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Download full textObari, 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
Direct linkMatthew 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
Kessler, Greg – Foreign Language Annals, 2018
We are living in a time with unprecedented opportunities to communicate with others in authentic and compelling linguistically and culturally contextualized domains. In fact, language teachers today are faced with so many fascinating options for using technology to enhance language learning that it can be overwhelming. Even for those who are…
Descriptors: Futures (of Society), Second Language Learning, Second Language Instruction, Social Media
Polyzou, Agoritsa; Karypis, George – International Educational Data Mining Society, 2018
Developing tools to support students and learning in a traditional or online setting is a significant task in today's educational environment. The initial steps towards enabling such technologies using machine learning techniques focused on predicting the student's performance in terms of the achieved grades. The disadvantage of these approaches…
Descriptors: Low Achievement, Predictor Variables, Classification, Student Characteristics
Yagci, Ali; Çevik, Mustafa – Education and Information Technologies, 2019
This study aims to predict the academic achievements of Turkish and Malaysian vocational and technical high school (VTS) students in science courses (physics, chemistry and biology) through artificial neural networks (ANN) and to put forth the measures to be taken against their failure. The study population consisted of 10th and 11th grade 922 VTS…
Descriptors: Prediction, Academic Achievement, Technical Education, Vocational High Schools
Barros, Thiago M.; Souza Neto, Plácido A.; Silva, Ivanovitch; Guedes, Luiz Affonso – Education Sciences, 2019
Predicting school dropout rates is an important issue for the smooth execution of an educational system. This problem is solved by classifying students into two classes using educational activities related statistical datasets. One of the classes must identify the students who have the tendency to persist. The other class must identify the…
Descriptors: Predictor Variables, Models, Dropout Rate, Classification
Ouherrou, Nihal; Elhammoumi, Oussama; Benmarrakchi, Fatimaezzahra; El Kafi, Jamal – Education and Information Technologies, 2019
Children with Learning Disabilities (LDs) show some emotional difficulties and behavioral problems in classroom compared with their peers without LDs. Emotions constitute an important part of the learning process. Recent evidence suggests that the use of Information and Communication Technology (ICT) in special education permits to remove barriers…
Descriptors: Children, Learning Disabilities, Emotional Response, Nonverbal Communication
Astle, Duncan E.; Bathelt, Joe; Holmes, Joni – Developmental Science, 2019
Our understanding of learning difficulties largely comes from children with specific diagnoses or individuals selected from community/clinical samples according to strict inclusion criteria. Applying strict exclusionary criteria overemphasizes within group homogeneity and between group differences, and fails to capture comorbidity. Here, we…
Descriptors: Cognitive Mapping, Learning Problems, Comorbidity, Identification
Auerbach, Joshua E.; Concordel, Alice; Kornatowski, Przemyslaw M.; Floreano, Dario – IEEE Transactions on Learning Technologies, 2019
It has often been found that students appreciate hands-on work, and find that they learn more with courses that include a project than those relying solely on conventional lectures and tests. This type of project driven learning is a key component of "Inquiry-based learning" (IBL), which aims at teaching methodology as well as content by…
Descriptors: Active Learning, Inquiry, Robotics, Artificial Intelligence
Porayska-Pomsta, Kaska – International Journal of Artificial Intelligence in Education, 2016
Evidence-based practice (EBP) is of critical importance in education where emphasis is placed on the need to equip educators with an ability to independently generate and reflect on evidence of their practices in situ--a process also known as "praxis." This paper examines existing research related to teachers' metacognitive skills and,…
Descriptors: Evidence Based Practice, Artificial Intelligence, Metacognition, Praxis
Conati, Cristina – International Journal of Artificial Intelligence in Education, 2016
This paper is a commentary on "Toward Computer-Based Support of Meta-Cognitive Skills: a Computational Framework to Coach Self-Explanation", by Cristina Conati and Kurt Vanlehn, published in the "IJAED" in 2000 (Conati and VanLehn 2010). This work was one of the first examples of Intelligent Learning Environments (ILE) that…
Descriptors: Metacognition, Intelligent Tutoring Systems, Skill Development, Artificial Intelligence
Aleven, Vincent; Roll, Ido; McLaren, Bruce M.; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2016
Help seeking is an important process in self-regulated learning (SRL). It may influence learning with intelligent tutoring systems (ITSs), because many ITSs provide help, often at the student's request. The Help Tutor was a tutor agent that gave in-context, real-time feedback on students' help-seeking behavior, as they were learning with an ITS.…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Help Seeking, Feedback (Response)
Rus, Vasile; Gautam, Dipesh; Swiecki, Zachari; Shaffer, David W.; Graesser, Arthur C. – International Educational Data Mining Society, 2016
Engineering virtual internships are simulations where students role play as interns at fictional companies, working to create engineering designs. To improve the scalability of these virtual internships, a reliable automated assessment system for tasks submitted by students is necessary. Therefore, we propose a machine learning approach to…
Descriptors: Engineering Education, Internship Programs, Computer Simulation, Models
Back to the Basics: Bayesian Extensions of IRT Outperform Neural Networks for Proficiency Estimation
Wilson, Kevin H.; Karklin, Yan; Han, Bojian; Ekanadham, Chaitanya – International Educational Data Mining Society, 2016
Estimating student proficiency is an important task for computer based learning systems. We compare a family of IRT-based proficiency estimation methods to Deep Knowledge Tracing (DKT), a recently proposed recurrent neural network model with promising initial results. We evaluate how well each model predicts a student's future response given…
Descriptors: Item Response Theory, Bayesian Statistics, Computation, Artificial Intelligence
Alsadoon, Reem – English Language Teaching, 2021
In the AI field of language learning, chatterbots are an interesting area for language learning and practice. This research investigates Arabic EFL vocabulary learning using an interactive storytelling chatterbot. A chatterbot was created and equipped with four vocabulary tools: a dictionary, images, an L1 translation tool, and a concordancer. The…
Descriptors: Vocabulary Development, Second Language Learning, Second Language Instruction, English (Second Language)
