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Say What? Automatic Modeling of Collaborative Problem Solving Skills from Student Speech in the Wild
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
Sha, Lele; Rakovic, Mladen; Li, Yuheng; Whitelock-Wainwright, Alexander; Carroll, David; Gaševic, Dragan; Chen, Guanliang – International Educational Data Mining Society, 2021
Classifying educational forum posts is a longstanding task in the research of Learning Analytics and Educational Data Mining. Though this task has been tackled by applying both traditional Machine Learning (ML) approaches (e.g., Logistics Regression and Random Forest) and up-to-date Deep Learning (DL) approaches, there lacks a systematic…
Descriptors: Classification, Computer Mediated Communication, Learning Analytics, Data Analysis
Zhou, Jianing; Bhat, Suma – Grantee Submission, 2021
Consistency of learning behaviors is known to play an important role in learners' engagement in a course and impact their learning outcomes. Despite significant advances in the area of learning analytics (LA) in measuring various self-regulated learning behaviors, using LA to measure consistency of online course engagement patterns remains largely…
Descriptors: Models, Online Courses, Learner Engagement, Learning Processes
Isaac M. Opper – Annenberg Institute for School Reform at Brown University, 2021
Researchers often include covariates when they analyze the results of randomized controlled trials (RCTs), valuing the increased precision of the estimates over the potential of inducing small-sample bias when doing so. In this paper, we develop a sufficient condition which ensures that the inclusion of covariates does not cause small-sample bias…
Descriptors: Randomized Controlled Trials, Sample Size, Statistical Bias, Artificial Intelligence
Juliana E. Raffaghelli; Bonnie Stewart – OTESSA Conference Proceedings, 2021
In the higher education context, an increasing concern on the technical or instrumental approach permeates attention to academics' data literacies and faculty development. The need for data literacy to deal specifically with the rise of learning analytics in higher education has been raised by some authors, though in spite of some focus on the…
Descriptors: Statistics Education, Faculty Development, Higher Education, Learning Analytics
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
New England Journal of Higher Education, 2018
New England Board of Higher Education's Commission on Higher Education & Employability has thought hard over the past year about the increasing role of artificial intelligence and robotics in the future of life and work. Many others are also waking up to this landscape, which not so long ago seemed like science fiction. Machines have changed…
Descriptors: Artificial Intelligence, Robotics, Decision Making, Moral Values
Pardos, Zachary A.; Dadu, Anant – Journal of Educational Data Mining, 2018
We introduce a model which combines principles from psychometric and connectionist paradigms to allow direct Q-matrix refinement via backpropagation. We call this model dAFM, based on augmentation of the original Additive Factors Model (AFM), whose calculations and constraints we show can be exactly replicated within the framework of neural…
Descriptors: Q Methodology, Psychometrics, Models, Knowledge Level
Montero, Shirly; Arora, Akshit; Kelly, Sean; Milne, Brent; Mozer, Michael – International Educational Data Mining Society, 2018
Personalized learning environments requiring the elicitation of a student's knowledge state have inspired researchers to propose distinct models to understand that knowledge state. Recently, the spotlight has shone on comparisons between traditional, interpretable models such as Bayesian Knowledge Tracing (BKT) and complex, opaque neural network…
Descriptors: Artificial Intelligence, Individualized Instruction, Knowledge Level, Bayesian Statistics
Jaeho Jeon – Computer Assisted Language Learning, 2024
Professionals within the field of language learning have predicted that chatbots would provide new opportunities for the teaching and learning of language. Despite the assumed benefits of utilizing chatbots in language classrooms, such as providing interactional chances or helping to create an anxiety-free atmosphere, little is known about…
Descriptors: Computer Assisted Instruction, Artificial Intelligence, Learning Analytics, Computer Software
National Centre for Vocational Education Research (NCVER), 2024
Research messages is a summary of research produced by NCVER each year. This year's compilation includes a range of research activities undertaken during 2023, comprising of research reports, summaries, occasional papers, presentations, webinars, consultancies, submissions, the 32nd 'No Frills' national research conference, and various additions…
Descriptors: Foreign Countries, Vocational Education, Rural Areas, Barriers
Wu-Yuin Hwang; Bo-Chen Guo; Anh Hoang; Ching-Chun Chang; Nien-Tsu Wu – Computer Assisted Language Learning, 2024
This study introduced an app, called Smart UEnglish, for helping EFL conversation practices in authentic contexts. These conversation practices were categorized into 'designed talk' and 'free talk', based on the content of an English textbook and authentic ambient environment that includes such things as transportation, weather and scenic…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Computer Software
Younes-Aziz Bachiri; Hicham Mouncif; Belaid Bouikhalene; Radoine Hamzaoui – Turkish Online Journal of Distance Education, 2024
This study examined the integration of artificial intelligence-powered speech recognition technology within early reading assessments in Morocco's Teaching at the Right Level (TaRL) program. The purpose was to evaluate the effectiveness of an automated speech recognition tool compared to traditional paper-based assessments in improving reading…
Descriptors: Foreign Countries, Artificial Intelligence, Speech Communication, Identification
Biju Theruvil Sayed; Zein Bassam Bani Younes; Ahmad Alkhayyat; Iroda Adhamova; Habesha Teferi – Language Testing in Asia, 2024
There has been a surge in employing artificial intelligence (AI) in all areas of language pedagogy, not the least among them language testing and assessment. This study investigated the effects of AI-powered tools on English as a Foreign Language (EFL) learners' speaking skills, psychological well-being, autonomy, and academic buoyancy. Using a…
Descriptors: Artificial Intelligence, Language Tests, Success, Speech Skills
Danial Hooshyar; Nour El Mawas; Yeongwook Yang – Knowledge Management & E-Learning, 2024
The use of learner modelling approaches is critical for providing adaptive support in educational computer games, with predictive learner modelling being among the key approaches. While adaptive supports have been shown to improve the effectiveness of educational games, improperly customized support can have negative effects on learning outcomes.…
Descriptors: Artificial Intelligence, Course Content, Tests, Scores

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