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Maarten van der Velde; Malte Krambeer; Hedderik van Rijn – International Educational Data Mining Society, 2025
Ensuring the integrity of results in online learning and assessment tools is a challenge, due to the lack of direct supervision increasing the risk of fraud. We propose and evaluate a machine learning-based method for detecting anomalous behaviour in an online retrieval practice task, using an XGBoost classifier trained on keystroke dynamics and…
Descriptors: Artificial Intelligence, Technology Uses in Education, Student Behavior, Information Retrieval
Joane Deneault; Natalie Lavoie – Journal of Educational Research, 2024
Little is known about students' engagement in school writing tasks and the methods that should be used by the researchers to assess this engagement. This exploratory study examines the relationship between the engagement reported by elementary school students (N = 136) in a handwriting and keyboarding activity (Likert scale questionnaire) and…
Descriptors: Learner Engagement, Handwriting, Keyboarding (Data Entry), Learning Activities
Edwards, John; Hart, Kaden; Shrestha, Raj – Journal of Educational Data Mining, 2023
Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as…
Descriptors: Data Analysis, Computer Science Education, Learning Analytics, Research Methodology
Pereira, Filipe D.; Oliveira, Elaine H. T.; Oliveira, David B. F.; Cristea, Alexandra I.; Carvalho, Leandro S. G.; Fonseca, Samuel C.; Toda, Armando; Isotani, Seiji – British Journal of Educational Technology, 2020
Tools for automatic grading programming assignments, also known as Online Judges, have been widely used to support computer science (CS) courses. Nevertheless, few studies have used these tools to acquire and analyse interaction data to better understand the students' performance and behaviours, often due to data availability or inadequate…
Descriptors: Introductory Courses, Programming, Outcomes of Education, Student Behavior
Peer reviewedFeldmann, Shirley C.; And Others – Computers in Human Behavior, 1991
This study examined the effects of five variables--student grouping at the computer, keyboarding status, academic discipline, student gender, and gender of partner--on student social behavior, both verbal and affective, in microcomputer classrooms in a public business high school. The effect of these variables on teacher behavior was also…
Descriptors: Affective Behavior, Business Education, Classroom Observation Techniques, Computer Assisted Instruction

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