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Salas-Rueda, Ricardo-Adan; Salas-Rueda, Erika-Patricia; Salas-Rueda, Rodrigo-David – Turkish Online Journal of Distance Education, 2020
This quantitative research analyzes the impact of the Web Application for the Educational Process on Compound Interest (WAEPCI) considering the machine learning and data science. The sample is composed of 46 students who studied the Financial Mathematics course in a Mexican university during the 2017 school year. WAEPCI presents the calculation of…
Descriptors: Foreign Countries, College Students, Credit (Finance), Computation
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Bass, Randy – Change: The Magazine of Higher Learning, 2018
The future of human learning will be shaped by technology, but in ways completely different from those of the past. Over recent decades, the emergence and development of educational technology has been largely divorced from the broader cultural conversation about the impact of machine intelligence on the future of humanity. Technology can best…
Descriptors: Futures (of Society), Educational Technology, Educational Trends, Artificial Intelligence
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Heys, Jeffrey J. – Chemical Engineering Education, 2018
The application of Machine Learning (ML) tools to a wide range of problems from image recognition to movie recommendations is increasing rapidly. After a brief overview of ML, select ML tools are demonstrated through the analysis of student grades in various chemical engineering courses. ML tools are shown to help in the identification of…
Descriptors: Artificial Intelligence, Man Machine Systems, Teaching Methods, Grades (Scholastic)
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Qazdar, Aimad; Er-Raha, Brahim; Cherkaoui, Chihab; Mammass, Driss – Education and Information Technologies, 2019
The use of machine learning with educational data mining (EDM) to predict learner performance has always been an important research area. Predicting academic results is one of the solutions that aims to monitor the progress of students and anticipates students at risk of failing the academic pathways. In this paper, we present a framework for…
Descriptors: Data Analysis, Academic Achievement, At Risk Students, High School Students
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Barata, Gabriel; Gama, Sandra; Jorge, Joaquim; Gonçalves, Daniel – IEEE Transactions on Learning Technologies, 2016
State of the art research shows that gamified learning can be used to engage students and help them perform better. However, most studies use a one-size-fits-all approach to gamification, where individual differences and needs are ignored. In a previous study, we identified four types of students attending a gamified college course, characterized…
Descriptors: Prediction, Performance, Profiles, Games
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Sen, Ayon; Patel, Purav; Rau, Martina A.; Mason, Blake; Nowak, Robert; Rogers, Timothy T.; Zhu, Xiaojin – International Educational Data Mining Society, 2018
In STEM domains, students are expected to acquire domain knowledge from visual representations that they may not yet be able to interpret. Such learning requires perceptual fluency: the ability to intuitively and rapidly see which concepts visuals show and to translate among multiple visuals. Instructional problems that engage students in…
Descriptors: Visual Aids, Visual Perception, Data Analysis, Artificial Intelligence
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Lagus, Jarkko; Longi, Krista; Klami, Arto; Hellas, Arto – ACM Transactions on Computing Education, 2018
The computing education research literature contains a wide variety of methods that can be used to identify students who are either at risk of failing their studies or who could benefit from additional challenges. Many of these are based on machine-learning models that learn to make predictions based on previously observed data. However, in…
Descriptors: Computer Science Education, Transfer of Training, Programming, Educational Objectives
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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
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Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – Grantee Submission, 2017
This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension. We compared the accuracy of seven different machine learning classification algorithms in predicting human ratings of student essays about…
Descriptors: Artificial Intelligence, Natural Language Processing, Reading Comprehension, Literature
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Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – International Educational Data Mining Society, 2017
This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension. We compared the accuracy of seven different machine learning classification algorithms in predicting human ratings of student essays about…
Descriptors: Artificial Intelligence, Natural Language Processing, Reading Comprehension, Literature
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Qin, Ying – International Journal of Computer-Assisted Language Learning and Teaching, 2019
This study extracts the comments from a large scale of Chinese EFL learners' translation corpus to study the taxonomy of translation errors. Two unsupervised machine learning approaches are used to obtain the computational evidences of translation error taxonomy. After manually revision, ten types of English to Chinese (E2C) and eight types…
Descriptors: Taxonomy, Translation, Computer Assisted Instruction, Second Language Learning
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Mughaz, Dror; Cohen, Michael; Mejahez, Sagit; Ades, Tal; Bouhnik, Dan – Interdisciplinary Journal of e-Skills and Lifelong Learning, 2020
Aim/Purpose: Using Artificial Intelligence with Deep Learning (DL) techniques, which mimic the action of the brain, to improve a student's grammar learning process. Finding the subject of a sentence using DL, and learning, by way of this computer field, to analyze human learning processes and mistakes. In addition, showing Artificial Intelligence…
Descriptors: Artificial Intelligence, Teaching Methods, Brain Hemisphere Functions, Grammar
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Magnus, Douglas de Matos; Carbonera, Luis Felipe Bianchi; Pfitscher, Luciano Lopes; Farret, Felix Alberto; Bernardon, Daniel Pinheiro; Tavares, Andre Abelardo – IEEE Transactions on Education, 2020
Contribution: A distinct instructional approach to laboratory activities for power system machines, based on hybrid project-based learning (h-PBL). Students develop their own laboratory procedures and explain their results. This approach creates a positive environment to improve students' problem-solving, and their control design and…
Descriptors: Student Projects, Laboratories, Educational Technology, Technology Uses in Education
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Astutik, Sri; Susantini, Endang; Madlazim; Nur, Mohamad; Supeno – International Journal of Instruction, 2020
This research aims to describe the effectiveness of collaborative creativity learning models to train student's scientific creativity learning outcome for secondary student. This research was conducted using one group pre- and post-test design. The results showed (1) an improved indicator of achievement in motion material with average g-score =…
Descriptors: Cooperative Learning, Creativity, Instructional Effectiveness, Secondary School Students
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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
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