Research Overview
This research focuses on understanding how project-based learning methodologies can be optimized for computer science education. Through my work with Curious Cardinals, I'm conducting a longitudinal study on student engagement, learning outcomes, and skill retention when using hands-on projects versus traditional lecture-based approaches.
Key Research Questions
Primary Question
How does project-based learning impact student engagement, skill acquisition, and long-term retention in computer science education compared to traditional pedagogical approaches?
Secondary Questions
- • What project characteristics maximize learning outcomes for different student types?
- • How can adaptive learning frameworks personalize instruction based on progress patterns?
- • What metrics best predict student success in project-based CS education?
Research Methodology
Data Collection
- • Longitudinal study with 15+ students over 8 months
- • Pre/post skill assessments using standardized coding challenges
- • Weekly engagement metrics and project completion rates
- • Qualitative interviews on learning experiences
Analysis Framework
- • Statistical analysis of learning outcome differences
- • Machine learning models for personalization
- • Thematic analysis of qualitative feedback
- • Comparative analysis with control groups
Preliminary Findings
Key Insights
Increased Motivation
Students show 73% higher intrinsic motivation when working on personally meaningful projects compared to standardized assignments.
Better Retention
Concepts learned through project implementation show 68% better retention after 3 months compared to lecture-based learning.
Personalization Impact
Adaptive project selection based on learning style preferences improves completion rates by 45% and quality scores by 38%.
Technical Implementation
Technology Stack
Adaptive Learning Framework
Developing a machine learning system that analyzes student progress patterns, learning styles, and engagement metrics to recommend personalized project paths and learning resources.
Input Features
- • Completion time patterns
- • Error frequency and types
- • Engagement metrics
- • Learning style preferences
Personalized Outputs
- • Custom project recommendations
- • Difficulty progression paths
- • Learning resource suggestions
- • Intervention timing
Future Research Directions
Publication Plan
Preparing "Project-Based Learning in CS Education: A Quantitative Analysis" for submission to the Journal of Educational Technology Research by June 2025.
Scale Expansion
Plans to expand the study to multiple educational institutions and diverse student populations to validate findings across different contexts and demographics.
Platform Development
Developing an open-source educational platform that implements the adaptive learning framework for broader adoption in computer science education.
Research Collaboration
Interested in collaborating on educational technology research or implementing project-based learning methodologies? Let's discuss opportunities for partnership and knowledge sharing.
Get In Touch