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Peer reviewed
Direct linkBastiaens, Theo J., Ed. – Association for the Advancement of Computing in Education, 2021
The Association for the Advancement of Computing in Education (AACE) is an international, non-profit educational organization. The Association's purpose is to advance the knowledge, theory, and quality of teaching and learning at all levels with information technology. The "EdMedia + Innovate Learning" conference took place online July…
Descriptors: Educational Media, Conferences (Gatherings), Electronic Learning, Distance Education
Burleson, Winslow; Lewis, Armanda – International Journal of Artificial Intelligence in Education, 2016
This essay imagines the role that artificial intelligence innovations play in the integrated living, learning and research environments of 2041. Here, in 2041, in the context of increasingly complex wicked challenges, whose solutions by their very nature continue to evade even the most capable experts, society and technology have co-evolved to…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Technology, Research
Pinkwart, Niels – International Journal of Artificial Intelligence in Education, 2016
This paper attempts an analysis of some current trends and future developments in computer science, education, and educational technology. Based on these trends, two possible future predictions of AIED are presented in the form of a utopian vision and a dystopian vision. A comparison of these two visions leads to seven challenges that AIED might…
Descriptors: Artificial Intelligence, Technology Uses in Education, Computer Science, Educational Technology
Devedzic, Vladan – International Journal of Artificial Intelligence in Education, 2016
If you ask me "Will Semantic Web 'ever' happen, in general, and specifically in education?", the best answer I can give you is "I don't know," but I know that today we are still far away from the hopes that I had when I wrote my paper "Education and The Semantic Web" (Devedzic 2004) more than 10 years ago. Much of the…
Descriptors: Web 2.0 Technologies, Semantics, Web Based Instruction, Visual Aids
Zhang, Lishan; VanLehn, Kurt – Research and Practice in Technology Enhanced Learning, 2016
Science instructors need questions for use in exams, homework assignments, class discussions, reviews, and other instructional activities. Textbooks never have enough questions, so instructors must find them from other sources or generate their own questions. In order to supply biology instructors with questions for college students in…
Descriptors: Questioning Techniques, Biology, Introductory Courses, Artificial Intelligence
Download full textShen, Shitian; Chi, Min – International Educational Data Mining Society, 2016
We explored a series of feature selection methods for model-based Reinforcement Learning (RL). More specifically, we explored four common correlation metrics and based on them, we proposed the fifth one named Weighed Information Gain (WIG). While much existing correlation-based feature selection methods mostly explored high correlation by default,…
Descriptors: Correlation, Selection, Methods, Intelligent Tutoring Systems
Joosten, Tanya; Lee-McCarthy, Kate; Harness, Lindsey; Paulus, Ryan – Online Learning Consortium, 2020
Underrepresented students enrolled in postsecondary educational institutions in the U.S. are faced with key barriers and challenges that have created an equity gap. By considering the needs of underrepresented students, faculty and institutions are able to implement digital course ware solutions to take a step towards closing the equity gap and…
Descriptors: Electronic Learning, Educational Innovation, Educational Trends, Technology Uses in Education
Obari, Hiroyuki; Lambacher, Steve; Kikuchi, Hisayo – Research-publishing.net, 2020
This study focuses on the use of emerging technologies such as Artificial Intelligence (AI) smart speakers and smartphone applications for improving the English language skills of L1 Japanese undergraduates. An empirical investigation was carried out with 82 Japanese students. Participants were required to study a variety of online English…
Descriptors: Artificial Intelligence, Computer Simulation, Audio Equipment, Handheld Devices
Matthew J. Salganik; Ian Lundberg; Alexander T. Kindel; Caitlin E. Ahearn; Khaled Al-Ghoneim; Abdullah Almaatouq; Drew M. Altschul; Jennie E. Brand; Nicole Bohme Carnegie; Ryan James Compton; Debanjan Datta; Thomas Davidson; Anna Filippova; Connor Gilroy; Brian J. Goode; Eaman Jahani; Ridhi Kashyap; Antje Kirchner; Stephen McKay; Allison C. Morgan; Alex Pentland; Kivan Polimis; Louis Raes; Daniel E. Rigobon; Claudia V. Roberts; Diana M. Stanescu; Yoshihiko Suhara; Adaner Usmani; Erik H. Wang; Muna Adem; Abdulla Alhajri; Bedoor AlShebli; Redwane Amin; Ryan B. Amos; Lisa P. Argyle; Livia Baer-Bositis; Moritz Büchi; Bo-Ryehn Chung; William Eggert; Gregory Faletto; Zhilin Fan; Jeremy Freese; Tejomay Gadgil; Josh Gagné; Yue Gao; Andrew Halpern-Manners; Sonia P. Hashim; Sonia Hausen; Guanhua He; Kimberly Higuera; Bernie Hogan; Ilana M. Horwitz; Lisa M. Hummel; Naman Jain; Kun Jin; David Jurgens; Patrick Kaminski; Areg Karapetyan; E. H. Kim; Ben Leizman; Naijia Liu; Malte Möser; Andrew E. Mack; Mayank Mahajan; Noah Mandell; Helge Marahrens; Diana Mercado-Garcia; Viola Mocz; Katariina Mueller-Gastell; Ahmed Musse; Qiankun Niu; William Nowak; Hamidreza Omidvar; Andrew Or; Karen Ouyang; Katy M. Pinto; Ethan Porter; Kristin E. Porter; Crystal Qian; Tamkinat Rauf; Anahit Sargsyan; Thomas Schaffner; Landon Schnabel; Bryan Schonfeld; Ben Sender; Jonathan D. Tang; Emma Tsurkov; Austin van Loon; Onur Varol; Xiafei Wang; Zhi Wang; Julia Wang; Flora Wang; Samantha Weissman; Kirstie Whitaker; Maria K. Wolters; Wei Lee Woon; James Wu; Catherine Wu; Kengran Yang; Jingwen Yin; Bingyu Zhao; Chenyun Zhu; Jeanne Brooks-Gunn; Barbara E. Engelhardt; Moritz Hardt; Dean Knox; Karen Levy; Arvind Narayanan; Brandon M. Stewart; Duncan J. Watts; Sara McLanahan – Grantee Submission, 2020
How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning…
Descriptors: Life Satisfaction, Family Life, Quality of Life, Disadvantaged
Goldin, Ilya; Narciss, Susanne; Foltz, Peter; Bauer, Malcolm – International Journal of Artificial Intelligence in Education, 2017
Formative feedback is well known as a key factor in influencing learning. Modern interactive learning environments provide a broad range of ways to provide feedback to students as well as new tools to understand feedback and its relation to various learning outcomes. This issue focuses on the role of formative feedback through a lens of how…
Descriptors: Formative Evaluation, Feedback (Response), Interaction, Technology Uses in Education
Crowe, Dale; LaPierre, Martin; Kebritchi, Mansureh – TechTrends: Linking Research and Practice to Improve Learning, 2017
With augmented intelligence/knowledge based system (KBS) it is now possible to develop distance learning applications to support both curriculum and administrative tasks. Instructional designers and information technology (IT) professionals are now moving from the programmable systems era that started in the 1950s to the cognitive computing era.…
Descriptors: Artificial Intelligence, Information Technology, Distance Education, Instructional Design
Suendermann-Oeft, David; Ramanarayanan, Vikram; Yu, Zhou; Qian, Yao; Evanini, Keelan; Lange, Patrick; Wang, Xinhao; Zechner, Klaus – ETS Research Report Series, 2017
We present work in progress on a multimodal dialog system for English language assessment using a modular cloud-based architecture adhering to open industry standards. Among the modules being developed for the system, multiple modules heavily exploit machine learning techniques, including speech recognition, spoken language proficiency rating,…
Descriptors: Language Tests, Computer Assisted Testing, Artificial Intelligence, English (Second Language)
Howard, Cynthia; Jordan, Pamela; Di Eugenio, Barbara; Katz, Sandra – International Journal of Artificial Intelligence in Education, 2017
Despite a growing need for educational tools that support students at the earliest phases of undergraduate Computer Science (CS) curricula, relatively few such tools exist--the majority being Intelligent Tutoring Systems. Since peer interactions more readily give rise to challenges and negotiations, another way in which students can become more…
Descriptors: Computer Science Education, Undergraduate Study, Intelligent Tutoring Systems, Artificial Intelligence
Wang, Lisa; Sy, Angela; Liu, Larry; Piech, Chris – International Educational Data Mining Society, 2017
Modeling student knowledge while students are acquiring new concepts is a crucial stepping stone towards providing personalized automated feedback at scale. We believe that rich information about a student's learning is captured within her responses to open-ended problems with unbounded solution spaces, such as programming exercises. In addition,…
Descriptors: Online Courses, Knowledge Level, Pedagogical Content Knowledge, Scaffolding (Teaching Technique)
Jim Webber – College Composition and Communication, 2017
Proponents of reframing argue that prophetic pragmatism entails redirecting contemporary education reforms. While this judgment may defend our professional standing, it overlooks the consequences of redirecting reform's appeals to global competition, which preclude public participation in defining the goals and measures of literacy education. This…
Descriptors: Evaluation Methods, Artificial Intelligence, Computer Assisted Testing, Grading
