A Score Recommendation System Towards Automating Assessment In Professional Courses

We present a preliminary version of an automated evaluation system that provides recommended scores for descriptive answers. The system is designed in the context of professional courses, such as from Applied Sciences, for questions that have an answer-key. The recommendation of a score is based on an analysis of the student's answer against an answer-key that comprises key phrases of the solution and alternatives, if any. We rely on five algorithms from literature on natural language processing to assess various aspects of a student's answer, such as the quantity and extent of match between keywords in a student's answer and the answer-key, while ensuring merely verbatim comparisons are not performed. The similarity scores are then combined using machine learning to output a recommended score for the answer. The system learns weights for the output of each of the five methods to map these to a score based on training data, comprising questions with answer-keys, student answers from actual exams and the scores provided by course instructors/ graders. It is noteworthy that the methods presented in this paper do not rely on any kind of a corpus or prior-knowledge that is subject-specific. Even though the accuracies are modest and improvements ought to be made to accommodate the kind of variety observed in students' answers, the avenue is a promising one towards enhancing learning assessment.