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Hayat Sahlaoui; El Arbi Abdellaoui Alaoui; Said Agoujil; Anand Nayyar – Education and Information Technologies, 2024
Predicting student performance using educational data is a significant area of machine learning research. However, class imbalance in datasets and the challenge of developing interpretable models can hinder accuracy. This study compares different variations of the Synthetic Minority Oversampling Technique (SMOTE) combined with classification…
Descriptors: Sampling, Classification, Algorithms, Prediction
Abdelmadjid Benmachiche; Abdelhadi Sahia; Soundes Oumaima Boufaida; Khadija Rais; Makhlouf Derdour; Faiz Maazouzi – Education and Information Technologies, 2025
In the context of massive open online courses (MOOCs), searching and retrieving information can be challenging because there is a huge amount of unstructured content, which creates a problem and makes it difficult for users to quickly find relevant lessons or resources. As a result, learners and teachers face significant barriers to accessing the…
Descriptors: MOOCs, Natural Language Processing, Artificial Intelligence, Search Engines
Lonneke Boels; Alex Lyford; Arthur Bakker; Paul Drijvers – Frontline Learning Research, 2023
Many students persistently misinterpret histograms. Literature suggests that having students solve dotplot items may prepare for interpreting histograms, as interpreting dotplots can help students realize that the statistical variable is presented on the horizontal axis. In this study, we explore a special case of this suggestion, namely, how…
Descriptors: Data Interpretation, Interpretive Skills, Statistical Distributions, Graphs
Costu, Fatma – Journal of Baltic Science Education, 2023
Several studies compared three different types of questions (conceptual, algorithmic, and graphical) across various topics, however, few focused specifically on gifted students. This study addressed this gap. The aim of the study, hence, was to determine whether there were notable differences in gifted students' performance in the three types of…
Descriptors: Academically Gifted, Concept Formation, Algorithms, Graphs
Taylor V. Williams – ProQuest LLC, 2022
Clustering, a prevalent class of machine learning (ML) algorithms used in data mining and pattern-finding--has increasingly helped engineering education researchers and educators see and understand assessment patterns at scale. However, a challenge remains to make ML-enabled educational inferences that are useful and reliable for research or…
Descriptors: Multivariate Analysis, Data Analysis, Student Evaluation, Large Group Instruction
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