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Automatic detection of inconsistencies between numerical scores and textual feedback in peer-assessment processes with machine learning
تشخیص خودکار ناسازگاری بین نمرات عددی و بازخورد متنی در فرآیندهای ارزیابی همتا با یادگیری ماشین-2019 The use of peer assessment for open-ended activities has advantages for both teachers and students.
Teachers might reduce the workload of the correction process and students achieve a
better understanding of the subject by evaluating the activities of their peers. In order to ease the
process, it is advisable to provide the students with a rubric over which performing the assessment
of their peers; however, restricting themselves to provide only numerical scores is detrimental,
as it prevents providing valuable feedback to others peers. Since this assessment produces
two modalities of the same evaluation, namely numerical score and textual feedback, it is possible
to apply automatic techniques to detect inconsistencies in the evaluation, thus minimizing
the teachers workload for supervising the whole process. This paper proposes a machine learning
approach for the detection of such inconsistencies. To this end, we consider two different approaches,
each of which is tested with different algorithms, in order to both evaluate the approach
itself and find appropriate models to make it successful. The experiments carried out with
4 groups of students and 2 types of activities show that the proposed approach is able to yield
reliable results, thus representing a valuable approach for ensuring a fair operation of the peer
assessment process. Keywords: Peer assessment | Open-ended works | Computer-aided assessment | Machine learning | Natural language processing |
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