FlashHack: Reflections on the Usage of a Micro Hackathon as an Assessment Tool in a Machine Learning Course

TL;DR

Engaging 229 third year CS undergraduate students in teams of four, FlashHack prompted them to tackle predefined challenges using machine learning techniques within a set timeframe, indicating high student engagement and satisfaction, alongside simplified assessment processes for instructors.

Abstract

Machine learning (ML) course for undergraduates face challenges in assessing student learning and providing practical exposure. Group project-based learning, an increasingly popular form of experiential learning in CS education, encounters certain limitation in participation and non-participation from a few students. Studies also suggest that students find longer programming assignments and project-based assessments distracting and struggle to maintain focus when they coincide with other courses. To tackle these issues, we introduced FlashHack: a monitored, incremental, in-classroom micro Hackathon that combines project-based learning with Hackathon elements. Engaging 229 third year CS undergraduate students in teams of four, FlashHack prompted them to tackle predefined challenges using machine learning techniques within a set timeframe. Assessment criteria emphasized machine learning application, problem-solving, collaboration, and creativity. Our results indicate high student engagement and satisfaction, alongside simplified assessment processes for instructors. This experience report outlines the Hackathon design and implementation, highlights successes and areas for improvement making it feasible for replication by interested computing educators.

Figures (4)

• Figure 1: The FlashHack Process with Details of each Phase
• Figure 2: Responses to Q2 (Opinion of FlashHack) of the Pre-Survey
• Figure 3: FlashHack helped me prepare and perform better in final exam
• Figure 4: FlashHack Score distribution