Automated assessment of written assignments employs computational linguistics and machine learning algorithms to evaluate student work. These systems analyze various features such as grammar, vocabulary, coherence, and argumentation to assign scores or provide feedback. For example, a system might identify instances of passive voice, assess the strength of evidence used to support a claim, or measure the overall readability of a text.
The implementation of these technologies offers numerous advantages, including increased efficiency in grading, reduced instructor workload, and enhanced consistency in evaluation. Historically, the manual grading of essays has been time-consuming and subject to inherent biases. Automation can provide rapid feedback to students, allowing for timely revisions and improvements. Furthermore, the application of standardized criteria can minimize inconsistencies across different graders or grading sessions.
