Bayesian inference. In this article, we describe how we…
Descriptors: Bayesian Statistics, Thinking Skills, Undergraduate Students, Psychology
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Ouyang, Liming; Zhang, Huizhan; Zhang, Xuexin; Wu, Haizhen – Biochemistry and Molecular Biology Education, 2019
Genomics is a core course for bioscience-majored undergraduates in many universities. However, the teaching of genomics faces many challenges such as the need for rapid updating of related knowledge, content overlap between genomics and molecular biology, lack of motivation of students in the traditional passive classroom-learning model, the…
Descriptors: Genetics, Science Instruction, Majors (Students), Undergraduate Students
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Fife, James H.; James, Kofi; Peters, Stephanie – ETS Research Report Series, 2020
The concept of variability is central to statistics. In this research report, we review mathematics education research on variability and, based on that review and on feedback from an expert panel, propose a learning progression (LP) for variability. The structure of the proposed LP consists of 5 levels of sophistication in understanding…
Descriptors: Mathematics Education, Statistics Education, Feedback (Response), Research Reports
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Lie, Richard; Abdullah, Christopher; He, Wenliang; Tour, Ella – CBE - Life Sciences Education, 2016
Primary literature offers rich opportunities to teach students how to "think like a scientist," but the challenges students face when they attempt to read research articles are not well understood. Here, we present an analysis of what master's students perceive as the most challenging aspects of engaging with primary literature. We…
Descriptors: Science Instruction, Teaching Methods, Thinking Skills, Scientists
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Kroes, James R.; Chen, Yuwen; Mangiameli, Paul – Decision Sciences Journal of Innovative Education, 2013
Many potential employers expect that newly hired students will arrive on-the-job with the ability to analyze data, utilize spreadsheets, and communicate findings and recommendations. We designed the Ocean State Circuits, Inc. Forecasting Project to address these gaps in our students' knowledge of analytical tools (such as the "vlookup()"…
Descriptors: Data Analysis, Student Improvement, Mathematical Formulas, Spreadsheets
Burne, Kevin – 1973
Phases two and three of an evaluation of an innovative approach to English Composition instruction at Long Beach City College are reported. In the innovative approach, each class was divided into two groups of 15 students each, which met twice each week to discuss topics for writing, to read and revise first drafts of papers, and to discuss basic…
Descriptors: College Students, Community Colleges, Comparative Analysis, Data Analysis
Stallings, William M. – 1993
To provide practice in making design decisions, collecting and analyzing data, and writing and documenting results, a professor of statistics has his graduate students in statistics and research methodology classes design and perform an experiment on the effects of fertilizers on the growth of radishes. This project has been required of students…
Descriptors: Course Evaluation, Data Analysis, Data Collection, Decision Making