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Fonseca, Samuel C.; Pereira, Filipe Dwan; Oliveira, Elaine H. T.; Oliveira, David B. F.; Carvalho, Leandro S. G.; Cristea, Alexandra I. – International Educational Data Mining Society, 2020
As programming must be learned by doing, introductory programming course learners need to solve many problems, e.g., on systems such as 'Online Judges'. However, as such courses are often compulsory for non-Computer Science (nonCS) undergraduates, this may cause difficulties to learners that do not have the typical intrinsic motivation for…
Descriptors: Programming, Introductory Courses, Computer Science Education, Automation
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Mu, Tong; Jetten, Andrea; Brunskill, Emma – International Educational Data Mining Society, 2020
In some computerized educational systems, there is evidence of students "wheel-spinning," where a student tries and repeatedly fails at an educational task for learning a skill. This may be particularly concerning in low resource settings. Prior research has focused on predicting and modeling wheel-spinning, but there has been little…
Descriptors: Computer Uses in Education, Artificial Intelligence, Academic Failure, Automation
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McCrohon, Mark; Tran, Ly Thi – Higher Education for the Future, 2019
This article discussed the development of a framework that visualizes the ontological reality of educational research participants that is incumbent on existing grounded and positioning theories. The proposed framework constructs the ontological reality of participants from qualitative data collected in semi-structured interviews. This framework…
Descriptors: Visualization, Educational Research, Participation, Grounded Theory
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Deb, Kaustuv; Banerjee, Sonali; Das, Atanu; Bag, Rajib – International Journal of Information and Communication Technology Education, 2019
Tutorial requirements of student modeling are traditionally identified by human tutors through crisp mental calculations, and the accuracy of this identification enhances with the experience of the tutor. These identifications become challenging when the mode of delivery is through e-learning. Automatic identification of tutorial requirements must…
Descriptors: Intelligent Tutoring Systems, Automation, Identification, Multiple Choice Tests
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McHugh, Meaghan C.; Saperstein, Sandra L.; Gold, Robert S. – Health Education & Behavior, 2019
Cyberbullying, defined as bullying that takes place using technology, includes similar tactics found in traditional bullying as well as unique approaches such as viral repetition. Nationally, prevalence rates for cyberbullying range from 10% to as high as 40% of school-aged children, depending on the definition and measurement tool applied. The…
Descriptors: Bullying, Computer Mediated Communication, Handheld Devices, Telecommunications
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
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Rieger, Tobias; Heilmann, Lydia; Manzey, Dietrich – Cognitive Research: Principles and Implications, 2021
Visual inspection of luggage using X-ray technology at airports is a time-sensitive task that is often supported by automated systems to increase performance and reduce workload. The present study evaluated how time pressure and automation support influence visual search behavior and performance in a simulated luggage screening task. Moreover, we…
Descriptors: Time Management, Travel, Air Transportation, Task Analysis
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Liaqat, Amna; Munteanu, Cosmin; Demmans Epp, Carrie – International Journal of Artificial Intelligence in Education, 2021
300,000 immigrants move to Canada each year in search of better economic opportunities, and many have limited English language skills. Improving written literacy of newcomers can enhance education, employment, or social integration opportunities. However, frequent, timely, and personalized feedback is not always possible for immigrants. Online…
Descriptors: Adult Students, English Language Learners, Feedback (Response), Automation
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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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Rafatbakhsh, Elaheh; Ahmadi, Alireza; Moloodi, Amirsaeid; Mehrpour, Saeed – Educational Measurement: Issues and Practice, 2021
Test development is a crucial, yet difficult and time-consuming part of any educational system, and the task often falls all on teachers. Automatic item generation systems have recently drawn attention as they can reduce this burden and make test development more convenient. Such systems have been developed to generate items for vocabulary,…
Descriptors: Test Construction, Test Items, Computer Assisted Testing, Multiple Choice Tests
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Chuang, Szufang – European Journal of Training and Development, 2021
Purpose: The purpose of this paper is to contribute to the literature on issues regarding the influence of skill-polarized workplace on jobs, human capital and organization from human resource development's (HRD's) perspective, this research identified 30 displaceable skills from endangered jobs and examined 423 adult employees' awareness and…
Descriptors: Job Skills, Robotics, Adults, Employees
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Pugh, Samuel L.; Subburaj, Shree Krishna; Rao, Arjun Ramesh; Stewart, Angela E. B.; Andrews-Todd, Jessica; D'Mello, Sidney K. – International Educational Data Mining Society, 2021
We investigated the feasibility of using automatic speech recognition (ASR) and natural language processing (NLP) to classify collaborative problem solving (CPS) skills from recorded speech in noisy environments. We analyzed data from 44 dyads of middle and high school students who used videoconferencing to collaboratively solve physics and math…
Descriptors: Problem Solving, Cooperation, Middle School Students, High School Students
Lynette Hazelton; Jessica Nastal; Norbert Elliot; Jill Burstein; Daniel F. McCaffrey – Grantee Submission, 2021
In writing studies research, automated writing evaluation technology is typically examined for a specific, often narrow purpose: to evaluate a particular writing improvement measure, to mine data for changes in writing performance, or to demonstrate the effectiveness of a single technology and accompanying validity arguments. This article adopts a…
Descriptors: Formative Evaluation, Writing Evaluation, Automation, Natural Language Processing
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Wang, Zhen; Zechner, Klaus; Sun, Yu – Language Testing, 2018
As automated scoring systems for spoken responses are increasingly used in language assessments, testing organizations need to analyze their performance, as compared to human raters, across several dimensions, for example, on individual items or based on subgroups of test takers. In addition, there is a need in testing organizations to establish…
Descriptors: Automation, Scoring, Speech Tests, Language Tests
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Vie, Jill-Jênn; Popineau, Fabrice; Bruillard, Éric; Bourda, Yolaine – International Journal of Artificial Intelligence in Education, 2018
In large-scale assessments such as the ones encountered in MOOCs, a lot of usage data is available because of the number of learners involved. Newcomers, that just arrive on a MOOC, have various backgrounds in terms of knowledge, but the platform hardly knows anything about them. Therefore, it is crucial to elicit their knowledge fast, in order to…
Descriptors: Automation, Test Construction, Measurement, Online Courses
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