A novel algorithm for dynamic student profile adaptation based on learning styles

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.E-learning recommendation systems are used to enhance student performance and knowledge by providing tailor- made services based on the students’ preferences and learning styles, which are typically stored in student profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the students’ changing behaviour. In this paper, we introduce new algorithms that are designed to track student learning behaviour patterns, capture their learning styles, and maintain dynamic student profiles within a recommendation system (RS). This paper also proposes a new method to extract features that characterise student behaviour to identify students’ learning styles with respect to the Felder-Silverman learning style model (FSLSM). In order to test the efficiency of the proposed algorithm, we present a series of experiments that use a dataset of real students to demonstrate how our proposed algorithm can effectively model a dynamic student profile and adapt to different student learning behaviour. The results revealed that the students could effectively increase their learning efficiency and quality for the courses when the learning styles are identified, and proper recommendations are made by using our method

Role of Cognitive Style of a Manager in the Development of Tourism Companies’ Dynamic Capabilities

Purpose – The purpose of this paper is to investigate the relationship between cognitive styles of managers working in tourism companies and dynamic capabilities of these companies. Design – The research relies on a quantitative questionnaire. Methodology – To answer the research question, the bivariate (Pearson) correlation was applied. A number of 268 answers from people working in tourism were received. Findings – We found a positive correlation between different dimensions of dynamic capabilities of tourism companies. These capabilities are influenced by managers’ cognitive characteristics. The organizational culture plays a mediating role in the latter relationship. Implications for theory – The paper offers an alternative understanding of dynamic capabilities in tourism and hospitality; the paper also opens new paths for academic research on the impact of cognitive characteristics of managers on the dynamic capabilities of tourism companies. Implications for practitioners – Making accurate psychological portrait of the candidate can predict his/her behavior in certain situation, such as response towards environmental change using dynamic capabilities and when making the necessary changes to the organizational culture. Originality – This study proposes model of influence of a manager’s cognitive style on dynamic capabilities, whereby organizational culture moderates this relationship

On Analysis and Evaluation for Predicting Students’ Academic Performance GPA Considering an Engineering Institution (Neural Networks’ Modeling Approach)

Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results. Educational Institutions face numerous challenges today in providing quality and student-centric education to Students Individual learners prefer their own strategies originated from diverse learning styles. Learning style models may include collective strategies for mental, emotional, and physiological components. On the basis of such components, this piece of research suggests a specific quantified learning style preferred by learners in engineering education. By following average learners’ achievements (marks) at specific courses closely related to the specialization, interesting analytical results for Grade Point Average (GPA) evaluation are obtained. Moreover, an ANN model with supervised learning is presented to simulate diverse learning styles performance. Accordingly, optimal guided advise is suggested in fulfillment of probabilistically best GPA of graduated engineers. Obtained simulation results are well supported by the findings of experimental case study

First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

Use of deep multi-target prediction to identify learning styles

It is possible to classify students according to the manner they recognize, process, and store information. This classification should be considered when developing adaptive e-learning systems. It also creates a comprehension of the different styles students demonstrate while in the process of learning, which can help adaptive e-learning systems offer advice and instructions to students, teachers, administrators, and parents in order to optimize students’ learning processes. Moreover, e-learning systems using computational and statistical algorithms to analyze students’ learning may offer the opportunity to complement traditional learning evaluation methods with new ones based on analytical intelligence. In this work, we propose a method based on deep multi-target prediction algorithm using Felder–Silverman learning styles model to improve students’ learning evaluation using feature selection, learning styles models, and multiple target classification. As a result, we present a set of features and a model based on an artificial neural network to investigate the possibility of improving the accuracy of automatic learning styles identification. The obtained results show that learning styles allow adaptive e-learning systems to improve the learning processes of students105Applied machine learnin

Teaching Ways and Learning Ways Revisited

In learning and teaching of languages, numerous theories have been put forward. These theories, normally influenced by developments in the fields of linguistics and psychology, have inspired several approaches to the teaching of second and foreign languages. This paper revisits English language teaching approaches, both traditional and modern, as well as learning styles and teaching styles. Such learning style models as The Myers-Briggs Type Indicator (MBTI); Kolb’s Experiential Learning Model; the Felder-Silverman Learning Styles Model are explored in-depth in this paper. Keywords: teaching approach; learning style; teaching styl

Adaptive learning: a cluster-based literature review (2011-2022)

Adaptive learning is a personalized instruction system that adjusts to the needs, preferences, and progress of learners. This paper reviews the current and future developments of adaptive learning in higher education, especially in relation to the digital education strategy of the European Union. It also uses a cluster analysis framework to explore the main themes and their relationships in the academic literature on adaptive learning. The paper highlights the potential of emerging technologies such as AI, eye-tracking, and physiological measurements to improve the personalization and effectiveness of adaptive learning systems. It presents various methods, algorithms, and prototypes that incorporate learning styles into adaptive learning. It also stresses the importance of continuous professional development in e-learning, media literacy, computer security, and andragogy for teachers who use adaptive learning systems. The paper concludes that adaptive learning can promote creativity, innovation, and lifelong learning in Ukrainian higher education, but it also acknowledges the challenges and suggests further research to assess its impact

Effects of Felder-Silverman and Honey-Mumford learning model on students’ in technical college

In Nigeria, enhancing instructional delivery in Radio, Television and Electronics (RTVE) as a trade subject in technical colleges has become a great concern for technical teachers as it focuses on overcoming the challenges of assisting the learner to learn by enhancing their cognitive achievement and interest. The purpose of this study was to determine the effect of Felder-Silverman and Honey-Mumford learning models on students’ achievement and interest in RTVE. Two research questions and three hypotheses tested at .05 level of significance guided the study. The study adopted quasi-experimental design. The population for the study consisted of 60 National Technical Certificate level II (NTC II) RTVE students in Technical colleges in Nigeria Federal Capital Territory, Abuja. A population of 60 students consisting of 50 males and 10 females were assigned to two treatment groups. The instruments for data collection were Radio, Television and Electronics Achievement Test (RTVEAT) and Radio, Television and Electronics Interest Inventory (RTVEII). The RTVEAT and RTVEII; Felder-Silverman lesson plans and Honey-Mumford lesson plans were validation. The test-retest reliability was determined using Pearson Product Moment Correlation Coefficient and was found to be .91, while the internal consistency of the RTVEAT was determined by Kuder-Richardson 20 (KR20). The RTVEII was subjected to construct validation using principal component analysis. A total of 24 items were selected for the study in the interest inventory. The internal consistency estimate of the RTVEII was determined using Cronbach Alpha technique and was found to be .82. Mean was used to answer the research questions while, Analysis of Covariance (ANCOVA) was used to test the four hypotheses that guided the study at .05 level of probability. The study found out, among others, that Felder-Silverman learning model is more effective than Honey-Mumford learning model in improving students’ achievement and interest in RTVE. There was an effect of gender on students’ achievement and interest in favour of females. Gender had no significant effect on students’ achievement. The study found no significant effect of treatments and gender on students’ achievement and interest in RTVE. The study recommended among others that Felder-Silverman learning model should be adopted in the teaching/learning of RTVE in Technical Colleges. In addition, workshops, seminars and conferences should be organized by Federal and State Science and Technical Schools Board to enlighten and train RTVE teachers on the application of Felder-Silverman learning model for improving students’ achievement and interest in studying RTVEInstitute for Science and Technology Education (ISTE

Mining online diaries for blogger identification

In this paper, we present an investigation of authorship identification on personal blogs or diaries, which are different from other types of text such as essays, emails, or articles based on the text properties. The investigation utilizes couple of intuitive feature sets and studies various parameters that affect the identification performance. Many studies manipulated the problem of authorship identification in manually collected corpora, but only few utilized real data from existing blogs. The complexity of the language model in personal blogs is motivating to identify the correspondent author. The main contribution of this work is at least three folds. Firstly, we utilize the LIWC and MRC feature sets together, which have been developed with Psychology background, for the first time for authorship identification on personal blogs. Secondly, we analyze the effect of various parameters, and feature sets, on the identification performance. This includes the number of authors in the data corpus, the post size or the word count, and the number of posts for each author. Finally, we study applying authorship identification over a limited set of users that have a common personality attributes. This analysis is motivated by the lack of standard or solid recommendations in literature for such task, especially in the domain of personal blogs. The results and evaluation show that the utilized features are compact while their performance is highly comparable with other larger feature sets. The analysis also confirmed the most effective parameters, their ranges in the data corpus, and the usefulness of the common users classifier in improving the performance, for the author identification task