LEAP -- Live Experiments for Active Pedagogy

TL;DR

LEAP is a lightweight framework that enables active student engagement in live classrooms through remotely callable functions, real-time participation visualization, and reusable interactive labs, fostering collaborative learning and instructor insights.

Contribution

This paper introduces LEAP, a novel framework for creating shareable, interactive computational labs that enhance engagement and provide real-time analytics in active pedagogy.

Findings

Enabled real-time visualization of student participation

Demonstrated applications in numerical analysis, machine learning, and algorithms

Facilitated community sharing of interactive labs

Abstract

Interactive computational environments can help students explore algorithmic concepts through collaborative hands-on experimentation. However, static and instructor controlled demos in lectures limit engagement. Even when interactive visualizations are used, interactions are solely controlled by the instructor, leaving students as passive observers. In addition, the tools used for demonstration often vary significantly, as they are typically developed by individual instructors. Consequently, the visualizations remain confined to a single classroom, rather than being shared and adapted across courses or reused by other instructors. To address this gap and foster active engagement in live classrooms, we present a lightweight and seamless software framework named LEAP for developing interactive computational lab exercises using a simple idea: remotely callable instructor-defined functions. Using API endpoints and a provided client, students can discover and then call instructor defined functions remotely from their coding environment using scripts or interactive notebooks. Each function call is time-stamped and persistently logged in a database, allowing real-time visualization of participation, diverse solution paths, common pitfalls, and live feedback through collaboration, gamification, and quizzes. Labs are packaged as self-contained folders, each containing their own remotely callable functions. We provide example labs to demonstrate applications relevant for numerical analysis, machine learning, algorithms courses and mention some in electrical engineering (EE), economics, and physics. These capabilities enhance engagement and provide instructors with actionable insights into learning processes. With a standardized lab format and an online directory for community-contributed labs, we aim to foster a global ecosystem for exchanging and expanding interactive pedagogy enabled by LEAP.