Journal articles: 'Learning preferences'

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Ch, Abdul Rashid, Irshad Nabi Sandhu, Muhammad Arif Ali, and Asad Ali Sandhu. "LEARNING PREFERENCES." Professional Medical Journal 22, no. 10 (October 10, 2015): 1351–55. http://dx.doi.org/10.29309/tpmj/2015.22.10.1042.

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Introduction: This study has focused to know the learning preferences amongfaculty considering different methodologies and considering about CME to improve healthcare.Objectives: To identify the gaps in knowledge regarding CME in medical faculty of Lahore andto see the awareness of the CME among them. Study Design: This is a cross-sectional study ofmedical faculty in Lahore. Settings: Three hospitals in Lahore are included from both private/public sectors. Period: It was conducted over a period of 2 months from JULY, 2014 to August,2014 Methods: A questionnaire comprising of 21 questions was distributed at random for datacollection among doctors having done post graduation. Results: Most of the faculty memberswho attended CME found it as useful tool for improving the knowledge and techniques forbetter patient care. Conclusions: Majority teachers are not interested in CME and do notparticipate and ask for some incentives.

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Li, Nan, William Cushing, Subbarao Kambhampati, and Sungwook Yoon. "Learning User Plan Preferences Obfuscated by Feasibility Constraints." Proceedings of the International Conference on Automated Planning and Scheduling 19 (October 16, 2009): 370–73. http://dx.doi.org/10.1609/icaps.v19i1.13393.

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It has long been recognized that users can have complex preferences on plans. Non-intrusive learning of such preferences by observing the plans executed by the user is an attractive idea. Unfortunately, the executed plans are often not a true representation of user preferences, as they result from the interaction between user preferences and feasibility constraints. In the travel planning scenario, a user whose true preference is to travel by a plane may well be frequently observed traveling by car because of feasibility constraints (perhaps the user is a poor graduate student). In this work, we describe a novel method for learning true user preferences obfuscated by such feasibility constraints. Our base learner induces probabilistic hierarchical task networks (pHTNs) from sets of training plans. Our approach is to rescale the input so that it represents the user's preference distribution on plans rather than the observed distribution on plans.

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Oh, Jaeho, Mincheol Kim, and Sang-Woo Ban. "Deep Learning Model with Transfer Learning to Infer Personal Preferences in Images." Applied Sciences 10, no. 21 (October 29, 2020): 7641. http://dx.doi.org/10.3390/app10217641.

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In this paper, we propose a deep convolutional neural network model with transfer learning that reflects personal preferences from inter-domain databases of images having atypical visual characteristics. The proposed model utilized three public image databases (Fashion-MNIST, Labeled Faces in the Wild [LFW], and Indoor Scene Recognition) that include images with atypical visual characteristics in order to train and infer personal visual preferences. The effectiveness of transfer learning for incremental preference learning was verified by experiments using inter-domain visual datasets with different visual characteristics. Moreover, a gradient class activation mapping (Grad-CAM) approach was applied to the proposed model, providing explanations about personal visual preference possibilities. Experiments showed that the proposed preference-learning model using transfer learning outperformed a preference model not using transfer learning. In terms of the accuracy of preference recognition, the proposed model showed a maximum of about 7.6% improvement for the LFW database and a maximum of about 9.4% improvement for the Indoor Scene Recognition database, compared to the model that did not reflect transfer learning.

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Žnidaršič, Martin, Aljaž Osojnik, Peter Rupnik, and Bernard Ženko. "Improving Effectiveness of a Coaching System through Preference Learning." Technologies 10, no. 1 (January 31, 2022): 24. http://dx.doi.org/10.3390/technologies10010024.

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The paper describes an approach for indirect data-based assessment and use of user preferences in an unobtrusive sensor-based coaching system with the aim of improving coaching effectiveness. The preference assessments are used to adapt the reasoning components of the coaching system in a way to better align with the preferences of its users. User preferences are learned based on data that describe user feedback as reported for different coaching messages that were received by the users. The preferences are not learned directly, but are assessed through a proxy—classifications or probabilities of positive feedback as assigned by a predictive machine learned model of user feedback. The motivation and aim of such an indirect approach is to allow for preference estimation without burdening the users with interactive preference elicitation processes. A brief description of the coaching setting is provided in the paper, before the approach for preference assessment is described and illustrated on a real-world example obtained during the testing of the coaching system with elderly users.

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Kuznar, Elaine, Grace Falciglia, Linda Wood, and Judith J. Frankel. "Learning Style Preferences." Journal of Nutrition For the Elderly 10, no. 3 (June 3, 1991): 21–34. http://dx.doi.org/10.1300/j052v10n03_02.

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Black, Joyce M. "Assessing Learning Preferences." Plastic Surgical Nursing 24, no. 2 (April 2004): 68–69. http://dx.doi.org/10.1097/00006527-200404000-00010.

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Sengsouliya, Souksakhone, Sithane Soukhavong, Say Phonekeo, Vanmany Vannasy, Vanthala Souvanxay, and Chanmany Rattanavongsa. "The Effect of Contextual Factor on Learning Styles Preferences of English Majors in Lao Public Universities." Journal of English Language Teaching and Linguistics 6, no. 3 (December 15, 2021): 683. http://dx.doi.org/10.21462/jeltl.v6i3.667.

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<em>This research is a quantitative correlational design, which investigates the English learning styles preferences of English major in Laotian public universities in Lao PDR and tests the effect of contextual factors on the participants’ learning styles preferences. The sample of this research involved 542 university-level students who major in English at a bachelor-degree program in four public universities in Lao PDR. The instrument of the study was Reid’s (1987) Perceptual Learning Style Preference Questionnaire (PLSPQ), which includes six different learning styles (Audio, Visual, Kinesthetic, Tactile, Individual, and Group learning styles). Participants were invited to rate their learning style preference towards the scale. The analysis was conducted based on</em> <em>Reid’s (1995) guide of categorizing preference levels, such as Major, Minor Learning Style Preference, and Negligible. The results indicated that the participants had three major preferences towards Kinesthetic learning (M=41.20), Audio learning (M=39.18), and Tactile learning styles (M=38.14), respectively. The study also found that there are significant differences in English learning styles preferences among students from different institutional contexts. Further research on testing the effect of contextual factors on learners’ learning choice is strongly recommended.</em>

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Ismail, Nadia Nur Afiqah, Tina Abdullah, and Abdul Halim Abdul Raof. "INSIGHTS INTO LEARNING STYLES PREFERENCE OF ENGINEERING UNDERGRADUATES: IMPLICATIONS FOR TEACHING AND LEARNING." Journal of Nusantara Studies (JONUS) 7, no. 1 (January 13, 2022): 390–409. http://dx.doi.org/10.24200/jonus.vol7iss1pp390-409.

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Background and Purpose: Education at higher institutions prepares graduates for the real world. To develop and maintain quality, the focus must not only be on what institutions can offer but also on the learning needs and styles of learners. Despite many studies on engineering learners’ learning styles, limited research has been conducted to compare the learning styles of Engineering and Engineering Education learners. This study was conducted to ascertain the learning style preferences of first-year undergraduates from both groups in a science and technology-driven university in Malaysia. Methodology: This descriptive study consisted of 40 Engineering and 40 Engineering Education learners who attended an English language course at the university. Perceptual Learning Style Preference Questionnaire was adopted as the survey instrument. The data were analysed using self-scoring sheet and Statistical Package for the Social Sciences. Findings: While both groups chose Kinaesthetic as a major learning style preference, the Engineering Education learners also chose Group, Tactile, and Auditory learning styles as their other major preferences. Both groups chose Visual and Individual as their minor preferences. Contributions: The findings extend research demonstrating the significant role of specific disciplines in Engineering to determine the learning style preferences of learners. The findings also provide useful insights that suggest implications for practice and policy. Keywords: Engineering, engineering education, English language, learning styles, teaching and learning. Cite as: Ismail, N. N. A., Abdullah, T., & Abdul Raof, A. H. (2022). Insights into learning styles preference of engineering undergraduates: Implications for teaching and learning. Journal of Nusantara Studies, 7(1) 390-409. http://dx.doi.org/10.24200/jonus.vol7iss1pp390-409

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Ruiz, Luis Miguel, Jose Luis Graupera, Juan Antonio Moreno, and Isabel Rico. "Social Preferences for Learning among Adolescents in Secondary Physical Education." Journal of Teaching in Physical Education 29, no. 1 (January 2010): 3–20. http://dx.doi.org/10.1123/jtpe.29.1.3.

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The purpose of the current study was to explore social interaction preferences for learning in Physical Education (PE) among Spanish secondary students. The sample consists of 6,654 students (3,500 girls and 3,154 boys, aged 12–17 years) from public and private urban and rural schools in two communities in Spain. All participants completed the Graupera/Ruiz Scale of Social Interaction Preferences in PE Learning (GR–SIPPEL) which explores four learning preference dimensions: cooperation, competition, affiliation, and individualism. Results indicated that the ordinal profile of students’ preferences in PE classes was: cooperative (very high preference), competitive and affiliate (high-moderate preference), and individualistic (moderate-low preference). Gender differences emerged: girls were less competitive and individualistic than boys, and slightly more cooperative and affiliate. Weak grade level differences were also observed.

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Mistry, Sajib, Sheik Mohammad Mostakim Fattah, and Athman Bouguettaya. "Sequential Learning-based IaaS Composition." ACM Transactions on the Web 15, no. 3 (July 3, 2021): 1–37. http://dx.doi.org/10.1145/3452332.

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We propose a novel Infrastructure-as-a-Service composition framework that selects an optimal set of consumer requests according to the provider’s qualitative preferences on long-term service provisions. Decision variables are included in the temporal conditional preference networks to represent qualitative preferences for both short-term and long-term consumers. The global preference ranking of a set of requests is computed using a k -d tree indexing-based temporal similarity measure approach. We propose an extended three-dimensional Q-learning approach to maximize the global preference ranking. We design the on-policy-based sequential selection learning approach that applies the length of request to accept or reject requests in a composition. The proposed on-policy-based learning method reuses historical experiences or policies of sequential optimization using an agglomerative clustering approach. Experimental results prove the feasibility of the proposed framework.

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Farman, Farman, Arbain Arbain, and Fitriyani Hali. "Learning Style Preferences Based on Class and Gender." AL-ISHLAH: Jurnal Pendidikan 13, no. 1 (April 18, 2021): 164–72. http://dx.doi.org/10.35445/alishlah.v13i1.368.

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This study aims to identify and describe students' learning style preferences in the mathematics education study program based on class and gender. This research is a type of quantitative research with a descriptive design that describes the percentage of learning style trends in mathematics education students based on class and gender and then draws conclusions on the percentage of learning style preference. The sample of this research was 44 students of the Mathematics Education study program at USN Kolaka. The instrument used in this study was a questionnaire containing 16 questions related to VARK learning styles (Visual, Auditorial, Read/Write, Kinesthetic). The VARK questionnaire instrument used is a standard questionnaire adapted from The VARK Questionnaire Version 7.1. The results showed that students’ learning styles had a multimodal preference(23% bimodal, 27% trimodal, 18% quadmodal).When viewed from class, class A and class B also have a multimodal preference where ARK is the most dominant preference for class A while class B's most dominant preferences are AK and VARK. In terms of gender, women had a multimodal preference (7% bimodal, 33% trimodal and 13% quadmodal) and men had a multimodal preference (31% bimodal, 24% trimodal and 21% quadmodal).

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Chakravarty, Soumitra, Mohd Khan, Shafali Singh, Bhawesh Bhushan, Geeta Jaiswal, Shweta Dwivedi, and Abha Pandey. "A study of the different learning styles of the present first professional MBBS students at United Institute of Medical Sciences, United Medicity, Prayagraj, India." National Journal of Physiology, Pharmacy and Pharmacology 12, no. 12 (2022): 1. http://dx.doi.org/10.5455/njppp.2022.12.09441202204112022.

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Background: The study is designed to evaluate the different learning styles of the present first professional MBBS students. Students were requested to complete the visual-aural-read/write-kinesthetic (VARK) questionnaire at VARK-LEARN.COM. Results of the questionnaire were collected by email and analyzed using Microsoft Excel. We found that 68.2% had multimodal learning preferences and 31.7% had single learning preference. Aims and Objectives: The objective of this study is to analyze the different learning styles of MBBS 1st year students of United Institute of Medical Sciences, Prayagraj, using the VARK questionnaire. Materials and Methods: This study includes the informed consent taken from each student before participating. The Institutional Ethics Committee approved the study. Copyright permission was taken from the website vark-learn.com, from VARK Learn Limited, New Zealand. One hundred and two students had consented, and out of them, 85 students participated in the study. The students were asked to complete the questionnaire on the website. The data on their learning styles were collected and analyzed using Microsoft Excel. Results: Out of 85 Students, 58 (68.2%) had multimodal learning preferences. Of these, 28.2% of students had four-part VARK or quadrimodal preferences, 28.2% of students had bimodal, and 11.7% had trimodal learning preferences. Twenty-seven (31.7%) had single learning preferences, and, out of these 27 students, 17 (20%) had kinesthetic, 6 (7.05%) had visual learning preferences, and 4 (4.7%) students had aural learning preferences. Conclusion: With the help of this study, we concluded that most of the students who participated in our study are multimodal learners, and out of the students who had a single learning preference, most had kinesthetic learning preferences.

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CASTELLANO, GIOVANNA, CIRO CASTIELLO, DANILO DELL'AGNELLO, ANNA MARIA FANELLI, CORRADO MENCAR, and MARIA ALESSANDRA TORSELLO. "LEARNING FUZZY USER PROFILES FOR RESOURCE RECOMMENDATION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 18, no. 04 (August 2010): 389–410. http://dx.doi.org/10.1142/s0218488510006611.

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Recommender systems are systems capable of assisting users by quickly providing them with relevant resources according to their interests or preferences. The efficacy of a recommender system is strictly connected with the possibility of creating meaningful user profiles, including information about user preferences, interests, goals, usage data and interactive behavior. In particular, analysis of user preferences is important to predict user behaviors and make appropriate recommendations. In this paper, we present a fuzzy framework to represent, learn and update user profiles. The representation of a user profile is based on a structured model of user cognitive states, including a competence profile, a preference profile and an acquaintance profile. The strategy for deriving and updating profiles is to record the sequence of accessed resources by each user, and to update preference profiles accordingly, so as to suggest similar resources at next user accesses. The adaption of the preference profile is performed continuously, but in earlier stages it is more sensitive to updates (plastic phase) while in later stages it is less sensitive (stable phase) to allow resource recommendation. Simulation results are reported to show the effectiveness of the proposed approach.

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LeVesseur, Cheyne A., Julie Q. Morrison, and Melissa Nantais. "Educators’ Preferences for Professional Learning Formats by Learning Objective." Journal of Education and Training Studies 10, no. 3 (March 5, 2022): 43. http://dx.doi.org/10.11114/jets.v10i3.5495.

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Innovations in information technology have generated an array of options for the professional learning for educators. Face-to-face, remote, and hybrid formats for professional learning each have their advantages and disadvantages for advancing educators’ knowledge and skills in evidence-based practices to promote student achievement. The purpose of this study was to better understand teacher and educational leaders’ preferences for professional learning formats in relation to the intended learning objective. The results indicate that educators preferences for professional learning format varied by the intended learning objective of the session. Remote professional learning was preferred relative to face-to-face and hybrid formats for a session in which the primary objective was to share information uni-directionally from the presenter to the audience (53% versus 25% and 22% preferred hybrid). The respondents were equally split in their preference for either a face-to-face or remote format when the learning objective was to understand new concepts and learn how to apply the concepts to their setting. The results have important implications for designing professional learning.

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Mei Ph’ng, Lee. "Teaching Styles, Learning Styles and the ESP Classroom." MATEC Web of Conferences 150 (2018): 05082. http://dx.doi.org/10.1051/matecconf/201815005082.

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Learner diversity that exists in the classroom plays a role in influencing the teaching and learning process in the classroom. It should be acknowledged in order for the teaching and learning process to be a meaningful and effective process. Thus, this study examined the learning styles preference of engineering students and the teaching styles preferences of their Technical Communication lecturers. The study also looked at whether the students’ learning styles preferences were influenced by their field of study, gender and ethnic backgrounds. Felder and Solomon’s Index of Learning Styles was administered to 588 engineering students while Grasha and Riechmann-Hruska’s Teaching Style Survey was administered to 10 Technical Communication lecturers. The findings revealed that the students have a marked preference for the visual learning style but balanced preferences for the other learning styles dimensions. The students’ field of study, gender and ethnic backgrounds did not seem to influence the students’ learning styles preferences. As for their Technical Communication lecturers, they seem to favour the student-centered teaching approach. All the data support the notion of adopting a balanced teaching approach in the Technical Communication classroom.

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Alhourani, Alaa Q. "Investigating the Match and Mismatch between Students’ Learning Styles and Teacher’s Teaching Styles in a Saudi School: A Case Study." Journal of World Englishes and Educational Practices 3, no. 2 (February 27, 2021): 10–20. http://dx.doi.org/10.32996/jweep.2021.3.2.2.

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This study examines if there is a match or mismatch between students’ learning styles and the teacher’s teaching styles in a classroom at Khalid Bin Alwlid School, Tabarjal, Saudi Arabia. The main objectives of this study are to determine dominant learning styles of the whole secondary grade students and the teacher’s teaching style. This study was carried out as a case study, and the data were analyzed qualitatively and quantitatively. Participants of this study were a class of 15 students and their English language teacher to the first secondary stage. The instruments used for collecting data were Reid’s Perceptual Learning Style Preference Questionnaire (1995), Teachers Teaching Style Preferences Questionnaire (Peacock 2001), and semi-structured interview. The findings of this study indicated that the majority of the students had multiple learning styles with at least auditory and group as their major learning style preferences, followed by kinesthetic, tactile, and visual as their minor learning style preferences. They showed negative preference towards individual style. The major learning style preferences of the English language teacher were visual and individual, followed by tactile and kinesthetic as his minor style preferences, while his negligible teaching style preferences were auditory and group. There was a mismatch between the teacher’s teaching style and the students’ learning style at Khalid Bin Alwlid School, Tabarjal, Saudi Arabia

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Alharbi, Homood A., Adel F. Almutairi, Eyad M. Alhelih, and Abdualrahman S. Alshehry. "The Learning Preferences among Nursing Students in the King Saud University in Saudi Arabia: A Cross-Sectional Survey." Nursing Research and Practice 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/3090387.

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Objective.The present study aimed to identify the most common learning preferences among the nursing students in Saudi Arabia and to investigate the associations of certain demographic variables with the learning preferences.Methods.All the undergraduate nursing students in the nursing college were requested to participate in this descriptive cross-sectional study. An Arabic version of the Felder-Silverman learning style model (FSLSM) questionnaire was used to examine the learning preferences among undergraduate nursing students.Results.A total of 56 (43%) completed questionnaires were included in the final analysis. Results of the present study indicate that the most common learning preferences among the nursing students were visual (67.9%), followed by active (50%) and sequential (37.5%) learning preferences. The verbal style was the least common learning preference (3.6%) among the nursing students. There was no association between gender and learning preferences (p>.05).Conclusion.The present study concluded that the visual, active, and sequential styles are the commonest learning preferences among the nursing students. The nursing educators should emphasize the use of this information in their teaching methods to improve learning skills among the nursing students.

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Bender, William N. "Inferred Brain Hemispheric Preference and Behavior of Learning Disabled Students." Perceptual and Motor Skills 64, no. 2 (April 1987): 521–22. http://dx.doi.org/10.2466/pms.1987.64.2.521.

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Correlations between teachers' ratings of behavior and a paper-and-pencil score for brain hemispheric preferences were generated for 38 learning disabled and 38 nonlearning disabled children in Grades 3 to 6, to identify previously described relationships for nonhandicapped children. Also, the children were compared on scores for brain hemispheric preference. Only one correlation between the teachers' ratings of behavior and hemispheric preference was noted for each group. Also, F tests indicated no difference in hemispheric preferences between the groups.

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N, Veena. "Learning Preferences among Students." IOSR Journal Of Humanities And Social Science 15, no. 6 (2013): 26–32. http://dx.doi.org/10.9790/0837-1562632.

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Bednar, James A., and Risto Miikkulainen. "Learning Innate Face Preferences." Neural Computation 15, no. 7 (July 1, 2003): 1525–57. http://dx.doi.org/10.1162/089976603321891792.

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New born humans preferentially orient to facelike patterns at birth, but months of experience with faces are required for full face processing abilities to develop. Several models have been proposed for how the interaction of genetic and evironmental influences can explain these data. These models generally assume that the brain areas responsible for newborn orienting responses are not capable of learning and are physically separate from those that later learn from real faces. However, it has been difficult to reconcile these models with recent discoveries of face learning in newborns and young infants. We propose a general mechanism by which genetically specified and environment-driven preferences can coexist in the same visual areas. In particular, newborn face orienting may be the result of prenatal exposure of a learning system to internally generated input patterns, such as those found in PGO waves during REM sleep. Simulating this process with the HLISSOM biological model of the visualsystem, we demonstrate that the combination of learning and internal patterns is an efficient way to specify and develop circuitry for face perception. This prenatal learning can account for the newborn preferences for schematic and photographic images of faces, providing a computational explanation for how genetic influences interact with experience to construct a complex adaptive system.

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Masitowarni, Siregar, and Fahri Haswani. "Learner Types and Their Preferences in Learning English." Budapest International Research and Critics in Linguistics and Education (BirLE) Journal 3, no. 2 (May 8, 2020): 777–83. http://dx.doi.org/10.33258/birle.v3i2.939.

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The research was conducted to find out the most dominant learners’ types and to describe their preferences in learning English at the first semester in academic year 2018/2019English Education Study Program, Languages and Arts Faculty , Universitas Negeri Medan. The subject of the study was one class consisting 30 students. The collecting data in the research was by using learner types and preferences questionnaires and observing the learning process in the classroom. The result showed that there were some types of learners in this case. Concrete learner with their preferences are playing game, paying attention to thepictures, watching film is the most dominant one (40.00%). Preference of analytical learners relates to study grammar and English boo, it also relates to find the learners’ own problems (16.67%). Communicative learners (23.33%) with their preferences tend to listen to native speaker, to talk to friend in English and to watch English channel in you tube. The learners who are authority-oriented (20.00%) with their preferences are understanding the teacher’s explanation and writing everything in a notebook.

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Gao, Yang, Christian M. Meyer, and Iryna Gurevych. "Preference-based interactive multi-document summarisation." Information Retrieval Journal 23, no. 6 (November 19, 2019): 555–85. http://dx.doi.org/10.1007/s10791-019-09367-8.

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AbstractInteractive NLP is a promising paradigm to close the gap between automatic NLP systems and the human upper bound. Preference-based interactive learning has been successfully applied, but the existing methods require several thousand interaction rounds even in simulations with perfect user feedback. In this paper, we study preference-based interactive summarisation. To reduce the number of interaction rounds, we propose the Active Preference-based ReInforcement Learning (APRIL) framework. APRIL uses active learning to query the user, preference learning to learn a summary ranking function from the preferences, and neural Reinforcement learning to efficiently search for the (near-)optimal summary. Our results show that users can easily provide reliable preferences over summaries and that APRIL outperforms the state-of-the-art preference-based interactive method in both simulation and real-user experiments.

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Shrestha, Ashish, Vinay Marla, Sushmita Shrestha, and Jyotsna Rimal. "Learning preferences of undergraduate dental students using VARK analysis." Journal of Kathmandu Medical College 9, no. 4 (December 31, 2020): 207–12. http://dx.doi.org/10.3126/jkmc.v9i4.38093.

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Background: Learning preference is a complex character in which learners most efficiently perceive, process, store, and recall what they are learning. Objectives: This study was conducted with an objective to identify the preferred learning styles of undergraduate dental students such that suitable modification in the teaching learning environment could be made. Methodology: An analytical cross-sectional study was carried at B.P. Koirala Institute of Health Sciences, Nepal among undergraduate dental students from June to December 2019 after getting ethical approval. Convenience sampling method was used to include all undergraduate students from first to final year; of which 252 provided consent. VARK questionnaire (version 7.8) comprising 16 multiple choice questions were used to analyse the learning preferences and were accordingly categorised for further statistical analysis. Results: Out of 280 students, a total of 252 students (90%; 90 males, 162 females) responded to the questionnaire among whom 89 (35.3%) had uni-modal learning preferences. Of the total, 55 (21.8%) had bi-modal, 39 (15.4%) had tri-modal, and 69 (27.3%) had quadri-modal learning preferences. Among the uni-modal preferences, 'K mild' was most common (36, 14.3%) followed by 'A mild'. Among bi-modal learning preferences 'AK type' (35, 13.9%) was most common and VAK type was common among tri-modal learning preferences. 'VARK type two' (33, 13.1%) was common quadri-modal learning preferences. Conclusion: This study highlighted the variations in learning preferences among dental students suggesting the need for a flexible learning environment. Understanding learning preferences can help to plan lessons effectively and help to manage students in a better way.

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POSSAJENNIKOV, ALEX. "COOPERATION AND COMPETITION: LEARNING OF STRATEGIES AND EVOLUTION OF PREFERENCES IN PRISONERS' DILEMMA AND HAWK-DOVE GAMES." International Game Theory Review 07, no. 04 (December 2005): 443–59. http://dx.doi.org/10.1142/s0219198905000636.

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By means of simulations I investigate a two-speed dynamic on strategies and preferences in prisoners' dilemmas and in hawk-dove games. Players learn strategies according to their preferences while evolution leads to a change in the preference composition. With complete information about the preferences of the opponent, cooperation in prisoners' dilemmas is achieved temporarily, with "reciprocal" preferences. In hawk-dove games, a symmetric correlated strategy profile is played that does not place any weight on mutual restraint. Among preferences only "hawkish" preferences and "selfish" preferences survive. With incomplete information, the symmetric equilibrium of the game is played. In prisoners' dilemmas only "selfish" and "reciprocal" preferences survive. In hawk-dove games all preferences are present in the medium run.

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Slater, Jill A., Heidi L. Lujan, and Stephen E. DiCarlo. "Does gender influence learning style preferences of first-year medical students?" Advances in Physiology Education 31, no. 4 (December 2007): 336–42. http://dx.doi.org/10.1152/advan.00010.2007.

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Students have specific learning style preferences, and these preferences may be different between male and female students. Understanding a student's learning style preference is an important consideration when designing classroom instruction. Therefore, we administered the visual, auditory, reading/writing, kinesthetic (VARK) learning preferences questionnaire to our first-year medical students; 38.8% (97 of 250 students) of the students returned the completed questionnaire. Both male (56.1%) and female (56.7%) students preferred multiple modes of information presentation, and the numbers and types of modality combinations were not significantly different between genders. Although not significantly different, the female student population tended to be more diverse than the male population, encompassing a broader range of sensory modality combinations within their preference profiles. Instructors need to be cognizant of these differences and broaden their range of presentation styles accordingly.

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Aggarwal, Manish. "Learning attitudinal decision model through pair-wise preferences." Kybernetes 47, no. 8 (September 3, 2018): 1569–84. http://dx.doi.org/10.1108/k-10-2017-0396.

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Purpose This paper aims to learn a decision-maker’s (DM’s) decision model that is characterized in terms of the attitudinal character and the attributes weight vector, both of which are specific to the DM. The authors take the learning information in the form of the exemplary preferences, given by a DM. The learning approach is formalized by bringing together the recent research in the choice models and machine learning. The study is validated on a set of 12 benchmark data sets. Design/methodology/approach The study includes emerging preference learning algorithms. Findings Learning of a DM’s attitudinal choice model. Originality/value Preferences-based learning of a DM’s attitudinal decision model.

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Mohd Junaid, Thuwaibah, and Nadia Nur Afiqah Ismail. "Learning Style Preferences Among Degree Students in Academic English Course in UTHM." International Journal of Engineering & Technology 7, no. 3.21 (August 8, 2018): 52. http://dx.doi.org/10.14419/ijet.v7i3.21.17095.

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This study was conducted to identify the major learning style preference among UTHM degree students semester 2 2015/2016 in Academic English classroom as well as to identify the differences between the learning style preferences of male and female students. A total of 150students from eight different faculties comprised of 75 male and 75 female students were involved in this study. The Perceptual Learning Style Preferences Questionnaire (PLSPQ) which was developed by Reid (1987) has been used as the instrument of the study. The findings revealed that the major learning style preference of the students was Kinesthetic learning style. Moreover, the findings also revealed that there were no significance differences at α = 0.05 between the learning styles preference of male and female UTHM degree students’ semester 2 2015/2016 in Academic English classroom.

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Smith *, Peter J. "Learning preferences and readiness for online learning." Educational Psychology 25, no. 1 (February 2005): 3–12. http://dx.doi.org/10.1080/0144341042000294868.

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Nawaz, Sobia, Saleha Ahsan, Nadeem Razaq, Saima Rubab Khan, Junaid Iqbal, and Muhammad Saif Ullah. "Problem Based learning by Evaluating Students Learning Preferences Using VARK." Pakistan Journal of Medical and Health Sciences 16, no. 1 (January 30, 2022): 571–73. http://dx.doi.org/10.53350/pjmhs22161571.

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Introduction: Learning methodology preference is one of different components of learning fashion. Sensory learning methodology inclination is one of the various components of learning fashion which decides the person’s ability to obtain modern information. It is one of the dimensions of the complex framework of inclinations that make up a person’s learning fashion. Objective: The objective of the study is to describe the learning styles of medical students. Material & Method Study design: quantitative cross sectional Settings: Continental Medical College, Lahore Duration: Six months i.e. 1st July 2021 to 31st December 2021 Data Collection procedure: It was quantitative cross sectional study conducted on a private sector medical college. Pre validated questionnaire was used to evaluate the students learning preferences using VARK. Results: There are hundred students participating in the study in which sixty were females and forty was males. The average age of the students is around 20-24 years. Mean and standard deviation were calculated after pre and post test. Conclusion: Most students are able to memorize successfully as long as the instructor provides different learning exercises within the zones surveyed in VARK. Dynamic learning might be upgraded in huge classrooms by showing models and demonstrations, discussions, wrangles about, replying questions, and part playing. Keywords: Problem based learning, learning, Preferences, VARK

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Lui, Catherine J., Scott E. Ferrin, Donald R. Baum, and Vance E. Randall. "The Preferred Perceptual Learning Styles of Hispanic Higher Education Students." Journal of Hispanic Higher Education 19, no. 4 (September 26, 2018): 404–21. http://dx.doi.org/10.1177/1538192718801793.

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This article addresses the question of whether higher education Hispanic students of different nationalities have different perceptual learning style preferences. Independent samples t test results suggest students of non-Mexican heritage prefer visual learning styles more than students of Mexican heritage. ANOVA results show older students and students from families with greater levels of education have greater preference for visual learning, and higher household income is related to lower preferences for group learning style, and vice versa.

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Ally, F., D. J. Pillay, and N. Govender. "Teaching and learning considerations during the COVID-19 pandemic: Supporting multimodal student learning preferences." African Journal of Health Professions Education 14, no. 1 (March 18, 2022): 13–16. http://dx.doi.org/10.7196/ajhpe.2022.v14i1.1468.

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Background. The advent of COVID‐19 and the subsequent national lockdown has catapulted higher education institutions into emergency remote teaching (ERT). A principal challenge in this shift is the ability to stimulate student interest towards engagement with, and retention of, course content. The creation of teaching and learning (T&L) resources and activities using a combination of the visual, aural, read/write and kinaesthetic (VARK) modes is fundamental in ensuring student engagement. Objectives. To determine the learning style profiles of undergraduate students and to explore how student learning profiles may be incorporated in T&L approaches during ERT. Methods. This descriptive study profiles the learning preferences of undergraduate students in a health science faculty using the VARK questionnaire. The study further outlines modifications in T&L implemented to support the varied learning preferences during the COVID‐19 ERT response. Results. Our findings demonstrate that the majority of our students have a multimodal learning preference, with the kinaesthetic modality being the most preferred. Voice‐over PowerPoint presentations with transitioning images, and audio files, supported the visual and aural learners through asynchronous engagement. Additionally, online discussion forums and applied projects (such as theme park designs) enhanced asynchronous learning by stimulating the visual, read/write and kinaesthetic preferences, respectively. Microsoft Team sessions with PowerPoint presentations supported visual and aural learning preferences through synchronous engagement. Conclusions. Rethinking traditional T&L approaches towards supporting the diverse student learning preferences is critical in student‐centred T&L amidst the many challenges that ERT has precipitated. Academics need to be dynamic in their T&L approaches and intuitive in their awareness of how subject content may be modified/enhanced in the ERT environment.

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Wouters, Pieter, and Esmee S. van der Meulen. "The Role of Learning Styles in Game-Based Learning." International Journal of Game-Based Learning 10, no. 1 (January 2020): 54–69. http://dx.doi.org/10.4018/ijgbl.2020010104.

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Adapting learning to the level and preferences of learners and game-based learning have increasingly received much attention. The current study examined whether learning styles based on the Felder-Silverman classification (perception, input, processing and organization of information) influence learning in GBL. Only the input and processing scales were found to be reliable. 109 students from prevocational secondary education played a math game (proportional reasoning) after which learning gain and motivation were measured. Gameplay yielded no learning, but a median-split breakdown (prior knowledge) indicated that students with little prior knowledge did learn from the game, while those with much prior knowledge deteriorated. Subsequent multiple regression analyses revealed no predictive effect for motivation. The processing preference was predictive for learning: Students with an active processing preference performed better than students with a reflective processing preference. The discussion discusses consequences and a number of directions for follow-up research.

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Guo, Lei, Hongzhi Yin, Tong Chen, Xiangliang Zhang, and Kai Zheng. "Hierarchical Hyperedge Embedding-Based Representation Learning for Group Recommendation." ACM Transactions on Information Systems 40, no. 1 (January 31, 2022): 1–27. http://dx.doi.org/10.1145/3457949.

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Group recommendation aims to recommend items to a group of users. In this work, we study group recommendation in a particular scenario, namely occasional group recommendation, where groups are formed ad hoc and users may just constitute a group for the first time—that is, the historical group-item interaction records are highly limited. Most state-of-the-art works have addressed the challenge by aggregating group members’ personal preferences to learn the group representation. However, the representation learning for a group is most complex beyond the aggregation or fusion of group member representation, as the personal preferences and group preferences may be in different spaces and even orthogonal. In addition, the learned user representation is not accurate due to the sparsity of users’ interaction data. Moreover, the group similarity in terms of common group members has been overlooked, which, however, has the great potential to improve the group representation learning. In this work, we focus on addressing the aforementioned challenges in the group representation learning task, and devise a hierarchical hyperedge embedding-based group recommender, namely HyperGroup. Specifically, we propose to leverage the user-user interactions to alleviate the sparsity issue of user-item interactions, and design a graph neural network-based representation learning network to enhance the learning of individuals’ preferences from their friends’ preferences, which provides a solid foundation for learning groups’ preferences. To exploit the group similarity (i.e., overlapping relationships among groups) to learn a more accurate group representation from highly limited group-item interactions, we connect all groups as a network of overlapping sets (a.k.a. hypergraph), and treat the task of group preference learning as embedding hyperedges (i.e., user sets/groups) in a hypergraph, where an inductive hyperedge embedding method is proposed. To further enhance the group-level preference modeling, we develop a joint training strategy to learn both user-item and group-item interactions in the same process. We conduct extensive experiments on two real-world datasets, and the experimental results demonstrate the superiority of our proposed HyperGroup in comparison to the state-of-the-art baselines.

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Monteiro, Diana, and Dr Jogindra Vati. "Preferences for Online Learning among Nursing Students: A Systematic Review." Journal of Nursing Practices and Research 2, no. 2 (August 8, 2022): 17–23. http://dx.doi.org/10.36647/jnpr/02.02.a004.

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Background and objectives: The traditional teaching methods have transitioned to distance learning in all fields of education, including medical and nursing education during the Covid 19 pandemic. We aimed to perform a systematic review on preferences for online modes of learning among nursing students.. Materials and Methods: The investigator conducted a thorough literature search on PubMed and Google scholar using comprehensive search strategies to find published English free full-text scientific articles related to preferences of nursing students to online and face-to-face learning modes. Based on the inclusion criteria, articles were searched. Results: The studies that were selected based on databases give the evidence to convince the different perceptions of nursing students to online learning modes. Out of six, 4 studies show less than 50% of preferences for online learning. Interpretation and conclusions: Current evidence suggests that the online mode of learning has increasing preference along with face-to-face learning in nursing. However, further studies with a larger sample size and longer duration are required. Keywords : Online learning, preferences, nursing students

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Desai, Ruchi, and Manali Shah. "Understanding the Learning Styles of Physiotherapy Students." International Journal of Health Sciences and Research 11, no. 7 (July 12, 2021): 188–93. http://dx.doi.org/10.52403/ijhsr.20210727.

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Physiotherapy students have wide range of diversity in their learning preferences therefore this has been always a challenged for the teachers to meet their demands. Understanding learning style preference encourages both students and teachers to continuously update themselves resulting in greater educational satisfaction. Study was performed to find out differences of learning preferences from first to final year physiotherapy students of LJ Institute of physiotherapy, Ahmedabad, Gujarat. Total 220 physiotherapy students from all four years were invited to participate in study, out of which 161 students (male: 49, female: 112) voluntarily participated in study. A web-based survey was implemented in this study which included VARK questionnaire and we found 72.7% students have multimodal learning style and kinaesthetic was the preferred sensory modalities of learning for most of the years but final year also showed more aural learning. Most of the male students in our study showed kinaesthetic learning and previous year academic performance also has influence on learning preference. Key words: VARK, learning style, Physiotherapy.

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Pettigrew, Frank E., Dorothy B. Zakrajsek, Mary A. Bayless, and Grace Goc-Karp. "Compatibility of Students' Learning and Teaching Styles on Their Ratings of College Teaching." Perceptual and Motor Skills 61, no. 3_suppl (December 1985): 1215–20. http://dx.doi.org/10.2466/pms.1985.61.3f.1215.

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Student's ratings of teaching performance were compared for those whose preferences for learning style were strongly matched ( n = 77) and strongly mismatched ( n = 40) with their instructors' preference of teaching style. Canfield's Learning Styles Inventory and Canfield and Canfield's Instructional Styles Inventory measured learning and teaching style preferences. The rating scale was designed from a catalog of items. Analysis of variance indicated no significant differences in instructors' ratings between the two groups.

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Whillier, Stephney, Reidar P. Lystad, David Abi-Arrage, Christopher McPhie, Samara Johnston, Christopher Williams, and Mark Rice. "The learning style preferences of chiropractic students: A cross-sectional study." Journal of Chiropractic Education 28, no. 1 (March 1, 2014): 21–27. http://dx.doi.org/10.7899/jce-13-25.

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Objective The aims of our study were to measure the learning style preferences of chiropractic students and to assess whether they differ across the 5 years of chiropractic study. Methods A total of 407 (41.4% females) full-degree, undergraduate, and postgraduate students enrolled in an Australian chiropractic program agreed to participate in a cross-sectional survey comprised of basic demographic information and the Visual, Aural, Read/Write, Kinesthetic (VARK) questionnaire, which identifies learning preferences on four different subscales: visual, aural, reading/writing, and kinesthetic. Multivariate analysis of variance and the χ2 test were used to check for differences in continuous (VARK scores) and categorical (VARK category preference) outcome variables. Results The majority of chiropractic students (56.0%) were found to be multimodal learners. Compared to the other learning styles preferences, kinesthetic learning was preferred by a significantly greater proportion of students (65.4%, p < .001) and received a significantly greater mean VARK score (5.66 ± 2.47, p < .001). Conclusions To the best of our knowledge, this is the first time chiropractic students have been shown to be largely multimodal learners with a preference for kinesthetic learning. While this knowledge may be beneficial in the structuring of future curricula, more thorough research must be conducted to show any beneficial relationship between learning style preferences and teaching methods.

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Arif, Muhammad, Muhammad Danial, and Nurhaeni Nurhaeni. "STUDENTS’ PERCEPTUAL ENGLISH LEARNING STYLE; MAJOR AND MINOR PREFERENCES IN HIGHER EDUCATION." LLT Journal: A Journal on Language and Language Teaching 24, no. 2 (November 26, 2021): 401–11. http://dx.doi.org/10.24071/llt.v24i2.3499.

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Every students have different ways in collecting, processing, and understanding information. Those refers to the preferences of students in learning or known as learning styles. Therefore, this study aimed to identify and recognize the students’ major and minor perceptual learning style preferences in English Education Department at Universitas Sulawesi Barat. This research was used quantitative approach by using survey design. This research was conducted in English Education Department at Universitas Sulawesi Barat with 79 respondents or college students who were selected by Saturated sampling. The data of this research were collected by using Reid’s (1987) Perceptual Learning Style Preferences Questionnaire (PLSPQ). The data were analysed with self-scoring based on Reid (1978) and descriptive statistic by using SPSS Software Statistic Version 20. The findings of the research shows the averages of the learning style of college students in English Education Department at Universitas Sulawesi Barat the year 2019 was categorized as none major perceptual learning styles preference. On the other hand, they were categorized as minor perceptual learning styles preference, in which kinaesthetic learning style had highest mean with (37.84) than another learning style, and followed by group learning style with mean (37.67), tactile with mean (36.91), auditory with mean (36.55), visual with mean (35.87), and individual learning style with mean (34.73). Even though they were categorized as minor perceptual learning styles preference, but the students still be able to learn better by their high minor condition.

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Fitkov-Norris, Elena, and Ara Yeghiazarian. "Are learning preferences really a myth? Exploring the mapping between study approaches and mode of learning preferences." ACTA IMEKO 10, no. 2 (June 29, 2021): 185. http://dx.doi.org/10.21014/acta_imeko.v10i2.565.

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This article tested for the presence of the conversion effect in the mapping related to the strength of students’ preferences for receiving information in a visual, auditory, read/write or kinaesthetic modality and the study approaches they adopt when taking notes in class, learning new concepts and revising for exams. The results indicated that the conversion effect is not ubiquitous but is context specific and only present when students seek to learn a new concept and revise for exams. It was present for students with strong visual and read/write preferences but only when attempting to learn a new concept. It was also present for students with a strong auditory preference when revising for exams, while these students preferred to learn a new concept by reading about it. However, the conversion effect did not emerge with kinaesthetic-leaning students in any of the contexts studied, while these students were significantly more likely to utilise auditory input when learning a new concept. Overall, the findings suggest that traditional educational approaches such as lectures and tutorials can be effective in supporting the learning for diverse student groups.

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Yousef, Darwish Abdulrahman. "The use of the learning styles questionnaire (LSQ) in the United Arab Emirates." Quality Assurance in Education 24, no. 4 (September 5, 2016): 490–506. http://dx.doi.org/10.1108/qae-03-2016-0010.

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Purpose The purpose of this study is to examine the use of Honey and Mumford’s (1986) learning styles questionnaire (LSQ) in the context of United Arab Emirates (UAE) higher education. In particular, it aims at exploring the learning style preferences of United Arab Emirates University (UAEU) students using LSQ. It also investigates whether there are statistically significant differences in students’ learning style preferences because of their demographic and academic characteristics. Design/methodology/approach Data were collected from a sample of 1,463 undergraduate students at the UAEU. Descriptive and inferential statistics were used to present the main characteristics of respondents, to explore the learning style preferences of UAEU students and to find out whether there are significant differences in students’ learning style preferences because of their demographic and academic characteristics. Findings Results indicated that UAEU students have strong preferences for the four learning styles. Results showed that about 68 per cent of UAEU students have strong or very strong preferences for the activist leaning style, whereas about 84 per cent have strong or very strong preferences for the reflector learning style, 78 per cent have strong or very strong preferences for the theorist learning style; about 60 per cent have strong or very strong preferences for the pragmatist learning style. Furthermore, there were statistically significant differences in certain learning styles because of students’ demographic and academic characteristics. Research limitations/implications There are a number of limitations associated with this study. First, data were collected from a single university in the UAE. Second, the results are based on a self-report survey and this in turn might affect the reliability of the results. Another limitation is that this study is of snapshot type. Hence, it might not capture the dynamic nature of learning style. On the other hand, it has a number of implications for students, educators and administrators. Originality/value The present study is the first attempt to explore learning styles preference of undergraduate students using LSQ, not only in the content of UAE higher education but also in the Arab world.

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Khadka, Krishna Kumari. "Learning Strategies and Learner Preferences." Rupantaran: A Multidisciplinary Journal 3 (October 2, 2020): 62–75. http://dx.doi.org/10.3126/rupantaran.v3i0.31742.

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Learning and teaching is a difficult job. Moreover, there are different ways of learning for all students. This article highlights how different learners learn in a different way, i.e. what different strategies they use in learning. All learners are different. And, this is more specifically based up on the students in that they have their own individual techniques in learning. We have rarely been able to address the individual variations in a class. Building on a survey study, this article emphasizes the fact that the techniques and learning strategies of the learners be addressed. For the purpose of the study, 50 students from a school of Lalitpur District were purposively selected and inquired via a questionnaire regarding the strategies they more preferred. They were form the secondary level. The article concludes that like other level learners, they also have various ways of doing the things during learning and this is to be understood by every teacher.

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Rashid Ch, Dr Abdul, Dr Irshad Nabi Sandhu, Dr Muhammad Arif Ali, and Dr Asad Ali Sandhu. "LEARNING PREFERENCES; TEACHING MEDICAL FACULTY." Professional Medical Journal 20, no. 10 (October 1, 2015): 1351–55. http://dx.doi.org/10.17957/tpmj/15.2834.

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Feng, Yifan, René Caldentey, and Christopher Thomas Ryan. "Robust Learning of Consumer Preferences." Operations Research 70, no. 2 (March 2022): 918–62. http://dx.doi.org/10.1287/opre.2021.2157.

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When companies develop new products, there are often competing designs from which to choose to take to market. How to decide? Traditional methods, such as focus groups, do not scale to the modern marketplace in which tastes evolve rapidly. In “Robust Learning of Consumer Preferences,” Feng, Caldentey, and Ryan develop a data-driven approach to deciding which design to produce by displaying a sequence of subsets of possible designs to potential customers. Their framework finds solutions that are robust to any model of consumer choice within a broad family that includes common choice models studied in the literature as special cases. Previous research focuses on algorithms whose performances are tied to a given choice model. Their algorithm is shown to be asymptotically optimal in a worst-case sense. The promising practical performance of the algorithm is demonstrated through a comprehensive numerical study using real data.

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Huertas-Abril, Cristina A., and Barbara Muszyńska. "Learning Design Preferences in LMOOCs." International Journal of Computer-Assisted Language Learning and Teaching 12, no. 1 (January 2022): 1–17. http://dx.doi.org/10.4018/ijcallt.291106.

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Massive Open Online Courses (MOOCs) have come to stay supported by the development of educational technologies, and within them Language MOOCs (LMOOCs) are a phenomenon that has risen expectations but also shown their limitations. This study aims at comparing students’ preferences from two universities (UCO, Spain, and ULS, Poland) regarding the learning design preferences of LMOOCs and analyze whether there are differences based on sociocultural context, gender and educational stage. The respondents (n = 260) stated to be in favor of a balance between the constructivist and instructionist educational practices in online language courses. The findings reveal significant differences regarding LMOOCs learning and feedback design in terms of gender and sociocultural context, while no significant differences were found between undergraduate and postgraduate students. These results may be used to plan innovative and effective learning situations that suit learners’ needs and preferences, which should lead to higher quality of learning, and higher learner engagement and satisfaction.

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William, Ray D. "061 LEARNING PREFERENCES IN HORTICULTURE." HortScience 29, no. 5 (May 1994): 436f—436. http://dx.doi.org/10.21273/hortsci.29.5.436f.

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Learning style preferences contribute to predictable actions by people. Basic researchers are fundamentally different than applied researchers in horticulture. Dilemmas associated with pesticides, worker protection, water, and labor issues often are related to differences in perception by relational versus linear thinkers. A participatory discussion will focus on these differences and how they can be combined to create dynamic and creative learning around complex issues facing horticulturists and consumers during the 1990's.

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Almeida, Patrícia, and Rita Mendes. "Learning Style Preferences across Disciplines." International Journal of Diversity in Organizations, Communities, and Nations: Annual Review 10, no. 2 (2010): 285–302. http://dx.doi.org/10.18848/1447-9532/cgp/v10i02/39847.

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Farias, Vivek F., and Andrew A. Li. "Learning Preferences with Side Information." Management Science 65, no. 7 (July 2019): 3131–49. http://dx.doi.org/10.1287/mnsc.2018.3092.

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PAUL, S., M. BOJANCZYK, and J. H. LANPHEAR. "Learning preferences of medical students." Medical Education 28, no. 3 (May 1994): 180–86. http://dx.doi.org/10.1111/j.1365-2923.1994.tb02696.x.

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Hartley, James. "Individual preferences in e-learning." British Journal of Educational Technology 35, no. 3 (May 2004): 383–84. http://dx.doi.org/10.1111/j.0007-1013.2004.397_5.x.

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Hartley, James. "Individual preferences in e-learning." British Journal of Educational Technology 36, no. 4 (July 2005): 695–96. http://dx.doi.org/10.1111/j.1467-8535.2005.00547_3.x.

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Journal articles on the topic 'Learning preferences'

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