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Hongwen Guo; Matthew S. Johnson; Kadriye Ercikan; Luis Saldivia; Michelle Worthington – Journal of Learning Analytics, 2024
Large-scale assessments play a key role in education: educators and stakeholders need to know what students know and can do, so that they can be prepared for education policies and interventions in teaching and learning. However, a score from the assessment may not be enough--educators need to know why students got low scores, how students engaged…
Descriptors: Artificial Intelligence, Learning Analytics, Learning Management Systems, Measurement
Burhan Ogut; Blue Webb; Juanita Hicks; Ruhan Circi; Michelle Yin – Grantee Submission, 2024
In this study, we explore the application of process mining techniques on assessment log data to explore problem-solving strategies in Algebra. By analyzing sequences of student activities, we demonstrate the significant potential of process mining in identifying problem-solving strategies that lead to successful and unsuccessful outcomes. Our…
Descriptors: Mathematics Skills, Problem Solving, Learning Analytics, Algebra
Congning Ni; Bhashithe Abeysinghe; Juanita Hicks – International Electronic Journal of Elementary Education, 2025
The National Assessment of Educational Progress (NAEP), often referred to as The Nation's Report Card, offers a window into the state of U.S. K-12 education system. Since 2017, NAEP has transitioned to digital assessments, opening new research opportunities that were previously impossible. Process data tracks students' interactions with the…
Descriptors: Reaction Time, Multiple Choice Tests, Behavior Change, National Competency Tests
Jiang, Yang; Gong, Tao; Saldivia, Luis E.; Cayton-Hodges, Gabrielle; Agard, Christopher – Large-scale Assessments in Education, 2021
In 2017, the mathematics assessments that are part of the National Assessment of Educational Progress (NAEP) program underwent a transformation shifting the administration from paper-and-pencil formats to digitally-based assessments (DBA). This shift introduced new interactive item types that bring rich process data and tremendous opportunities to…
Descriptors: Data Use, Learning Analytics, Test Items, Measurement
Patel, Nirmal; Sharma, Aditya; Shah, Tirth; Lomas, Derek – Journal of Educational Data Mining, 2021
Process Analysis is an emerging approach to discover meaningful knowledge from temporal educational data. The study presented in this paper shows how we used Process Analysis methods on the National Assessment of Educational Progress (NAEP) test data for modeling and predicting student test-taking behavior. Our process-oriented data exploration…
Descriptors: Learning Analytics, National Competency Tests, Evaluation Methods, Prediction
Bosch, Nigel – Journal of Educational Data Mining, 2021
Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The…
Descriptors: Accuracy, Learning Analytics, Models, National Competency Tests
Tate, Tamara P.; Warschauer, Mark – Technology, Knowledge and Learning, 2019
The quality of students' writing skills continues to concern educators. Because writing is essential to success in both college and career, poor writing can have lifelong consequences. Writing is now primarily done digitally, but students receive limited explicit instruction in digital writing. This lack of instruction means that students fail to…
Descriptors: Writing Tests, Computer Assisted Testing, Writing Skills, Writing Processes