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Peer-reviewed

Research Article

Differentiating the learning styles of college students in different disciplines in a college English blended learning setting

Roles Conceptualization, Formal analysis, Funding acquisition, Methodology, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Linguistics, School of International Studies, Zhejiang University, Hangzhou City, Zhejiang Province, China, Center for College Foreign Language Teaching, Zhejiang University, Hangzhou City, Zhejiang Province, China, Institute of Asian Civilizations, Zhejiang University, Hangzhou City, Zhejiang Province, China

Roles Formal analysis, Project administration, Writing – review & editing

Affiliation Department of Linguistics, School of International Studies, Zhejiang University, Hangzhou City, Zhejiang Province, China

Roles Formal analysis, Writing – original draft

Roles Writing – review & editing

• Jie Hu, 
• Yi Peng, 
• Xueliang Chen, 

• Published: May 20, 2021
• https://doi.org/10.1371/journal.pone.0251545
• Peer Review
• Reader Comments

Learning styles are critical to educational psychology, especially when investigating various contextual factors that interact with individual learning styles. Drawing upon Biglan’s taxonomy of academic tribes, this study systematically analyzed the learning styles of 790 sophomores in a blended learning course with 46 specializations using a novel machine learning algorithm called the support vector machine (SVM). Moreover, an SVM-based recursive feature elimination (SVM-RFE) technique was integrated to identify the differential features among distinct disciplines. The findings of this study shed light on the optimal feature sets that collectively determined students’ discipline-specific learning styles in a college blended learning setting.

Citation: Hu J, Peng Y, Chen X, Yu H (2021) Differentiating the learning styles of college students in different disciplines in a college English blended learning setting. PLoS ONE 16(5): e0251545. https://doi.org/10.1371/journal.pone.0251545

Editor: Haoran Xie, Lingnan University, HONG KONG

Received: May 15, 2020; Accepted: April 29, 2021; Published: May 20, 2021

Copyright: © 2021 Hu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This research was supported by the Philosophical and Social Sciences Planning Project of Zhejiang Province in 2020 [grant number 20NDJC01Z] with the recipient Jie Hu, Second Batch of 2019 Industry-University Collaborative Education Project of Chinese Ministry of Education [grant number 201902016038] with the recipient Jie Hu, SUPERB College English Action Plan with the recipient Jie Hu, and the Fundamental Research Funds for the Central Universities of Zhejiang University with the recipient Jie Hu.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Research background.

Learning style, as an integral and vital part of a student’s learning process, has been constantly discussed in the field of education and pedagogy. Originally developed from the field of psychology, psychological classification, and cognitive research several decades ago [ 1 ], the term “learning style” is generally defined as the learner’s innate and individualized preference for ways of participation in learning practice [ 2 ]. Theoretically, learning style provides a window into students’ learning processes [ 3 , 4 ], predicts students’ learning outcomes [ 5 , 6 ], and plays a critical role in designing individualized instruction [ 7 ]. Knowing a student’s learning style and personalizing instruction to students’ learning style could enhance their satisfaction [ 8 ], improve their academic performance [ 9 ], and even reduce the time necessary to learn [ 10 ].

Researchers in recent years have explored students’ learning styles from various perspectives [ 11 – 13 ]. However, knowledge of the learning styles of students from different disciplines in blended learning environments is limited. In an effort to address this gap, this study aims to achieve two major objectives. First, it investigates how disciplinary background impacts students’ learning styles in a blended learning environment based on data collected in a compulsory college English course. Students across 46 disciplines were enrolled in this course, providing numerous disciplinary factor resources for investigating learning styles. Second, it introduces a novel machine learning method named the SVM to the field of education to identify an optimal set of factors that can simultaneously differentiate students of different academic disciplines. Based on data for students from 46 disciplines, this research delves into the effects of a massive quantity of variables related to students’ learning styles with the help of a powerful machine learning algorithm. Considering the convergence of a wide range of academic disciplines and the detection of latent interactions between a large number of variables, this study aims to provide a clear picture of the relationship between disciplinary factors and students’ learning styles in a blended learning setting.

Literature review

Theories of learning styles..

Learning style is broadly defined as the inherent preferences of individuals as to how they engage in the learning process [ 2 ], and the “cognitive, affective and physiological traits” of students have received special attention [ 14 ]. To date, there has been a proliferation of learning style definitions proposed to explain people’s learning preferences, each focusing on different aspects. Efforts to dissect learning style have been contested, with some highlighting the dynamic process of the learner’s interaction with the learning environment [ 14 ] and others underlining the individualized ways of information processing [ 15 ]. One vivid explication involved the metaphor of an onion, pointing out the multilayer nature of learning styles. It was proposed that the outermost layer of the learning style could change in accordance with the external environment, while the inner layer is relatively stable [ 16 , 17 ]. In addition, a strong concern in this field during the last three decades has led to a proliferation of models that are germane to learning styles, including the Kolb model [ 18 ], the Myers-Briggs Type Indicator model [ 19 ] and the Felder-Silverman learning style model (FSLSM) [ 20 ]. These learning style models have provided useful analytical lenses for analyzing students’ learning styles. The Kolb model focuses on learners’ thinking processes and identifies four types of learning, namely, diverging, assimilating, converging, and accommodating [ 18 ]. The Myers-Briggs Type Indicator model classifies learners into extraversion and introversion types, with the former preferring to learn from interpersonal communication and the latter inclining to benefit from personal experience [ 19 ]. As the most popular available model, the FSLSM identifies eight categories of learners according to the four dimensions of perception, input, processing and understanding [ 20 ]. In contrast to other learning style models that divided students into only a few groups, the FSLSM describes students’ learning styles in a more detailed manner. The four paired dimensions delicately distinguish students’ engagement in the learning process, providing a solid basis for a steady and reliable learning style analysis [ 21 ]. In addition, it has been argued that the FSLSM is the most appropriate model for a technology-enhanced learning environment because it involves important theories of cognitive learning behaviors [ 22 , 23 ]. Therefore, a large number of scholars have based their investigations of students’ learning styles in the e-learning/computer-aided learning environment on FSLSM [ 24 – 28 ].

Learning styles and FSLSM.

Different students receive, process, and respond to information with different learning styles. A theoretical model of learning style can be used to categorize people according to their idiosyncratic learning styles. In this study, the FSLSM was adopted as a theoretical framework to address the collective impacts of differences in students’ learning styles across different disciplines (see Fig 1 ).

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This model specifies the four dimensions of the construct of learning style: visual/verbal, sensing/intuitive, active/reflective, and sequential/global. These four dimensions correspond to four psychological processes: input, perception, processing, and understanding.

https://doi.org/10.1371/journal.pone.0251545.g001

The FSLSM includes learning styles scattered among four dimensions.

Visual learners process information best when it is presented as graphs, pictures, etc., while verbal learners prefer spoken cues and remember best what they hear. Sensory learners like working with facts, data, and experimentation, while intuitive learners prefer abstract principles and theories. Active learners like to try things and learn through experimentation, while reflective learners prefer to think things through before taking action. Sequential learners absorb knowledge in a linear fashion and make progress step by step, while global learners tend to grasp the big picture before filling in all the details.

Learning styles and academic disciplines.

Learning styles vary depending on a series of factors, including but not limited to age [ 29 ], gender [ 30 ], personality [ 2 , 31 ], learning environment [ 32 ] and learning experience [ 33 ]. In the higher education context, the academic discipline seems to be an important variable that influences students’ distinctive learning styles, which echoes a multitude of investigations [ 29 , 34 – 41 ]. One notable study explored the learning styles of students from 4 clusters of disciplines in an academic English language course and proposed that the academic discipline is a significant predictor of students’ learning styles, with students from the soft-pure, soft-applied, hard-pure and hard-applied disciplines each favoring different learning modes [ 42 ]. In particular, researchers used the Inventory of Learning Styles (ILS) questionnaire and found prominent disparities in learning styles between students from four different disciplinary backgrounds in the special educational field of vocational training [ 43 ]. These studies have found significant differences between the learning styles of students from different academic disciplines, thus supporting the concept that learning style could be domain dependent.

Learning styles in an online/blended learning environment.

Individuals’ learning styles reflect their adaptive orientation to learning and are not fixed personality traits. Consequently, learning styles can vary among diverse contexts, and related research in different contexts is vital to understanding learning styles in greater depth. Web-based technologies eliminate barriers of space and time and have become integrated in individuals’ daily lives and learning habits. Online and blended learning have begun to pervade virtually every aspect of the education landscape [ 40 ], and this warrants close attention. In addition to a series of studies that reflected upon the application of information and communication technology in the learning process [ 44 , 45 ], recent studies have found a mixed picture of whether students in a web-based/blended learning environment have a typical preference for learning.

Online learning makes it possible for students to set their goals and develop an individualized study plan, equipping them with more learning autonomy [ 46 ]. Generally, students with a more independent learning style, greater self-regulating behavior and stronger self-efficacy are found to be more successful in an online environment [ 47 ]. For now, researchers have made substantial contributions to the identification and prediction of learning styles in an online learning environment [ 27 , 48 – 51 ]. For instance, an inspiring study focused on the manifestation of college students’ learning styles in a purely computer-based learning environment to evaluate the different learning styles of web-learners in the online courses, indicating that students’ learning styles were significantly related to online participation [ 49 ]. Students’ learning styles in interactive E-learning have also been meticulously investigated, from which online tutorials have been found to be contributive to students’ academic performance regardless of their learning styles [ 51 ].

As a flexible learning method, blended courses have combined the advantages of both online learning and traditional teaching methods [ 52 ]. Researchers have investigated students’ learning styles within this context and have identified a series of prominent factors, including perceived satisfaction and technology acceptance [ 53 ], the dynamics of the online/face-to-face environment [ 54 ], and curriculum design [ 55 ]. Based on the Visual, Aural, Reading or Write and Kinesthetic model, a comprehensive study scrutinized the learning styles of K12 students in a blended learning environment, elucidating the effect of the relationship between personality, learning style and satisfaction on educational outcomes [ 56 ]. A recent study underscored the negative effects of kinesthetic learning style, whereas the positive effects of visual or auditory learning styles on students’ academic performance, were also marked in the context of blended learning [ 57 ].

Considering that academic disciplines and learning environment are generally regarded as essential predictors of students’ learning styles, some studies have also concentrated on the effects of academic discipline in a blended learning environment. Focusing on college students’ learning styles in a computer-based learning environment, an inspiring study evaluated the different learning styles of web learners, namely, visual, sensing, global and sequential learners, in online courses. According to the analysis, compared with students from other colleges, liberal arts students, are more susceptible to the uneasiness that may result from remote teaching because of their learning styles [ 11 ]. A similar effort was made with the help of the CMS tool usage logs and course evaluations to explore the learning styles of disciplinary quadrants in the online learning environment. The results indicated that there were noticeable differences in tool preferences between students from different domains [ 12 ]. In comparison, within the context of blended learning, a comprehensive study employed chi-square statistics on the basis of the Community of Inquiry (CoI) presences framework, arguing that soft-applied discipline learners in the blended learning environment prefer the kinesthetic learning style, while no correlations between the learning style of soft-pure and hard-pure discipline students and the CoI presences were identified. However, it is noted that students’ blended learning experience depends heavily on academic discipline, especially for students in hard-pure disciplines [ 13 ].

Research gaps and research questions

Overall, the research seems to be gaining traction, and new perspectives are continually introduced. The recent literature on learning styles mostly focuses on the exploration of the disciplinary effects on the variation in learning styles, and some of these studies were conducted within the blended environment. However, most of the studies focused only on several discrete disciplines or included only a small group of student samples [ 34 – 41 ]. Data in these studies were gathered through specialized courses such as academic English language [ 42 ] rather than the compulsory courses available to students from all disciplines. Even though certain investigations indeed boasted a large number of samples [ 49 ], the role of teaching was emphasized rather than students’ learning style. In addition, what is often overlooked is that a large number of variables related to learning styles could distinguish students from different academic disciplines in a blended learning environment, whereas a more comprehensive analysis that takes into consideration the effects of a great quantity of variables related to learning styles has remained absent. Therefore, one goal of the present study is to fill this gap and shed light on this topic.

Another issue addressed in this study is the selection of an optimal measurement that can effectively identify and differentiate individual learning styles [ 58 ]. The effective identification and differentiation of individual learning styles can not only help students develop greater awareness of their learning but also provide teachers with the necessary input to design tailor-made instructions in pedagogical practice. Currently, there are two general approaches to identify learning styles: a literature-based approach and a data-driven approach. The literature-based approach tends to borrow established rules from the existing literature, while the data-driven approach tends to construct statistical models using algorithms from fields such as machine learning, artificial intelligence, and data mining [ 59 ]. Research related to learning styles has been performed using predominantly traditional instruments, such as descriptive statistics, Spearman’s rank correlation, coefficient R [ 39 ], multivariate analysis of variance [ 56 ] and analysis of variance (ANOVA) [ 38 , 43 , 49 , 57 ]. Admittedly, these instruments have been applied and validated in numerous studies, in different disciplines, and across multiple timescales. Nevertheless, some of the studies using these statistical tools did not identify significant results [ 36 , 53 , 54 ] or reached only loose conclusions [ 60 ]; this might be because of the inability of these methods to probe into the synergistic effects of variables. However, the limited functions of comparison, correlation, prediction, etc. are being complemented by a new generation of technological innovations that promise more varied approaches to addressing social and scientific issues. Machine learning is one such approach that has received much attention both in academia and beyond. As a subset of artificial intelligence, machine learning deals with algorithms and statistical models on computer systems, performing tasks based on patterns and inference instead of explicit instruction. As such, it can deal with high volumes of data at the same time, perform tasks automatically and independently, and continuously improve its performance based on past experience [ 54 ]. Similar machine learning approaches have been proposed and tested by different scholars to identify students’ learning styles, with varying results regarding the classification of learning styles. For instance, a study that examined the precision levels of four computational intelligence approaches, i.e., artificial neural network, genetic algorithm, ant colony system and particle swarm optimization, found that the average precision of learning style differentiation ranged between 66% and 77% [ 61 ]. Another study that classified learning styles through SVM reported accuracy levels ranging from 53% to 84% [ 62 ]. A comparison of the prediction performance of SVM and artificial neural networks found that SVM has higher prediction accuracy than the latter [ 63 ]. This was further supported by another study, which yielded a similar result between SVM and the particle swarm optimization algorithm [ 64 ]. Moreover, when complemented by a genetic algorithm [ 65 ] and ant colony system [ 66 ], SVM has also shown improved results. These findings across different fields point to the reliability of SVM as an effective statistical tool for identification and differentiation analysis.

Therefore, a comprehensive investigation across the four general disciplines in Biglan’s taxonomy using a strong machine learning approach is needed. Given the existence of the research gaps discussed above, this exploratory study seeks to address the following questions:

• Can students’ learning styles be applied to differentiate various academic disciplines in the blended learning setting? If so, what are the differentiability levels among different academic disciplines based on students’ learning styles?
• What are the key features that can be selected to determine the collective impact on differentiation by a machine learning algorithm?
• What are the collective impacts of optimal feature sets?

Materials and methods

This study adopted a quantitative approach for the analysis. First, a modified and translated version of the original ILS questionnaire was administered to collect scores for students’ learning styles. Then, two alternate data analyses were performed separately. One analysis involved a traditional ANOVA, which tested the main effect of discipline on students’ learning styles in each ILS dimension. The other analysis involved the support vector machine (SVM) technique to test its performance in classifying students’ learning styles in the blended learning course among 46 specializations. Then, SVM-based recursive feature elimination (SVM-RFE) was employed to specify the impact of students’ disciplinary backgrounds on their learning styles in blended learning. By referencing the 44 questions (operationalized as features in this study) in the ILS questionnaire, SVM-RFE could rank these features based on their relative importance in differentiating different disciplines and identify the key features that collectively differentiate the students’ learning style. These steps are intended to not only identify students’ learning style differences but also explain such differences in relation to their academic disciplinary backgrounds.

Participants

The participants included 790 sophomores taking the blended English language course from 46 majors at Z University. Sophomore students were selected for this study for two reasons. First, sophomores are one of the only two groups of students (the other group being college freshmen) who take a compulsory English language course, namely, the College English language course. Second, of these two groups of students, sophomores have received academic discipline-related education, while their freshmen counterparts have not had disciplinary training during the first year of college. In the College English language course, online activities, representing 55% of the whole course, include e-course teaching designed by qualified course teachers or professors, courseware usage for online tutorials, forum discussion and essay writing, and two online quizzes. Offline activities, which represent 45% of the whole course, include role-playing, ice-breaker activities, group presentations, an oral examination, and a final examination. Therefore, the effects of the academic discipline on sophomores’ learning styles might be sufficiently salient to warrant a comparison in a blended learning setting [ 67 ]. Among the participants, 420 were male, and 370 were female. Most participants were aged 18 to 19 years and had taken English language courses for at least 6 years. Based on Biglan’s typology of disciplinary fields, the students’ specializations were classified into the four broad disciplines of hard-applied (HA, 289/37.00%), hard-pure (HP, 150/19.00%), soft-applied (SA, 162/20.00%), and soft-pure (SP, 189/24.00%).

Biglan’s classification scheme of academic disciplines (hard (H) vs. soft (S) disciplines and pure (P) vs. applied (A) disciplines) has been credited as the most cited organizational system of academic disciplines in tertiary education [ 68 – 70 ]. Many studies have also provided evidence supporting the validity of this classification [ 69 ]. Over the years, research has indicated that Biglan’s typology is correlated with differences in many other properties and serves as an appropriate mechanism to organize discipline-specific knowledge or epistemologies [ 38 ] and design and deliver courses for students with different learning style preferences [ 41 ]. Therefore, this classification provides a convenient framework to explore differences across disciplinary boundaries. In general, HA disciplines include engineering, HP disciplines include the so-called natural sciences, SA disciplines include the social sciences, and SP disciplines include the humanities [ 41 , 68 , 71 ].

In learning style research, it is difficult to select an instrument to measure the subjects’ learning styles [ 72 ]. The criteria used for the selection of a learning style instrument in this study include the following: 1) successful use of the instrument in previous studies, 2) demonstrated validity and reliability, 3) a match between the purpose of the instrument and the aim of this study and 4) open access to the questionnaire.

The Felder and Soloman’s ILS questionnaire, which was built based on the FSLSM, was adopted in the present study to investigate students’ learning styles across different disciplines. First, the FSLSM is recognized as the most commonly used model for measuring individual learning styles on a general scale [ 73 ] in higher education [ 74 ] and has remained popular for many years across different disciplines in university settings and beyond. In the age of personalized instruction, this model has breathed new life into areas such as blended learning [ 75 ], online distance learning [ 76 ], courseware design [ 56 ], and intelligent tutoring systems [ 77 , 78 ]. Second, the FSLSM is based on previous learning style models; the FSLSM integrates all their advantages and is, thus, more comprehensive in delineating students’ learning styles [ 79 , 80 ]. Third, the FSLSM has a good predictive ability with independent testing sets (i.e., unknown learning style objects) [ 17 ], which has been repeatedly proven to be a more accurate, reliable, and valid model than most other models for predicting students’ learning performance [ 10 , 80 ]. Fourth, the ILS is a free instrument that can be openly accessed online (URL: https://www.webtools.ncsu.edu/learningstyles/ ) and has been widely used in the research context [ 81 , 82 ].

The modified and translated version of the original ILS questionnaire includes 44 questions in total, and 11 questions correspond to each dimension of the Felder-Silverman model as follows: questions 1–11 correspond to dimension 1 (active vs. reflective), questions 12–22 correspond to dimension 2 (sensing vs. intuitive), questions 23–33 correspond to dimension 3 (visual vs. verbal), and questions correspond 34–44 to dimension 4 (sequential vs. global). Each question is followed by five choices on a five-point Likert scale ranging from “strongly agree with A (1)”, “agree with A (2)”, “neutral (3)”, “agree with B (4)” and “strongly agree with B (5)”. Option A and option B represent the two choices offered in the original ILS questionnaire.

Ethics statements

The free questionnaires were administered in a single session by specialized staff who collaborated on the investigation. The participants completed all questionnaires individually. The study procedures were in accordance with the ethical standards of the Helsinki Declaration and were approved by the Ethics Committee of the School of International Studies, Zhejiang University. All participants signed written informed consent to authorize their participation in this research. After completion of the informed consent form, each participant was provided a gift (a pen) in gratitude for their contribution and participation.

Data collection procedure

Before the questionnaires were distributed, the researchers involved in this study contacted faculty members from various departments and requested their help. After permission was given, the printed questionnaires were administered to students under the supervision of their teachers at the end of their English language course. The students were informed of the purpose and importance of the study and asked to carefully complete the questionnaires. The students were also assured that their personal information would be used for research purposes only. All students provided written informed consent (see S2 File ). After the questionnaires were completed and returned, they were thoroughly examined by the researchers such that problematic questionnaires could be identified and excluded from further analysis. All questionnaires eligible for the data analysis had to meet the following two standards: first, all questions must be answered, and second, the answered questions must reflect a reasonable logic. Regarding the few missing values, the median number of a given individual’s responses on 11 questions per dimension included in the ILS questionnaire was used to fill the void in each case. In statistics, using the median number to impute missing values is common and acceptable because missing values represent only a small minority of the entire dataset and are assumed to not have a large impact on the final results [ 83 , 84 ].

In total, 850 questionnaires were administered to the students, and 823 of these questionnaires were retrieved. Of the retrieved questionnaires, the remaining 790 questionnaires were identified as appropriate for further use. After data screening, these questionnaires were organized, and their respective results were translated into an Excel format.

Data analysis method

During the data analysis, as a library of the SVM, the free package LIBSVM ( https://www.csie.ntu.edu.tw/~cjlin/libsvm/ ) was first applied as an alternative method of data analysis. Then, a traditional ANOVA was performed to examine whether there was a main effect of academic discipline on Chinese students’ learning styles. ANOVA could be performed using SPSS, a strong data analysis software that supports a series of statistical analyses. In regard to the examination of the effect of a single or few independent variables, SPSS ANOVA can produce satisfactory results. However, SVM, a classic data mining algorithm, outperforms ANOVA for dataset in which a large number of variables with multidimensions are intertwined and their combined/collective effects influence the classification results. In this study, the research objective was to efficiently differentiate and detect the key features among the 44 factors. Alone, a single factor or few factors might not be significant enough to discriminate the learning styles among the different disciplines. Selected by the SVM, the effects of multiple features may collectively enhance the classification performance. Therefore, the reason for selecting SVM over ANOVA is that in the latter case, the responses on all questions in a single dimension are summed instead of treated as individual scores; thus, the by-item variation is concealed. In addition, the SVM is especially suitable for statistical analysis with high-dimensional factors (usually > 10; 44-dimensional factors were included in this study) and can detect the effects collectively imposed by a feature set [ 85 ].

Originally proposed in 1992 [ 86 ], the SVM is a supervised learning model related to machine learning algorithms that can be used for classification, data analysis, pattern recognition, and regression analysis. The SVM is an efficient classification model that optimally divides data into two categories and is ranked among the top methods in statistical theory due to its originality and practicality [ 85 ]. Due to its robustness, accurate classification, and prediction performance [ 87 – 89 ], the SVM has high reproducibility [ 90 , 91 ]. Due to the lack of visualization of the computing process of the SVM, the SVM has been described as a “black box” method [ 92 ]; however, future studies in the emerging field of explainable artificial intelligence can help solve this problem and convert this approach to a “glass box” method [ 67 ]. This algorithm has proven to have a solid theoretical foundation and excellent empirical application in the social sciences, including education [ 93 ] and natural language processing [ 94 ]. The mechanism underlying the SVM is also presented in Fig 2 .

Hyperplanes 1 and 2 are two regression lines that divide the data into two groups. Hyperplane 1 is considered the best fitting line because it maximizes the distance between the two groups.

https://doi.org/10.1371/journal.pone.0251545.g002

The SVM contains the following two modules: one module is a general-purpose machine learning method, and the other module is a domain-specific kernel function. The SVM training algorithm is used to build a training model that is then used to predict the category to which a new sample instance belongs [ 95 ]. When a set of training samples is given, each sample is given the label of one of two categories. To evaluate the performance of SVM models, a confusion matrix, which is a table describing the performance of a classifier on a set of test data for which the true values are known, is used (see Table 1 ).

https://doi.org/10.1371/journal.pone.0251545.t001

ACC represents the proportion of true results, including both positive and negative results, in the selected population;

SPE represents the proportion of actual negatives that are correctly identified as such;

SEN represents the proportion of actual positives that are correctly identified as such;

AUC is a ranking-based measure of classification performance that can distinguish a randomly chosen positive example from a randomly chosen negative example; and

F-measure is the harmonic mean of precision (another performance indicator) and recall.

The ACC is a good metric frequently applied to indicate the measurement of classification performance, but the combination of the SPE, SEN, AUC, F-measure and ACC may be a measure of enhanced performance assessment and was frequently applied in current studies [ 96 ]. In particular, the AUC is a good metric frequently applied to validate the measurement of the general performance of models [ 97 ]. The advantage of this measure is that it is invariant to relative class distributions and class-specific error costs [ 98 , 99 ]. Moreover, to some extent, the AUC is statistically consistent and more discriminating than the ACC with balanced and imbalanced real-world data sets [ 100 ], which is especially suitable for unequal samples, such as the HA-HP model in this study. After all data preparations were completed, the data used for the comparisons were extracted separately. First, the processed data of the training set were run by using optimized parameters. Second, the constructed model was used to predict the test set, and the five indicators of the fivefold cross-validation and fivefold average were obtained. Cross-validation is a general validation procedure used to assess how well the results of a statistical analysis generalize to an independent data set, which is used to evaluate the stability of the statistical model. K-fold cross-validation is commonly used to search for the best hyperparameters of SVM to achieve the highest accuracy performance [ 101 ]. In particular, fivefold, tenfold, and leave-one-out cross-validation are typically used versions of k-fold cross-validation [ 102 , 103 ]. Fivefold cross-validation was selected because fivefold validation can generally achieve a good prediction performance [ 103 , 104 ] and has been commonly used as a popular rule of thumb supported by empirical evidence [ 105 ]. In this study, five folds (groups) of subsets were randomly divided from the entire set by the SVM, and four folds (training sample) of these subsets were randomly selected to develop a prediction model, while the remaining one fold (test sample) was used for validation. The above functions were all implemented with Python Programming Language version 3.7.0 (URL: https://www.python.org/ ).

Then, SVM-RFE, which is an embedded feature selection strategy that was first applied to identify differentially expressed genes between patients and healthy individuals [ 106 ], was adopted. SVM-RFE has proven to be more robust to data overfitting than other feature selection techniques and has shown its power in many fields [ 107 ]. This approach works by removing one feature each time with the smallest weight iteratively to a feature rank until a group of highly weighted features were selected. After this feature selection procedure, several SVM models were again constructed based on these selected features. The performance of the new models is compared to that of the original models with all features included. The experimental process is provided in Fig 3 for the ease of reference.

https://doi.org/10.1371/journal.pone.0251545.g003

The classification results produced by SVM and the ranking of the top 20 features produced by SVM-RFE were listed in Table 2 . Twenty variables have been selected in this study for two reasons: a data-based reason and a literature-based reason. First, it is clear that models composed of 20 features generally have a better performance than the original models. The performance of models with more than 20 is negatively influenced. Second, SVM-based studies in the social sciences have identified 20 to 30 features as a good number for an optimal feature set [ 108 ], and 20 features were selected for inclusion in the optimal feature set [ 95 ]. Therefore, in this study, the top 20 features were selected for subsequent analysis, as proposed in previous analyses that yielded accepted measurement rates. These 20 features retained most of the useful information from all 44 factors but with fewer feature numbers, which showed satisfactory representation [ 96 ].

https://doi.org/10.1371/journal.pone.0251545.t002

Results of RQ (1) What are the differentiability levels among different academic disciplines based on students’ learning styles?

To further measure the performance of the differentiability among students’ disciplines, the collected data were examined with the SVM algorithm. As shown in Table 2 , the five performance indicators, namely, the ACC, SPE, SEN, AUC and F-measure, were utilized to measure the SVM models. Regarding the two general performance indicators, i.e., the ACC value and AUC value, the HA-HP, HA-SA, and HA-SP-based models yielded a classification capacity of approximately 70.00%, indicating that the students in these disciplines showed a relatively large difference. In contrast, the models based on the H-S, A-P, HP-SA, HP-SP, and SA-SP disciplines only showed a moderate classification capacity (above 55.00%). This finding suggests that these five SVM models were not as effective as the other three models in differentiating students among these disciplines based on their learning styles. The highest ACC and AUC values were obtained in the model based on the HA-HP disciplines, while the lowest values were obtained in the model based on the HP-SA disciplines. As shown in Table 2 , the AUCs of the different models ranged from 57.76% (HP-SA) to 73.97% (HA-HP).

To compare the results of the SVM model with another statistical analysis, an ANOVA was applied. Prior to the main analysis, the students’ responses in each ILS dimension were summed to obtain a composite score. All assumptions of ANOVA were checked, and no serious violations were observed. Then, an ANOVA was performed with academic discipline as the independent variable and the students’ learning styles as the dependent variable. The results of the ANOVA showed that there was no statistically significant difference in the group means of the students’ learning styles in Dimension 1, F(3, 786) = 2.56, p = .054, Dimension 2, F(3, 786) = 0.422, p = .74, or Dimension 3, F(3, 786) = 0.90, p = .443. However, in Dimension 4, a statistically significant difference was found in the group means of the students’ learning styles, F (3, 786) = 0.90, p = .005. As the samples in the four groups were unbalanced, post hoc comparisons using Scheffé’s method were performed, demonstrating that the means of the students’ learning styles significantly differed only between the HA (M = 31.04, SD = 4.986) and SP (M = 29.55, SD = 5.492) disciplines, 95.00% CI for MD [0.19, 2.78], p = .016, whereas the other disciplinary models showed no significant differences. When compared with the results obtained from the SVM models, the three models (HA-HP, HA-SA, and HA-SP models) presented satisfactory differentiability capability of approximately 70.00% based on the five indicators.

In the case of a significant result, it was difficult to determine which questions were representative of the significant difference. With a nonsignificant result, it was possible that certain questions might be relevant in differentiating the participants. However, this problem was circumvented in the SVM, where each individual question was treated as a variable and a value was assigned to indicate its relative importance in the questionnaire. Using SVM also circumvented the inherent problems with traditional significance testing, especially the reliance on p-values, which might become biased in the case of multiple comparisons [ 109 ].

Results of RQ (2) What are the key features that can be selected to determine the collective impact on differentiation by a machine learning algorithm?

To examine whether the model performance improved as a result of this feature selection procedure, the 20 selected features were submitted to another round of SVM analysis. The same five performance indicators were used to measure the model performance (see Table 2 ). By comparing the performance of the SVM model and that of the SVM-RFE model presented in Table 2 , except for the HA-SP model, all other models presented a similar or improved performance after the feature selection process. In particular, the improvement in the HA-HP and HP-SA models was quite remarkable. For instance, in the HA-HP model, the ACC value increased from 69.32% in the SVM model to 82.59% in the SVM-RFE model, and the AUC score substantially increased from 73.97% in the SVM model to 89.13% in the SVM-RFE model. This finding suggests that the feature selection process refined the model’s classification accuracy and that the 20 features selected, out of all 44 factors, carry substantive information that might be informative for exploring disciplinary differences. Although results for the indicators of the 20 selected features were not very high, all five indicators above 65.00% showed that the model was still representative because only 20 of 44 factors could present the classification capability. Considering that there was a significant reduction in the number of questions used for the model construction in SVM-RFE (compared with those used for the SVM model), the newly identified top 20 features by SVM-RFE were effective enough to preserve the differential ability of all 44 questions. Thus, these newly identified top 20 factors could be recognized as key differential features for distinguishing two distinct disciplines.

To identify these top 20 features in eight models (see Table 2 ), SVM-RFE was applied to rank order all 44 features contained in the ILS questionnaire. To facilitate a detailed understanding of what these features represent, the questions related to the top 20 features in the HA-HP model are listed in Table 3 for ease of reference.

https://doi.org/10.1371/journal.pone.0251545.t003

Results of RQ (3) What are the collective impacts of optimal feature sets?

The collective impacts of optimal feature sets could be interpreted from four aspects, namely, the complexities of students’ learning styles, the appropriate choice of SVM, the ranking of SVM-RFE and multiple detailed comparisons between students from different disciplines. First, the FSLSM considers the fact that students’ learning styles are shaped by a series of factors during the growth process, which intertwine and interact with each other. Considering the complex dynamics of the learning style, selecting an approach that could detect the combined effects of a group of variables is needed. Second, recent years have witnessed the emergence of data mining approaches to explore students learning styles [ 28 , 48 – 50 , 110 ]. Specifically, as one of the top machine learning algorithms, the SVM excels in identifying the combined effects of high-order factors [ 87 ]. In this study, the SVM has proven to perform well in classifying students’ learning styles across different disciplines, with every indicator being acceptable. Third, the combination of SVM with RFE could enable the simultaneous discovery of multiple features that collectively determine classification. Notably, although SVM-FRE could rank the importance of the features, they should be regarded as an entire optimal feature set. In other words, the combination of these 20 features, rather than a single factor, could differentiate students’ learning styles across different academic disciplines. Last but not least, the multiple comparisons between different SVM models of discipline provide the most effective learning style factors, giving researchers clues to the nuanced differences between students’ learning styles. It can be seen that students from different academic disciplines understand, see and reflect things from individualized perspectives. The 20 most effective factors for all models scattered within 1 to 44, verifying students’ different learning styles in 4 dimensions. Therefore, the FSLSM provides a useful and effective tool for evaluating students’ learning styles from a rather comprehensive point of view.

The following discussions address the three research questions explored in the current study.

Levels of differentiability among various academic disciplines based on students’ learning styles with SVM

The results suggest that SVM is an effective approach for classification in the blended learning context in which students with diverse disciplinary backgrounds can be distinguished from each other according to their learning styles. All performance indicators presented in Tables 2 and 3 remain above the baseline of 50.00%, suggesting that between each two disciplines, students’ learning style differences can be identified. To some extent, these differences can be identified with a relatively satisfactory classification capability (e.g., 69.32% of the ACC and 73.97% of the AUC in the HA-HP model shown in Table 2 ). Further support for the SVM algorithm is obtained from the SVM-RFE constructed to assess the rank of the factors’ classification capacity, and all values also remained above the baseline value, while some values reached a relatively high classification capability (e.g., 82.59% of the ACC and 89.13% of the AUC in the HA-HP model shown in Table 2 ). While the results obtained mostly show a moderate ACC and AUC, they still provide some validity evidence supporting the role of SVM as an effective binary classifier in the educational context. However, while these differences are noteworthy, the similarities among students in different disciplines also deserve attention. The results reported above indicate that in some disciplines, the classification capacity is not relatively high; this was the case for the model based on the SA-SP disciplines.

Regarding low differentiability, one explanation might be the indistinct classification of some emerging “soft disciplines.” It was noted that psychology, for example, could be identified as “a discipline that can be considered predominantly ‘soft’ and slightly ‘purer’ than ‘applied’ in nature” [ 111 ] (p. 43–53), which could have blurred the line between the SA and SP disciplines. As there is now no impassable gulf separating the SA and SP disciplines, their disciplinary differences may have diminished in the common practice of lecturing in classrooms. Another reason comes from the different cultivation models of “soft disciplines” and “hard disciplines” for sample students. In their high school, sample students are generally divided into liberal art students and science students and are then trained in different environments of knowledge impartation. The two-year unrelenting and intensive training makes it possible for liberal art students to develop a similar thinking and cognitive pattern that is persistent. After the college entrance examination, most liberal art students select SA or SP majors. However, a year or more of study in university does not exert strong effects on their learning styles, which explains why a multitude of researchers have traditionally investigated the SA and SP disciplines together, calling them simply “social science” or “soft disciplines” compared with “natural science” or “hard disciplines”. There have been numerous contributions pointing out similarities in the learning styles of students from “soft disciplines” [ 37 , 112 – 114 ]. However, students majoring in natural science exhibit considerable differences in learning styles, demonstrating that the talent cultivation model of “hard disciplines” in universities is to some extent more influential on students’ learning styles than that of the “soft disciplines”. Further compelling interpretations of this phenomenon await only the development of a sufficient level of accumulated knowledge among scholars in this area.

In general, these results are consistent with those reported in many previous studies based on the Felder-Silverman model. These studies tested the precision of different computational approaches in identifying and differentiating the learning styles of students. For example, by means of a Bayesian network (BN), an investigation obtained an overall precision of 58.00% in the active/reflective dimension, 77.00% in the sensing/intuitive dimension and 63.00% in the sequential/global dimension (the visual/verbal dimension was not considered) [ 81 ]. With the help of the keyword attributes of learning objects selected by students, a precision of 70.00% in the active/reflective dimension, 73.30% in the sensing/intuitive dimension, 73.30% in the sequential/global dimension and 53.30% in the visual/verbal dimension was obtained [ 115 ].

These results add to a growing body of evidence expanding the scope of the application of the SVM algorithm. Currently, the applications of the SVM algorithm still reside largely in engineering or other hard disciplines despite some tentative trials in the humanities and social sciences [ 26 ]. In addition, as cross-disciplines increase in current higher education, it is essential to match the tailored learning styles of students and researchers studying interdisciplinary subjects, such as the HA, HP, SA and SP disciplines. Therefore, the current study is the first to incorporate such a machine learning algorithm into interdisciplinary blended learning and has broader relevance to further learning style-related theoretical or empirical investigations.

Verification of the features included in the optimal feature sets

Features included in the optimal feature sets provided mixed findings compared with previous studies. Some of the 20 identified features are verified and consistent with previous studies. A close examination of the individual questions included in the feature sets can offer some useful insights into the underlying psychological processes. For example, in six of the eight models constructed, Question 1 (“I understand something better after I try it out/think it through”) appears as the feature with the number 1 ranking, highlighting the great importance attached to this question. This question mainly reflects the dichotomy between experimentation and introspection. A possible revelation is that students across disciplines dramatically differ in how they process tasks, with the possible exception of the SA-SP disciplines. This difference has been supported by many previous studies. For example, it was found that technical students tended to be more tactile than those in the social sciences [ 116 ], and engineering students (known as HA in this study) were more inclined toward concrete and pragmatic learning styles [ 117 ]. Similarly, it was explored that engineering students prefer “a logical learning style over visual, verbal, aural, physical or solitary learning styles” [ 37 ] (p. 122), while social sciences (known as SA in this study) students prefer a social learning style to a logical learning style. Although these studies differ in their focus to a certain degree, they provide an approximate idea of the potential differences among students in their relative disciplines. In general, students in the applied disciplines show a tendency to experiment with tasks, while those in the pure disciplines are more inclined towards introspective practices, such as an obsession with theories. For instance, in Biglan’s taxonomy of academic disciplines, students in HP disciplines prefer abstract rules and theories, while students in SA disciplines favor application [ 67 ]. Additionally, Question 10 (“I find it easier to learn facts/to learn concepts”) is similar to Question 1, as both questions indicate a certain level of abstraction or concreteness. The difference between facts and concepts is closely related to the classification difference between declarative knowledge and procedural knowledge in cognitive psychology [ 35 , 38 ]. Declarative knowledge is static and similar to facts, while procedural knowledge is more dynamic and primarily concerned with operational steps. Students’ preferences for facts or concepts closely correspond to this psychological distinction.

In addition, Questions 2, 4, 7, and 9 also occur frequently in the 20 features selected for the different models. Question 2 (“I would rather be considered realistic/innovative”) concerns taking chances. This question reflects a difference in perspective, i.e., whether the focus should be on obtaining pragmatic results or seeking original solutions. This difference cannot be easily connected to the disciplinary factor. Instead, there are numerous factors, e.g., genetic, social and psychological factors, that may play a strong role in defining this trait. The academic discipline only serves to strengthen or diminish this difference. For instance, decades of research in psychology have shown that males are more inclined towards risk taking than females [ 118 – 121 ]. A careful examination of the current academic landscape reveals a gender difference; more females choose soft disciplines than males, and more males choose hard disciplines than females. This situation builds a disciplinary wall classifying students into specific categories, potentially strengthening the disciplinary effect. For example, Question 9 (“In a study group working on difficult material, I am more likely to jump in and contribute ideas/sit back and listen”) emphasizes the distinction between active participation and introspective thinking, reflecting an underlying psychological propensity in blended learning. Within this context, the significance of this question could also be explained by the psychological evaluation of “loss and gain”, as students’ different learning styles are associated with expected reward values and their internal motivational drives, which are determined by their personality traits [ 122 ]. When faced with the risk of “losing face”, whether students will express their ideas in front of a group of people depends largely on their risk and stress management capabilities and the presence of an appropriate motivation system.

The other two questions also convey similar messages regarding personality differences. Question 4 concerns how individuals perceive the world, while Question 7 concerns the preferred modality of information processing. Evidence of disciplinary differences in these respects was also reported [ 35 , 123 – 125 ]. The other questions, such as Questions 21, 27, and 39, show different aspects of potential personality differences and are mostly consistent with the previous discussion. This might also be a vivid reflection of the multi-faceted effects of blended learning, which may differ in their consonance with the features of each discipline. First, teachers from different domains use technology in different ways, and student from different disciplines may view blended learning differently. For instance, the characteristics of soft-applied fields entail specialized customization in blended courses, further broadening the gulf between different subjects [ 126 ]. Second, although blended learning is generally recognized as a stimulus to students’ innovation [ 127 ], some students who are used to an instructivist approach in which the educator acts as a ‘sage on the stage’ will find it difficult to adapt to a social constructivist approach in which the educator serves as a ‘guide on the side’ [ 128 ]. This difficulty might not only negatively affect students’ academic performance but also latently magnify the effects of different academic disciplines.

Interpretation of the collective impact of optimal feature sets

In each SVM model based on a two-discipline model, the 20 key features (collectively known as an optimal feature set) selected exert a concerted effect on students’ learning styles across different disciplines (see Table 2 ). A broad examination of the distribution of collective impact of each feature set with 20 features in the eight discipline models suggests that it is especially imperative considering the emerging cross-disciplines in academia. Current higher education often involves courses with crossed disciplines and students with diverse disciplinary backgrounds. In addition, with the rise of technology-enhanced learning, the design of personalized tutoring systems requires more nuanced information related to student attributes to provide greater adaptability [ 59 ]. By identifying these optimal feature sets, such information becomes accessible. Therefore, understanding such interdisciplinary factors and designing tailor-made instructions are essential for promoting learning success [ 9 ]. For example, in an English language classroom in which the students are a blend of HP and SP disciplines, instructors might consider integrating a guiding framework at the beginning of the course and stepwise guidelines during the process such that the needs of both groups are met. With the knowledge that visual style is dominant across disciplines, instructors might include more graphic presentations (e.g., Question 11) in language classrooms rather than continue to use slides or boards filled with words. Furthermore, to achieve effective communication with students and deliver effective teaching, instructors may target these students’ combined learning styles. While some methods are already practiced in real life, this study acts as a further reminder of the rationale underlying these practices and thus increases the confidence of both learners and teachers regarding these practices. Therefore, the practical implications of this study mainly concern classroom teachers and educational researchers, who may draw some inspiration for interdisciplinary curriculum design and the tailored application of learning styles to the instructional process.

Conclusions

This study investigated learning style differences among students with diverse disciplinary backgrounds in a blended English language course based on the Felder-Silverman model. By introducing a novel machine learning algorithm, namely, SVM, for the data analysis, the following conclusions can be reached. First, the multiple performance indicators used in this study confirm that it is feasible to apply learning styles to differentiate various disciplines in students’ blended learning processes. These disciplinary differences impact how students engage in their blended learning activities and affect students’ ultimate blended learning success. Second, some questions in the ILS questionnaire carry more substantive information about students’ learning styles than other questions, and certain underlying psychological processes can be derived. These psychological processes reflect students’ discipline-specific epistemologies and represent the possible interaction between the disciplinary background and learning style. In addition, the introduction of SVM in this study can provide inspiration for future studies of a similar type along with the theoretical significance of the above findings.

Despite the notable findings of this study, it is subject to some limitations that may be perfected in further research. First, the current analysis examined the learning styles without allowing for the effects of other personal or contextual factors. The educational productivity model proposed by Walberg underlines the significance of the collected influence of contextual factors on individuals’ learning [ 129 ]. For example, teachers from different backgrounds and academic disciplines are inclined to select various teaching methods and to create divergent learning environments [ 130 ], which should also be investigated thoroughly. The next step is therefore to take into account the effects of educational background, experience, personality and learning experience to gain a more comprehensive understanding of students’ learning process in the blended setting.

In conclusion, the findings of this research validate previous findings and offer new perspectives on students’ learning styles in a blended learning environment, which provides future implications for educational researchers, policy makers and educational practitioners (i.e., teachers and students). For educational researchers, this study not only highlights the merits of using machine learning algorithms to explore students’ learning styles but also provides valuable information on the delicate interactions between blended learning, academic disciplines and learning styles. For policy makers, this analysis provides evidence for a more inclusive but personalized educational policy. For instance, in addition to learning styles, the linkage among students’ education in different phases should be considered. For educational practitioners, this study plays a positive role in promoting student-centered and tailor-made teaching. The findings of this study can help learners of different disciplines develop a more profound understanding of their blended learning tendencies and assist teachers in determining how to bring students’ learning styles into full play pedagogically, especially in interdisciplinary courses [ 131 – 134 ].

Supporting information

https://doi.org/10.1371/journal.pone.0251545.s001

S2 File. Informed consent for participants.

https://doi.org/10.1371/journal.pone.0251545.s002

S1 Dataset.

https://doi.org/10.1371/journal.pone.0251545.s003

Acknowledgments

The authors would like to thank the anonymous reviewers for their constructive comments on this paper and Miss Ying Zhou for her suggestions during the revision on this paper.

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94 Learning Styles Essay Topics

🏆 best essay topics on learning styles, 👍 good learning styles research topics & essay examples, 🎓 most interesting learning styles research titles, 💡 simple learning styles essay ideas, ❓ research questions about learning styles.

• Learning and Teaching Styles
• Teaching Methodologies for Various Learning Styles
• The VARK Questionnaire: Learning Styles
• Auditory Learning Style, Its Features and Application
• Personal Learning Style According to Kolb’s Theory
• Visual Learning Style and Improvement Strategy
• Multimodal Learning Style and Strategies
• Vark Learning Style Assessment Analysis In this paper, the VARK assessment results show that my preferred learning style is the multimodal style with strong emphasis on read/write, kinesthetic, and aural learning styles.
• Visual, Aural, Read/Write, Kinesthetic Learning Styles The VARK analysis indicates that the most prominent aspects of the learning method are lists, notes, and texts that provide descriptions and other learning aspects.
• Learning Style and Reading Achievement Learning styles are ways of learning which involve methods of educating that are specific to individuals thought to allow individuals to learn best.
• Learning Styles Analysis in Nursing This essay analysis various learning styles, which are adopted by nurse students. The essay uses the Kolb’s learning model to classify learning styles.
• VARK: A Guide to Learning Styles The VARK guide recommends that people with reading/writing learning preferences form a habit of making learnable packages.
• Learning Styles and Leadership in Self-Reflection The research on personal learning styles was conducted using the Belbin questionnaire technique and Honey and Mumford learning styles questionnaire.
• The VARK Questionnaire and Learning Styles People who use read/write and kinesthetic style prefer the printed word as a source of information, as well as pictures/personal experience to complete tasks.
• Styles in the Scholarship of Teaching and Learning Huber concludes this essay by saying that the practice of the scholarship of teaching and learning is increasingly being appreciated.
• Learning Styles and Approaches Among Medical Education Participants According to the VARK questionnaire, my learning style is mild kinesthetic. In general, this style relies on practical information and real-world phenomena.
• Personal Learning Style and Recommended Strategies The peculiarities of the learning style precondition the choice of learning strategies and methods to understand information.
• Learning Style: How to Choose and How to Use? This paper will discuss the different learning style dimensions, how to identify the best learning style, and explore strategies for working with one’s preferred learning style.
• Visual Learning Style and Its Application According to the test results, I am a visual learner. Visual learners are distracted by sounds and people around them, and I can say it is true about my learning style.
• Learning Styles for Better Academic Performance Learning style is a set of unique and specific approaches employed by individuals during the process of studying. Using a learning style correlates with school performance.
• Which Style of Teaching and Learning Do PMU Students Prefer Familiarity with particular predispositions towards specific types of classes is necessary for achieving proper student engagement and efficiency.
• Multiple Learning Styles and Strategies This analysis has underscored the importance of knowing the preferred learning style and associated strategies. The analysis has also demonstrated that students can have multiple learning styles.
• Kinesthetic Learning Preference and Other Styles This paper presents the assessment of results acquired after completing the VARK questionnaire that identified a person’s preferred learning techniques.
• Learning Style and Read/Write Study Strategies The preferred learning style is determined to be mostly in the area of reading and writing. Points and determinants are related to visual, aural and kinesthetic.
• Health Literacy: Diverse Needs and Learning Styles This paper identifies individual learning styles and the unique learning needs of diverse learners integrating health literacy considerations into health assessment.
• Multimodal Learning Preference and Other Styles This paper presents the analysis of the VARK Questionnaire which allows to identify preferred learning strategies and empower the learning process.
• Individual Learning Styles for Training Programs The learning styles have a direct impact on education and training programs because this consideration is important for developing different teaching and learning techniques.
• Cognitive Styles and Learning Strategies The process of learning is not a simple summation of the individual feelings or the result of the simple association of the features. This process is different for each person.
• Visual, Aural, Read, Kinaesthetic Learning Styles Considering the results of the test using VARK questionnaire, the paper is aimed at summarizing the author’s learning style, specifying the preferred learning strategies.
• The Learning Styles and Personalities of Biomedical Engineering Students
• Learning Styles and Effective Study Habits
• Kolb’s Learning Styles and Experiential Learning Cycle Assignment
• Teaching Students With Different Learning Styles on a Virtual Platform?
• Brain Dominance and Various Learning Styles
• Relationship Between Learning Styles Gender and Mathematics Scores
• Skills and Learning Styles of Innovative Companies’ Employees
• Learning Styles and Self-regulated Learning for Sciences
• Erroneous Learning Styles Educational Approach
• Adult Learning Styles and Curriculum Development
• Assessment Between Learning Styles and Overall Academic Achievement
• Different Learning Styles Make Better Students
• Child Learning Styles and Factors That Effect Them
• Learning Styles and Different Levels of Understanding
• Vark Learning Styles: Read/Write Learning Style
• Learning Styles and Technology in a Ninth-grade High School Population
• Individuals With Different Learning Styles and Personality Types Assignment
• Learning Styles and Comprehension of Secondary Special Education Students
• Why Learning Styles Matter for Student Achievement in College Economics
• Learning Styles and Reaching Full Potential
• Accommodating Different Learning Styles in the Classroom
• Learning Styles and the Accounting Profession
• Diversity, Religion, and Learning Styles
• Therapeutic Recreation for Different Learning Styles
• Learning Styles and Personality Types in a Group Dynamic
• Teaching and Learning Styles in Music Reading
• Learning Styles for Children With Down Syndrome
• The Learning Cycle and Learning Styles of Kolb, Honey and Mumford
• Variances Between Learning Styles and Academics Education
• Differentiation Learning Styles and Inclusive Education
• Learning Styles and Their Strengths, Weaknesses, and Preference
• Educational Theories and Learning Styles on Teaching Practice
• Learning Styles and Strategies by Richard Felder and Barbra
• Technology and Learning Styles Among High School Freshman
• Learning Styles, Multiple Intelligence, and Deep Learning
• Aboriginal and Torres Strait Islander Learning Styles
• Language Learning Styles Used by Students in Globalization
• Different Learning Styles Used by Students
• Understanding Multiple Intelligences and Learning Styles
• Types of Charter Schools and a New Approach to Children’s Learning Style
• Can Educators Take Advantage of Individual Learning Styles?
• How Do Learning Styles Affect Society and Education System?
• What Is the Importance of Learning the Different Learning Styles of Children?
• How Can Learning Styles Improve a Person’s Way of Learning?
• Why Is It Important for Teachers to Know the Different Learning Styles of Learners or Their Multiple Intelligences?
• What Is the Difference Between Learning Styles and Learning Theories?
• How Do Learning Styles Affect Students’ Learning?
• What Are the Benefits of Learning Styles?
• How Can Learning Styles Help Personal Development?
• Why Is It Important to Understand the Personality and Learning Styles of Individuals?
• How Might Being Aware of Your Learning Styles Help You to Achieve Success in College?
• What Is the Benefit of Identifying the Learning Style at College and Why?
• How Do Learning Styles Affect Student Performance?
• Do Learning Styles Affect Study Duration and Academic Success?
• Is There a Significant Relationship Between Learning Styles and Academic Performance?
• Why Is It Important to Cater to Different Learning Styles?
• What Factors Can Influence the Learning Style of Students?
• Does Learning Style Influence Academic Performance in Different Forms of Assessment?
• Which Learning Style Is the Most Effective?
• Why Do Learning Styles Matter in the Workplace?
• Is There a Best Learning Style in All Learning Situations?
• Which Learning Style Is Best for Learners Who Learn Best Through Words?
• What Role Do Learning Styles Play in Teacher Effectiveness?
• Is Learning Style Important in Patient Education?
• What Are the Characteristics of the Visual Learning Style?

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• Published: 26 April 2024

Relationship between learning styles and clinical competency in nursing students

• Seyed Kazem Mousavi 1 , 2 ,
• Ali Javadzadeh 3 ,
• Hanieh Hasankhani 3 &
• Zahra Alijani Parizad 3  

BMC Medical Education volume  24 , Article number:  469 ( 2024 ) Cite this article

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The acquisition of clinical competence is considered the ultimate goal of nursing education programs. This study explored the relationship between learning styles and clinical competency in undergraduate nursing students.

A descriptive-correlational study was conducted in 2023 with 276 nursing students from the second to sixth semesters at Abhar School of Nursing, Zanjan University of Medical Sciences, Iran. Data were collected using demographic questionnaires, Kolb’s learning styles, and Meretoja’s clinical competence assessments completed online by participants. Data were analyzed using SPSS version 16, employing descriptive statistics and inferential tests (independent T-test, ANOVA, Pearson correlation) at a significance level 0.05.

The predominant learning styles among nursing students were divergent (31.2%), and the least common was convergent (18.4%). The overall clinical competency score was 77.25 ± 12.65. Also, there was a significant relationship between learning styles and clinical competency, so the clinical competency of students with accommodative and converging learning styles was higher. ( P  < 0.05).

The results of this study showed the association between learning styles and clinical competence in nursing students. It is recommended that educational programs identify talented students and provide workshops tailored to strengthen various learning styles associated with enhanced clinical competence.

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Introduction

Clinical competency is a multifaceted and nuanced concept that has been extensively explored and examined from various perspectives in recent years [ 1 ]. Its significance is underscored by the World Health Organization (WHO), which has identified the assessment and enhancement of nurses’ competencies as fundamental principles to uphold the quality of care. WHO defines nurses as competent when they can fulfill their professional responsibilities at the appropriate level, grade, and standard [ 2 ]. Factors such as evolving healthcare systems, the imperative for safe and cost-effective services, heightened community awareness of health issues, escalating expectations for quality care, and the demand for skilled healthcare professionals have elevated the importance of clinical competence in nursing and related fields [ 3 ]. Clinical competency is considered the ultimate objective and benchmark of nursing education effectiveness [ 4 ]. Notably, clinical education constitutes a pivotal component of nursing training, with over half of nursing programs dedicated to practical training [ 5 ]. A nursing student’s ability to become a proficient nurse at the bedside hinges on acquiring essential skills during their academic journey and attaining requisite qualifications [ 6 ]. Scholars argue that continual efforts to enhance educational quality are essential for upholding nursing care standards and improving clinical competence [ 7 , 8 ]. Understanding how learners acquire knowledge is crucial for enhancing educational quality, with learning styles playing a pivotal role in this process [ 9 ].

Learning refers to the relatively enduring behavioral changes from experiences [ 10 ]. Learning styles, a concept widely embraced by educational theorists in recent decades, pertain to individuals’ distinct approaches to processing information and acquiring knowledge [ 11 ]. These styles encompass cognitive and psychosocial traits that are relatively stable indicators of learners’ engagement with and response to their learning environments [ 12 ]. Among the myriad theories on learning styles, Kolb’s learning theory is particularly influential [ 13 ]. According to Kolb, learning can be categorized into four primary modes: concrete experience, abstract conceptualization, reflective observation, and active experimentation, yielding four learning styles—converging, diverging, assimilating, and accommodating [ 14 ].

People with a converging learning style excel at problem-solving, decision-making, and practical application by engaging in abstract conceptualization and active experimentation [ 15 ]. Diverging learners thrive on experiencing and closely observing situations, possessing a unique ability to view scenarios from multiple perspectives and synthesize information into a cohesive whole [ 16 ]. The assimilating learning style is characterized by a preference for deep thinking and thorough examination, with individual’s adept at organizing information and employing abstract concepts to comprehend complex situations [ 13 ]. Accommodating learners learn best through hands-on experiences and activities, demonstrating proficiency in working with tangible objects and gaining new insights through practical engagement [ 10 ]. Learning styles are essential in nursing education because the primary mission of nursing education programs is to train nurses who have the necessary knowledge, attitude, and skills to maintain and improve the health of society members, and in other words, have sufficient competence in providing their job duties [ 17 ].

So far, separate studies have been conducted on nursing students’ learning styles and clinical competency [ 4 , 8 , 13 , 17 ]. However, the relationship between these two concepts has received less attention from researchers. The first step in ensuring students’ academic success is to determine their learning style [ 11 ]. Professors’ awareness of the student’s learning styles and the relationship between these styles and the level of clinical competency provide a favorable opportunity to identify the styles with higher clinical competency and encourage students to use them as much as possible. Considering this importance, the researchers decided to design and implement the present study to investigate the relationship between learning styles and clinical competency in nursing students.

Materials and methods

Study design and sampling.

This study was a descriptive-correlational study conducted in 2023, investigating the relationship between learning styles and the clinical competency of undergraduate nursing students. The research involved all second to sixth-semester undergraduate nursing students from the Abhar School of Nursing affiliated with Zanjan University of Medical Sciences, Iran. Sampling was carried out as a census, with 276 students selected to participate in the study. The inclusion criteria included willingness to participate in the study, full-time employment in nursing, no prior clinical work experience, and no reported psychiatric diseases or medication use. Incomplete questionnaire completion was set as an exclusion criterion.

Instruments

The data collection tools included demographic questionnaires, Kolb’s learning styles questionnaire, and a modified Meretoja nursing clinical competency questionnaire. The demographic questionnaire gathered age, gender, marital status, semester, Grade Point Average (GPA), and interest in nursing.

The data collection tools included demographic questionnaires, Kolb’s learning styles questionnaire, and a modified Meretoja nursing clinical competency questionnaire. The demographic questionnaire gathered information such as age, gender, marital status, semester, Grade Point Average (GPA), and interest in nursing.

Kolb’s III learning styles questionnaire comprised 12 questions with four options each, requiring the student to select the option most similar to them. Each option represented one of the four main learning methods: concrete experience (CE), reflective observation (RO), abstract conceptualization (AC), and active experimentation (AE). Scores for the four learning styles were obtained from the total questions across the sections. By subtracting scores, two dimensions (AC - CE) and (AE - RO) were derived, determining the student’s learning styles as converging, diverging, accommodating, or assimilating [ 18 ]. This questionnaire has been used in various studies over the past 30 years, demonstrating validity and reliability. In Iran, Ghahrani et al. used the internal consistency method to determine the reliability of the questionnaire. They obtained Cronbach’s alpha of 71% in concrete experience, 68% in reflective observation, 71% in abstract conceptualization, and 71% in active experimentation [ 19 ]. Also, In the present study, the reliability value of this questionnaire was determined using Cronbach’s alpha method of 0.94.

Meretoja’s revised nursing clinical competency questionnaire contained 47 items across 5 areas of clinical competency: assisting patients (7 skills), teaching and guidance (12 skills), diagnostic measures (8 skills), therapeutic measures (5 skills), and occupational responsibilities (15 skills). Skills were rated on a four-point Likert scale, assessing the degree of skill utilization [ 20 ]. This questionnaire was psychometrically evaluated in Iran by Bahreini et al., and its validity was qualitatively determined at the optimal level, and its reliability was determined between 70 and 85%. Also, in this study, the reliability value of this questionnaire was determined using Cronbach’s alpha method of 0.91 [ 21 ].

Data collection and statistical analysis

Following ethical approval and research permission, questionnaires, consent forms, and contact information for the researchers were provided to students online through the Porsline system ( www.porsline.ir ) for completion. Data analysis was performed using SPSS version 16 software, employing descriptive (frequency, percentage, mean, and standard deviation) and inferential (independent T-test, ANOVA, Pearson correlation) statistics at a significance level of 0.05.

Out of 276 participants, 10 students were excluded due to incomplete questionnaire responses, leaving 266 participants for analysis. The average age of students was 22.33, with 63.4% being female. Most participants were single (79.2%), and 46.2% had a GPA between 16 and 18. Also, 80.7% of students declared that they are interested in nursing. Then, the results of the questionnaires on learning styles and clinical competency were examined. Based on this, the findings showed that divergent (31.2%) and convergent (18.4%) styles were the study participants’ most and least-used learning styles, respectively. Also, the overall students’ clinical competence score was 77.25 ± 12.65 (Table  1 ).

The relationship between the participants’ learning styles and clinical competency was examined in the next step. Initially, the study data underwent a normality assessment. The Kolmogorov-Smirnov test results indicated that parametric statistical tests were applicable ( p  > 0.05). Subsequently, an ANOVA test was conducted to explore the relationship between learning styles and clinical competency, revealing a significant association between learning style and clinical competency with moderate effect size ( p  < 0.05) (Table  2 ).

The correlation between demographic variables, learning styles, and student clinical competency was investigated in the final phase of analyzing the findings. Parametric independent t-tests, Pearson’s correlation coefficient, and ANOVA were employed for this analysis. The results indicated that none of the learning styles exhibited a statistically significant relationship with the demographic characteristics of the participants ( p  > 0.05). However, a significant correlation was observed between participants’ demographic variables, such as age, academic semester, GPA, and interest in nursing, and their clinical competencies ( p  < 0.05) (Table  3 ).

This study explored the relationship between learning styles and clinical competency in undergraduate nursing students. The research initially focused on examining the variables and subsequently explored their interrelation. According to this, the most prevalent learning style among nursing students was divergent. This finding aligns with the outcomes of various domestic studies like Mehni et al. [ 22 ] and Shirazi et al. [ 16 ], as well as numerous international studies such as those by Campos et al. in Brazil and the United States [ 23 ], Nosheen in Pakistan [ 24 ], Madu et al. in Nigeria [ 25 ], and AbuAssi et al. in Saudi Arabia [ 26 ]. It should be said that the dominant abilities of people with divergent styles are concrete experience and reflective observation. They see the situation from multiple angles, emphasize brainstorming and generating ideas, have a strong imagination, are more sensitive to values, respect the feelings of others, and listen with an open mind and without bias [ 5 ]. Therefore, these people have high cultural interests and are more inclined towards humanities fields such as sociology, psychology, counseling, and nursing.

Upon reviewing studies in this field, it is concluded that findings often vary. They can be influenced by factors including individual student traits, educators’ teaching styles, learning environments, and tasks [ 12 ]. Also, this study noted no significant association between learning styles and participants’ demographic characteristics, consistent with similar research in the field [ 22 , 23 ]. In this regard, Dantas et al. emphasized that learning styles predominantly reflect individuals’ traits and are minimally impacted by demographic variables [ 12 ].

Furthermore, the clinical competency level of nursing students was reported to be at an average level, consistent with findings from studies conducted in Iran [ 6 , 27 , 28 ] and other countries [ 29 , 30 , 31 ]. Some studies, however, have yielded differing results compared to the present study. For instance, Ghafari et al. [ 1 ], Katebi et al. [ 32 ], and Fung et al. [ 33 ] found that nursing students participating in their studies exhibited a higher level of clinical competency. Notably, participants in all three mentioned studies were in their final year of study undergoing the arena course. Hence, the emphasis on passing diverse training units and gaining more clinical experiences could justify the high clinical competency score achieved. Also, In the present study, the relationship between academic semesters and students’ clinical competency was significant, which confirms the above argument. Conversely, certain studies have reported a lower level of clinical competency among nursing students. For example, Getie et al. found that only one-third of nursing students demonstrated acceptable clinical competency [ 34 ]. These discrepancies in findings can stem from the questionnaire and the data collection method used. Notably, Getie et al. evaluated students’ clinical competency through assessments by cooperating nurses rather than self-assessment. Additionally, adjusting the questionnaire averages to reflect higher clinical competence could impact the reported competency levels. Various factors, such as individual, environmental, organizational, and educational characteristics, influence the acquisition of clinical competency in nursing students [ 2 ]. Hence, diverse study outcomes exist in this field. Also, besides academic semesters, relationships were observed between age, GPA, interest in the field, and nursing students’ clinical competency. These results align with the findings of many studies in this area [ 27 , 28 , 29 , 32 , 33 ]. Older students are often in their final semesters, potentially showcasing higher clinical competency due to their exposure to clinical environments. Madjid et al. highlighted in their study that good grades obtained by learners in any field indicate their interest in the subject. This aspect holds particular significance in nursing—a complex and demanding profession where success hinges on a genuine interest and academic excellence [ 35 ].

Also, the study revealed a significant relationship between nursing students’ learning styles and clinical competency. According to this, students employing accommodating and converging learning styles reported heightened levels of clinical competency. In their study, Ebrahimi Fakhar et al. noted that medical students utilizing reflective observation and active experimentation learning methods exhibited enhanced clinical competency upon course completion. As these attributes align with accommodative and convergent learning style characteristics, the present study’s results are consistent with these findings [ 36 ]. Moon et al. found that nursing students with converging and accommodating styles reported increased competence in clinical practices [ 37 ]. Similarly, Lundell Rudberg et al. revealed that nursing students with these learning styles demonstrated greater professional responsibility, a key aspect of clinical competence. This correlation indirectly supports the present study’s outcomes [ 38 ]. Generally, it can be said that people with convergent and accommodating learning styles typically gravitate toward practical learning. They are inclined towards hands-on activities, deriving their learning mainly through experience and active participation [ 12 , 15 ]. Therefore, they are expected to engage more in clinical settings, fostering heightened clinical competency. Furthermore, the study’s latest findings indicated that students with an assimilating learning style exhibited lower levels of clinical competency, aligning with Moon et al.‘s study [ 37 ]. Similarly, Figueiredo et al. explored nurses’ learning styles based on Kolb’s theory in qualitative research, noting that nurses with assimilating learning styles are inclined towards abstract and subjective concepts over practical content and may have less enthusiasm for immersive clinical environments [ 39 ].

Limitations

One limitation of this study was the potential for inaccuracies in questionnaire completion due to the electronic data collection method and the extensive number of questions. To address this concern, participants were provided with researchers’ contact details for clarifications during data collection. Another limitation was the reliance on self-report tools and the omission of considering individuals’ personality traits in measuring the research variables, factors beyond researchers’ direct control.

The study’s findings underscore the relationship between learning styles and clinical competency in undergraduate nursing students. Therefore, considering the high importance of acquiring clinical competency in these students, it is recommended that educational administrators identify students prone to declining clinical competency based on their learning styles and organize workshops to enhance styles associated with superior clinical competency. Also, Given the complexity of learning styles and clinical competency as constructs, future studies may benefit from exploring additional theories and tools to delve deeper into these concepts, employing qualitative or mixed-method approaches for comprehensive analysis.

Data availability

Data is provided within the manuscript or supplementary information files.

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Acknowledgements

The authors acknowledge the students who participated in the study.

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Seyed Kazem Mousavi

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Department of Nursing, School of Nursing and Midwifery, Guilan University of Medical Sciences, Rasht, Iran

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All the authors helped design the study. AJ and HH collected the data. SKM, and AJ analyzed and interpreted the data. All the authors helped write the manuscript and read and approved the final version.Funding.

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Permission to conduct the present study was obtained from the Research Department and Ethics Committee of Zanjan University of Medical Sciences (IR.ZUMS.REC.1402.095. available at: https://ethics.research.ac.ir/ ). All the study participants were informed about the objectives of the study, the confidentiality of the information, and the voluntary nature of their participation, and all students completed the informed consent form.

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Mousavi, S.K., Javadzadeh, A., Hasankhani, H. et al. Relationship between learning styles and clinical competency in nursing students. BMC Med Educ 24 , 469 (2024). https://doi.org/10.1186/s12909-024-05432-z

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Creating Inclusive Play Environments for Infants and Toddlers

This brief focuses on supporting play for all infants and toddlers. Play comes in different varieties, so it is important to learn strategies and practices that ensure each child is supported. Find the most up-to-date information to answer these prompts:

What does research say?

What does it look like.

Also check out the companion resource, Connecting at Home. It includes simple tips for families to support their children's engagement and learning.

Research Notes

• Approaches to Learning
• Social and Emotional Learning
• Perceptual, Motor, and Physical Development
• Language and Literacy

Creating inclusive play environments ensures that every child can participate in meaningful ways. When we understand how to make play more inclusive, we support children’s development and learning.

The Take-home

• Play looks different for each child. Their interests, cultural background, temperament, and family values and practices will affect how they play.
• Adult attitudes and mindsets are consistently identified as the most important factors in adults’ ability to effectively support inclusive play.
• Play needs to feel meaningful to children. Look for ways to increase their engagement and expression during play. Support families in helping children explore their interests and feel a sense of belonging.

Play style varies among children.

Play is universal, but how we play and what play looks like varies across individuals. Cultural influences can affect how children play. Children might play independently or socially, engage in competition or cooperation, be comfortable or uncomfortable playing with adults, or engage less in make-believe and more in exploration. All types of play expressions are valid and should be supported.

Our biases affect what kind of play is allowed or encouraged.

To help you better understand and support inclusive play for the children in your program, it is important to investigate your values around play. Do you dislike messy play, or avoid risky play? Do you encourage boys and girls to engage equally in block play and dramatic play?

Our internal ideas of what is “acceptable play” influence how we interact with and support families’ values around play. Take the time to ask families questions about how they view play. Find ways to expand your practice in ways that align with the families’ values about play.

Intentional supports create opportunities for inclusive play.

Expanding play opportunities can provide meaningful experiences for young children. This can include observing a child’s play interests, working with families to identify materials to expand their play, and talking with children’s families about their observations. Supporting inclusive play means learning from families as well as sharing strategies with them. This helps them build emotional connections with their infant or toddler. Children who feel secure in their spaces and relationships are more likely to explore.

Look for these opportunities to support inclusive play for infants and toddlers.

• Notice items and materials the child likes to play with. Can they access them easily on their own in all of their learning spaces? Modifying toys or materials to make them more accessible can be simple, such as taping paper to the floor to create easy access to an easel or moving baskets of objects to lower shelves.
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• Help families notice and pay attention to nonverbal cues like eye gaze, pointing, gestures, and other body language. Infants and toddlers use a variety of communication cues. It’s important to validate and reward their attempts at communication!
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The parent is the child's most important teacher, and you are their "guide on the side." Use these tips with families to help them support how their child learns:

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• Model exploring objects through every sense. If you and the parent are exploring leaves, describe the experience: Can you hear it? Can you smell it? What do you see? What does it feel like? Including a multisensory experience allows children to engage in different ways.
• Use open-ended materials like boxes, bowls, and blocks to encourage creative play. Support parents in identifying materials their children can play with. For a crawling baby, suggest placing different materials on the floor for them to crawl over and explore. As they do, talk about different textures, shapes, colors, and patterns.
• Ask families if you can help them find items around the home that nurture children's curiosity, creativity, attention, and persistence.
• Help families adapt materials and activities to their child's ability level. Families might remove a couple of rings from a stacking toy to simplify the activity. Adding popsicle sticks to book pages can make them easier to turn.
• Encourage families to expand on everyday routines with their children. During bath time, they can use different words to describe the movement of water or introduce a new household item like a sponge.
• Inclusive Learning Environments for Infants and Toddlers
• Playful and Fun Learning Environments for Infants and Toddlers
• Learning at Home and Homelike Environments

Connecting at Home

Play is natural for all children, though it looks different for each child. Follow your child's lead and see where the magic will take you!

Play with Me

With just a few simple changes, you can boost your success as a play partner with your child. Facing each other while playing, taking turns together, and using simple language like “put the ball in the box” can help boost your child’s motivation and interest in play.

Make It for Me

Be ready to adapt different activities for your child. Do they get overwhelmed when choosing what to play with? Try offering two to three different toy choices for them to choose from, and cycle in other toys another time. Experiment with different places to play. For example, move their blocks to the table where they can be seated, and see how that affects their attention.

Turn Out the Lights

Create a fun hide-and-seek game with your toddler by hiding toys in various safe places. Turn the lights off and guide them with a flashlight to the objects. Let them guide you to find objects too!

What’s inside?

Take a familiar object, like a spoon or a small toy, and hide it inside a sock or wrap it in a scarf. Allow your child to explore the different textures and feel the object inside. Narrate what you see them do and what textures they might be feeling. When they pull out the object, it will be a surprise discovery!

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NeurIPS 2024

Conference Dates: (In person) 9 December - 15 December, 2024

Homepage: https://neurips.cc/Conferences/2024/

Call For Papers 

Author notification: Sep 25, 2024

Camera-ready, poster, and video submission: Oct 30, 2024 AOE

Submit at: https://openreview.net/group?id=NeurIPS.cc/2024/Conference  

The site will start accepting submissions on Apr 22, 2024 

Subscribe to these and other dates on the 2024 dates page .

The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024) is an interdisciplinary conference that brings together researchers in machine learning, neuroscience, statistics, optimization, computer vision, natural language processing, life sciences, natural sciences, social sciences, and other adjacent fields. We invite submissions presenting new and original research on topics including but not limited to the following:

• Applications (e.g., vision, language, speech and audio, Creative AI)
• Deep learning (e.g., architectures, generative models, optimization for deep networks, foundation models, LLMs)
• Evaluation (e.g., methodology, meta studies, replicability and validity, human-in-the-loop)
• General machine learning (supervised, unsupervised, online, active, etc.)
• Infrastructure (e.g., libraries, improved implementation and scalability, distributed solutions)
• Machine learning for sciences (e.g. climate, health, life sciences, physics, social sciences)
• Neuroscience and cognitive science (e.g., neural coding, brain-computer interfaces)
• Optimization (e.g., convex and non-convex, stochastic, robust)
• Probabilistic methods (e.g., variational inference, causal inference, Gaussian processes)
• Reinforcement learning (e.g., decision and control, planning, hierarchical RL, robotics)
• Social and economic aspects of machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior)
• Theory (e.g., control theory, learning theory, algorithmic game theory)

Machine learning is a rapidly evolving field, and so we welcome interdisciplinary submissions that do not fit neatly into existing categories.

Authors are asked to confirm that their submissions accord with the NeurIPS code of conduct .

Formatting instructions:   All submissions must be in PDF format, and in a single PDF file include, in this order:

• The submitted paper
• Technical appendices that support the paper with additional proofs, derivations, or results 
• The NeurIPS paper checklist  

Other supplementary materials such as data and code can be uploaded as a ZIP file

The main text of a submitted paper is limited to nine content pages , including all figures and tables. Additional pages containing references don’t count as content pages. If your submission is accepted, you will be allowed an additional content page for the camera-ready version.

The main text and references may be followed by technical appendices, for which there is no page limit.

The maximum file size for a full submission, which includes technical appendices, is 50MB.

Authors are encouraged to submit a separate ZIP file that contains further supplementary material like data or source code, when applicable.

You must format your submission using the NeurIPS 2024 LaTeX style file which includes a “preprint” option for non-anonymous preprints posted online. Submissions that violate the NeurIPS style (e.g., by decreasing margins or font sizes) or page limits may be rejected without further review. Papers may be rejected without consideration of their merits if they fail to meet the submission requirements, as described in this document. 

Paper checklist: In order to improve the rigor and transparency of research submitted to and published at NeurIPS, authors are required to complete a paper checklist . The paper checklist is intended to help authors reflect on a wide variety of issues relating to responsible machine learning research, including reproducibility, transparency, research ethics, and societal impact. The checklist forms part of the paper submission, but does not count towards the page limit.

Supplementary material: While all technical appendices should be included as part of the main paper submission PDF, authors may submit up to 100MB of supplementary material, such as data, or source code in a ZIP format. Supplementary material should be material created by the authors that directly supports the submission content. Like submissions, supplementary material must be anonymized. Looking at supplementary material is at the discretion of the reviewers.

We encourage authors to upload their code and data as part of their supplementary material in order to help reviewers assess the quality of the work. Check the policy as well as code submission guidelines and templates for further details.

Use of Large Language Models (LLMs): We welcome authors to use any tool that is suitable for preparing high-quality papers and research. However, we ask authors to keep in mind two important criteria. First, we expect papers to fully describe their methodology, and any tool that is important to that methodology, including the use of LLMs, should be described also. For example, authors should mention tools (including LLMs) that were used for data processing or filtering, visualization, facilitating or running experiments, and proving theorems. It may also be advisable to describe the use of LLMs in implementing the method (if this corresponds to an important, original, or non-standard component of the approach). Second, authors are responsible for the entire content of the paper, including all text and figures, so while authors are welcome to use any tool they wish for writing the paper, they must ensure that all text is correct and original.

Double-blind reviewing:   All submissions must be anonymized and may not contain any identifying information that may violate the double-blind reviewing policy.  This policy applies to any supplementary or linked material as well, including code.  If you are including links to any external material, it is your responsibility to guarantee anonymous browsing.  Please do not include acknowledgements at submission time. If you need to cite one of your own papers, you should do so with adequate anonymization to preserve double-blind reviewing.  For instance, write “In the previous work of Smith et al. [1]…” rather than “In our previous work [1]...”). If you need to cite one of your own papers that is in submission to NeurIPS and not available as a non-anonymous preprint, then include a copy of the cited anonymized submission in the supplementary material and write “Anonymous et al. [1] concurrently show...”). Any papers found to be violating this policy will be rejected.

OpenReview: We are using OpenReview to manage submissions. The reviews and author responses will not be public initially (but may be made public later, see below). As in previous years, submissions under review will be visible only to their assigned program committee. We will not be soliciting comments from the general public during the reviewing process. Anyone who plans to submit a paper as an author or a co-author will need to create (or update) their OpenReview profile by the full paper submission deadline. Your OpenReview profile can be edited by logging in and clicking on your name in https://openreview.net/ . This takes you to a URL "https://openreview.net/profile?id=~[Firstname]_[Lastname][n]" where the last part is your profile name, e.g., ~Wei_Zhang1. The OpenReview profiles must be up to date, with all publications by the authors, and their current affiliations. The easiest way to import publications is through DBLP but it is not required, see FAQ . Submissions without updated OpenReview profiles will be desk rejected. The information entered in the profile is critical for ensuring that conflicts of interest and reviewer matching are handled properly. Because of the rapid growth of NeurIPS, we request that all authors help with reviewing papers, if asked to do so. We need everyone’s help in maintaining the high scientific quality of NeurIPS.  

Please be aware that OpenReview has a moderation policy for newly created profiles: New profiles created without an institutional email will go through a moderation process that can take up to two weeks. New profiles created with an institutional email will be activated automatically.

Venue home page: https://openreview.net/group?id=NeurIPS.cc/2024/Conference

If you have any questions, please refer to the FAQ: https://openreview.net/faq

Ethics review: Reviewers and ACs may flag submissions for ethics review . Flagged submissions will be sent to an ethics review committee for comments. Comments from ethics reviewers will be considered by the primary reviewers and AC as part of their deliberation. They will also be visible to authors, who will have an opportunity to respond.  Ethics reviewers do not have the authority to reject papers, but in extreme cases papers may be rejected by the program chairs on ethical grounds, regardless of scientific quality or contribution.  

Preprints: The existence of non-anonymous preprints (on arXiv or other online repositories, personal websites, social media) will not result in rejection. If you choose to use the NeurIPS style for the preprint version, you must use the “preprint” option rather than the “final” option. Reviewers will be instructed not to actively look for such preprints, but encountering them will not constitute a conflict of interest. Authors may submit anonymized work to NeurIPS that is already available as a preprint (e.g., on arXiv) without citing it. Note that public versions of the submission should not say "Under review at NeurIPS" or similar.

Dual submissions: Submissions that are substantially similar to papers that the authors have previously published or submitted in parallel to other peer-reviewed venues with proceedings or journals may not be submitted to NeurIPS. Papers previously presented at workshops are permitted, so long as they did not appear in a conference proceedings (e.g., CVPRW proceedings), a journal or a book.  NeurIPS coordinates with other conferences to identify dual submissions.  The NeurIPS policy on dual submissions applies for the entire duration of the reviewing process.  Slicing contributions too thinly is discouraged.  The reviewing process will treat any other submission by an overlapping set of authors as prior work. If publishing one would render the other too incremental, both may be rejected.

Anti-collusion: NeurIPS does not tolerate any collusion whereby authors secretly cooperate with reviewers, ACs or SACs to obtain favorable reviews. 

Author responses:   Authors will have one week to view and respond to initial reviews. Author responses may not contain any identifying information that may violate the double-blind reviewing policy. Authors may not submit revisions of their paper or supplemental material, but may post their responses as a discussion in OpenReview. This is to reduce the burden on authors to have to revise their paper in a rush during the short rebuttal period.

After the initial response period, authors will be able to respond to any further reviewer/AC questions and comments by posting on the submission’s forum page. The program chairs reserve the right to solicit additional reviews after the initial author response period.  These reviews will become visible to the authors as they are added to OpenReview, and authors will have a chance to respond to them.

After the notification deadline, accepted and opted-in rejected papers will be made public and open for non-anonymous public commenting. Their anonymous reviews, meta-reviews, author responses and reviewer responses will also be made public. Authors of rejected papers will have two weeks after the notification deadline to opt in to make their deanonymized rejected papers public in OpenReview.  These papers are not counted as NeurIPS publications and will be shown as rejected in OpenReview.

Publication of accepted submissions:   Reviews, meta-reviews, and any discussion with the authors will be made public for accepted papers (but reviewer, area chair, and senior area chair identities will remain anonymous). Camera-ready papers will be due in advance of the conference. All camera-ready papers must include a funding disclosure . We strongly encourage accompanying code and data to be submitted with accepted papers when appropriate, as per the code submission policy . Authors will be allowed to make minor changes for a short period of time after the conference.

Contemporaneous Work: For the purpose of the reviewing process, papers that appeared online within two months of a submission will generally be considered "contemporaneous" in the sense that the submission will not be rejected on the basis of the comparison to contemporaneous work. Authors are still expected to cite and discuss contemporaneous work and perform empirical comparisons to the degree feasible. Any paper that influenced the submission is considered prior work and must be cited and discussed as such. Submissions that are very similar to contemporaneous work will undergo additional scrutiny to prevent cases of plagiarism and missing credit to prior work.

Plagiarism is prohibited by the NeurIPS Code of Conduct .

Other Tracks: Similarly to earlier years, we will host multiple tracks, such as datasets, competitions, tutorials as well as workshops, in addition to the main track for which this call for papers is intended. See the conference homepage for updates and calls for participation in these tracks. 

Experiments: As in past years, the program chairs will be measuring the quality and effectiveness of the review process via randomized controlled experiments. All experiments are independently reviewed and approved by an Institutional Review Board (IRB).

Financial Aid: Each paper may designate up to one (1) NeurIPS.cc account email address of a corresponding student author who confirms that they would need the support to attend the conference, and agrees to volunteer if they get selected. To be considered for Financial the student will also need to fill out the Financial Aid application when it becomes available.

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Computer Science > Computer Vision and Pattern Recognition

Title: few-shot calligraphy style learning.

Abstract: We introduced "Presidifussion," a novel approach to learning and replicating the unique style of calligraphy of President Xu, using a pretrained diffusion model adapted through a two-stage training process. Initially, our model is pretrained on a diverse dataset containing works from various calligraphers. This is followed by fine-tuning on a smaller, specialized dataset of President Xu's calligraphy, comprising just under 200 images. Our method introduces innovative techniques of font image conditioning and stroke information conditioning, enabling the model to capture the intricate structural elements of Chinese characters. The effectiveness of our approach is demonstrated through a comparison with traditional methods like zi2zi and CalliGAN, with our model achieving comparable performance using significantly smaller datasets and reduced computational resources. This work not only presents a breakthrough in the digital preservation of calligraphic art but also sets a new standard for data-efficient generative modeling in the domain of cultural heritage digitization.

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