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Areej ElSayary, Editor – IGI Global, 2025
By creating specific prompts, educators can harness the power of AI models to generate tailored content, provide instant feedback, and simulate real-world scenarios for deeper learning engagement. Whether it's creating personalized lesson plans, generating creative writing prompts, or assisting with problem-solving exercises, generative AI creates…
Descriptors: Prompting, Engineering, Artificial Intelligence, Technology Uses in Education
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Dina Fitria Murad; Meta Amalya Dewi; Arbaiah Inn; Silvia Ayunda Murad; Noor Udin; Taufik Darwis – Journal of Educators Online, 2025
This study aims to produce a more personalized recommendation system for online learning using multicriteria in collaborative filtering and data from the Binus Online Learning repository as a knowledge base. The study uses forecasting (regression) and consists of three stages: (1) collecting data on the results of the learning process; (2) adding…
Descriptors: Electronic Learning, Data Collection, Context Effect, Learning Processes
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Li, Kam Cheong; Wong, Billy Tak-Ming – Interactive Learning Environments, 2021
This paper provides a comprehensive review of the features and trends of personalised learning. The review covers a total of 203 journal articles collected from Scopus, which were published from 2001 to 2018 and involved personalised learning practices. Comparing the practices between 2001-2009 and 2010-2018, there was a clear trend that they…
Descriptors: Individualized Instruction, Educational Trends, Literature Reviews, Technology Uses in Education
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Atsunori Fujii – Discover Education, 2024
Learner autonomy is particularly important in higher education, where students are fully responsible for their own learning. Despite this, there is a lack of research on aspects of autonomy support in higher education compared with that of primary and secondary education. To address this gap, this study explored autonomy support and learning…
Descriptors: Higher Education, Personal Autonomy, Preferences, Educational Environment
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Johnson, Evelyn; Masser, Julianne S.; Spears, Lindsey – Exceptionality, 2023
Recent advances in the science of learning and development suggest that education reform efforts will require approaches that attend to the needs of the whole student, to include intensive interventions for students who face barriers to learning, such as students with learning and attention disorders. Although the science of learning and…
Descriptors: Metacognition, Students with Disabilities, Special Education Teachers, Educational Change
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Agrati, Laura Sara; Burgos, Daniel; Ducange, Pietro; Limone, Pierpaolo; Pecori, Riccardo; Perla, Loredana; Picerno, Pietro; Raviolo, Paolo; Stracke, Christian M. – Education Sciences, 2021
In this paper, we report the scientific experience of HELMeTO 2020, the second edition of the International Workshop on Higher Education Learning Methodologies and Technologies Online, held virtually in Bari (Italy) in September 2020 because of the COVID-19 pandemic. The call received 59 proposals from nine countries, 39 papers were accepted to…
Descriptors: Distance Education, Higher Education, Electronic Learning, Technology Uses in Education
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Kuhnel, Matthias; Seiler, Luisa; Honal, Andrea; Ifenthaler, Dirk – Interactive Technology and Smart Education, 2018
Purpose: The purpose of the study was to test the usability of the MyLA app prototype by its potential users. Furthermore, the Web app will be introduced in the framework of "Mobile Learning Analytics", a cooperation project between the Cooperative State University Mannheim and University of Mannheim. The participating universities focus…
Descriptors: Electronic Learning, Higher Education, Data Collection, Usability
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Kuhnel, Matthias; Seiler, Luisa; Honal, Andrea; Ifenthaler, Dirk – International Association for Development of the Information Society, 2017
This study aims to test the usability of MyLA (My Learning Analytics), an application for students at two German universities: The Cooperative State University Mannheim and University of Mannheim. The participating universities focus on the support of personalized and self-regulated learning. MyLA collects data such as learning behavior and…
Descriptors: Electronic Learning, Higher Education, Data Collection, Usability
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Keppell, Mike – International Perspectives on Higher Education Research, 2014
This chapter will explore how the places of learning might look in next generation learning spaces where learners traverse physical and virtual spaces using personalised learning strategies. It will examine how learning spaces may represent ubiquitous spaces in which the learner undertakes some form of study or learning. Although there has been…
Descriptors: Learning Strategies, Higher Education, Computer Simulation, Instructional Design
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Ola, Ade G.; Bai, Xue; Omojokun, Emmanuel E. – Research in Higher Education Journal, 2014
Over the years, companies have relied on On-Line Analytical Processing (OLAP) to answer complex questions relating to issues in business environments such as identifying profitability, trends, correlations, and patterns. This paper addresses the application of OLAP in education and learning. The objective of the research presented in the paper is…
Descriptors: Profiles, Database Management Systems, Information Management, Progress Monitoring
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Kinchin, Ian M.; Baysan, Aylin; Cabot, Lyndon Bruce – Journal of Further and Higher Education, 2008
The development of teaching in higher education towards a more learner-orientated model has been supported by the literature on individual learning differences and on learning styles in particular. This has contributed to the evolution of university pedagogy away from a medieval transmission model than runs counter to contemporary understanding of…
Descriptors: Learning Strategies, Learning Experience, Teaching Methods, Higher Education
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Weston, Cynthia; Cranton, P. A. – Journal of Higher Education, 1986
A survey of instructional strategies for higher education is presented. The issue of selecting the most appropriate methods and materials is discussed in relation to the type and level of the expected student learning, special needs of the student group, and the practical constraints of the instructional setting. (Author/MLW)
Descriptors: College Instruction, Conventional Instruction, Educational Strategies, Experiential Learning
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Pear, Joseph J.; Crone-Todd, Darlene E. – Computers & Education, 2002
Describes a computer-mediated teaching system called computer-aided personalized system of instruction (CAPSI) that incorporates a social constructivist approach, maintaining that learning occurs primarily through a socially interactive process. Discusses use of CAPSI in an undergraduate course at the University of Manitoba that showed students…
Descriptors: Computer Assisted Instruction, Computer Mediated Communication, Feedback, Foreign Countries
Campbell, Martha – 1983
According to Benjamin Bloom's (1981) concept of of mastery learning, most students--given the appropriate instruction and learning resources, adequate time spent learning, and perseverance--can learn a particular academic skill. Because a classroom often does not provide enough time or resources for developmental students to master necessary basic…
Descriptors: Higher Education, Individualized Instruction, Learning Laboratories, Learning Strategies
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Protter, Murray H. – American Mathematical Monthly, 1991
With reference to a self-paced calculus course, the following aspects are reviewed: the structure and the mechanisms, the grading and credit arrangements, the essentials of proper record keeping, the restrictions and prerequisites for enrollment, the cost and the effectiveness, and the potential for application to other mathematics courses. (JJK)
Descriptors: Calculus, College Mathematics, Higher Education, Individualized Instruction
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