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Kahn, Ken; Winters, Niall – British Journal of Educational Technology, 2021
Constructionism, long before it had a name, was intimately tied to the field of Artificial Intelligence. Soon after the birth of Logo at BBN, Seymour Papert set up the Logo Group as part of the MIT AI Lab. Logo was based upon Lisp, the first prominent AI programming language. Many early Logo activities involved natural language processing,…
Descriptors: Artificial Intelligence, Man Machine Systems, Programming Languages, Programming
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Thrall, Elizabeth S.; Lee, Seung Eun; Schrier, Joshua; Zhao, Yijun – Journal of Chemical Education, 2021
Techniques from the branch of artificial intelligence known as machine learning (ML) have been applied to a wide range of problems in chemistry. Nonetheless, there are very few examples of pedagogical activities to introduce ML to chemistry students in the chemistry education literature. Here we report a computational activity that introduces…
Descriptors: Undergraduate Students, Artificial Intelligence, Man Machine Systems, Science Education
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Goudouris, Cesar; de Abreu Mol, Antônio Carlos; Legey, Ana Paula; de Carvalho, Paulo Victor Rodrigues; Freire, Joana Loureiro; Martins, Bianca Maria Rego; Jatobá, Alessandro – Education and Information Technologies, 2020
Teaching computer programming to children and adolescents has become popular in recent years. This popularity has resulted in increased research into techniques for teaching introductory programming using visual languages, especially block-based languages. This study aims to explore new possibilities for teaching programming by adopting a hybrid…
Descriptors: High School Students, Computer Science Education, Programming, Teaching Methods
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Anna Y. Q. Huang; Jei Wei Chang; Albert C. M. Yang; Hiroaki Ogata; Shun Ting Li; Ruo Xuan Yen; Stephen J. H. Yang – Educational Technology & Society, 2023
To improve students' learning performance through review learning activities, we developed a personalized intervention tutoring approach that leverages learning analysis based on artificial intelligence. The proposed intervention first uses text-processing artificial intelligence technologies, namely bidirectional encoder representations from…
Descriptors: Academic Achievement, Tutoring, Artificial Intelligence, Individualized Instruction
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Hew, Khe Foon; Qiao, Chen; Tang, Ying – International Review of Research in Open and Distributed Learning, 2018
Although massive open online courses (MOOCs) have attracted much worldwide attention, scholars still understand little about the specific elements that students find engaging in these large open courses. This study offers a new original contribution by using a machine learning classifier to analyze 24,612 reflective sentences posted by 5,884…
Descriptors: Learner Engagement, Large Group Instruction, Online Courses, Man Machine Systems
Kirschenbaum, Matthew – Chronicle of Higher Education, 2009
The author advocates that humanities scholars should seek and study programming languages. He believes that, increasingly, an appreciation of how complex ideas can be imagined and expressed as a set of formal procedures--rules, models, algorithms--in the virtual space of a computer will be an essential element of a humanities education. Students…
Descriptors: Programming Languages, Student Motivation, Artificial Intelligence, Computer Mediated Communication
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Duval, Beverly K.; Main, Linda – Library Software Review, 1994
Describes expert systems and discusses their use in libraries. Highlights include parts of an expert system; expert system shells; an example of how to build an expert system; a bibliography of 34 sources of information on expert systems in libraries; and a list of 10 expert system shells used in libraries. (Contains five references.) (LRW)
Descriptors: Expert Systems, Information Sources, Library Automation, Man Machine Systems
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Chan, Tak-Wai – Journal of Artificial Intelligence in Education, 1996
Describes the development of learning companion systems and their contributions to the class of social learning systems that integrate artificial intelligence agents and use machine learning to tutor and interact with students. Outlines initial social learning projects, their programming languages, and weakness. Future improvements will include…
Descriptors: Artificial Intelligence, Computer Assisted Instruction, Futures (of Society), Global Education