In this study, we explored the relationship between students’ learning preferences, as assessed by the GRLSS, and their information acquisition and processing processes, as measured by ILS. While weak correlations were found between the two measurement tools, the weak positive correlation between the collaborative sub-simension (GRLSS) and the active-reflective information process (ILS) stands out. This suggests that students who prefer collaborative learning may be more likely to process information actively and reflectively, though the relationship is weak. Despite the weak correlations observed, these findings highlight the complexity of the relationship between learning preferences and information processing processes, suggesting that multiple factors may influence how students engage with learning. Furthermore, we found that academic achievement was related to the avoidant learning preference, one of the sub-dimensions of GRLSS. The lack of significant differences in ILS scores across CGPA groups and the significant differences observed in GRLSS sub-dimension scores between the very high CGPA group and others suggest that students with higher CGPA scores tend to exhibit more adaptable and engaged learning styles.
The VARK Questionnaire and GRLSS assess individuals’ learning preferences, whereas the ILS and Kolb Learning Style Inventory evaluate how individuals acquire and process information. These tools measure different dimensions of learning, with VARK and GRLSS focusing on preferences, and ILS on cognitive processes. In our study, GRLSS was chosen for its comprehensive assessment of various learning preferences, while ILS was selected to examine the cognitive processes behind information acquisition and processing. Previous studies have used these questionnaires separately to examine the learning styles of physiotherapy and rehabilitation students [6, 22, 23, 28,29,30, 35, 36, 42,43,44,45]. However, our study is the first to combine these two distinct tools within the same analysis, aiming to investigate the relationship between DPR students’ learning preferences and their information acquisition and processing approaches. This approach provides a novel perspective on how learning preferences and cognitive processing styles interact, even if the correlations are not strong.
Gender has been seen as an important factor in many studies evaluating learning styles. In studies with physiotherapy and rehabilitation students, gender differences in learning preferences have been observed. For instance, Argut et al. found that kinesthetic learning preferences were more common in male students, while auditory learning preferences were more frequent in female students [28]. Similar results were obtained in the study conducted by Desai and Shah using The VARK Questionnaire with 112 female and 49 male students in 2021 [30]. A total of 372 students, 287 female and 85 males, were included in a study conducted to determine the learning styles of students in the teaching programmes of the Faculty of Education. GRLSS was used in the study, and it was reported that learning preferences differed according to gender, with female students exhibiting more participant and dependent learning preferences than male students [31]. In another study conducted with undergraduate and graduate students at Tehran University, a total of 1039 participants, 493 females and 546 males, were included. GRLSS was used, and it was concluded that female students had significantly higher averages than male students in dependent, participant and collaborative learning preferences, while male students had higher averages than female students in independent and avoidant learning preferences [32]. In contrast to these studies, our research found weak correlations between male and female students’ learning preferences and information processing, with no significant differences between genders. This discrepancy may indicate that gender-based learning differences are influenced by sample size, study design, or cultural factors, warranting further investigation.
In the literature, Kolb Learning Style Inventory has been widely used to assess the learning styles of physiotherapy and rehabilitation students [33,34,35]. Milanese et al. investigated the learning styles of senior year students of physiotherapy and rehabilitation department by using Kolb Learning Style Inventory and found that the preferred learning styles (Decomposition, Assimilation and Accommodation) were equally distributed, with decomposition being the least preferred style [29]. Similarly, according to the results of other studies, the preferred learning styles among physiotherapy and rehabilitation students were convergent and assimilative [33, 36], while the least preferred were divergent and accommodative [29, 34]. In contrast, a study involving 545 students in Malaysia using the GRLSS found that competitive and collaborative learning preferences were dominant [37]. Another study conducted at Gazi University with 170 students from the medical faculty revealed a preference for competitive and collaborative sub-dimensions as well [38]. In the study conducted by Demir et al. in 2014 using ILS with DPR students, it was found that students understood more easily in teaching environments where the ‘sensing’, ‘visual’, and ‘sequential’ dimensions were emphasized [23]. Similarly, Şahin et al. used ILS in a study conducted with nursing, social work, and physiotherapy and rehabilitation students in 2021, and concluded that students from all three different departments used ‘visual’, ‘sensing’, and ‘sequential’ learning methods more intensively [6]. While these studies focused on evaluating students’ preferred learning methods using a single evaluation scale, the relationship between different evaluation scales with various sub-dimensions has not been explored. Our study, however, is the first to examine this relationship by combining two distinct scales (GRLSS and ILS) within the same analysis. This allows for a more comprehensive understanding of learning preferences and cognitive processing may function independently rather than being directly related. In our study, we found that the dependent, participant, avoidant, and collaborative sub-dimensions of the GRLSS, as well as the sensing and visual sub-dimensions of the ILS, emerged prominently in the score range groupings. Despite this, the correlations observed in our study were predominantly weak and low. Specifically, no significant relationships were found between the two measurement tools with different sub-dimensions, emphasizing that the relationship between students’ learning preferences and their information acquisition and processing methods may not be as straightforward as previous studies might suggest. This finding indicates that when selecting a questionnaire to assess learning preferences and information processing, it is essential to determine which specific dimension to evaluate based on research focus.
Although many studies in the literature have used the GRLSS [22, 37,38,39], only one study evaluated the learning style of Turkish DPR students [22]. İlçin et al. included 184 participants in their study [22]. One strength of our study is that we evaluated the learning preferences of DPR students with a larger sample size (n = 377) using the GRLSS. By increasing the sample size, our study provides a more robust dataset, which strengthens the reliability of our findings despite the weak correlations. İlçin et al. used the GRLSS to examine the relationship between learning styles and academic performance in Turkish DPR students [22]. Their results showed that while Turkish DPR students predominantly exhibited a collaborative sub-dimension, the participant sub-dimension was associated with significantly higher academic performance [22]. In contrast, when we compared the sub-dimensions of the GRLSS across CGPA groups, significant differences were found in all dimensions of the scale, except for the competitive sub-dimension (p > 0.05). Specificaly, the scores for the independent, dependent, participant, and collaborative sub-dimensions were significantly higher in the group with a very high CGPA compared to other groups. Conversely, the avoidant sub-dimension score of the low CGPA group was significantly larger than those of the high and very high CGPA groups. Based on these findings, it can be concluded that avoidant sub-dimension is associated with lower academic achievement. These results suggest that students with a very high CGPA exhibit more adaptable and flexible sub-dimensions, with a preference for independent, dependent, participant, and collaborative sub-dimension. In contrast, students with medium and low CGPA scores tend to favor the avoidant sub-dimension. This indicates that students who are more sociable and capable of adapting to different learning conditions are more likely to achieve higher academic success.
Studies have demonstrated that education plans designed in accordance with students’ learning styles contribute to improved course performance [40, 41]. For effective teaching, instructional environments and processes must be structured appropriately [15]. A study utilizing the GRLSS observed a strong alignment between students’ learning styles and the teaching methods employed by instructors, which was found to facilitate positive interactions between students and faculty members [39]. Şahin et al. examined students from three different departments and identified both similarities and differences in their learning methods. They concluded that aligning teaching strategies and materials with the dominant learning styles of each department and incorporating multiple methods instead of relying on a single approach could improve learning effectiveness [6]. Studies evaluating DPR students using the VARK Questionnaire or Learning Style Questionnaire have identified kinesthetic learning as the most preferred learning style [42,43,44]. This preference suggests that DPR students adopt a ‘hands-on’ learning approach as the most effective way to acquire knowledge [45]. Previous research has further emphasized that this approach is associated with increased engagement in applied learning environments [46, 47]. In a study conducted by İlçin et al., a negative correlation was identified between the avoidant sub-dimension of the GRLSS and academic performance [22]. Similarly, in our study, a comparison of the sub-dimensions of the GRLSS across CGPA groups revealed that students in the low CGPA group exhibited significantly higher scores in the avoidant sub-dimension compared to those in the high and very high CGPA groups. This finding suggests that students who score higher in the avoidant sub-dimension may be less inclined to engage in active learning processes. Based on our reseach findings, we associate students with high scores in the avoidant sub-dimension tend to disengage from learning activities with the stucture of the DPR curriculum, which is predominantly practice-based. Rather than aiming to enhance academic performance, our study provides insight into the interaction between learning preferences and instructional methods within an applied education context. In this context, it is recommended that intructional strategies be adapted to foster greater participation among students exhibiting high scores in the avoidant sub-dimension, ensuring that they can better navigate and benefit from applied educational experiences.
Limitations
Our study had some limitations. First, as data were collected from DPR students at a single university, the findings may not be generalizable to other institutions or departments. Additionally, the absence of a grade point average for first-year students may have impacted the analysis of learning preferences and academic performance. The measurement tools were analyzed for the entire group. However, separate analyses for each dimension could have been conducted by including only students from one side of the poles, ensuring that each student was assigned to a single range group. Our grouping method may have influenced the correlation outcomes. Additionally, since the measurement tools were administered in a face-to-face setting, participants’ responses may have been influenced by the Hawthorne effect, where individuals alter their behavior due to being observed. Although anonymity was ensured, this potential bias should be considered. A key strength of our study is that, to the best of our knowledge, it is the first study to examine the relationship between learning preference and information acquisition processes of DPR students using two different measurement tools.
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