Cluster profiles of university students’ conceptions of learning according to gender, educational level, and academic disciplines

Highlights

• A person-oriented approach was assumed to identify meaningful profiles of university students’ conceptions of learning.

• The three profiles, “disengaged students”, “overwhelmed by emotions students”, and “helmsman students”, emerged.

• The three profiles are discussed in terms of strengths and weaknesses of students’ disposition and motivation to learn.

• Interactions between the profiles and gender, educational level, as well as academic disciplines are discussed.

Abstract

Describing university students’ profiles of conceptions of learning is an important pre-requisite in personalizing a learning process to increase its effectiveness. A person-oriented approach was assumed to identify meaningful profiles of university students’ conceptions referring to cognitive and socio-cultural, affective/motivational, and attributional/regulative components of learning. Profiles were also analyzed in relation to gender, educational level, and academic disciplines. University students (N = 243) completed a validated self-report instrument (LCQ: Learning Conceptions Questionnaire). A non-hierarchical cluster analysis (two-step method) yielded three profiles: “disengaged students”, “overwhelmed by emotions students”, and “helmsman students”. The profiles included strength and vulnerability aspects of university students’ disposition and motivation to learn. The study also revealed interactions between the profiles and gender, educational level, and academic disciplines. These findings are discussed in relation to the current literature, as well as to theoretical and practical implications.

Introduction

Researchers have long been interested in how students understand the process of learning, given that learners’ visions affect motivation, learning approaches, and outcomes (Prosser & Trigwell, 1999; Vermunt, 2005; Vettori, Vezzani, Bigozzi, & Pinto, 2018). In the context of higher education, scholars have defined the construct of “conceptions of learning” (Marton, Dall’Alba, & Beaty, 1993; Säljö, 1979) as a set of beliefs about what students mean by learning. More recently, the construct has become more multifaceted (Vermunt & Donche, 2017) and context specific (Cantoia, Giordanelli, Pérez-Tello, & Antonietti, 2011). A significant number of studies have shown that students’ conceptions of learning are related to and reflect different components of the learning process. For example, they are related to beliefs about the general features of the process of learning (i.e., learning conditions and scopes, expectations about changes) as highlighted by conceptions of learning as “construction of knowledge” (Vermunt, 2005), as “an interpretative process aimed at the understanding of reality” (Säljö, 1979), and as “getting a new perspective” (Eklund-Myrskog, 1998). Furthermore, they are related to learning activities, including memorization, reproduction, integration, and understanding as highlighted by conceptions of learning as “remembering and keeping something in mind” (Eklund-Myrskog, 1998). In addition, they are related to a cooperative view of learning, as highlighted by the conceptions of learning as “cooperative learning” (Vermunt & Vermetten, 2004).

A recent multidimensional approach to the studying of conceptions of learning suggests including several components of learning, such as cognitive and socio-cultural, affective/motivational, attributional/regulative (e.g., Vermunt & Donche, 2017). From this multidimensional perspective, conceptions related to the cognitive and socio-cultural component of learning highlight learning activities and processes (e.g., memorization, concentration), beliefs about the nature of learning (e.g., individual vs. social focus), and about themselves (e.g., active vs. passive learner). Conceptions related to the affective/motivational component of learning highlight students’ academic emotions (e.g., enjoyment, satisfaction, boredom, frustration, and anxiety) (e.g., Pekrun, 2006), as well as their personal orientations and motivations (e.g., goals, motives and values) (e.g., Vermunt, 2005). Conceptions related to the attributional/regulative component of learning highlight different attributions for academic success and failure (e.g., internal vs. external; Weiner, 2010). The multidimensional approach has been applied to the studying of conceptions of learning across educational levels and countries. These studies showed partially overlapping and partially specific conceptions of learning detected among middle-school (Pinto, Bigozzi, Vettori, & Vezzani, 2018; Vezzani, Vettori, & Pinto, 2018), upper-level secondary school students in European and extra-European countries (Cantoia et al., 2011), and university students (Vezzani, Vettori, & Pinto, 2018). For the university educational level, which is the focus of this study, three conceptions related to the cognitive and socio-cultural component of learning emerged as follows: a conception of learning as “Learning as co-constructive and cultural process” (item examples: “I learn when I collaborate with others”) mirrored an idea of learning as a process merged in social practices and interactions, partially referable to a conception as cooperative learning (Vermunt, 2005). A conception of learning as “Learning as a reduction of deficit knowledge and passive-receptive role” (item examples: “The student is a person who has no knowledge of what the school will teach him”) mirrored a passive view of the learner. It is partially referable to a conception as intake of knowledge for storage in the “reproduction-directed learning” pattern (external regulation with performance goal orientation), negatively associated with mean exam scores (Vermunt, 1998, 2005). A conception of learning as “concentration and individual work” (item examples: “Learning is mostly a matter of concentration and commitment”) spread light on the learners’ strong commitment at a cognitive level. With regard to the affective/motivational component of learning, three conceptions emerged as follows: a conception of learning as “negative experiences and anxiety” (item examples: “Learning as something that makes me anxious”) highlights an idea of learning involving unpleasant feelings and emotions, such as boredom, frustration, and anxiety. A conception of learning as “volition and personal growth” (item examples: “Learning as a time of personal growth and change”) portrayed learning as imbued with meaning of growth, personal choice, enjoyment, and to positive expectations about changes (e.g., Marton et al., 1993). A conception of learning as “opportunities and self-efficacy” (item examples: “Learning as the opportunity to show what I am worth”) mirrored an idea of learning as connotated by enjoyment and the belief in one’s abilities to achieve prospective goals. With respect to the attributional/regulative component of learning, two conceptions emerged as follows: a conception of learning as “external attribution for success” (item examples: “The last time I successfully passed a school test I felt lucky”) characterized by the identification of uncontrollable factors (e.g., teachers’ mood, task difficulty, luck) as the causes of positive learning achievements (Weiner, 2010). A conception of learning as “internal attribution for success and gratitude towards the teacher” (item examples: “The last time I successfully passed a school test I felt capable”) highlights an active role of the students who can evaluate, control themselves, and make predictions of their learning activities (see, Maddux & Gosselin, 2012).

Studies in the literature provided evidence about the specificity and variability of students’ conceptions of learning in relation to gender, educational level, and academic disciplines (e.g., Smith et al., 2007; Vermunt, 2005; Vezzani et al., 2018a). The variable of gender resulted relevant, because some studies showed that females’ conceptions of learning stand out for a cooperative view of learning (e.g., Vermunt, 2005). Furthermore, conceptions related to a socio-constructive view of learning, to an internal attribution of scholastic achievements, as well as to personal growth (Vezzani et al., 2018a) were linked to scholastic achievements in a more significantly positive way, compared to males.

A further remarkable source of information was the exploration of the degree of differentiation or similarities of conceptions of learning between different cohorts of educational level of study (e.g., Smith et al., 2007). Vermunt (2005) found that older students were more likely to show a “meaning-directed learning pattern”, positively associated with exam scores. This pattern included a conception of learning as construction of knowledge driven by personal effort and responsibility, self-regulation, and a master goal orientation sustained by personal interest and motivation. Furthermore, Vezzani et al. (2018a) found that university students on the master’s degree program had a higher score on the conception of learning as “opportunities and self-efficacy” than university students on the bachelor’s degree program. Considering those findings, the idea that increasing with the educational level of study, it is easier to find a conception mirroring a positive and proactive role of learners, connotated by enjoyment and motivation to reach their personal goals.

Finally, with respect the association between conceptions of learning and academic disciplines, the results were quite contrasting (e.g., Eklund-Myrskog, 1998; Lee, Johanson, & Tsai, 2008; Tsai, 2004; Tynjälä, 1997). Some studies supported the hypothesis that differences in students’ learning approach and motives might be linked to students’ efforts to adjust their learning disposition and expectations to the specific academic discipline, which in turn may affect the construction of their conceptions of learning. In this perspective, soft disciplines (e.g., social sciences) support a learning process focused on internal motivation, and critical and holistic thinking strategies (Parpala, Lindblom-Ylänne, Komulainen, Litmanen, & Hirsto, 2010; Ylijoki, 2000) and meaning-directed learning, while hard disciplines (e.g., natural and technical sciences) focused on logical and analytical thinking strategies and reproduction directed learning (Vermunt, 2005). According to Lonka and Lindblom-Ylänne (1996), constructivist visions of learning were most easily found in psychology students, whereas visions of learning as intake of knowledge were typical of medical students. Furthermore, Smith and Miller (2005) observed that psychology students had a higher score for deep learning strategy and motives, while business students had higher scores for surface learning strategy and motives. Conversely, no difference was found between conceptions of learning and academic discipline by Vezzani et al. (2018a) considering university students who studied in humanities or technical-scientific faculties. These contradictory findings point out the necessity to further investigate these relations.

Some major findings can be summarized. Firstly, a wide and multifaced range of conceptions emerged; secondly, they might be sensitive to gender, educational level of study, and academic disciplines. These findings are crucial to sustain a comprehensive understanding of students’ conceptions of learning as motivational factors. Recent results (Pinto et al., 2018; Vettori et al., 2018) demonstrated that the motivational power of conceptions of learning is differentiated and some show higher probability than others to activate motivation to learn, as well as a strategic use of learning strategies, which in turn affect scholastic achievements.

From a methodological perspective, the corpus of studies about how students conceptualize learning has been spread by applying a variable-oriented approach (e.g., Hayenga & Corpus, 2010), leaving questions open as to whether university students could be profiled based on specific dynamic interplays of conceptions related to cognitive and socio-cultural, affective/motivational, attributional/regulative components of learning. The possibility of profiling students and then collecting evidence about their idiosyncratic and specific mindset represents an important step towards the possibility of reaching a more comprehensive vision about the motivating power of students’ conceptions of learning. From a learning theory perspective, the studying of motivation could benefit from the identification of university students’ profiles of conceptions of learning, since different students’ mindsets might involve different motivational framing (conditional vs. hierarchical vs. mixed), which in turn could affect performance and psychological experiences (Murthy, Villatte, & McHugh, 2019). Thus, the variable-oriented perspective needs to be enriched by a person-oriented approach towards the identification of meaningful subgroups of students with similar scores on learning variables (Vermunt & Donche, 2017; Vettori, Bigozzi, Miniati, Vezzani, & Pinto, 2019) which might indicate different motivational processes and then, help in targeting specific interventions.

The person-oriented approach has already been applied to the studying of some significant learning variables by adopting different variants of cluster analysis. For example, latent class clustering was used by Heikkilä, Niemivirta, Nieminen, and Lonka (2011) to identify three groups of first-year teacher-training students: (1) non-regulating students (50 %) (highest levels of stress, exhaustion, and lack of interest); (2) self-directed students (28 %) (highest levels of deep understanding, critical evaluation, and optimism as well as the lowest levels of surface approach, and lack of regulation); and (3) non-reflective students (22 %) (lowest levels of deep understanding, critical evaluation, and task-avoidance, meanwhile surface approach, lack of regulation, and optimism had average loadings). The findings of Vanthournout, Coertjens, Gijbels, Donche, and Van Petegem (2013), deriving from a hierarchical cluster analysis, showed four groups of students in a teacher-training program: (1) surface approach profile (32 %) (low score on deep approach scale, high score on surface approach scale); (2) all-low profile (22 %) (below average score both on deep and surface approach scale); all-high profile (30 %) (above average score both on deep and surface approach scale); (4) deep approach profile (16 %) (high score on deep approach scale, low score on surface approach scale).

Considering the important contribution of the person-oriented approach, we decided to apply this approach to the studying of university students’ conceptions of learning. The exploration of the dynamic interplay between qualitatively different visions of learning may shed light on meaningful patterns of different components of learning (e.g., Hickendorff, Edelsbrunner, McMullen, Schneider, & Trezise, 2018; Vermunt & Donche, 2017). The application of a person-oriented approach allows one to conceive university students’ representational world in a complex way including both the more traditionally studied, cognitive and socio-cultural aspects as well as, the more recently studied, affective/motivational and attributional/regulative aspects (Pekrun & Perry, 2014). A recent study (Karagiannopoulou, Milienos, Kamtsios, & Rentzios, 2019) highlighted the association between learning approach and emotion regulation (e.g., immature, neurotic, and mature defense styles) among undergraduate students. Their findings showed three different profiles: (1) restricted maturity and dissonant-unorganized profile (low deep and surface approaches, low emotional engagement); (2) defensive and reproduction oriented profile (high disadaptive defense styles, high surface approach, mid-range deep and strategic approaches); (3) mature and learning advanced profile (high deep and strategic approaches, high adaptive defense style). According to Postareff, Mattson, Lindblom-Ylänne, and Hailikari (2017), the following three clusters involving learning approaches, emotions, and study success support the existence of adaptive and less adaptive learning patterns: (1) quickly progressing successful students experiencing positive emotions (high deep approach, feelings of competence, and successful outcomes); (2) quickly progressing successful students experiencing negative emotions (high deep approach, unpleasant feelings of anxiety and frustration, and successful outcomes); (3) slowly progressing students experiencing negative emotions (high surface approach, high presence of unpleasant feelings of anxiety and incompetence, low study-success score). The findings showed a key role of emotions in students’ learning, even though their relation to learning approach and study success needs to be further explored. For example, the interesting result that some students were grouped in a cluster for their high presence of negative emotions along with high deep approaches, and successful learning outcomes is not fully in line with previous studies showing that deep learning approaches relate to positive emotions, while surface learning approaches relate to unpleasant emotions (e.g., Trigwell, Ellis, & Han, 2012). According to the control-value theory of academic emotions (Pekrun & Perry, 2014) and Bandura’s (1982) theorizations, we might think that students’ self-efficacy beliefs help in transferring emotions into learning strategies, which in turn influence learning outcomes (see, Postareff et al., 2017). Similarly, according to locus of control theories (see, Weiner, 2010), we might think that students’ attributional and regulative mechanisms (e.g., one feels internal locus of control of one’s successes and failures) might combine with affective/motivational learning components and thus, influence learning outcomes. The application of a person-oriented approach to the studying of conceptions of learning may help to shed light on this point.

Assuming the subject of study as the unit of analysis provides a template for integrating knowledge derived from the widespread variable-centered approach (see, Fryer, 2017; Lindblom-Ylanne, Parpala, & Postareff, 2013). It might advance our understanding of how different significant components of learning processes combine and of whether they are related to personal and contextual factors. The purpose of the current study is twofold. Firstly, to identify meaningful distinct profiles of university students’ conceptions referred to cognitive and socio-cultural, affective/motivational, and attributional/regulative components of learning. Secondly, to examine the different associations of students’ profiles with gender, educational level, and academic disciplines. Regarding the first aim, in line with recent findings in the literature (e.g., Karagiannopoulou et al., 2019; Postareff et al., 2017), we expected a three-cluster solution of adaptive and less adaptive profiles. The adaptive profile might show a desirable combination of conceptions of learning (e.g., personal growth along with internal attribution and pleasant feelings), the less adaptive profile should show a negative almost exclusively (e.g., gaining knowledge along with external attribution and unpleasant feelings), whereas a further profile might show both equally. Regarding the second aim, according to results grounded on a variable-oriented research perspective, we expected different associations between cluster profiles of university students’ conceptions of learning and gender, educational level, and academic disciplines.