A Cognitive Preference Profiles in Engineering Students: A Cluster Analysis of Learning Tendencies and Cerebral Hemispheric Orientation

Available online at www.rajournals.in RA JOURNAL OF APPLIED RESEARCH ISSN: 2394-6709 DOI:10.47191/rajar/v11i12.12 Volume: 11 Issue: 12 December 2025 International Open Access Impact Factor8.553 Page no.- 1163-1178 1163 Pricopie-Filip Alina1, RAJAR Volume 11 Issue 12 December 2025 A Cognitive Preference Profiles in Engineering Students: A Cluster Analysis of Learning Tendencies and Cerebral Hemispheric Orientation Pricopie-Filip Alina1, Andrei Mihaela2 1,2Faculty of Automation, Computers Sciences, Electrical and Electronics, “Dunarea de Jos” University of Galati, Romania ARTICLE INFO ABSTRACT Published Online: 23 December 2025 Corresponding Author: Pricopie-Filip Alina School dropout in the first year of engineering faculties is a current topic. Possible solutions depend on many variables like the students’ learning style, the teaching methods, learning material, etc; Therefore, solutions can only be proposed adapted to the particularities of the study group (students learning style, teacher methods, material, etc). This study analyzes the cognitive preference profiles in a sample of engineering students. In this respect, a correlation between the learning style and the cerebral hemispheric orientation for each student in sample has been done using the “Preference learning style questionnaire” and the “Cerebral hemispheric preference” questionnaire, both created by Ricki Linksman (1999). Furthermore, cluster analyses were used to determine the student’s cognitive profiles based on which pedagogical recommendations and teaching methods for engineering education are made. KEYWORDS: learning styles; cerebral hemisphere, super-link for learning, cognitive preferences, teaching methods I. INTRODUCTION Every student, person or learner experiences educational process and understands information in a way that is uniquely their own. These differences appear from a combination of psychophysiological factors or personal approaches to reasoning and problem-solving [1]. These aspects are very important not only in how students assimilate new material, but also in how they integrate the abstract concepts in real life. Because students do not think, perceive, or process in the same way, a uniform educational model does not have better performances in training future engineering for examples. Modern education requires individualization and differentiation of instructional approaches [2]. In other words, each student has a learning profile and style which are helpful in creating new teaching methods. Today, there are also machine learning techniques used to identify them [3]. When pedagogical methods are adapted to these profiles, learning becomes more meaningful, more efficient, and more sustainable, because the learning process is designed in such a way that all learners have support: those who are visually, those who prefer structured verbal explanations, or those who learn best through doing [1,4]. This perspective was successfully applied to engineering education, where students from technical faculties have to understand and integrate many abstract mathematical concepts, spatial–visual representations, algorithmic thinking, laboratory experimentation, or different design tasks [5]. Students differ not only in learning styles: visual, auditory, tactile, or kinesthetic [6], but also in the cognitive orientation through which they analyze or synthesize information [7]. These tendencies, often referred to as cognitive preferences, include both perceptual learning styles and broader hemispheric processing orientations [8,9]. The first one reflects favored modes of interaction with content. Visual students tend to learn fast from diagrams, spatial representations, and symbolic structures; auditoryoriented learners respond well to verbal explanation and sequential narration; tactile and kinesthetic learners excel in manipulation, hands-on experimentation, and active movement [10,11]. Hemispheric processing orientation describes how learners analyze and organize information. Although cognitive functions are distributed across both hemispheres, the research suggests that each person uses most one of them. Learners with a left-oriented hemisphere typically prefer structured, sequential, and detail-focused reasoning, relying on rules, symbolic representations, and stepwise procedures. Those with a right-oriented tendency respond more to holistic, visual–spatial, and intuitive approaches, organizing information through patterns, images, and global conceptual frames. Many students exhibit mixed profiles, flexibly shifting between analytical and holistic strategies depending on task demands, while a smaller number has an integrated profile that combines both modes simultaneously, allowing them to coordinate detailed analysis with higher-level conceptual understanding [8,9,12]. These “A Cognitive Preference Profiles in Engineering Students: A Cluster Analysis of Learning Tendencies and Cerebral Hemispheric Orientation” 1164 Pricopie-Filip Alina1, RAJAR Volume 11 Issue 12 December 2025 heterogeneous processing tendencies play a particularly important role in engineering education, where students must alternate between mathematical formalism, spatial visualization, system-level reasoning, and hands-on experimentation. The students performance and cognitive profiles were investigated based on learning styles, individual [1,4,6,7,9,13] or grouped in clusters [14–17], but none of these researches did not take into account the hemispheric orientation. Students rarely function according to a single dominant mode and as a result, there is increasing interest in building integrated learner profiles that combine several behavioral indicators into a single representation of learning variability. Despite their relevance, learning styles and cerebral hemispheric preference have rarely been examined together in engineering education. This correlation is favors the development of accelerated learning competencies [18]. Existing work relies heavily on frequency comparisons and chi-square tests. Cluster analysis is a powerful technique widely used in psychology and learning analytics. Starting from the idea that school dropout is mostly due to the learning difficulties that students encounter in the first year, this paper proposes a teaching method based on Cognitive Preference Profiles. The main contributions of the present paper are: Identify learning styles and cerebral hemispheric preference for 91 students from a technical faculty. We used two instruments proposed by [18]. Analyze the relationship between those aforementioned factors that influence learning process: we test whether they are statistically associated or whether they operate independently. Identify integrated cognitive profiles: using cluster analysis, we examine whether students naturally group into distinct profiles that combine learning styles and hemispheric orientations into cognitive patterns. Based on those clusters, we propose some pedagogical recommendations and teaching methods for engineering education. Introduce a novel computational indicator: the Cognitive Profile Index (CPI). One of the key original contributions of this paper is the proposed of a numerical index that integrates perceptual and hemispheric tendencies into a single continuous score ranging from -1 to +1. This provides an engineering-friendly, quantitative representation of each student's cognitive profile or allows numerical comparison between clusters. II. THEORETICAL BACKGROUND A. Learning Styles Learning styles are a current topic in the recent research in the fields of education, psychology and neuroscience. The field of learning styles and methods encompasses not only many research studies in the field, but lately also a series of commercial activities addressed to the public audience. In [19,20] a review can be consulted of all these materials and fields that address this concept of learning style. There are mentioned both aspects related to the impressive number of studies carried out in this field, as well as the textbooks and not least the websites that facilitate learning style testing and determination activities. In scientific literature there are perhaps as many definitions of learning style as there are theories and models that have been developed. A comprehensive definition that declares learning styles as being the “way in which each learner begins to concentrate on, process, absorb, and retain new and difficult information” [21-23]. [24] highlights that this concept cand lead to different ways in which theorists understand this concept, so that their methods for assessment and observations may be different. This is the reason why there are so many different definitions and models for the learning style assessment in scientific literature. Specialized studies conducted in recent decades have shown that learning styles represent a combination of natural and acquired habits. We receive information about the outside world through all five human senses but depending on the stimuli to which our brain was exposed, interconnections were created between nerve cells. The more stimuli we receive, the more interconnections - learning patterns are formed. The more often the brain is exposed to stimuli of the same type, the simpler, faster and more automatic the corresponding learning pattern becomes and thus, the best learning style is formed [25]. If the learning material is presented in such a manner that facilitates its reception through the appropriate sense - the preferred learning style - then a greater efficiency of the learning process can be ensured. One categorization of learning style is related to the four human senses through which we receive information from the surrounding world. Therefore, according to [25-26] there are four learning styles: visual, auditory, tactile and kinesthetic. Visual learners receive information through the visual sense; therefore, they are receptive to displayed materials and demonstrations of new information. Auditory learn by reading aloud or discussing and processing thoughts out loud. Tactile learners receive information through the sense of touch, so they learn easily if the new information is presented in such a way that allows them to use their hands, fingers, their perceptions sense. Kinesthetic learners learn easily being in motion: by activating their motor muscles, by engaging in movement activities or exploiting new concepts. Even if there is a preferable cognitive way of receiving information, it must be mentioned that there are people who use two, three or all of four styles to learn. B. Cerebral Hemisphere Lately research in the neuroscience field has been progressed enormously and today offers an understanding of “A Cognitive Preference Profiles in Engineering Students: A Cluster Analysis of Learning Tendencies and Cerebral Hemispheric Orientation” 1165 Pricopie-Filip Alina1, RAJAR Volume 11 Issue 12 December 2025 how the brain works. Human brain is divided into two hemispheres. Generally, people use both cerebral hemispheres, but the new information processing and its storage is done predominantly by using one of these hemispheres, demonstrating a cerebral hemispheric preference. When we predominantly use certain neural pathways, we are practically strengthening those neural pathways, transforming it into the preferable cerebral hemisphere. We become more efficient in using that hemisphere and so; by finding it simpler and more comfortable to use it, we will use it more frequently. Ideally, people should be able to use both cerebral hemispheres appropriately for the task at hand. But to be able to do this, a certain amount of training is necessary. Scientific discoveries in this field are relatively recent, and the academic environment is trying to maximize the efficiency of these discoveries. In the meantime, however, the cerebral hemisphere development is an aspect that it is left to chance - people are developing their cerebral hemispheres purely randomly - depending on the stimuli frequency they are subjected to in childhood. The correlation between learning style and the preferred cerebral hemisphere was called by R. Linksman with the suggestive term “the super link for learning”. The learning style and the cerebral hemispheres work together. Learning style is associated with different ways of receiving information from the world and transmitting information from the senses to the brain. Cerebral hemispheric preference deals with how we work with information – how we process it and how we store it, once it reaches to the brain. Sensory data received by sight, hearing, touch or through the muscles of the body can be channeled to the left or to the right cerebral hemisphere. They process and store the information in different ways as follows: symbolic/sensory or step-by-step/simultaneously. The left cerebral hemisphere processes data symbolically, in the form of numbers, letters, words and abstract ideas, while the right hemisphere does it in a sensory way, perceiving the world through the senses, without words. The left cerebral hemisphere is the one that deals with language. The right hemisphere processes data without language. It perceives visual, auditory, gustatory, olfactory, tactile, movement, music, human voice sounds, and nature without assigning labels to them. In other words, the right cerebral hemisphere perceives life as a movie without dialogue. The left hemisphere stores information step by step. It absorbs information in a linear order, successively, one at a time. It has difficulty perceiving the big picture at once. In contrast, the right hemisphere stores information globally, simultaneously – it sees the big picture at once. It has difficulty rehearsing information step by step, if it does not get the big picture. Although the two hemispheres have different tasks, the humans merge them and use both hemispheres. The aspect that is being studied in recent research is that when we have to understand and learn new concepts, we tend to use our preferred cerebral hemisphere - the one with which we find it easiest/comfortable/handy to process the new information. Therefore, if we would receive data through our preferred hemisphere, it would be easier and more natural to accelerate the learning process. This is the aspect this paper aims to enhance in building new teaching methods and pedagogical proposals. III. MATHERIALS AND METHODS All paragraphs must be indented as well as justified, i.e. both left-justified and right-justified. A. Participants (Size 10 & Bold) The entire document should be in Times New Roman or Times font. Other font types may be used if needed for special purposes. Type 3 fonts should not be used. To identify individual learning style, the cerebral hemisphere preference and the correlation between them, we applied a specific set of questionnaires to a sample of 91 technical engineering students, from different specializations of Faculty of Automation, Computers, Electrical and Electronic Engineering: Electrical Engineering, Electronics and Telecommunications and Electrical Engineering and Computers. Demographic distribution, presented in Table 1 and Figure 1, shows that 85.7% of participants were male, while 14.3% were female. The study specialization distribution (Figure 2) shows that IE engineering students were preponderant in the student sample. Table 1. Demographic distribution Figure 1. Gender distribution “A Cognitive Preference Profiles in Engineering Students: A Cluster Analysis of Learning Tendencies and Cerebral Hemispheric Orientation” 1166 Pricopie-Filip Alina1, RAJAR Volume 11 Issue 12 December 2025 Figure 2. Studies specialization distribution B. Instruments This study utilizes a descriptive quantitative research design to determine the correlation between learning styles and the dominant cerebral hemisphere in the sense of determining what is called in the specialized literature the “super link for learning”. In this perspective we used the “Preference learning style questionnaire” and the “Cerebral hemispheric preference” questionnaire, both created by Ricki Linksman (1999). These two questionnaires were centralized and the sstatistical analysis have been done with IBM SPSS Statistic. C. Procedure The questionnaires were completed successively, within the same time frame. Completion of the questionnaires was completely voluntary. Students were informed that questionnaire completion is anonymous, and the research data will be strictly confidential. Also, they have been informed about the fact that the research results will be used exclusively for academic purposes and that they can quit the questionnaires completion whenever they want to - if they changed their mind regarding their participation in this research. The students in sample completed the two questionnaires, performed the score calculation by themselves and after that they have completed a Google form with their obtained results. This allows the researchers to centralize the research results without any external implication in the process D. Statistical Analysis The collected data using the two Rikki Linksman questionnaires were centralized and statistically analyzed using IBM SPSS Statistic. In the first part of the article, a statistical analysis of possible correlations between the learning style and, respectively, the cerebral hemisphere and the variables that could possibly influence them (gender, specialization, etc) was carried out. For the analysis of the correlation between the Learning Style and the Cerebral Hemisphere, the Crosstabulation and Chis-Square Tests have been used. Based on the analyses of the correlation tendencies results, a Two Step Cluster analyses have been done to determine the Cognitive Profile Index. E. Cognitive Profile Index To integrate the information obtained from the learning styles questionnaire and the hemispheric processing orientation instrument into a single, comparable metric, we propose a Cognitive Profile Index (CPI). This index transforms two categorical, qualitatively different constructs into a numerical variable that reflects each student’s overall cognitive profile. This approach enables a more compact representation of learner variability, supports quantitative comparisons between clusters, and provides an engineeringfriendly metric. The CPI was constructed in two stages. First, learning styles were coded along an ordinal perceptual continuum ranging from predominantly visual to predominantly kinesthetics. The four categories (Visual, Auditory, Tactile, Kinesthetics) were mapped to values 1–4 and then linearly normalized to the interval [-1, +1] using a symmetric transformation, centered at 2.5 and divided by 1.5 (which is (4-1)/2), resulting in a symmetric and interpretable score. This score has the following interpretation: negative values indicating a tendency toward visual and sequential information processing, and positive values reflecting a preference for tactile or kinesthetics’, action-oriented modes of engagement. The proposed formula for this normalization is: The Snorm values for the mentioned styles are: Table 2. Snorm values Second, cerebral hemispheric preferences were encoded in the same mode as styles, on a scale ranging from -1 to +1. Based on established behavioral descriptors, left-oriented profiles were assigned a value of -1, right-oriented profiles +1, while mixed and integrated profiles received intermediate values (-0.3 and +0.3, respectively) to reflect blended or balanced orientation. The value in this case has the same relation as Snorm, noted with H. The final CPI score for each student was computed as the arithmetic means of the normalized score Snorm and the hemispheric orientation code: “A Cognitive Preference Profiles in Engineering Students: A Cluster Analysis of Learning Tendencies and Cerebral Hemispheric Orientation” 1167 Pricopie-Filip Alina1, RAJAR Volume 11 Issue 12 December 2025 The resulting index ranges from -1 (visual–left/mixed preference) to +1 (tactile/kinesthetic–right/integrated orientation), with values near zero indicating balanced profiles. This index was used to examine distributional differences between the clusters identified in SPSS, to visualize cognitive patterns, and to quantify the degree of separation between groups. F. Teaching Strategy Proposal The objective of present research is to analyze the super link for learning/cognitive profile for a sample of students in engineering faculty. Once it has been determined, the objective is to propose, develop and implement a teaching strategy to help students to lean easily and implicitly decrease the school dropout rate in the first year of study. The teaching strategy proposal will be founded on the present research results and conclusions. IV. RESULTS A. Learning tendencies No more than three levels of headings should be used. All headings must be in 10pt font. Every word in a heading must be capitalized except for short minor words as listed in Section III-B. The analysis was done in IBM SPSS Statistic. The results reflecting the learning tendencies, in Figure 3, revealed noticeable differences between technical specialization. Figure 3. Learning styles tendencies on technical domains Table 3 (Style/Domain Crosstabulation) expose the correlation between the learning style and study specialization. It can be observed that visual learners were most frequently found in Electrical Engineering (50%), whereas tactile and kinesthetic learners were more prevalent in Electronics (42.9% and 52.2% respectively). In contrast, students in Electrical Engineering and Computer Science showed lower representation of tactile (14.3%) and kinesthetic (8.7%) preferences. Auditory learners were evenly distributed across all specializations (33.3% in each group). These descriptive results suggest that learning style patterns vary across academic programs at a surface level. Table 3. Style Domain Crosstabulation The style/domain crosstabulation reveals not a significant statistical relationship between specialization and learning style for this sample, as proven also by chi-square analysis. Considering the technical specifics of the faculty, it was expected that there would not be a significant difference between learning styles preferred by specializations, but that the visual learning style would be the preferred one. There could be a significant statistical difference between the learning style but reported to the specific of the faculty (not the specialization in the same faculty profile)– this will make the subject of future research. The Chi-Square test examining the association between learning style and specialization (Table 4) was not statistically significant, χ²(6) = 8.57, p = .199. Fisher’s Exact Test confirmed the absence of a significant association (p = .196). Table 4. Chi-Square Tests for Stile/Domain Figure 4. Learning styles tendencies “A Cognitive Preference Profiles in Engineering Students: A Cluster Analysis of Learning Tendencies and Cerebral Hemispheric Orientation” 1168 Pricopie-Filip Alina1, RAJAR Volume 11 Issue 12 December 2025 The distribution of learning style preferences per global (Figure 4) indicated a dominant inclination toward visual (almost 40%). Kinesthetic learning styles were the second most prevalent (≈25%), suggesting that a considerable proportion of students benefit from movement, interaction, and experiential engagement during learning tasks. Auditory learners represented approximately 20% of the sample, while tactile learners accounted for the lowest percentage (≈15%). These results highlight a heterogeneous cognitive profile within the student population, with a particularly strong visual preference consistent with patterns frequently reported in technical and educational engineering contexts. B. Cerebral Hemisphere tendencies Figure 5. Cerebral Hemispheric tendencies on technical domain Figure 6. Distribution of cerebral hemispheric tendencies per quantity Analysis of hemispheric preference (Figure 6) showed a relatively balanced distribution between left (about 35%) and mixed dominance (33%), followed by right hemispheric preference (30%). Only a negligible percent is for an integrated hemispheric profile (2-3%), indicating that simultaneous bilateral processing is uncommon among the participants. These results suggest that most students tend to rely predominantly on either sequential-analytical (left) or flexible/multimodal (mixed) processing styles, with a notable proportion showing right-hemispheric reliance associated with global and intuitive reasoning patterns. Table 5. Hemisphere Domain Crosstabulation The distribution of hemispheric dominance across specializations showed no statistically significant association, χ²(9) = 15.07, p = .089. Left-, right-, and mixed-hemisphere respondents appeared across all academic programs in comparable proportions, indicating that hemispheric preference is not linked to specialization. Although integrated hemisphere responses were recorded only within the Electrical Engineering and Computer Science group, the frequency was too small (n = 2) to support interpretation. Table 6 Chi-Square Tests for Hemisphere/Domain The Chi-Square test assessing the association between hemispheric dominance and specialization (Table 6) was not statistically significant, χ²(6) = 8.76, p = .188, with Fisher’s Exact Test confirming the absence of an association (p = .372). C. Learning Style - Cerebral Hemispheric corelated tendencies Figure 7. Learning Style - Cerebral Hemispheric corelated tendencies “A Cognitive Preference Profiles in Engineering Students: A Cluster Analysis of Learning Tendencies and Cerebral Hemispheric Orientation” 1169 Pricopie-Filip Alina1, RAJAR Volume 11 Issue 12 December 2025 Table 7 Learning Style/ Cerebral Hemispheric Crosstabulation The cross-classification between learning styles and hemispheric dominance (Table 7) revealed distinct distribution patterns. Visual learners were mainly associated with left-hemispheric dominance (16) and mixed dominance (14), while only a small number aligned with righthemispheric processing (4) or integrated profiles (2). Auditory learners showed a relatively balanced distribution across left, right and mixed hemispheric preferences (6). Tactile learners were most frequently linked to righthemispheric dominance (8), with fewer cases falling into left or mixed categories. Kinesthetic learners demonstrated a stronger association with right dominance (9) and mixed profiles (8), while fewer aligned with left-hemispheric patterns. Table 8 Chi-Square Tests for Learning Style/ Cerebral Hemispheric A chi-square test was conducted to examine whether learning style was associated with hemispheric dominance (Table8). The association did not reach statistical significance, χ²(9, N = 91) = 15.07, p = .089, suggesting that the distribution of learning styles across hemispheric categories does not differ from what would be expected by chance. Although descriptive patterns indicated tendencies such as visual learners aligning more frequently with left or mixed dominance and kinesthetic learners showing stronger right-hemisphere representation, these differences were not statistically supported. Questioning if there is any determination between the student’s learning style and the student’s cerebral dominant hemisphere, we made a symmetric analysis. Table 9 Symmetric measures The symmetric measures analysis, presented in Table 9, showed no linear association between learning style and hemispheric dominance. Pearson’s correlation was negligible and non-significant (r = –0.014, p = .898), and Spearman’s rank-order correlation likewise indicated no significant monotonic relationship (ρ = .032, p = .761). These results confirm that learning style does not correlate linearly or ordinally with hemispheric dominance in the sample (N = 91). D. Correlation between Gender, Learning Style and Cerebral Hemisphere To see if there is any correlation between gender and the cerebral hemisphere preference or between gender and the learning style we proceed to make the following analysis. 1. Gender - Cerebral Hemisphere Figure 8. Gender - Cerebral Hemispheric corelated tendencies The grouped bar chart (Figure 8) illustrates the distribution of hemispheric dominance across gender categories. Female participants showed a higher proportion of right-hemispheric dominance, followed by left dominance, with very few cases categorized as mixed and none classified “A Cognitive Preference Profiles in Engineering Students: A Cluster Analysis of Learning Tendencies and Cerebral Hemispheric Orientation” 1170 Pricopie-Filip Alina1, RAJAR Volume 11 Issue 12 December 2025 as integrated. In contrast, male participants demonstrated a different pattern, with mixed dominance being the most frequent, followed by left-hemispheric and right-hemispheric profiles. Integrated dominance appeared only among males and at very low frequency. Table 10. Gender/Cerebral Hemisphere Crosstabulation The cross-tabulation between gender and hemispheric dominance in Table 10 showed different distribution patterns for male and female students. Among female participants (n=13), right-hemispheric dominance was most frequent (46.2%), followed by left dominance (38.5%), while mixed dominance was less common (15.4%) and no cases showed integrated hemispheric processing. In contrast, male students (n=78) exhibited a different pattern, with mixed hemispheric dominance being the most prevalent (35.9%), followed by left dominance (34.6%), right dominance (26.9%), and a small proportion classified as integrated (2.6%). At the overall sample level, left (35.2%) and mixed (33.0%) dominance were most common, followed by right dominance (29.7%) and integrated dominance (2.2%). Table 11. Chi-Square Tests for Gender/Cerebral Hemisphere The chi-square test (Table 11) was performed to examine the association between gender and hemispheric dominance. The results indicated that the association was not statistically significant, χ²(3, N = 91) = 3.19, p = .363, suggesting that hemispheric dominance did not differ systematically between male and female students. Fisher’s Exact Test confirmed the non-significant result (p = .398). Therefore, although descriptive percentages suggested different dominance tendencies across genders, these differences were not statistically supported. Together, the chi-square and Fisher’s Exact tests demonstrate that gender does not significantly influence hemispheric dominance in this student population. 2. Gender – Learning Style The grouped bar chart (Figure 9) illustrates how learning style categories are distributed across genders. Female students showed the highest proportions of auditory and kinesthetic learning preferences, while visual learning was the least represented among them. In contrast, male students demonstrated a markedly different pattern, with visual learning being the most frequent style, followed by kinesthetic, auditory, and tactile preferences. These visual trends correspond with the numerical frequencies observed in the cross-tabulation. Figure 9. Gender – Learning Styles corelated tendencies Table 12. Gender/Learning Style Crosstabulation The cross-tabulation between gender and learning style revealed different distribution patterns for male and female participants (Table 12). Among female students (n = 13), auditory (30.8%) and kinesthetic (30.8%) learning styles were the most common, followed by tactile (23.1%) and visual (15.4%) preferences. In contrast, male students (n = 78) showed a markedly different distribution, with visual learning being the most prevalent (43.6%), followed by kinesthetic (24.4%), auditory (17.9%), and tactile (14.1%) styles. At the total sample level, visual learning was dominant (39.6%), followed by kinesthetic (25.3%), auditory (19.8%), and tactile (15.4%). A chi-square test was conducted to determine whether learning style distribution differed by gender. In Table 13 it can be observe that the association was not statistically “A Cognitive Preference Profiles in Engineering Students: A Cluster Analysis of Learning Tendencies and Cerebral Hemispheric Orientation” 1171 Pricopie-Filip Alina1, RAJAR Volume 11 Issue 12 December 2025 significant, χ²(3, N = 91) = 3.93, p = .269, indicating that learning style preferences did not vary in a systematic way between male and female students. Fisher’s Exact Test confirmed the non-significant result (p = .202). Thus, although descriptive percentages suggested different tendencies across genders, these differences were not statistically supported. Table 13. Chi-Square Tests for Gender/Learning Style 3. Correspondence Analysis between Learning Style and Cerebral Hemisphere dominance A Spearman rank-order correlation was computed to assess the relationship between cerebral hemispheric dominance and learning style. The analysis showed a very weak positive correlation that was not statistically significant, ρ(91) = .032, p = .761. These results indicate that learning style preference is not associated with hemispheric dominance in this sample. Table 14. Learning Style – Cerebral hemispheric correlation The association between learning style and hemispheric dominance was further examined using Cramer’s V, which indicated a small-to-moderate effect size (V = .235). The result did not reach statistical significance (p = .083), suggesting that although some associative tendency may be present, it is not strong enough to be considered statistically reliable within this sample (N = 91). Table 15. Learning Style – Cerebral hemispheric symmetric measures Table 16. Learning Style – Cerebral Hemisphere correspondence analysis Table 17. Summary of proportion inertia and confidence standard deviation Table 18. Overview Row Points for Learning Styles The overview of row points shows the contributions of each attribute in variable. In the overview row points for learning style (Table 18), visual and tactile learning style contribute the most to inertia of the first dimension. On the other side, tactile and kinesthetic learning style contribute the most to inertia of the second dimension. All the first dimensions make significant contributions to all row points Table 19. Overview Row Points for Cerebral Hemisphere In the overview row points for the cerebral hemisphere (Table 19), the right hemisphere contributes the most to the inertia of the first dimension and the mixed hemisphere contributed the most to the second dimension. The first dimensions are those who make significant contributions to all row points. “A Cognitive Preference Profiles in Engineering Students: A Cluster Analysis of Learning Tendencies and Cerebral Hemispheric Orientation” 1178 Pricopie-Filip Alina1, RAJAR Volume 11 Issue 12 December 2025 25. Linksman, Ricki. How to learn anything quickly. Barnes & Noble Books, 2001. 26. Beltran, K., Abonita, A., Brazal, A., Relente, J., Relente, J. M., & Urbana, J. (2025). Visual, Auditory, Read/Write and Kinesthetic (VARK) Model-Based Peer Teaching Approach. Educative: Jurnal Ilmiah Pendidikan, 3(1), 17–24. https://doi.org/10.70437/educative.v3i1.937