Schools and universities face multiple challenges when they target initiating or expanding undergraduate programs. Education has traditionally utilized a teacher-centered educational and assessment approach. Only few attempts exist to involve objective feedback and non-traditional assessment methods and technologies to improve the processes of teaching, learning, and education in general. This paper addresses a novel objective multi-parameter assessment methodology based on Soft computing technology to discover the effect of students’ groupings by exploiting the interrelationships between the grades the students received for their laboratory subjects and the grade they obtained in the university enrolment exam. The research results allow for exploring non-desirable discordant teaching and assessment practices for individuals or groups. In addition, the results obtained illustrate opportunities to focus on the individual student during the education process and determine adaptive teaching strategies based on the particular level of knowledge and experience. Toward these results statistical and Soft computing models implementing Unsupervised Neural and Exploratory Projection Techniques have been applied to carry on the objective assessment of the students’ skills development during the entire higher education period. Empirical verification of the proposed assessment model is performed in a real environment, where a case study is defined, and analysed. The real data set to validate the performance of the proposed approach has been collected at the School of Dentistry of the Complutense University of Madrid
