Decision trees for predicting dropout in Engineering Course students in Brazil.
Ari Melo MarianoArthur Bandeira de Magalhães Lelis FerreiraMaíra Rocha SantosMara Lucia CastilhoAnna Carla Freire Luna Campêlo BastosPublished in: ITQM (2022)
Keyphrases
- decision trees
- engineering students
- engineering education
- electrical engineering
- engineering courses
- student learning
- applied sciences
- learning experience
- learning environment
- high school
- learning outcomes
- higher education
- learning styles
- high school students
- software engineering
- undergraduate engineering
- predictive accuracy
- engineering design
- undergraduate students
- distance learning
- learning tools
- computer science
- college students
- collaborative learning
- machine learning
- computer programming
- undergraduate and graduate
- university level
- practical experiences
- learning analytics
- learning process
- learning activities
- mobile learning
- programming course
- e learning
- remote laboratories
- virtual laboratories
- cooperative learning
- educational institutions
- graduate students
- learning algorithm
- intelligent tutoring systems
- distance education
- online course
- decision tree induction
- middle school students
- university students
- students learning
- elementary school
- decision tree learning
- science learning
- control group
- computer engineering
- virtual laboratory
- conceptual understanding
- chemical engineering
- machine learning algorithms
- training data
- secondary school