Year: 2022
Type: dissertation
Abstract: A core part of Computer Science education, and Database Systems education in particular, is the use of machine problems to both develop and assess students' abilities.In order to conserve resources, these assignments are often automatically graded via auto-grading systems that verify that they produce the correct outputs.Unfortunately, these systems lack essential insights into the approaches students use to solve the assignments being graded, allowing subtle flaws in student intuition to go unseen.Furthermore, manual analysis of students' code submissions at scale ranges from costly to impossible, depending on course size and assignment frequency, making these drawbacks difficult to avoid.In this thesis paper, we rigorously define a series of metrics for evaluating a system that captures nuances in students' approach, and then make use of these metrics to develop a system that is capable of serving as a significant force multiplier for Computer Science faculty.This system, Echelon, functions by extracting features that instructors deem significant from students' SQL queries and using them to generate clusters that capture the key approaches taken, and then projecting these clusters to an interactive dashboard that can be used to help teaching staff quickly identify the major trends in students' approaches to a problem.We conclude with a full analysis of Echelon on real data.
Author: Weston, Matthew Charles
Institution: University of Illinois Urbana-Champaign
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
Funder: National Science Foundation
OpenAlex ID: W7220803465