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Melina Verger; Chunyang Fan; Sébastien Lallé; François Bouchet; Vanda Luengo – Journal of Educational Data Mining, 2024
Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against certain individuals and harmful long-term…
Descriptors: Algorithms, Prediction, Bias, Classification
Dragos-Georgian Corlatescu; Micah Watanabe; Stefan Ruseti; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2024
Modeling reading comprehension processes is a critical task for Learning Analytics, as accurate models of the reading process can be used to match students to texts, identify appropriate interventions, and predict learning outcomes. This paper introduces an improved version of the Automated Model of Comprehension, namely version 4.0. AMoC has its…
Descriptors: Computer Software, Artificial Intelligence, Learning Analytics, Natural Language Processing
Brittany C. Bradford; Debshila Basu Mallick; Richard G. Baraniuk – Grantee Submission, 2023
Greater financial literacy is critically needed among young adults in the United States, but many financial literacy education courses have been less effective than hoped for by educators and researchers. Additionally, many have not been designed around established curricula or learning science principles, rendering findings difficult for…
Descriptors: Money Management, Open Source Technology, Textbooks, Financial Literacy
Blobstein, Ariel; Gal, Kobi; Kim, Hyunsoo Gloria; Facciotti, Marc; Karger, David; Sripathi, Kamali – International Educational Data Mining Society, 2022
Emoji are commonly used in social media to convey attitudes and emotions. While popular, their use in educational contexts has been sparsely studied. This paper reports on the students' use of emoji in an online course forum in which students annotate and discuss course material in the margins of the online textbook. For this study, instructors…
Descriptors: Computer Mediated Communication, Nonverbal Communication, Social Media, Online Courses
Ramanarayanan, Vikram; Lange, Patrick; Evanini, Keelan; Molloy, Hillary; Tsuprun, Eugene; Qian, Yao; Suendermann-Oeft, David – ETS Research Report Series, 2017
Predicting and analyzing multimodal dialog user experience (UX) metrics, such as overall call experience, caller engagement, and latency, among other metrics, in an ongoing manner is important for evaluating such systems. We investigate automated prediction of multiple such metrics collected from crowdsourced interactions with an open-source,…
Descriptors: Automation, Prediction, Man Machine Systems, Open Source Technology
Knowles, Jared E. – Journal of Educational Data Mining, 2015
The state of Wisconsin has one of the highest four year graduation rates in the nation, but deep disparities among student subgroups remain. To address this the state has created the Wisconsin Dropout Early Warning System (DEWS), a predictive model of student dropout risk for students in grades six through nine. The Wisconsin DEWS is in use…
Descriptors: Dropouts, Models, Prediction, Risk
Waters, John K. – T.H.E. Journal, 2013
If anyone had thought to recognize a K-12 educational buzzword of the year for 2012, it would surely have been "open educational resources" (OER). Ed tech media has fairly hummed with the topic, largely with exciting predictions: OER would give cash-strapped K-12 educators access to high-quality tools and content for their classrooms at little or…
Descriptors: State Standards, Prediction, Instructional Materials, Elementary Secondary Education
Valdés Aguirre, Benjamín; Ramírez Uresti, Jorge A.; du Boulay, Benedict – International Journal of Artificial Intelligence in Education, 2016
Sharing user information between systems is an area of interest for every field involving personalization. Recommender Systems are more advanced in this aspect than Intelligent Tutoring Systems (ITSs) and Intelligent Learning Environments (ILEs). A reason for this is that the user models of Intelligent Tutoring Systems and Intelligent Learning…
Descriptors: Intelligent Tutoring Systems, Models, Open Source Technology, Computers
Zafra, Amelia; Ventura, Sebastian – International Working Group on Educational Data Mining, 2009
The ability to predict a student's performance could be useful in a great number of different ways associated with university-level learning. In this paper, a grammar guided genetic programming algorithm, G3P-MI, has been applied to predict if the student will fail or pass a certain course and identifies activities to promote learning in a…
Descriptors: Foreign Countries, Programming, Academic Achievement, Grades (Scholastic)
International Association for Development of the Information Society, 2012
The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…
Descriptors: Academic Achievement, Academic Persistence, Academic Support Services, Access to Computers