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Michelle Pauley Murphy; Woei Hung – TechTrends: Linking Research and Practice to Improve Learning, 2024
Constructing a consensus problem space from extensive qualitative data for an ill-structured real-life problem and expressing the result to a broader audience is challenging. To effectively communicate a complex problem space, visualization of that problem space must elucidate inter-causal relationships among the problem variables. In this…
Descriptors: Information Retrieval, Data Analysis, Pattern Recognition, Artificial Intelligence
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Berg, Arthur; Hawila, Nour – Teaching Statistics: An International Journal for Teachers, 2021
This article is presented in two parts: in the first part we discuss the use of R and R-related tools when implementing a data science curriculum in the classroom and direct readers to helpful R resources in education, and in the second part, we demonstrate the use of R in exploring COVID-19 data. In particular, we explore ethnic/racial…
Descriptors: Data, Data Analysis, Programming Languages, COVID-19
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Green, Michael; Chen, Xiaobo – Journal of Chemical Education, 2020
For undergraduate students to be prepared for graduate school and industry, it is imperative that they understand how to merge the theoretical insights gleaned through their undergraduate education with the raw data sets acquired through materials analysis. Thus, the ability to implement data analysis is a vital skill that students should develop.…
Descriptors: Undergraduate Students, Data, Chemistry, Programming Languages
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Hao, Jiangang; Ho, Tin Kam – Journal of Educational and Behavioral Statistics, 2019
Machine learning is a popular topic in data analysis and modeling. Many different machine learning algorithms have been developed and implemented in a variety of programming languages over the past 20 years. In this article, we first provide an overview of machine learning and clarify its difference from statistical inference. Then, we review…
Descriptors: Artificial Intelligence, Statistical Inference, Data Analysis, Programming Languages
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Maranga, Jemar Jude A.; Matugas, Leilla Keith J.; Lim, Jorge Frederick W.; Romana, Cherry Lyn C. Sta. – International Association for Development of the Information Society, 2019
Teaching an introductory programming course to an average of 40 students while monitoring their performance can be a challenge for instructors. Preparing coding exercises with test cases and checking students' programs can prove to be time consuming at times. Moreover, programming has been known to be quite difficult for students to learn. To…
Descriptors: Online Courses, Programming Languages, Introductory Courses, Computer Science Education
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Brown, Neil C. C.; Altadmri, Amjad – ACM Transactions on Computing Education, 2017
Teaching is the process of conveying knowledge and skills to learners. It involves preventing misunderstandings or correcting misconceptions that learners have acquired. Thus, effective teaching relies on solid knowledge of the discipline, but also a good grasp of where learners are likely to trip up or misunderstand. In programming, there is much…
Descriptors: Novices, Programming Languages, Programming, Error Patterns
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Castro-Schez, J. J.; del Castillo, E.; Hortolano, J.; Rodriguez, A. – IEEE Transactions on Education, 2009
Educational software tools are considered to enrich teaching strategies, providing a more compelling means of exploration and feedback than traditional blackboard methods. Moreover, software simulators provide a more motivating link between theory and practice than pencil-paper methods, encouraging active and discovery learning in the students.…
Descriptors: Foreign Countries, Computer Software, Data Analysis, Web Based Instruction