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Williams, Peter – Journal of Higher Education Policy and Management, 2019
New technologies and the knowledge economy are destabilising graduate professions, with artificial intelligence and the analysis of 'big data' making significant impacts on formerly secure jobs. Blockchain technology, offering automated secure credentialling of undergraduate students' activities and achievements, may significantly erode existing…
Descriptors: Competency Based Education, Technology Uses in Education, Institutional Mission, Universities
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Aksu, Gökhan; Güzeller, Cem Oktay; Eser, Mehmet Taha – International Journal of Assessment Tools in Education, 2019
In this study, it was aimed to compare different normalization methods employed in model developing process via artificial neural networks with different sample sizes. As part of comparison of normalization methods, input variables were set as: work discipline, environmental awareness, instrumental motivation, science self-efficacy, and weekly…
Descriptors: Sample Size, Artificial Intelligence, Classification, Statistical Analysis
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Sulmont, Elisabeth; Patitsas, Elizabeth; Cooperstock, Jeremy R. – ACM Transactions on Computing Education, 2019
Given its societal impacts and applications to numerous fields, machine learning (ML) is an important topic to understand for many students outside of computer science and statistics. However, machine-learning education research is nascent, and research on this subject for non-majors thus far has only focused on curricula and courseware. We…
Descriptors: Man Machine Systems, Artificial Intelligence, Nonmajors, College Faculty
Holstein, Kenneth; McLaren, Bruce M.; Aleven, Vincent – Grantee Submission, 2019
Involving stakeholders throughout the creation of new educational technologies can help ensure their usefulness and usability in real-world contexts. However, given the complexity of learning analytics (LA) systems, it can be challenging to meaningfully involve non-technical stakeholders throughout their design and development. This article…
Descriptors: Learning Analytics, Technology Uses in Education, Artificial Intelligence, Stakeholders
Selwyn, Neil – John Wiley & Sons, Inc, 2019
Developments in AI, robotics and big data are changing the nature of education. Yet the implications of these technologies for the teaching profession are uncertain. While most educators remain convinced of the need for human teachers, outside the profession there is growing anticipation of a technological reinvention of the ways in which teaching…
Descriptors: Educational Technology, Technology Uses in Education, Robotics, Artificial Intelligence
Tappert, Charles C.; Agerwala, Tilak – Association Supporting Computer Users in Education, 2019
This paper discusses our experiences teaching a doctoral-level course in emerging information technologies. The concept of emerging technologies is put into context by describing the technology life cycle. The emerging information technologies of current interest -- Artificial Intelligence and related areas, Collective Human-Computer Intelligence,…
Descriptors: Graduate Study, Information Technology, Artificial Intelligence, Quantum Mechanics
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Petkovic, Dalibor; Denic, Nebojša; Perenic, Goran – International Journal of Technology in Education and Science, 2017
Dramatic increase of learning resources has made the process of learning a timeconsuming task for learners to find relevant resources. Recommender systems are increasingly being developed in E-Learning systems to find relevant resources and facilitate both learning and teaching process. The learning style is defined as the learners' preferences in…
Descriptors: Electronic Learning, Artificial Intelligence, Cognitive Style, Taxonomy
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Han Yu; Chunyan Miao; Cyril Leung; Timothy John White – npj Science of Learning, 2017
Massive Open Online Courses (MOOCs) represent a form of large-scale learning that is changing the landscape of higher education. In this paper, we offer a perspective on how advances in artificial intelligence (AI) may enhance learning and research on MOOCs. We focus on emerging AI techniques including how knowledge representation tools can enable…
Descriptors: Artificial Intelligence, MOOCs, Individualized Instruction, Psychological Patterns
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Lin, Zhiqing – English Language Teaching, 2021
The traditional linear regression in applied linguistics (AL) suffers from the drawbacks arising from the strict assumptions namely: linearity, and normality, etc. More advanced methods are needed to overcome the shortcomings of the traditional method and grapple with intricate linguistic problems. However, there is no previous review on the…
Descriptors: Applied Linguistics, Computer Software, Computational Linguistics, Interdisciplinary Approach
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Yin, Jiaqi; Goh, Tiong-Thye; Yang, Bing; Xiaobin, Yang – Journal of Educational Computing Research, 2021
This study investigated the impact of a chatbot-based micro-learning system on students' learning motivation and performance. A quasi-experiment was conducted with 99 first-year students taking part in a basic computer course on number system conversion. The students were assigned to a traditional learning group or a chatbot-based micro-learning…
Descriptors: Educational Technology, Technology Uses in Education, Student Motivation, Academic Achievement
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Suk, Youmi; Kim, Jee-Seon; Kang, Hyunseung – Journal of Educational and Behavioral Statistics, 2021
There has been increasing interest in exploring heterogeneous treatment effects using machine learning (ML) methods such as causal forests, Bayesian additive regression trees, and targeted maximum likelihood estimation. However, there is little work on applying these methods to estimate treatment effects in latent classes defined by…
Descriptors: Artificial Intelligence, Statistical Analysis, Statistical Inference, Classification
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Sung, Shannon H.; Li, Chenglu; Chen, Guanhua; Huang, Xudong; Xie, Charles; Massicotte, Joyce; Shen, Ji – Journal of Science Education and Technology, 2021
In this paper, we demonstrate how machine learning could be used to quickly assess a student's multimodal representational thinking. Multimodal representational thinking is the complex construct that encodes how students form conceptual, perceptual, graphical, or mathematical symbols in their mind. The augmented reality (AR) technology is adopted…
Descriptors: Observation, Artificial Intelligence, Knowledge Representation, Grade 9
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Lajoie, Susanne P. – International Journal of Artificial Intelligence in Education, 2021
I first met Jim Greer at the NATO Advanced Study Institute on Syntheses of Instructional Sciences and Computing Science for Effective Instructional Computing Systems in 1990 in Calgary, Canada. It was during this meeting that I came to realize that Jim was one of those rare individuals that could help "translate" computer science…
Descriptors: Models, Student Characteristics, Artificial Intelligence, Computer Uses in Education
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Kewalramani, Sarika; Palaiologou, Ioanna; Dardanou, Maria; Allen, Kelly-Ann; Phillipson, Sivanes – Australasian Journal of Early Childhood, 2021
This Australian study examines whether and how technologies such as Artificially Intelligent (AI) toys in a home-based setting might socially and emotionally support children with diverse needs through play. Building on the concept of 'emotional capital', and employing a design-based research approach, parents during the COVID-19 lockdown periods…
Descriptors: Foreign Countries, Robotics, Toys, Social Emotional Learning
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Fancsali, Stephen E.; Li, Hao; Sandbothe, Michael; Ritter, Steven – International Educational Data Mining Society, 2021
Recent work describes methods for systematic, data-driven improvement to instructional content and calls for diverse teams of learning engineers to implement and evaluate such improvements. Focusing on an approach called "design-loop adaptivity," we consider the problem of how developers might use data to target or prioritize particular…
Descriptors: Instructional Development, Instructional Improvement, Data Use, Educational Technology
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