Influence of personality on peer assessment evaluation perceptions using Machine Learning techniques

Abstract

The successful instructional design of self and peer assessment in higher education poses several challenges that instructors need to be aware of. One of these is the influence of students’ personalities on their intention to adopt peer assessment. This paper presents a quasi-experiment in which 85 participants, enrolled in the first-year of a Computer Engineering programme, were assessed regarding their personality and their acceptance of three modalities of peer assessment (individual, pairs, in threes). Following a within-subjects design, the students applied the three modalities, in a different order, with three different activities. An analysis of the resulting 1195 observations using ML techniques shows how the Random Forest algorithm yields significantly better predictions for three out of the four adoption variables included in the study. Additionally, the application of a set of eXplainable Artificial Intelligence (XAI) techniques shows that Agreeableness is the best predictor of Usefulness and Ease of Use, while Extraversion is the best predictor of Compatibility, and Neuroticism has the greatest impact on global Intention to Use. The discussion highlights how, as it happens with other innovations in educational processes, low levels of Consciousness is the most consistent predictor of resistance to the introduction of peer assessment processes in the classroom. Also, it stresses the value of peer assessment to augment the positive feelings of students scoring high on Neuroticism, which could lead to better performance. Finally, the low impact of the peer assessment modality on student perceptions compared to personality variables is debated.