Code, Clicks, and Classrooms: Exploring the Learning Behaviors of Code.org Users

Rachel (Yiqiu) Zhou

University of Illinois at Urbana-Champaign | yiqiuz3@illinois.edu

Abstract

This report provides a comprehensive analysis of user engagement data from the Code.org platform, with a focus on both student and teacher interactions. The goal is to uncover patterns and trends that offer valuable insights into how students engage with Code.org. These findings aim to assist stakeholders like educators and platform developers in making data-driven decisions to improve engagement and learning outcomes.

Through narrative, visualizations, and code, this report explores the dataset to identify key trends and derive meaningful implications. The subsequent sections provide an in-depth yet accessible analysis, starting with exploratory overviews and building to more advanced statistical interpretations.

By diving into the chapters within, readers can uncover the stories hidden in the data - stories that illuminate the intricate dynamics shaping the Code.org user experience. The insights gleaned serve as a compass for stakeholders seeking to enhance platform effectiveness and foster engaging learning for all users.

Dataset Overview

• 2. Overview of Datasets

Descriptive Analysis

• Teacher-Centric Analysis
  • 3. Teacher-Centric Descriptive Analysis
• Student-Centric Analysis
  • 4.1 Activity Performance Analysis
  • 4.1 Rention and Drop-Out
  • 4.3 Assessment Analysis

Advanced Exploratory Analysis

• Learning Analytics
  • 5.1 Analysis of Teacher Platform Implementation
  • 5.2 Investigation of Student Learning Behaviors
  • 5.3 Predictive Modeling of Assessment Performance

Conclusion

• Conclusion