1. Digital Show-How Extreme active learning for Introductory Programming
David Stotts
Dept. of Computer Science
Univ. of North Carolina
In cooperation with UNC Center for Faculty Excellence

2. The Problem Massive demand growth in CS classes Static resources
How can we do more with less?
Active learning is a long-running campus plan

3. Goals Engage students in active learning during class
Increase class attendance
Reduce load on TA office hours
Provide consistent and rapid evaluation of work
Increase student comprehension, improve mastery
Ameliorate technical mystery from many IDEs
Make programming success possible for students not likely to become programmers

4. Bricks: Extreme Active Learning
To learn programming, find someone who knows what they are doing, do what they do
Learn by doing… Type What I Type
Online help monitored by LA for real-time answers
Bricks grades the code for function and style as each student submits
Get a few points for attending class and coding with me
DIY programs for homework

5. Bricks IDE

10. Student Matrix

13. Bricks Help Request

14. Bricks System Architecture

15. Bricks scales to large classes
Program analysis is done on individual laptops
Analysis results object (JSON) is sent to grading server along with the code submission
Server-side runs the program for functional correctness
Server-side does not have to analyze code… it checks the data in the analysis object (for things like did user declare and use the correct objects, did the use employ while loops rather than for loops, etc.

16. Bricks data science All student work is saved in the Mongo DB
Every submission is saved: correct or incorrect, along with the analysis object for the code
Every help request is saved: student question and instructor reply, with code for each
Student has access to the full history
Data can be mined for improving Bricks responses

17. Results of Bricks Use
12 offerings of COMP 110 Intro Programming (JavaScript), class sizes from 15 up to 150
Students love it, very enthusiastic in exit interviews
Scores on exams (showing comprehension) are up a half letter grade
Demand on TA office hours way down, to where we run a class of 150 with one TA
Class attendance mid 90s% (we think this contributes big to lower TA usage). Up from about 70%

18. Current Directions
ML techniques for clustering “wrong programs”, improved feedback suggests where to fix the code
Online courses, video based
Collaborative learning, pair programming
Crowd sourcing solutions… IDE combines JavaScript chunks gathered from class members