Work

Satisfactory for All: Supporting Mastery Learning with Human-in-the-loop Assessments in a Discrete Math Course

Year: 2025

Type: conference-paper

Abstract: This experience report documents an attempt at embracing the "A's for all" and equitable grading frameworks in an introductory, proof writing-based discrete mathematics course for computer science majors (with N=138 students) at a medium-sized research-oriented university in the US. Unlike in introductory programming contexts, there is so far no reliable automated grading system that gives formative and adaptive feedback supporting the scope of a proof-based discrete mathematics course. We therefore faced the unique challenge of being unable to automate all assessments and directly offer all students unlimited attempts toward mastery.

Source: ACM Technical Symposium on Computer Science Education (SIGCSE)

DOI: 10.1145/3641554.3701854

Authors: Shao-Heng Ko, Alex Chao, Violet Pang

Institution: Duke University

Language: English

Topic: Teaching and Learning Programming

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

Funder: Universitas Brawijaya