Learning Patterns of University Students

Interest in how university students approach learning has increased in the last decades, with the purpose of giving the students a central role and contribute to the achievement of meaningful learning and better academic performance. Learning at university should be conceived not only as professional training but also “as a place where epistemological and professional experiences are shared, experiences that provide students with training in various aspects of life” (^{Martínez-Fernández, Rávida, Adams, 2019: 567}).

From this point different theoretical frameworks and lines of study have emerged in order to explain how students self-regulate their learning (^{Cash, 2016}; ^{Pintrich, 2000}; ^{Rosário et al., 2014}; ^{Schunk, Usher, 2013}; ^{Zimmerman, 2008}), develop their academic motivation (^{Ames, 1992}; ^{Boekaerts, 2009}; ^{González, 2015}), personal epistemology (^{Hofer, Pintrich, 1997}; ^{Hofer, Bendixen, 2012}; ^{Muis, Chevrier, Singh, 2018}), and learning styles (Gargallo-López, Pérez-Pérez, Verde-Peleato, García-Félix, 2018; ^{Marton, Säljö, 1976}; ^{Sue, 2014}; ^{Tagg, 2003}).

Learning patterns constitute an integrative construct to explain students’ learning experiences, addressing cognitive, motivational-affective and regulative activities in an interrelated and dynamic way, as well as beliefs about learning in a certain context and period of time (^{Vermunt, Donche, 2017}; ^{Busato, Prins, Elshout & Hamaker, 1998}); ^{Vázquez, 2009}). From this perspective, ^{Vermunt (1998)} proposed a model that has been considered one of the most complete in regard to the theoretical-conceptual aspects that it integrates and defines, as well as the different research work based on it (^{Vermunt, Donche, 2017}).

Vermunt’s proposal encompasses personal (self-regulation) and contextual (external regulation) processes, through which students show certain abilities and preferences “to access, process, regulate, produce and motivationally guide their learning actions’’ (^{Martínez-Fernández, García-Ravidá, 2012: 168}) which, even though they do not fully explain the complex process of learning, are considered key elements for the study and optimal development of the students’ learning. These processes are integrated into the following dimensions (^{Vermunt, 1998}; ^{Vermunt, Donche, 2017}):

Conceptions of learning, which refer to a student’s cognitive construction of knowledge and ways of learning. That is the nature of knowledge and the roles that teachers, classmates, and students themselves should assume.

Motivations or academic guidelines, the set of student’s intentions, purposes, attitudes, and concerns related to their studies.

Processing strategies, which refer to the combination of cognitive activities aimed at processing the contents of learning, understanding, and skill development. They are also known as learning approaches.

Regulation strategies, the number of metacognitive strategies that learners use to guide, monitor, and review, or not, their learning process and outcomes.

Based on the different combinations of sub-categories that integrate the dimensions above, four learning patterns have been identified (^{Vermunt, 1998}): meaning-directed; application-directed; reproduction-directed and undirected.

Students with a meaning-directed pattern see learning as the construction of knowledge itself for which they are responsible. They are intrinsically motivated, adopt a deep processing approach, and learn in a self-regulated way.

Students who have an application-directed pattern tend to place greater value on the knowledge they can use and usually try to find a connection between what they learn and reality, and their vocational reasons are those which often underlie this pattern. They also use elaboration strategies in order to build deeper learning, and combine both self-regulation processes and external regulations.

In the reproduction-directed pattern, students conceive learning as a knowledge set to be “absorbed”; their main motivation is to pass and demonstrate their competence. They try to memorize contents and conduct a certain level of analysis, in addition to paying attention to external regulations.

Students with an undirected pattern usually conceive learning as something that should be encouraged by the teacher, they have an ambivalent motivational orientation, show poor processing, and they often experience a lack of regulation.

The term “style”, employed initially by Jan Vermunt, the author of the model followed in this work, could give rise to what is understood as an invariant attribute, a personality trait. For that reason, he replaced it for pattern (^{Vermunt, 2005}), which refers to a way of studying and considering the learning activity as relatively stable but not immutable. In this way, a different understanding of what has been conceptualized as learning styles is established and presents different interrelated factors, with specific patterns of analysis according to the context in which they are developed.

The Vermunt model has been used in the context of European universities (^{Donche, Van Petegem, 2009}; ^{Martínez-Fernández, García-Ravidá, 2012}), in the Asian context (^{Law, Meyer, 2011}; ^{Marambe, Vermunt, Boshuizen, 2012}) and more recently in the Latin American context (^{Gaeta-González et al., 2020}; ^{Martínez-Fernández, Vermunt, 2015}; ^{Vázquez, 2009}), in order to research into the relationship between learning patterns and some personal and contextual factors. These studies have shown the links between the different learning patterns adopted by the students and their gender, age, field of studies and study term.

However, to date the results are not conclusive regarding the composition of the dimensions that integrate the learning patterns, and there are still questions about their behavior in different sociocultural contexts (^{Vermunt, Donche, 2017}), particularly concerning the link between the learning patterns and other academic variables, including their self-perception as students, the effort made to study and their academic performance.

Based on the above, our research work has a dual objective: on the one hand, to analyze the way in which the dimensions of learning patterns are integrated into the Mexican private university context, specifically in the educational sciences, and on the other hand, to analyze the relationship between some academic variables (study term, self-perception as student, effort, and academic performance) and the learning patterns in students of two universities, based on the four dimensions proposed by ^{Vermunt (1998)}: learning conceptions, academic motivation, processing strategies, and self-regulation strategies. The knowledge obtained from this research could be useful for the design and implementation of teaching and learning strategies, within a discipline and according to a particular university context.

Based on the objective proposed, we developed the following hypotheses: 1) We expect that the composition of the dimensions that make up the learning patterns will vary in the Mexican context, with respect to the original proposal stated by ^{Vermunt (1998)}; 2) We expect to find significant correlations between academic variables (study term, perception as a student, effort and academic performance) and the dimensions of the learning patterns adopted by students.

Participants

The training of education professionals in Mexico, in addition to having the variable of greater female participation (^{García, Ávila, Vargas and Hernández, 2015}), does not represent a relevant enrollment compared to other areas. According to ^{ANUIES (2017)}, in Mexico there are around 264,000 students of majors related to the field of education, about 200,000 of which are women and the rest men. Within this universe is the sample of this study, which is limited to the states of Aguascalientes (N = 3,766), Mexico City (N = 24,813), Jalisco (N = 9,654) and Puebla (N = 13,075).

Participants were 175 university students enrolled in Pedagogy (44%) and Psychopedagogy (56%) programs of two Mexican private universities. The first, University A (n = 99), has campuses located in Mexico City, Aguascalientes, and Guadalajara, and the second, University B (n = 76), has a campus in the city of Puebla. Out of the total of participants, 167 (95%) were women, and 8 (5%) were male, aged between 17 and 28 years old (average 20; S.D. = 1.88). The academic variables of the sample are shown in Table 1. For the development of the research, participants were selected by convenience sampling, their participation was voluntary, and they did not receive any compensation or incentive in exchange.

Instrument

In order to identify the learning patterns of university students, the study used the inventory of learning styles (ILS) designed by ^{Vermunt (1998)} for university student samples translated into Spanish by ^{Martínez-Fernández et al. (2009)}.

The questionnaire consists of 120 items grouped into four dimensions: (1) learning conceptions (40 items), (2) motivational orientation for learning (25 items), (3) processing strategies (27 items), and (4) learning regulation strategies (28 items). For the dimensions of learning conceptions and motivational orientation for learning, the questionnaire was answered using a five-point Likert scale ranging from (1) “totally disagree” to (5) “totally agree”. In the case of processing strategies and learning regulation strategies, the questionnaire was answered using a five-point Likert-type scale ranging from (1) “never” to (5) “almost always”. The total reliability indexes (Cronbach’s alphas) and the index from each factor that make up the original questionnaire are above .60 (^{Vermunt, 1998}).

As an introductory part of the instrument, questions about the students’ sociodemographic characteristics (sex, age) and academic variables (university, degree, study term, self-perception as students, effort devoted to learning, and academic performance) were included.

Procedure

Data were collected in groups, within the academic schedule, and with the authorization of the Council Board and the faculty professors at both universities. The questionnaires were applied in an electronic format, in the presence of the teacher, with an approximate duration of 20 minutes. Students were informed of the purpose of the study, their participation was voluntary, and the confidentiality of their information was guaranteed.

Data Analysis

The data analysis was carried out with the support of the statistical software package SPSS, version 22. Firstly, the psychometric properties of the ILS were analyzed through exploratory factor analysis (EFA). The principal components method was used, and a varimax rotation was applied to maximize the weights at the factor level. The reliability analyses of the total scale and the factors that comprise it were performed using Cronbach’s alpha. Secondly, the normality assumptions of the data were estimated using the Kolmogorov-Smirnov test. Afterward, a comparative analysis of the learning patterns adopted by the students according to the academic variables (semester of studies, degree program, perception as a student, effort and academic performance) was carried out through the Mann-Whitney U test, as well as a correlation analysis of learning patterns and academic performance through Spearman’s correlation. Finally, a comparative analysis between both university students’ learning patterns was performed using the Mann-Whitney U test.

Composition of the Learning Patterns Dimension

First, the internal consistency of the ILS was confirmed. As shown in Table 2, four different patterns (factors) in the way students learn were identified, which account for 61.06% of the cumulative variance. The instrument subscales in each factor and the designated name given to each one are described below.

In Factor 1, Meaning, the four subscales became saturated: deep-processing (relationship, structuring and critical thinking), step-by-step processing (analysis), concrete processing and self-regulation strategies (processes, results and learning contents) - which would correspond to a learning pattern directed towards meaning - as well as the external regulation (of results) typical of a reproduction-oriented pattern.

In Factor 2, Multiple Orientation, there is a distribution of the four patterns: construction of learning conceptions, increase and use of knowledge, cooperative learning and educational stimulation, as well as motivational orientations of personal interest, and vocation and orientation to grades and evaluation, which are specific to the learning patterns directed towards understanding, application and reproduction, respectively.

Factor 3, Reproduction, consists of step-by-step processing subscales (memorization and essay) and external regulations, typical of a learning pattern directed towards reproduction.

Factor 4, Undirected pattern, involves an ambivalent motivational orientation and the absence of self-regulation strategies.

The instrument showed a reliability index of .93, which is considered excellent, as well as each one of the factors above .70, which is considered acceptable, except for factor 4, considered weak (Table 3).

Academic Variables and Learning Patterns Adopted by Students

Semester of Studies, Degree Program and Learning Patterns

When comparing students’ semester of studies (1^st-4^th; 5^th-9^th), statistically significant differences were found in the reproduction pattern (z = 2.83; p =.005) and multiple orientation (z = 2.86; p =.004). That is, more students from the 1st to 4th semester (average range = 96.85), compared to the 5th to 9th semester (average range = 74.72), showed a reproduction pattern. Also, more students from the 1^st-4^th semester (average range = 96.85), compared to the 5^th-9^th semester (average range = 74.72), showed a multiple-orientation pattern. This implies that more students from the first semesters on try to memorize learning materials without analyzing them thoroughly. Even when they sometimes try to direct their learning and prepare for their profession, they rely more on external regulation and the prescribed materials than on their resources.

No significant differences were found regarding the degree program (Pedagogy, Psychopedagogy).

Perception as a Student, Effort and Learning Patterns

When analyzing the perception as a student, most of the participants considered that in the previous levels they had had more successes (90.3%) than failures (9.7%). Further, most of them valued positively the effort they dedicated to learn (mean = 8.54; S.D. 1.00), and had an average grade of 8.87 (S.D. = 1.17. = .65), which shows that students’ learning processes and results are related to their previous outcomes attained.

Academic Performance and Learning Patterns

There was a significant positive correlation between academic performance and meaning-oriented learning pattern (r_s= .256; p < .01), which implies the adoption of a deeper approach and motivation to learning, as well as the self-regulation of learning. On the other hand, there was a significant negative correlation between academic performance and the undirected pattern (r_s=-.222; p <.01), which implies an ambivalent motivational orientation and the absence of self-regulation (Table 5).

When comparing students’ learning patterns according to their academic performance (7-8; 9-10), more students with an average grade seven-eight (on a scale of ten) showed an undirected pattern (z = 2.57; p =.010), which means that they do not know well how to approach their learning and experience a lack of self-regulation. In contrast, students with an average grade of nine to ten presented a meaning-oriented pattern (z = 2.57, p =.010), which involves the use of self-regulated and deep processing strategies.

University of Origin and Learning Patterns

Regarding the learning patterns of students from University A (n = 99), as shown in Figure 1, most of them showed a multiple orientation pattern (median = 241; S.D. 16.72), followed by students with the meaning-oriented pattern (median = 124.00; S.D. 22: 30) and, to a lesser degree, students with a reproduction-oriented pattern (median = 36.00; S.D. 6.95) and with an undirected pattern (median = 26.00; S.D. 6.64).

Figure 1 Learning patterns by University 

As for the learning patterns of students from University B (n = 76), as shown in Figure 2, most of them showed a multiple orientation pattern (median = 231; S.D. 29.86), followed by students with a meaning-oriented pattern (median = 121.50; S.D. 24: 03) and, to a lesser degree, students with a reproduction-oriented pattern (median = 31.50; S.D. 8.18) and with an undirected pattern (median = 26.00; S.D. 9.09).

When comparing the university of origin, analysis showed significant differences in the multiple-orientation (z = 2.32; p =.020) and reproduction (z = 4.08; p =. 000) patterns. A greater number of students from University A showed a multiple-orientation pattern (mean average = 95.80), in contrast to the students of University B (mean average = 77.84). Also, more students from University A (average range = 101.67) than University B (average range = 70.20) showed a reproduction-oriented pattern. However, there were no significant differences between the meaning-oriented and the undirected learning patterns, and no significant differences in the academic performance of students from both universities.