Standards-Based Grading Derived Data to Monitor Grading and Student Learning
Author(s) -
Heidi DiefesDux,
Hossein Ebrahiminejad
Publication year - 2020
Publication title -
2018 asee annual conference and exposition proceedings
Language(s) - English
Resource type - Conference proceedings
DOI - 10.18260/1-2--30981
Subject(s) - grading (engineering) , computer science , machine learning , data science , artificial intelligence , natural language processing , data mining , engineering , civil engineering
Grading of student work is the primary practice for evaluating students’ learning and performance in a course. As such, the data generated from grading can be a powerful source of evidence for course-level decision-making by stakeholders. This paper demonstrates, through a specific large engineering course example, how standards-based grading (SBG) derived data can be used to monitor student learning and grading. Three criteria for using SBG data confidently and effectively for this purpose are established. First, the grading data have to be of high quality. Second, the grading data results need to accessible through simple visual representations. Third, there needs to be a clear path forward from grading data, to interpretation, to actions.
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