To Dev or to Doc? Predicting College IT Students' Prominent Functions in Software Teams Using LMS Activities and Academic Profiles

Keyphrases

• Academic Performance 50%
• Academic Profile 100%
• Activity Performance 50%
• Activity Profile 100%
• Best Model 50%
• Classification Strategy 50%
• College Students 50%
• Complement Naive Bayes 50%
• Course Grades 50%
• Deep Learning Model 50%
• Deep Machine Learning 50%
• Development Team 50%
• Diverse Backgrounds 50%
• Document Identification 50%
• Education Technology 50%
• F1 Value 50%
• Gaussian Naive Bayes 50%
• Granularity 50%
• Identification Task 50%
• Intelligent Education 50%
• Intervention Strategies 50%
• IT Students 100%
• Log Grading 50%
• Long Short-term Memory 100%
• Machine Learning Learning 50%
• Multilayer Perceptron 100%
• Multivariate Time Series Classification 50%
• Personalized Education 50%
• Programmer 50%
• Proposed Strategy 50%
• Random Forest 50%
• Social Desirability Bias 50%
• Software Development 100%
• Software Professionals 50%
• Software Skills 50%
• Software Tasks 50%
• Software Teams 100%
• Stochastic Gradient Descent 50%
• Student Characteristics 50%
• Student Expertise 50%
• Student Learning Outcomes 50%
• Student Needs 50%
• Technology Application 50%
• XGBoost 50%

Computer Science

• Case Study 33%
• Deep Learning Model 33%
• Development Team 33%
• Diverse Background 33%
• Extreme Gradient Boosting 33%
• Gradient Descent 33%
• Granularity 33%
• Learning Experiences 33%
• Long Short-Term Memory Network 66%
• Machine Learning 33%
• Multilayer Perceptron 66%
• Multivariate Time Series 33%
• Random Decision Forest 33%
• Social Desirability 33%
• Software Development 100%
• Software Practitioner 33%
• Support Vector Machine 33%