Investigation of the relationship between learning preferences and information acquisition and processing processes of physiotherapy and rehabilitation department students

Abstract

Background

The Department of Physiotherapy and Rehabilitation (DPR) provides both theoretical and practical education, requiring students to engage in diverse learning processes. While learning preferences and information processing have been studied separately in health sciences, their relationship in DPR students remains unexplored. This study investigates this relationship by assessing learning preferences with the Grasha-Riechmann Learning Style Survey (GRLSS) and measuring information acquisition and processing with the Felder and Soloman Index of Learning Styles (ILS).

Objective

The aim of this study is to investigate the relationship between the learning preferences of physiotherapy and rehabilitation students and their processes of information acquisition and processing.

Methods

In this descriptive cross-sectional study, 377 students from the DPR were evaluated. A data recording form was used to collect information on the students’ age, gender, and year of study/program. Their cumulative grade point average (CGPA) was recorded as a measure of academic performance. Learning preferences were evaluated using the GRLSS, while information acquisition and processing were measured using the Felder and Soloman ILS.

Results

A weak positive correlation was found between the collaborative sub-dimension of GRLSS and the active-reflective dimension of ILS (r = 0.228, p < 0.05). Gender-based analysis revealed a negative correlation between the avoidant sub-dimension of GRLSS and the sensing-intuitive of ILS dimension in females (r=-0.216, p < 0.05). In males, CGPA showed a weak correlation with the sensing-intuitive dimension (r = 0.200, p < 0.05). Students with low CGPA scored higher in the avoidant sub-dimension than those with high and very high CGPA (p = 0.011, p < 0.001). No significant differences were found in ILS scores across CGPA groups.

Conclusions

This study investigated the relationship between DPR students’ learning preferences and their processes of information acquisition and processing. While significant differences were found in GRLSS sub-dimensions among CGPA groups, no significant correlation was observed between GRLSS and ILS scores. Academic performance was associated with the avoidant sub-dimension of the GRLSS. These findings indicate that other factors may influence this interaction. To gain deeper insight into the complexity of learning preferences and information processing, further research with diverse methodologies is needed.

Clinical trial number

Not applicable.

Introduction

The learning process is different for each individual and understanding students’ learning behaviours is an important part of this process [1]. In the most general sense, learning style is the characteristics that show an individual’s tendencies or preferences towards learning. These characteristics show how the individual or student perceives learning, how he/she interacts with his/her environment and how he/she reacts to the elements in his/her environment. The student fulfils these characteristics in a certain consistency and continuity [2, 3]. Learning style is primarily focused on how the individual apprroaches learning, whereas learning preferences describe the specific strategies or methods that students tend to favor during learning activities. According to Huston and colleagues, the learning style refers to the preferred learning style of the individual and is related to how the student learns rather than what the student learns [4].

Determining the learning styles of individuals allows identifying their strengths and weaknesses during learning, taking measures to improve their weaknesses, bringing together the most suitable individuals to work together and considering the diversity in the classroom [5]. In other words, recognising the learning style of the student increases the quality of education and makes individual learning more appropriate [6]. Learning style preferences affect an individual’s learning and explain academic success [7]. In the literature, there are studies showing that there is a close relationship between learning style and academic achievement [8, 9]. In this study, academic success was measured using Cumulative Grade Point Average (CGPA), a commonly used metric that reflects students’ overall academic achievement and progression in higher education [6]. Determining the learning styles of the student and organising the education plan in accordance with the learning styles is very important in terms of increasing the efficiency of teaching [10, 11]. Studies show that when students are taught with the learning style they prefer, there is a statistically significant increase in their positive attitudes towards teaching and a positive improvement in classroom behaviour and discipline [12].

Learning styles are defined in different ways by various researchers [13,14,15]. Rita Dunn, who first introduced the concept of learning style, defined this concept as ‘each student’s use of unique and different ways of preparing to learn new and difficult information’ [13]. Kolb, another name at the forefront with his research on this subject, defined learning style as the individual preferred method of receiving and processing information [14]. Due to the different variables in the definitions of learning styles, many models have been put forward. According to De Bello, many learning style models are compatible with each other, but some models are based on all multidimensional features such as cognitive, affective and behavioural, while some models are limited to relying only on a single dimension such as cognitive or psychological [16]. Some researchers suggest that the individual’s tendency to act in a certain way also includes a preference. Thus, the concept of preference comes to the fore as one of the main concepts in definitions [12]. Grasha and Riechmann classified the preferred learning methods of individuals into different sub-dimensions such as collaborative, competitive, participant, avoidant, dependent, independent [15]. In 1988, Felder and Silverman defined learning style as ‘characteristic strengths of individuals in the process of receiving, retaining and processing information’ [17]. In the literature, information acquisition and processing processes are commonly assessed using standardized questionnaires and scales. These instruments provide detailed insights into how individuals perceive, acquire, and process information. The Felder and Soloman Index of Learning Styles (ILS) evaluates cognitive processing preferences, Kolb’s Learning Style Inventory examines experiential learning. These tools are widely used to analyze individual learning differences [12, 14, 17]. In this study, the Grasha-Riechmann Learning Style Survey (GRLSS) was used to assess students’ engagement in the learning process, while the Felder and Soloman Index of Learning Styles (ILS) measured their information acquisition and processing preferences. These two scales were chosen as they provide complementary insights. GRLSS evaluates behavioral and attitudinal engagement in learning, whereas ILS identifies cognitive processing preferences [15, 17].

The Department of Physiotherapy and Rehabilitation (DPR) offers a clinical and professional education program consisting of theoretical and practical courses, as well as internship studies. Therefore, it is important for students in the department to effectively utilize different dimensions of learning. To gain a detailed understanding of students’ learning styles, a comprehensive evaluation should be conducted using questionnaires that address various sub-dimensions. Determining whether the learning style is related to the information processing process, and aligning the learning style with this process, will support the learning process. Improving the education quality of DPR students is possible by understanding learning styles correctly and developing appropriate teaching methods. In the literature, the learning styles of students in different health science departments [18,19,20,21] and DPR students have been examined separately in terms of learning preferences [22] and information acquisition and processing processes [6, 23]. To the best of our knowledge, there is no research that evaluates the learning style of DPR students using two questionnaires with different sub-dimensions within the scope of the same study and examines the relationship between them. Therefore, the primary aim of our research is to examine the relationship between the learning preferences of DPR students and their processes of acquiring and processing information. Understanding these relationships may help in selecting teaching approaches that better align students’ learning preferences and information processing, potentially improving the quality of education.

Methods

Study design

This descriptive cross-sectional study (Supplementary File 1) was conducted between December 2022 and February 2023 with formal and secondary education students who were studying at Manisa Celal Bayar University (MCBU), Health Sciences Faculty (HSF), DPR and who voluntarily agreed to participate in the study.

Participants

The study was conducted with students enrolled in the formal and secondary education programs of MCBU, HSF, DPR, who voluntarily agreed to participate in the study. Students were approached through announcements made in classes, where they were provided with information about the purpose of the study, the methods, possible risks and benefits, protection of personal data and contact information. After the participants were informed, written consent was obtained indicating their willingness to participate. To be included in the study, participants had to be 18 years of age or older and provide their consent by signing the informed consent form. Students who did not participate in the study or who signed the consent form but did not answer any questions were excluded. Since the exclusion criteria were limited to these conditions, no participants met the exclusion criteria, and therefore, no one was excluded from the study.

Ethical consideration

This study adhered to the principles of the Declaration of Helsinki and was approved by the Manisa Celal Bayar University, Faculty of Medicine, Health Sciences Ethics Committee (Ethics Board Approval Number 07/12/2022 20.478.486/1597). After the participants were informed (purpose of the study, methods, possible risks and benefits, protection of personal data and contact information), written consent was obtained that they volunteered to participate in the study, and then data collection was carried out.

Sample size calculation

In our study, power was accepted as 80% and type 1 error value was accepted as 5% in sample size calculation. Hawa and Tılfarlıoğlu examined the relationship between social interaction and learning styles in their study [24]. According to the results of the study, the lowest correlation was found in the group of students who learned with the “group work” method and was calculated as r = 0.144. According to this result, the sample size calculation was made using the online power analysis calculator https://samplesize.net/correlation-sample-size/ and it was concluded that at least 376 participants should be included in our study.

Data collection

In the study, measurement tools were administered to the participants using a face-to-face interview technique. Participants’ age, gender, grade level, and academic achievement were assessed. The Grasha-Riechmann Learning Style Survey (GRLSS) and The Felder and Soloman Index of Learning Styles (ILS) were selected as data collection tools due to their established validity and reliability in assessing learning preferences and information processing styles. GRLSS is a comprehensive learning style measurement tool consisting of 60 items, which helps determine individual learning preferences across various dimensions, including Collaborative/Competitive, Participant/Avoidant and Independent/Dependent. The ILS, which consists of 44 questions, evaluates information acquisition and processing by assessing participants’ preferences in learning strategies, such as Sensing/Intuitive, Visual/Verbal, Active/Reflective and Sequential/Global. The measurement tools were self-administered by participants in a supervised face-to-face setting and were completed in approximately 10–15 min. The measurement tools were administered by three researchers who were not involved in teaching the students and did not have grading authority. The only researcher with grading authority was responsible solely for collecting CGPA data.

Academic achievement was measured using the students’ CGPA, which reflects their overall academic performance during their studies. CGPA is calculated automatically by the university and is available to students on their transcripts. It was obtained by asking students about their CGPA during the research. CGPA groups were created with 0.50-point intervals as follows: low (< 2.50), medium (2.51-3.00), high (3.01–3.50) and very high (3.51-4.00). This measure was included to analyze the relationship between students’ learning styles and their academic success.

Grasha- Riechmann learning style survey (GRLSS)

The Turkish adaptation of the GRLSS was conducted by Zereyak in 2005. The scale consists of 60 items across three bipolar sub-dimensions: Collaborative/Competitive, Participant/Avoidant, Independent/Dependent. The GRLSS was chosen for this study as it assesses students’ engagement in learning by identifying their behavioral and attitudinal preferences in different educational settings. Each sub-dimension represents different learning preferences: Collaborative learners prefer working in groups and sharing knowledge, while Competitive learners focus on outperforming their peers. Participant learners actively engage in classroom activities and take responsibility for their learning, whereas Avoidant learners tend to be disengaged and reluctant to participate. Independent learners favor self-directed learning and rely on their own abilities, while Dependent learners seek structured guidance from instructors and peers [15].

Each item is rated on a five-point Likert scale: “strongly disagree”, “disagree”, “undecided”, “agree” and “strongly agree”, with scores ranging from 1 to 5. The student can get a score between a minimum of 1.00 and a maximum of 5.00 from each pole of the sub-dimension and he/she can be included in the very low, low, medium, high and very high clusters in the relevant sub-dimension according to the score he/she receives. The lowest and highest score ranges of the clustering process were used from the results obtained in Zereyak’s study [25]. According to that study, the lowest score ranges for the very low clusters of the dependent, independent, cooperative, competitive, participatory and avoidant sub-dimensions were determined as 1.00-2.81, 1.00-2.81, 1.00-2.58, 1.00-1.78, 1.00-2.13, 1.00-1.91, respectively. The score ranges for the very high clusters of the sub-dimensions were determined as 4.31-5.00, 4.33-5.00, 4.30-5.00, 3.76-5.00, 3.82-5.00, 3.37-5.00, in the same order. The Cronbach’s Alpha internal consistency coefficient of the Turkish adaptation of the GRLSS was calculated as 0.83, with sub-dimension coefficients ranging between 0.53 and 0.78. The test-retest reliability coefficients for the sub-dimensions ranged from 0.474 to 0.888 [15, 25].

The Felder and Soloman index of learning styles (ILS)

ILS is a scale that evaluates information acquisition and processing processes, consisting of 44 questions and four bipolar sub-dimensions: Active/Reflective, Sensing/Intuitive, Visual/Verbal and Sequential/Global. In this study, it was used to evaluate how students acquire and process information, providing insight into their learning approaches. Each dimension represents different learning processes: Active learners learn best by doing and interacting, while Reflective learners prefer to process information internally. Sensing learners favor concrete facts and structured methods, whereas Intuitive learners focus on abstract concepts and innovation. Visual learners retain information better through images, while Verbal learners benefit more from written or spoken explanations. Sequential learners progress step by step in a logical order, while Global learners grasp the bigger picture first and may struggle with details initially [26].

Each sub-dimension consists of 11 statements, each with two response options: ‘a’ and ‘b’. Many ‘a’ responses indicate the sensing, visual, doing and sequential poles, while many ‘b’ responses correspond to the intuitive, auditory, thinking and holistic poles, depending on the sub-dimension being assessed. The individual’s dominant learning style is obtained by subtracting the number of ‘b’ responses from the number of ‘a’ responses given in the survey sub-dimension. The student’s sub-dimension score can vary within the ranges of 1–3, 5–7, 9–11. The range of 1–3 corresponds to low, the range of 5–7 to medium, and the range of 9–11 corresponds to the high cluster in the relevant pole of the sub-dimension. The scale was adapted into Turkish by Samancı and Keskin, and the Cronbach’s Alpha internal consistency coefficient was found to be 0.64 [26, 27].

Statistical analysis

In accordance with the data structure of the research group’s age, gender, grade level, academic achievement, learning preferences, and information acquisition and processing process variables, the lowest and highest values were expressed as mean, standard deviation and percentage distributions. IBM SPSS Statics version 26.0 software was used for statistical analysis of the findings obtained in the study. The conformity of the variables to normal distribution was analyzed by Kolmogorov-Simirnov/Shapiro-Wilk tests. The relationships between dependent and independent variables were examined with Spearman/Pearson correlation tests according to their conformity to normal distribution; the relationship between students’ learning preferences and information acquisition and processing processes was examined. For this purpose, students who were administered GRLSS and ILS were evaluated separately in each sub-dimension of the scale dimensions. The poles of the GRLSS and ILS were determined, and then it was analyzed whether there was a correlation between these tendencies.

Results

Participants

The sample of this study consisted of 377 students, 267 female and 110 male students, from the formal and secondary education students of the DPR. The mean age of the whole group was 20.70 ± 1.92 years and the general weighted CGPA was 2.26 ± 1.41. Descriptive statistics and percentage values of the research group are given in detail in Tables 1 and 2.

Table 1 Descriptive characteristics and survey scores of the subjects according to gender

Table 2 Percentage values of the subjects according to CGPA levels

Clusters according to questionnaire subdimensions

In the score range grouping of the GRLSS dependent/independent dimension most of the students were grouped in the high range of dependent pole. For the participant/avoidant dimension most of the students were in the high range of participant pole. While for the collaborative/competitive dimension students were mostly in high range of collaborative pole. When each of the dimension poles is examined individually, students were mostly clustered in medium range of independent style, high range of dependent style, high range of participant style, high range of avoidant style, high range of collaborative style and medium range of competitive style. In the score range grouping of ILS, in the sensing/intuitive dimension students clustered mostly in medium range of sensing pole. For visual/verbal dimensions they were in medium range of visual and for active/reflective dimension in low range of reflective at most. And finally for the sequential/global dimension students were mostly in low range of sequential pole. The percentage values of the scale sub-dimensions are given in Table 2.

Correlation analysis

In our research, the correlations of the whole group survey results and other variables were evaluated. The results obtained are generally at very low and low correlation levels. A statistically significant correlation of r = 0.228 was obtained between the GRLSS collaborative sub-dimension and the ILS active-reflective dimension. The correlations we obtained between CGPA and GRLSS sub-dimensions and ILS dimensions were at negligible levels (statistically significant r values were in between 0.140 and 0.167).

When grouped according to gender, the correlations of the survey results and variables were also examined. A significant correlation of r=-0.216 was found between the GRLSS avoidant sub-dimension and the ILS sensing-intuitive dimension in females. A significant correlation of r = 0.237 was found between the GRLSS collaborative sub-dimension and the ILS active-reflective dimension in both females and males. A correlation of r = 0.200 was found between the CGPA and the ILS sensing-intuitive dimension in males. The results of all group correlations are detailed in Table 3, Supplementary Table 1, Supplementary Table 2.

Table 3 Correlations between age, CGPA and survey scores

Differences between subgroups

The participant group was classified according to CGPA, and the survey results were compared between these groups by Kruskal-Wallis Test. There was no significant difference between the groups in any sub-dimension of ILS scores (p < 0.05). When the sub-dimensions of the GRLSS were compared in CGPA groups, Kruskal-Wallis Tests showed significant results in all dimensions of the scale except the competitive sub-dimension (p > 0.05). When the sub-dimensions of the GRLSS were compared between the CGPA groups in pairs with the Mann-Whitney U Test, it was found that the avoidant sub-dimension score of the group with low CGPA was significantly larger than the groups with high and very high CGPA. (Low and high group p = 0,011, low and very high group p < 0,001). The independent, dependent, participant and collaborative sub-dimension scores of the group with very high CGPA were generally significantly higher than the other groups. The results of pairwise comparisons between the groups are given in Table 4.

Table 4 Pairwise comparisons in CGPA groups according to GRLSS

Discussion

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.

Conclusions

In this study, we examined the relationship between the learning preferences of DPR students and their information acquisition and processing processes. Our findings highlighted the dependent, participant, avoidant, and collaborative sub-dimensions of the GRLSS scale in the score range groups, as well as the sensing and visual sub-dimensions of the ILS scale. No significant relationship was found between these two measurement tools, which assess different sub-dimensions. These results have been reconsidered considering the lack of significant correlation, and the findings have been interpreted within this context. However, academic performance was found to be associated with the avoidant sub-dimension of the GRLSS. Furthermore, when comparing GRLSS sub-dimensions across CGPA groups, significant differences were found in all dimensions except for the competitive sub-dimension. The absence of a relationship between the two measurement tools suggests that additional factors may influence this interaction. Therefore, future studies employing diverse methodologies are needed to better understand how learning preferences interact with various cognitive and academic factors.