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Seyed Parsa Neshaei; Richard Lee Davis; Paola Mejia-Domenzain; Tanya Nazaretsky; Tanja Käser – International Educational Data Mining Society, 2025
Deep learning models for text classification have been increasingly used in intelligent tutoring systems and educational writing assistants. However, the scarcity of data in many educational settings, as well as certain imbalances in counts among the annotated labels of educational datasets, limits the generalizability and expressiveness of…
Descriptors: Artificial Intelligence, Classification, Natural Language Processing, Technology Uses in Education
Nesrine Mansouri; Mourad Abed; Makram Soui – Education and Information Technologies, 2024
Selecting undergraduate majors or specializations is a crucial decision for students since it considerably impacts their educational and career paths. Moreover, their decisions should match their academic background, interests, and goals to pursue their passions and discover various career paths with motivation. However, such a decision remains…
Descriptors: Undergraduate Students, Decision Making, Majors (Students), Specialization
Luiz Rodrigues; Filipe Dwan Pereira; Marcelo Marinho; Valmir Macario; Ig Ibert Bittencourt; Seiji Isotani; Diego Dermeval; Rafael Mello – Education and Information Technologies, 2024
Intelligent Tutoring Systems (ITS) have been widely used to enhance math learning, wherein teacher's involvement is prominent to achieve their full potential. Usually, ITSs depend on direct interaction between the students and a computer. Recently, researchers started exploring handwritten input (e.g., from paper sheets) aiming to provide…
Descriptors: Intelligent Tutoring Systems, Handwriting, Equal Education, Access to Education
Esther Ulitzsch; Qiwei He; Steffi Pohl – Grantee Submission, 2024
This is an editorial for a special issue "Innovations in Exploring Sequential Process Data" in the journal Zeitschrift für Psychologie. Process data refer to log files generated by human-computer interactive items. They document the entire process, including keystrokes, mouse clicks as well as the associated time stamps, performed by a…
Descriptors: Educational Innovation, Man Machine Systems, Educational Technology, Computer Assisted Testing
Olga Ovtšarenko – Discover Education, 2024
Machine learning (ML) methods are among the most promising technologies with wide-ranging research opportunities, particularly in the field of education, where they can be used to enhance student learning outcomes. This study explores the potential of machine learning algorithms to build and train models using log data from the "3D…
Descriptors: Artificial Intelligence, Algorithms, Technology Uses in Education, Opportunities
Tiffany Tseng; Matt J. Davidson; Luis Morales-Navarro; Jennifer King Chen; Victoria Delaney; Mark Leibowitz; Jazbo Beason; R. Benjamin Shapiro – ACM Transactions on Computing Education, 2024
Machine learning (ML) models are fundamentally shaped by data, and building inclusive ML systems requires significant considerations around how to design representative datasets. Yet, few novice-oriented ML modeling tools are designed to foster hands-on learning of dataset design practices, including how to design for data diversity and inspect…
Descriptors: Artificial Intelligence, Models, Data Processing, Design
Bondaryk, Leslie G.; Hsi, Sherry; Van Doren, Seth – IEEE Transactions on Learning Technologies, 2021
Sensor systems have the potential to make abstract science phenomena concrete for K-12 students. Internet of Things (IoT) sensor systems provide a variety of benefits for modern classrooms, creating the opportunity for global data production, orienting learners to the opportunities and drawbacks of distributed sensor and control systems, and…
Descriptors: Internet, Systems Development, Computer Uses in Education, Secondary School Science
Andreas Bueckle – ProQuest LLC, 2021
In the age of big data, interactive data visualizations are becoming increasingly prevalent. The ability to focus on subsets of data is essential for exploring large temporal, geospatial, topical, and network datasets. In order to categorize all parts of data visualizations, various attempts have been made to build frameworks for how to interpret,…
Descriptors: Computer Simulation, Visual Aids, Computer Interfaces, Technology Uses in Education

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