Using students’ cognitive, affective, and demographic characteristics to predict their understanding of computational thinking concepts: A machine learning-based approach

Keyphrases

• Affective Data 33%
• Affective Factors 100%
• Cognitive Characteristics 100%
• Cognitive Information 33%
• Cognitive-affective Processes 100%
• Computational Concepts 100%
• Copyright 33%
• Demographic Characteristics 100%
• Demographic Data 33%
• Design Support 33%
• Embedding Algorithm 33%
• Extreme Gradient Boosting(XGBoost) 33%
• K-means Algorithm 33%
• Logistic Regression Model 100%
• Machine Learning Algorithms 33%
• Machine Learning Approach 100%
• Machine Learning Based 33%
• Machine Learning Models 66%
• Optimal number of Clusters 33%
• Pedagogical Design 33%
• Pedagogical Resource 33%
• Prediction Accuracy 33%
• Primary Students 33%
• Resource Support 33%
• Student Cognition 100%
• Student Conceptual Understanding 33%
• Student Learning 33%
• Student Population 100%
• T-distributed Stochastic Neighbor Embedding (t-SNE) 33%
• Unsupervised Clustering Techniques 33%
• World Wide Web 33%

Social Sciences

• Computational Thinking 100%
• Conceptual Understanding 33%
• Learning System 100%
• Logistic Regression Analysis 100%
• Logit Model 100%
• Machine Learning 100%
• Machine Learning Algorithm 33%
• Machine Learning Model 66%
• Mathematics 33%
• Prior experience 33%
• Research Approach 33%
• Stochastics 33%
• Student Learning 33%
• UK 33%
• Xgboost 33%