Rico-Juan, Juan Ramón, Gallego, Antonio-Javier, Calvo-Zaragoza, Jorge
Automatic detection of inconsistencies between numerical scores and textual feedback in peer-assessment processes with machine learning
Rico-Juan, Juan Ramón; Gallego, A.-Javier; Calvo-Zaragoza, Jorge. “Automatic detection of inconsistencies between numerical scores and textual feedback in peer-assessment processes with machine learning”. En: Badía Contelles, José Manuel; Grimaldo Moreno, Francisco (eds.). Actas de las XXVI Jornadas sobre la Enseñanza Universitaria de la Informática, València, 8-9 de julio de 2020. València: Asociación de Enseñantes Universitarios de la Informática, 2020, p. 357
URI: http://hdl.handle.net/10045/125241
DOI: 
ISSN: 2531-0607
Abstract: 
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:Evaluación por pares, Trabajos abiertos, Evaluación asistida por ordenador, Aprendizaje automático, Inteligencia artificial, Procesamiento del lenguaje natural
Asociación de Enseñantes Universitarios de la Informática (AENUI)
info:eu-repo/semantics/conferenceObject