Learning patterns of first year students

Abstract

During the last decades, technological development has allowed universities to build complex systems to collect information about the students. However, this information is organized thinking in administration issues ignoring the improvement of the quality of teaching and learning as a primary objective. We do not know the students in the sense of the student-centered educative model as promoted by the European Higher Education Area. We have plenty of information about students who enter university but this information is not organized in a learning process sense. This concrete research involves gathering relevant information from students in terms of improving their learning practice. The work consists of a description of ¿learning patterns¿ of freshmen regarding variables of gender, level of knowledge and type of education. Participants were 699 first year students (cohort 2006-07) who belong to all academic disciplines (Technical, Humanities, Health, Education, Business, Experimental Science and Law) in representative percentages by means of a convenience sampling strategy. The theoretical basis of the learning patterns concept lies in the interactive learning model (ILM), developed by Johnston (1996, 2009). This model states that learning takes place with the interplay of three components: cognitive (knowledge), conative (acting) and affective (feeling). The action of these elements composes an individual profile, which consists of four different learning patterns: sequential, precise, technical and confluent. Data collection was performed using a Learning Connections Inventory (LCI). LCI is a validated instrument in all educational levels and is linked to a specific protocol that facilitates the transfer interpretation of the results to be concrete practices in the educational process. The fact of that the students know themselves could be useful to face different learning situations. The analysis was conducted using statistical methods such as MANOVA analysis, ANOVA, comparison of means for independent samples and the calculation of effect sizes.