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Lafuente, Deborah; Cohen, Brenda; Fiorini, Guillermo; Garci´a, Agusti´n Alejo; Bringas, Mauro; Morzan, Ezequiel; Onna, Diego – Journal of Chemical Education, 2021
Machine learning, a subdomain of artificial intelligence, is a widespread technology that is molding how chemists interact with data. Therefore, it is a relevant skill to incorporate into the toolbox of any chemistry student. This work presents a workshop that introduces machine learning for chemistry students based on a set of Python notebooks…
Descriptors: Undergraduate Students, Chemistry, Electronic Learning, Artificial Intelligence
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Zhang, Yingbin; Pinto, Juan D.; Fan, Aysa Xuemo; Paquette, Luc – Journal of Educational Data Mining, 2023
The second CSEDM data challenge aimed at finding innovative methods to use students' programming traces to model their learning. The main challenge of this task is how to decide which past problems are relevant for predicting performance on a future problem. This paper proposes a set of weighting schemes to address this challenge. Specifically,…
Descriptors: Problem Solving, Introductory Courses, Computer Science Education, Programming
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Chen Zhong; J. B. Kim – Journal of Information Systems Education, 2024
Data Analytics has emerged as an essential skill for business students, and several tools are available to support their learning in this area. Due to the students' lack of programming skills and the perceived complexity of R, many business analytics courses employ no-code analytical software like IBM SPSS Modeler. Nonetheless, generative…
Descriptors: Business Education, Regression (Statistics), Programming, Artificial Intelligence
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Pang, Bo; Nijkamp, Erik; Wu, Ying Nian – Journal of Educational and Behavioral Statistics, 2020
This review covers the core concepts and design decisions of TensorFlow. TensorFlow, originally created by researchers at Google, is the most popular one among the plethora of deep learning libraries. In the field of deep learning, neural networks have achieved tremendous success and gained wide popularity in various areas. This family of models…
Descriptors: Artificial Intelligence, Regression (Statistics), Models, Classification
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Akar, Sacide Guzin Mazman; Altun, Arif – Contemporary Educational Technology, 2017
The purpose of this study is to investigate and conceptualize the ranks of importance of social cognitive variables on university students' computer programming performances. Spatial ability, working memory, self-efficacy, gender, prior knowledge and the universities students attend were taken as variables to be analyzed. The study has been…
Descriptors: Individual Differences, Learning Processes, Programming, Self Efficacy
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Simkin, Mark; Stiver, Debra – Journal of Education for Business, 2016
Allowing students to grade their own homework promises many advantages. But can students perform such grading tasks honestly and accurately? Also, do such assessments vary by gender? To answer these questions, the authors analyzed the homework scores of 266 students in seven introductory programming classes. The statistical results were favorable…
Descriptors: Homework, Self Evaluation (Individuals), Student Evaluation, Gender Differences
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Liu, Ming; Rus, Vasile; Liu, Li – IEEE Transactions on Learning Technologies, 2017
Question generation is an emerging research area of artificial intelligence in education. Question authoring tools are important in educational technologies, e.g., intelligent tutoring systems, as well as in dialogue systems. Approaches to generate factual questions, i.e., questions that have concrete answers, mainly make use of the syntactical…
Descriptors: Chinese, Questioning Techniques, Automation, Natural Language Processing
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Maguire, Phil; Maguire, Rebecca; Kelly, Robert – Computer Science Education, 2017
We report on an intervention in which informal programming labs were switched to a weekly machine-evaluated test for a second year Data Structures and Algorithms module. Using the online HackerRank system, we investigated whether greater constructive alignment between course content and the exam would result in lower failure rates. After…
Descriptors: Programming, Computer Science Education, Teaching Methods, Test Scoring Machines
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Wong, Wang-chan – Information Systems Education Journal, 2015
In this paper, the author reports the results of an empirical study on the relationship between a student's programming experience and their success in a traditional Systems Analysis and Design (SA&D) class where technical skills such as dataflow analysis and entity relationship data modeling are covered. While it is possible to teach these…
Descriptors: Programming, Information Systems, Systems Analysis, Design
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Maaliw, Renato R. III; Ballera, Melvin A. – International Association for Development of the Information Society, 2017
The usage of data mining has dramatically increased over the past few years and the education sector is leveraging this field in order to analyze and gain intuitive knowledge in terms of the vast accumulated data within its confines. The primary objective of this study is to compare the results of different classification techniques such as Naïve…
Descriptors: Classification, Cognitive Style, Electronic Learning, Decision Making
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Bringula, Rex P.; Manabat, Geecee Maybelline A.; Tolentino, Miguel Angelo A.; Torres, Edmon L. – World Journal of Education, 2012
This descriptive study determined which of the sources of errors would predict the errors committed by novice Java programmers. Descriptive statistics revealed that the respondents perceived that they committed the identified eighteen errors infrequently. Thought error was perceived to be the main source of error during the laboratory programming…
Descriptors: Error Patterns, Programming, Programming Languages, Predictor Variables
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Erhardt, Robert J.; Shuman, Michael P. – Journal of Statistics Education, 2015
At Wake Forest University, a student who is blind enrolled in a second course in statistics. The course covered simple and multiple regression, model diagnostics, model selection, data visualization, and elementary logistic regression. These topics required that the student both interpret and produce three sets of materials: mathematical writing,…
Descriptors: Blindness, Statistics, College Mathematics, College Students
Mynyk, John – ProQuest LLC, 2012
Organizations that depend on the use of IT in their business models must maintain their systems and keep their systems current to survive (Filipek, 2008; Kulkarni, Kumar, Mookerjee, & Sethi, 2009; Unterkalmsteiner et al., 2012). As most IT departments allocate as much as 80% of their budget to maintain stability while leaving only the other…
Descriptors: Mixed Methods Research, Information Technology, Standards, Programming
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Benakli, Nadia; Kostadinov, Boyan; Satyanarayana, Ashwin; Singh, Satyanand – International Journal of Mathematical Education in Science and Technology, 2017
The goal of this paper is to promote computational thinking among mathematics, engineering, science and technology students, through hands-on computer experiments. These activities have the potential to empower students to learn, create and invent with technology, and they engage computational thinking through simulations, visualizations and data…
Descriptors: Calculus, Probability, Data Analysis, Computation
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Cigdem, Harun – Contemporary Educational Technology, 2015
This study focuses on learners' self-regulation which is one of the essential skills for student achievement in blended courses. Research on learners' self-regulation skills in blended learning environments has gained popularity in recent years however only a few studies investigating the correlation between self-regulation skills and student…
Descriptors: Metacognition, Computer Science Education, Measures (Individuals), Blended Learning
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