scholarly journals Bioinformatics-Based Adaptive System towards Real-Time Dynamic E-learning Content Personalization

2020 ◽  
Vol 10 (2) ◽  
pp. 42
Author(s):  
Othmar Othmar Mwambe ◽  
Phan Xuan Tan ◽  
Eiji Kamioka
Keyword(s):  
Learning Styles ◽  
Real Time ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Accurate Information ◽  
Interactive Learning Environment ◽  
Time Dynamic ◽  
E Learning ◽  
Time Basis ◽  
User Friendly

Adaptive Educational Hypermedia Systems (AEHS) play a crucial role in supporting adaptive learning and immensely outperform learner-control based systems. AEHS’ page indexing and hyperspace rely mostly on navigation supports which provide the learners with a user-friendly interactive learning environment. Such AEHS features provide the systems with a unique ability to adapt learners’ preferences. However, obtaining timely and accurate information for their adaptive decision-making process is still a challenge due to the dynamic understanding of individual learner. This causes a spontaneous changing of learners’ learning styles that makes hard for system developers to integrate learning objects with learning styles on real-time basis. Thus, in previous research studies, multiple levels navigation supports have been applied to solve this problem. However, this approach destroys their learning motivation because of imposing time and work overload on learners. To address such a challenge, this study proposes a bioinformatics-based adaptive navigation support that was initiated by the alternation of learners’ motivation states on a real-time basis. EyeTracking sensor and adaptive time-locked Learning Objects (LOs) were used. Hence, learners’ pupil size dilation and reading and reaction time were used for the adaption process and evaluation. The results show that the proposed approach improved the AEHS adaptive process and increased learners’ performance up to 78%.

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

2018 ◽  
Vol 2 (4) ◽  
pp. 271 ◽  
Author(s):  
Outmane Bourkoukou ◽  
Essaid El Bachari
Keyword(s):  
Learning Style ◽  
Recommender System ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Research Field ◽  
Learning System ◽  
K Nearest Neighbors ◽  
E Learning ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.

scholarly journals From Cognitive Landscapes to Digital Hyperscapes

2003 ◽  
Vol 4 (2) ◽  
Author(s):  
Jose Bidarra ◽  
Ana Dias
Keyword(s):  
Learning Styles ◽  
Learning Management Systems ◽  
Learning Objects ◽  
Management Systems ◽  
Digital Information ◽  
Cognitive Profiles ◽  
E Learning ◽  
Learning Management ◽  
Key Terms ◽  
Massive Information

<P> The widespread diffusion of e-Learning in organizations has encouraged the discovery of more effective ways for conveying digital information to learners, for instance, via the commonly called Learning Management Systems (LMS). A problem that we have identified is that cognitive variables and pedagogical processes are rarely taken into consideration and sometimes are confused with the mere use by learners of “diversified” hypermedia resources. Within the context of widespread dissemination of multimedia content that has followed the emergence of massive information resources, we discuss the need for more powerful and effective learner-centered tools capable of handling all kinds of design configurations and learning objects. </p> <P class=abstract><B>Key Terms: </B>cognitive profiles, learning styles, mind mapping, multimedia and hypermedia content, hyperscapes, e-Learning, learning objects, Learning Management Systems (LMS).</P>

A Real-time Dynamic Concept Adaptive Learning Algorithm for Exploitability Prediction

2021 ◽  
Author(s):  
Jiao Yin ◽  
Ming Jian Tang ◽  
Jinli Cao ◽  
Hua Wang ◽  
Mingshan You
Keyword(s):  
Real Time ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Time Dynamic

scholarly journals Towards an Educational Platform with Real-Time Collaboration and Monitoring of Students Achievement

2020 ◽  
Author(s):  
Paulo De Souza ◽  
Wagner Marques ◽  
Jaline Mombach
Keyword(s):  
Software Engineering ◽  
Real Time ◽  
Learning Environments ◽  
Virtual Learning ◽  
Learning Objects ◽  
Virtual Learning Environments ◽  
Educational Systems ◽  
E Learning

Several studies have been undertaken aiming to improve the efficiency of e-learning through the development of features to Virtual Learning Environments. However, such researches have no focus on the use of collaboration of learning objects and analysis of students’ progress in real-time. Hence, this paper presents an educational platform that allows real-time co-authorship and monitoring of students’ progress in learning objects, through the implementation of software engineering techniques and patterns designed for educational systems.

scholarly journals Development & Evaluation of E-Learning Module Based on Visual and Global Preferences Using a User-Centered Design Approach

2021 ◽  
Vol 16 (15) ◽  
pp. 139
Author(s):  
Harry Budi Santoso ◽  
Panca O. Hadi Putra ◽  
Febrian Fikar Farras Hendra S
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Interaction Design ◽  
Interface Design ◽  
Learning Objects ◽  
User Centered Design ◽  
Alternative Design ◽  
Learning Module ◽  
E Learning ◽  
User Centered

Students develop various learning styles based on their preferences and learning habits. To serve different learning styles in a class with a number of students using the conventional face-to-face teaching method is not practical; therefore, the idea of personalized e-Learning to accommodate differences in learning style has arisen. Building on this idea, this research intends to provide an alternative interaction design for e-Learning modules by developing content based on user needs using the User-Centered Design methodology. Due to a lack of e-Learning content for visual and global preferences in the Felder-Silverman learning styles, User-Centered Design is chosen as the basis to design the e-Learning module. The result consists of an alternative design and a proposed interface design. The alternative design describes learning objects and navigation of the e-Learning module. The proposed interface design is a prototype of an interactive e-Learning module. After being evaluated, the prototype satisfies the user's expectations in terms of content translation, content navigation, and interactivity throughout the module.

An Adaptive e-learning Platform for Personalized Course Generation

2008 ◽  
pp. 262-282 ◽  
Author(s):  
Enver Sangineto
Keyword(s):  
Learning Styles ◽  
Learning Objects ◽  
Learning Preferences ◽  
Domain Specific ◽  
Learning Platform ◽  
E Learning ◽  
Funded Project ◽  
Learning Platforms ◽  
Didactic Material ◽  
Methodological Aspects

In this chapter we show the technical and methodological aspects of an e-learning platform for automatic course personalization built during the European funded project Diogene. The system we propose is composed of different knowledge modules and some inference tools. The knowledge modules represent the system’s information about both the domain-specific didactic material and the student model. By exploiting such information the system automatically builds courses whose didactic material is customized to meet the current student’s degree of knowledge and her/his learning preferences. Concerning the latter, we have adopted the Felder and Silverman’s pedagogical approach in order to match the student’s learning styles with the system Learning Objects’ types. Finally, we take care to describe the system’s didactic material by means of some present standards for e-learning in order to allow knowledge sharing with other e-learning platforms and knowledge searching by means of possible Semantic Web information retrieval facilities.

Validation of Learning Effort Algorithm for Real-Time Non-Interfering Based Diagnostic Technique

2014 ◽  
pp. 206-219
Author(s):  
Pi-Shan Hsu ◽  
Te-Jeng Chang
Keyword(s):  
Real Time ◽  
Questionnaire Survey ◽  
Diagnostic Technique ◽  
Learning Context ◽  
Learning Platform ◽  
E Learning ◽  
Tutor System ◽  
Intelligent Tutor System ◽  
User Friendly ◽  
Intelligent Tutor

The objective of this research is to validate the algorithm of learning effort which is an indicator of a new real-time and non-interfering based diagnostic technique. IC3 Mentor, the adaptive e-learning platform fulfilling the requirements of intelligent tutor system, was applied to 165 university students. The learning records of the subjects who attended IC3 Mentor were converted into Characteristic Learning Effort (CLE) curves through the algorithms of learning effort. By evaluating CLE curves and questionnaire survey reports, the findings indicate that the learning effort algorithm is verified to be an effective real-time and non-interfering diagnostic technique. Furthermore, CLE curve is proven to be an effective user-friendly tool for learners and instructors in diagnosing learning progress under adaptive e-learning context. The CLE curve generated by the algorithm of learning effort is a visualized graphic tool which can be applied in the adaptive e-learning platform of education and industry fields.

. Learning analytics technology in adaptive electronic learning environments.

2021 ◽  
Vol 92 (2) ◽  
pp. 144-153
Author(s):  
M.R. Attia ◽  
Keyword(s):  
Learning Styles ◽  
Learning Environments ◽  
Adaptive Learning ◽  
Learning Analytics ◽  
Rapid Identification ◽  
Electronic Learning ◽  
E Learning ◽  
Mental Abilities ◽  
The Difference ◽  
As If

Adaptive e-learning environments are based on diversifying the presentation of content according to the learning styles of each learner, where the content is presented as if it is directed to each student separately, and activities and tests are presented so that they are sensitive to the different styles of learners and suitable for their mental abilities. These environments depend in their design on intelligence, therefore, these environments can analyze the characteristics and capabilities of learners, each separately, and this is done through learning analytics technology that helps in the rapid identification of the patterns of learners and the development of their behavior within the environment. In this article, firstly we review what adaptive learning environments and its characteristics are; the difference between adaptable and adaptive environments; components of adaptive learning environments. Learning analytics technology is also highlighted; and its importance in adaptive e-learning environments.

scholarly journals Teaching and Learning Styles on Moodle: An Analysis of the Effectiveness of Using STEM and Non-STEM Qualifications from a Gender Perspective

2021 ◽  
Vol 13 (3) ◽  
pp. 1166
Author(s):  
María Sáiz-Manzanares ◽  
Raúl Marticorena-Sánchez ◽  
Natalia Muñoz-Rujas ◽  
Sandra Rodríguez-Arribas ◽  
María-Camino Escolar-Llamazares ◽  
...  
Keyword(s):  
Gender Differences ◽  
Learning Styles ◽  
Gender Gap ◽  
Teaching And Learning ◽  
Detailed Examination ◽  
Learning Objects ◽  
Technological Gap ◽  
Teachers And Students ◽  
E Learning ◽  
And Mathematics

Teaching in Higher Education is with increasing frequency completed within a Learning Management System (LMS) environment in the Blended Learning modality. The use of learning objects (activities and resources) offered by LMS means that both teachers and students require training. In addition, gender differences relating to the number of students in STEM (Science, Technology, Engineering, and Mathematics) and Non-STEM courses might have some influence on the use of those learning objects. The study involves 13 teachers (6 experts in e-Learning and 7 non-experts) on 13 academic courses (4 STEM and 9 Non-STEM) and a detailed examination of the logs of 626 students downloaded from the Moodle platform. Our objectives are: (1) To confirm whether significant differences may be found in relation to the use of learning objects (resources and activities) on Moodle, depending on the expertise of the teacher (expert vs. non-expert in e-Learning); (2) To confirm whether there are significant differences between students regarding their use of learning objects, depending on the expertise of the teacher (expert vs. non-expert in e-Learning); (3) To confirm whether there are significant differences for the use of learning objects among students as a function of gender. Differences were found in the use of Moodle learning objects (resources and activities) for teachers and for students depending on the expertise of the teacher. Likewise, differences were found for the use of some learning objects as a function of gender and the degrees that the students were following. Increased technological training for both teachers and students is proposed, especially on Non-STEM qualifications, in order to mitigate the effects of the technological gap and its collateral relation with the gender gap and the digital divide.

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