User Learning Style Research Articles

Hybrid attribute-based recommender system for personalized e-learning with emphasis on cold start problem

This article introduces a recommendation system that merges a knowledge-based (attribute-based) approach with collaborative filtering, specifically addressing the challenges of the pure-cold start scenario in personalized e-learning. The system generates learning recommendations by assessing item similarities, utilizing the Rogers-Tanimoto similarity measure for materials and users, and Jaccard's similarity for user comparisons. Unlike traditional collaborative methods relying on prior ratings, this approach depends on attributes. Additionally, user and learning material profiling structures were created to serve as fundamental inputs for the recommendation algorithm. These profiles represent student and material knowledge in a two-dimensional space to facilitate matching. Our processes incorporate user learning styles, preferences, and prior knowledge as metrics for achieving the desired level of personalization. The system produces a list of top recommendations based on predicted ratings. To validate its efficacy, a website resembling our learning platform was developed and tested by users. The primary results demonstrate the system's ability to identify similar users even in a pure cold start condition without existing ratings. Consequently, the system proves its capability in recommending suitable materials, modeling students, and identifying similar user groups. The evaluation results of the proposed system showed a good level of satisfaction by the testimonials, quantified by a score of 82% for the recommended materials (16% higher than exiting cold-start systems), and an average score of 90% in terms of satisfaction about the generated student profiles. As they proved the capability of the framework in recommending suitable materials, and its capability in modeling students, finding similar groups of users.

Microlearning-based Tourism Arabic Teaching Material Model for Tourism Awareness Groups in Tourism Villages

The present imperative for members of the tourism awareness group (Pokdarwis) in the tourist village is to acquire proficiency in Arabic communication skills. This study aims to provide instructional materials for teaching Arabic language skills to members of the tourism village Pokdarwis, with a specific focus on the field of tourism. To achieve this, a micro-learning technique was employed. The study primarily addressed the constraints of the problem, which pertained to the absence of tourist Arabic teaching materials specifically designed for members of the tourism village Pokdarwis. The study also aimed to explore the process of developing these materials. The research employed the methodology of development research, commonly referred to as R&D. The development paradigm employed in this study is Thiagarajan’s 4D model, comprising four distinct stages: definition, design, development, and dissemination. The research methodology employed in this study encompassed the utilization of questionnaires and unstructured interviews as the primary instruments. The research was conducted on 30 individuals who were members of the tourism town of Pokdarwis. Based on the research results, it was evident that the individuals affiliated with the Pokdarwis tourist village required Arabic language competency to facilitate the growth and advancement of tourist villages. Hence, there is a necessity for the development of Arabic instructional resources tailored specifically for the aim of facilitating tourism in tourist villages, with an emphasis on the incorporation of micro-learning principles characterized by brevity and technology-driven approaches. The design of the teaching materials developed aligns with the learning style of prospective users, as indicated by the assessment results from learning design experts and learning media experts. Additionally, the design of the teaching materials adheres to the principles of micro-learning-based instructional materials.
 Keywords: character education, Pancasila learner profile, proverbs, Sumbawanese

Modeling students' preferences and knowledge for improving educational achievements

Student modeling is a fundamental aspect in customized learning environments. It enables unified representation of students' characteristics that supports creating personalized learning experiences. This paper aims to build an effective student model by combining learning preferences with skill levels. A student profile is formulated upon detecting the user's learning styles and learning preferences, as well as their knowledge level and misconceptions. The pieces of information are collected through an interactive online platform, by completing personal and knowledge assessment quizzes. Moreover, a learner can make his/her profile open for other learners as a starting point for supporting collaborative learning. The results showed an improvement of students' educational achievements who used the platform, and the satisfaction level reported by non-neutral users was averaged as a score of 90%. The evaluation of this platform showed promising results regarding its ability in describing students in a comprehensive manner.

Empowering Operator 5.0: human-centric design of an augmented reality tool for a learning factory

This study presents a human-centric design and prototype development of an Augmented Reality (AR)-based assistance tool aimed at supporting industrial maintenance technicians. Situated within the LEONARDO learning factory at the University of Calabria, the case study explores the potential of AR technology to prioritize the well-being, safety, and skill development of operators. Leveraging the 3I design thinking model, the study emphasizes the importance of inspiration, ideation, and implementation in developing effective AR solutions. Through user interviews and iterative design processes, the research identifies key insights into user preferences and learning styles, highlighting the need for practical, interactive educational methodologies. The resulting AR tool features intuitive guidance, task management support, safety reminders, troubleshooting assistance, training resources, and performance tracking functionalities, aligning with the principles of human-centric augmentation. The study underscores the significance of integrating human-centric values with technological advancements in fostering a proactive learning attitude among operators in smart manufacturing environments.

Integration of Gamified Elements and Learning Style Data in Online Learning System

The increase in usage of Online Learning systems has provided a challenge to deliver e-contents based on user needs. This study uses learning style data to determine the learning profile and provide the content based on the user's needs, whereas gamified elements are used to increase user engagement. The combination of these two approaches would eventually increase the overall user motivation and interest in the platform. The model developed is used to predict the Learning Style (LS) preference by analyzing the activity data obtained from the student interaction in the course. Further, clustering is done to determine the LS preference of the user in the system. Content is designed based on user's learning styles and preferences and uploaded to the platform. Data were collected through surveys after the end of the course to measure students’ perception of the three variables: motivation, learning satisfaction, and learning outcomes. The findings from the study suggest that the students showed high motivation, positive learning outcomes, and satisfaction with the developed contents.

Napping Areas and Nap Pods in Academic Library

Background of the study: The library is a place for users to find information, as well as a place to improve the intellectual quality of users. Along with changes in learning styles and obtaining changing user information, libraries are required to follow the development and learning styles of users by providing supporting facilities and infrastructure, one of which is napping areas or nap pods. Purpose: The purpose of this study is to determine the importance of implementing a napping area in the library. Method: This study uses qualitative research with a literature study approach that emphasizes the use of reference sources to obtain data from literature. Findings: The results of the study indicate that the provision of a napping area as a temporary resting place for users needs to be facilitated by libraries in universities considering that developed countries have also implemented these facilities. This is done so that users feel comfortable and refreshed to restart their activities in doing academic tasks and improve quality in the world of education. Conclusion: The importance of implementing a napping area in the library can also provide a unique experience and increase the number of visitors who are decreasing day by day, on the other hand it also increases learning commons as an interesting renewal in the library with the existence of a napping area in the library.

Автоматизация разработки адаптивного электронного обучения с использованием технологии блокчейн

The low percentage of completion of online courses in the framework of the modern mass online education may be related to the lack of adaptation for a specific user of an online course of a learning path unifying all the stu-dents. The solution to the problem can be adaptive learning, which modifies the learning process for different students depending on their characteristics and learning conditions. The purpose of the study is to automate the process of developing adaptive e-learning by using the blockchain technology. The results of a study, in which adaptive learning was organized on the basis of the Felder-Silverman learning styles model was used as input data for the experiment. Applying the mechanism of smart contracts, the blockchain technology registered learning events, which made it possible to determine the learning style of users directly during the learning process. The chosen model of learning styles, as well as the number of categories of students, were configured exclusively in the smart contract code, which made it possible to talk about the universality and scalability of the process of organizing adaptive e-learning using the blockchain. The technology also ensured the reliability of data storage and traceability. It documents heterogeneous data in a single storage space adding a timemarker and the author's address to each recorded event. The proposed method also unifies the organization of adaptive e-learning, since it is suitable for different models of learning styles with different parameters. The automated process does not require the development of preliminary tests and is free from biased assessments from the students in the learning course

Exploring the Determinants of Behavioral Outcome: A Study of Online Learning Among College Students in Mexico

We investigated the effects of prior visits to a given web site, social media mode and learning style of Internet users on their Behavioral Outcome based on an online search task. Currently, little research attention is given to Mexico regarding this topic. The research methodology was a laboratory experiment conducted in a university in Mexico, with a total sample of 120 subjects. The experimental design was a randomized complete block design. There were eight block-treatment (2 x 4) effects, assigned randomly to the subjects. The data analysis of the results and contributions of the study are presented and interpreted within the framework of Kolb’s experiential learning theory.

Student learning style in vocational higher education

When student learning styles are known, teaching can be designed according to student interests through teaching strategies, teaching methods and techniques, and selecting and applying the necessary teaching tools. This research aims to describe vocational students' learning styles using Forster learning styles and investigate the relationship between these learning styles and mathematics learning achievement. Researchers analyzed vocational students' mathematics learning styles using the "How Do I Learn?" developed by Forster. By using a screening model and quantitative research methods, data collected. The collected data from the instrument containing questions related to the student's mathematics learning style are then processed using SPSS, analyzed using descriptive statistics and one-way analysis of variance (ANOVA). Descriptive statistics were applied to view participant demographics and an overview of each learning style score, while one-way analysis of variance (ANOVA) was conducted to assess differences in mathematics learning styles among students based on mathematics scores. The results showed no relationship found between reflective, curious, diligent, and user learning styles using the questionnaire developed by Foster and the students' mathematics scores.

Learning Styles and Dyslexia Types – Understanding Their Relationship and its Benefits in Adaptive E-learning Systems

Learning is a fundamental element of people’s everyday lives. Learning experiences can take the form of our interactions with others, through attending an educational establishment, etc. Not everyone learns in the same way, and even people who are considered to have a similar standard of abilities or proficiency will exhibit different learning styles. This does not necessarily mean that some students are better than others; it means that students are different from one another. Adaptive e-learning system should be capable of adapting the content to the user learning style, abilities and knowledge level. In this paper, we investigate the benefits of incorporating learning styles and dyslexia type in adaptive e-learning systems. Adaptivity aspects based on dyslexia type and learning styles enrich each other, enabling systems to provide learners with materials which fit their needs more accurately. Besides, consideration of learning styles and dyslexia type can contribute to more accurate student modelling. In this paper, the relationship between learning styles, the Felder–Silverman learning style model (FSLSM), and dyslexia type, is investigated. These relationships will lead to a more reliable student model.

Hybridisation of RF(Xgb) to improve the tree-based algorithms in learning style prediction

<span lang="EN-US">This paper presents hybridization of Random Forest (RF) and Extreme Gradient Boosting (Xgb), named RF(Xgb) to improve the tree-based algorithms in learning style prediction. Learning style of specific users in an online learning system is determined based on their interaction and behavior towards the system. The most common online learning theory used in determining the learning style is the Felder-Silverman’s Learning Style Model (FSLSM). Many researchers have proposed machine learning algorithms to establish learning style by using the log file attributes. This helps in determining the learning style automatically. However, current researches still perform poorly, where the range of accuracy is between 58%-89%. Hence, RF(Xgb) is proposed to help in improving the learning style prediction. This hybrid algorithm was further enhanced by optimizing its parameters. From the experiments, RF(Xgb) was proven to be more effective, with accuracy of 96% compared to J48 and LSID-ANN algorithm from previous literature.</span>

Hybrid Adaptive Educational Hypermedia Recommender Accommodating User’s Learning Style and Web Page Features

Personalized recommenders have proved to be of use as a solution to reduce the information overload ‎problem. Especially in Adaptive Hypermedia System, a recommender is the main module that delivers ‎suitable learning objects to learners. Recommenders suffer from the cold-start and the sparsity problems. ‎Furthermore, obtaining learner’s preferences is cumbersome. Most studies have only focused on similarity ‎between the interest profile of a user and those of others. However, it can lead to the gray-sheep problem, ‎in which users with consistently different opinions from the group do not benefit from this approach. On ‎this basis, matching the learner’s learning style with the web page features and mining specific attributes ‎is more desirable. The primary contribution of this research is to introduce a feature-based recommender ‎system that delivers educational web pages according to the user's individual learning style. We propose an ‎Educational Resource recommender system which interacts with the users based on their learning style ‎and cognitive traits. The learning style determination is based on Felder-Silverman theory. Furthermore, ‎we incorporate all explicit/implicit data features of a page and the elements contained in them that have an ‎influence on the quality of recommendation and help the system make more effective recommendations.‎

The Mathematics Learning Styles of Vocational College Students

<p style="text-align:justify">The researcher investigated the mathematics learning styles of vocational college students during fall 2017 and spring 2018. 94 students enrolled in the vocational college of a public university completed the questionnaire "How Do I Actually Learn?" developed by Forster. Scale has four learning styles: reflective, inquisitive, diligent and user. The results of the research revealed that the most preferred learning model was user learning style in mathematics learning. Most of the students preferred to learn mathematics by writing the solutions, and reading through their notes or work. There were also statistically significant differences in mathematics learning styles among students according to their academic achievement. According to the results, students with high level of success were more inquisitive than students with low levels.</p>

ERP Training: A Framework to Develop Appropriate Training to Accommodate the Variety of End-user Learning Styles in Thailand

This article proposes an ERP training framework to accommodate the various learning styles of Thai users by analyzing the current ERP training approach and the learning styles of Thai users, including learning style and cultural learning behavior, which are reflected through the training outcomes of various end-user learning style groups. A qualitative research approach was employed in this study. Semi-structured interview and questionnaire were used as instruments to collect data based on seventy-two end-users who had participated in ERP training in implementation projects at twenty-two companies in Thailand. The recommendations for an ERP training framework were developed based on four dimensions of the current approach, which largely failed to accommodate Thai users’ various learning styles. This framework would help to improve ERP training quality, which is a key success factor in ERP implementation.

Adaptive Educational Hypermedia Systems based on Learning Styles: the Case of Adaptation Rules

This paper investigates the pedagogical basis of Adaptive Educational Hypermedia Systems (AEHS) that incorporate Learning Styles in order to accommodate user's learning style preferences and needs. Therefore, AES adapt the learning content, its presentation and navigation to the user's learning style preferences. We collect thirty three (33) Adaptive and Intelligent Web-based Educational Systems (AIWBES) that incorporate learning styles and discuss twenty of them, namely the AEHS, as the remaining are Intelligent Tutorng Systems. The main achievement of this work is the investigation of AEHS' pedagogical basis in terms of adaptation rules. We conclude that these systems follow similar patterns in their adaptation logic.

Heterogeneous Link Prediction Technique in Personalized E-Learning System using SVM

Personalized learning system is an online educational system; it is used to predict the learner's most prepared learning styles. Preferences of learning styles are found through way of linking different types of materials over the web. Deal of these website links, the link prediction is introduced; it is a problem technique in many online social networks, online E-learning centers, its forums and communities. In general, the link prediction techniques deal with homogeneous way of predicting the user's learning style in online social networks and educational systems. However, a new challenging problem in link prediction is to analyze heterogeneous learner's (e.g. male and female) based on their preference of learning styles in selecting the links. A day-by-day research works are being incorporated in the technology-enhanced between heterogeneous entity and their perception level. This research work mainly focus on examining the information of different learning styles and prediction of more preferred learning styles links by classification models through Weka tool. The experiment was examined using various data mining classification models, where Support vector machine (SVM) classification model has achieved good performance result and has predicted the learner's potential by analyzing the most representative learner's’ features of perception level and gender. Finally the research work has concluded how these representative learner's features are incorporated with learning style dimensions as well as better learning model is used on the system.

Integrated Model for E-Learning Acceptance

E-learning is not going to work if the system is not used in accordance with user needs. User Interface is very important to encourage using the application. Many theories had discuss about user interface usability evaluation and technology acceptance separately, actually why we do not make it correlation between interface usability evaluation and user acceptance to enhance e-learning process. Therefore, the evaluation model for e-learning interface acceptance is considered important to investigate. The aim of this study is to propose the integrated e-learning user interface acceptance evaluation model. This model was combined some theories of e-learning interface measurement such as, user learning style, usability evaluation, and the user benefit. We formulated in constructive questionnaires which were shared at 125 English Language School (ELS) students. This research statistics used Structural Equation Model using LISREL v8.80 and MANOVA analysis.

The Minimum Normalized Dissimilarity between Objects based Rough Set Technique for Elucidating Learning Styles in E-learning

Objectives: The objective of this research work is to analyse the learning styles of individual users in an e-learning system and to formulate a mathematical model to determine it. Methods: This research work proposes MNDBO, a rough set based clustering technique for elucidating learning styles by finding minimum normalized dissimilarity between objects in e-learning. The proposed clustering technique uses a normalized score value for estimating the deviation between data’s through the equivalence property of rough set theory. Findings: Further, the result predicts that the clusters produced by MNDBO algorithm perform better than MADO by 11%, 14% than SDR and 16% than MMR in terms of cohesion. Furthermore, MNDBO algorithm also produces better results than MADO by 15%, 23% than SDR and 27% than MMR in terms of coupling. In addition MNDBO algorithm maximizes the cohesion and simultaneously reduces the coupling rate based on varying number of cluster size on an average 15% and 19% respectively. Applications/Improvements: If this Rough set based clustering technique is used means we can able to discover successfully relations with inconsistent or incomplete data.

Influence of learning styles on social structures in online learning environments

AbstractIn e‐learning settings, the interactions of students with one another, with the course content and with the instructors generate a considerable amount of information that may be useful for understanding how people learn online. The objective of the present research was to use social network analysis to explore the social structure of an e‐learning environment and analyse how the structure depends on user learning style. Applying Felder and Silverman's inventory of learning styles to an online course with 214 adult graduate‐level participants, we found that some styles occupied more central positions than others. These findings may help designers improve learning activities by tailoring them to the learning styles of the participants.

Service based AEHS for human centric learning environments

All of system and process in this world is for human. Thus, system designers and developers consider the human characteristics at first when they think and design their system. For this consideration, user oriented learning system was introduced in education area. Especially, AEHS aims to provide learning contents that fits the individual learning style of users. That is, AEHS analyzes users' learning patterns and their learning process and according to the analyzed learning performance, the system identifies the most appropriate process for each user. But, in AEHS, it is difficult for users to choose learning contents fitted for their preference or learning ability. Also, system development process has many changes from traditional ways to new technology, ubiquitous and cloud computing. This change is to provide more convenient and efficient system process to developers and users. In this paper, we proposed AEHS considering user's characteristics in cloud computing environment. For this environment, we use the process as a service for learning system. In order to consider user's characteristics, we use a method that enables users to choose a content delivery and a difficulty level of the learning contents according to their styles such as gender, learning experience and preferences. For this process, we added the module, learning contents preference and learning difficulty preference, to user profile of AEHS.