Adaptive E-learning Environment Research Articles

The impact of information technology for online learning with the implementation and involvement of students in the creation of conditions and environment

Adaptive e-learning is considered a stimulus to assist learning and increase student engagement. Hence, building suitable adaptive e-learning environments helps to individualize education to reinforce learning goals. This work aims to create an adaptable e-learning environment based on students' learning styles and examine the effect of the adaptive e-learning environment on student engagement. This study also contrasts the suggested adaptive e-learning environment with the standard e-learning strategy. The suggested adaptive e-learning strategy and findings may assist e-learning institutions in building and implementing more individualized and adaptable e-learning environments to increase student engagement. The work is based on mixed research methodologies used to examine the effect in the following manner: The adaptive e-learning environment is designed using the development technique, a quasi-experimental research methodology for performing the research experiment. The student engagement scale measures the following affective and behavioral factors: skills, participation/interaction, performance, and emotions. According to the data, the experimental group is statistically substantially higher than the control group. These experimental findings suggest that an adaptable e-learning environment can motivate pupils to learn. This study makes many practical recommendations: how to develop a foundation for adaptive e-learning based on learning styles and how to execute it; how to improve the impact of adaptive e-learning in education; and how to boost the cost-effectiveness of education.

Implementing adaptive e-learning for newborn care in Tanzania: an observational study of provider engagement and knowledge gains

IntroductionTo improve healthcare provider knowledge of Tanzanian newborn care guidelines, we developed adaptive Essential and Sick Newborn Care (aESNC), an adaptive e-learning environment. The objectives of this study were to...

The Adoption of AI-Enabled Adaptive e-Learning Environment in Palestinian Schools: Integrating Extended Technology Acceptance Model and System Success Model

The Adoption of AI-Enabled Adaptive e-Learning Environment in Palestinian Schools: Integrating Extended Technology Acceptance Model and System Success Model

Developing typical profiles of generation Z students to improve the quality of the educational process

Global digital platforms that influence the development of local digital platforms (from national-level platforms to the platform of a particular enterprise), in order to be more effective, must take into account the informational requirements of users, including those determined by their emotional and psychological characteristics. For this purpose, their developers need to know the typical emotional and psychological profiles of users.The construction of typical emotional and psychological profiles of Generation Z learners is necessary for correct and efficient operation of information and educational platforms, in particular, adaptive e-learning environments, which, among other things, implement gamification approaches.To identify the above-mentioned profiles of Generation Z learners, we use R. Cattell’s multifactor testing methodology (form C) and subsequent processing of the obtained results using statistical methods (in the form of cluster analysis). To ensure the representativeness of the results, the study involved 994 students from various universities (the survey results were used in an impersonal form).The analysis of clustering results makes it possible to identify the share of students who are able to independently form new knowledge and obtain original results of intellectual activity, to determine the share of students with a pragmatic attitude to life, as well as those who possess the qualities of business leadership, etc.Based on these results, recommendations for improving the quality of the educational process as a special case of adaptive e-learning environment are formulated.

Development of pediatric acute care education (PACE): An adaptive electronic learning (e-learning) environment for healthcare providers in Tanzania.

Globally, inadequate healthcare provider (HCP) proficiency with evidence-based guidelines contributes to millions of newborn, infant, and child deaths each year. HCP guideline proficiency would improve patient outcomes. Conventional (in person) HCP in-service education is limited in 4 ways: reach, scalability, adaptability, and the ability to contextualize. Adaptive e-learning environments (AEE), a subdomain of e-learning, incorporate artificial intelligence technology to create a unique cognitive model of each HCP to improve education effectiveness. AEEs that use existing internet access and personal mobile devices may overcome limits of conventional education. This paper provides an overview of the development of our AEE HCP in-service education, Pediatric Acute Care Education (PACE). PACE uses an innovative approach to address HCPs' proficiency in evidence-based guidelines for care of newborns, infants, and children. PACE is novel in 2 ways: 1) its patient-centric approach using clinical audit data or frontline provider input to determine content and 2) its ability to incorporate refresher learning over time to solidify knowledge gains. We describe PACE's integration into the Pediatric Association of Tanzania's (PAT) Clinical Learning Network (CLN), a multifaceted intervention to improve facility-based care along a single referral chain. Using principles of co-design, stakeholder meetings modified PACE's characteristics and optimized integration with CLN. We plan to use three-phase, mixed-methods, implementation process. Phase I will examine the feasibility of PACE and refine its components and protocol. Lessons gained from this initial phase will guide the design of Phase II proof of concept studies which will generate insights into the appropriate empirical framework for (Phase III) implementation at scale to examine effectiveness.

A Personality-Based Virtual Tutor for Adaptive Online Learning System

E-learning has become one of the most extensively used electronic systems in the field of education. Despite its benefits, there are some capabilities and concerns that may have a negative impact on students’ performance. As a result, personalized e-learning systems are being developed, which adapt e-learning systems to the users’ personality, knowledge, behavior, interests, or preferences. This will improve the overall learning experience and performance of the students. This study created and tested an e-learning system, called “Cybele” to help students learn cybersecurity in an online mode of learning. “Cybele” is a personality-based virtual instructor for cybersecurity online learning that includes a chatbot built using Rasa Open Source. The paper used Myers-Briggs Type Indicator (MBTI) personality model for initial learner assessment to address various student learning styles for a better online learning experience. Testing was done for the system functionality and the traditional learning approach was compared to the personalized e-learning system. Results show that students who participated in the developed adaptive e-learning environment performed better than those who pursue the traditional learning method.

In-service teachers' TPACK development through an adaptive e-learning environment (ALE).

Due to the effects of the COVID-19 crisis on educational institutions, schools had to close and switch to online education. Training in-service teachers to incorporate and utilize technology as part of Internet-based instructions was a challenge and pressing necessity. TPACK is an essential framework for comprehending how teachers employ technology in teaching. Despite the significance of adaptive learning environments in recent years, research has not addressed how to use these environments to improve the TPACK of in-service teachers, particularly during crises. Consequently, our objective was to design an adaptive learning environment that provides in-service math, science, and English teachers with substantial and continuing support for each TPACK component. A total of 173 in-service teachers were divided into two groups: an experimental group of 83 who used adaptive learning and a control group of 90 who used Zoom techniques. TPACK questionnaires were administered before and after the experiment. The experimental group improved TPACK more than the control group. All teachers believed that adaptive learning training helped them to build technology-integrated lesson plans. This study provides ideas and practices for developing an adaptive learning environment for the in-service teachers' TPACK development. The challenges to adaptive learning environments have been highlighted, identifying the potential for future investigations.

An Approach for Learner Categorization Based on Emotions in Intelligent Adaptive E-Learning Environment

The pandemic across the globe has constrained the change from a conventional face to face to e-learning platforms. The most challenging task during online learning is to be aware and support the emotional side of students. In existing environments, the emotion of the listener consideration is lagging. This can be provided by capturing the emotions of the listener through facial expressions. In general, the most common facial expressions are happy, sad, anger, fear, disgust, neutral and surprise. This knowledge can be used to classify different listeners. Hence in this article, we proposed a novel approach to identify an emotion based learner category in the development of Intelligent Adaptive E-Learning Environment by using Convolution Neural Network. The major work is composed of emotion detection model and learner categorization. The emotion detection model is trained by using a standard FER2013 dataset and it is extended with live streams of learners. The results of emotion detection model are extended to categorize the learners by fusing emotions and comprehend as Active, Evaluative, Passive and Non-Listener. The proposed model is trained using 100 epochs and achieved an accuracy of 94.44% in the training phase. This knowledge helps to interpret learner’s participation in e-learning environment.

MODELLING OF PEDAGOGICAL PATTERNS THROUGH E-LEARNING OBJECTS

Software platforms for e-learning support various options for presenting educational content. One of the ways to organize and structure it is via so-called pedagogical patterns. They are a method for describing and sharing knowledge and practical experience. Pedagogical patterns are used to describe pedagogical situations that occur repeatedly in the learning process. In the context of e-learning systems, there are various approaches to digitalization of pedagogical patterns. The purpose of the paper is to show how to build instances of pedagogical patterns using e-learning pedagogical objects, which can be easily and conveniently used as models in an adaptive e-learning environment. An e-learning pedagogical object is an abstract concept that can be presented in the concrete form of an e-learning object, an e-learning methodological object, an e-learning object for monitoring and diagnostics or an e-learning object with learning outcomes. These objects are building blocks for constructing instances of pedagogical patterns. This paper thoroughly discusses the issue of creating instances of pedagogical patterns of the four types of e-learning pedagogical objects. The instance of a pedagogical pattern is meant to serve to create subsections of educational topics. The instances of the patterns built of e-learning objects are learning units that are used depending on the context of a particular pedagogical situation. The e-learning pedagogical objects and the pedagogical pattern instances are intended to be applied in an adaptive e-learning environment as teaching aids. Their theoretical models are applied in Moodle LMS in line with the tendency for software to assist and replace some of the teacher functions, while the teacher’s role is raised to a higher organizational, pedagogical and methodological level. Three instances of pedagogical patterns have been created through e-learning pedagogical objects: “Early Feedback”, “Feedback Sandwich” and “Consistent Metaphor” in LMS Moodle, which have been tested in the training course “Modeling of training courses in Moodle” during the autumn trimester of the academic year 2021/2022 at the Faculty of Mathematics and Informatics of Paisii Hilendarski University of Plovdiv, Bulgaria.

Automatic Adaptation of Open Educational Resources: An Approach From a Multilevel Methodology Based on Students’ Preferences, Educational Special Needs, Artificial Intelligence and Accessibility Metadata

The need for adaptive e-learning environments that respond to learning variability is now a fundamental requirement in education, as it helps to ensure that students learn and pass their courses within a set time frame. Although guidelines, techniques and methods have been established in recent years to contribute to the development of accessible and adaptable e-learning environments that promote digital inclusion, their implementation is challenging due to the lack of knowledge of an adequate way to do it and because it is considered more of a technological competence for scholars in the area. In this context, automated support for adapting material that responds to the correct use of accessibility metadata not only provides a way to improve the description of adapted educational resources, but also facilitates their search according to the needs and preferences of students, particularly those with disabilities. In this article, we carry out a multilevel methodological proposal for the automatic adaptation of open educational resources, in order to provide a tool that contributes to the accessibility and correct use of their metadata in e-learning environments. A research is conducted with students with disabilities to establish their real needs and preferences, highlighting the need to strengthen the adequate description and coherent alternative text in images, the correct subtitling in videos and the conversion of audio to text, data that are relevant to our proposal. The research conducted aims to contribute with an automated support tool in the generation of accessible educational resources that are correctly labeled for search and reuse. This research also aims to support researchers in artificial intelligence applications to address challenges and opportunities in the field of virtual education, in addition to providing an overview that could help those who generate educational resources and maintain their interest in making them accessible.

The effectiveness of an adaptive e-learning environment based on the different pattern of educational preferences (individual - group) in developing the skills of designing and producing electronic courses according to quality standards for students of the Education Technology Division

The effectiveness of an adaptive e-learning environment based on the different pattern of educational preferences (individual - group) in developing the skills of designing and producing electronic courses according to quality standards for students of the Education Technology Division

The effectiveness of an adaptive e-learning environment based on the different pattern of educational preferences (individual - group) in developing the skills of designing and producing electronic courses according to quality standards for students of the Education Technology Division

The effectiveness of an adaptive e-learning environment based on the different pattern of educational preferences (individual - group) in developing the skills of designing and producing electronic courses according to quality standards for students of the Education Technology Division

Adaptive e-learning environment based on learning styles and its impact on development students' engagement

Adaptive e-learning is viewed as stimulation to support learning and improve student engagement, so designing appropriate adaptive e-learning environments contributes to personalizing instruction to reinforce learning outcomes. The purpose of this paper is to design an adaptive e-learning environment based on students' learning styles and study the impact of the adaptive e-learning environment on students’ engagement. This research attempts as well to outline and compare the proposed adaptive e-learning environment with a conventional e-learning approach. The paper is based on mixed research methods that were used to study the impact as follows: Development method is used in designing the adaptive e-learning environment, a quasi-experimental research design for conducting the research experiment. The student engagement scale is used to measure the following affective and behavioral factors of engagement (skills, participation/interaction, performance, emotional). The results revealed that the experimental group is statistically significantly higher than those in the control group. These experimental results imply the potential of an adaptive e-learning environment to engage students towards learning. Several practical recommendations forward from this paper: how to design a base for adaptive e-learning based on the learning styles and their implementation; how to increase the impact of adaptive e-learning in education; how to raise cost efficiency of education. The proposed adaptive e-learning approach and the results can help e-learning institutes in designing and developing more customized and adaptive e-learning environments to reinforce student engagement.

Examining the Effectiveness of Using Adaptive AI-Enabled e-Learning during the Pandemic of COVID-19.

This study aimed to identify the effect of using adaptive AI-enabled e-learning on developing digital content creative design skills among postgraduate students. The research tools included an achievement test and an observation checklist for rating the practical performance. Research results concluded that, regardless of learning styles, the proposed adaptive e-learning environment had a positive effect on developing both cognitive achievement and practical performance of digital content creative design skills. The results also indicated that there is a significant difference at the 0.01 level between the mean scores of the first experimental group's students using the global learning style-based adaptive e-learning environment and the second experimental group's students using the sequential learning style-based adaptive AI-enabled e-learning environment in the achievement test and observation checklist after measurement of digital content creative design skills in favor of the second experimental group's students. The study provided a number of suggestions and recommendations for making the utmost use of various design layouts of adaptive AI-enabled e-learning environments in developing different cognitive and performance aspects of learning as well as taking full advantage of digital content creative design skills mastery in producing a plethora of advanced electronic educational applications in the foreseeable future.

بيئة تعلم تکيفية وفقًا لأسلوب التعلم لتنمية القابلية للاستخدام لدي التلاميذ المعاقين عقليًا القابلين للتعلم

هدف البحث الحالي إلى تصميم بيئة تعلم تکيفية وفقا لأسلوب التعلم( السمعي- البصري) وقياس أثرها على تنمية القابلية للاستخدام لدي التلاميذ المعاقين عقليا القابلين للتعلم، وللوصول لهذا الهدف تم إعداد قائمة معايير بيئة التعلم الإلکتروني التکيفي في البحث الحالي، وتکونت عينة البحث من (12) تلميذ وتلميذه، وتم تقسيمهم إلى مجموعتين تجريبيتين طبقا لنتيجة حل مقاييس أسلوب التعلم لفيلمنج فارک(VARK)، واستخدم التصميم التجريبي العاملي ۲x1، وتم تطبيق أساليب المعالجة الإحصائية المناسبة باستخدام برنامج 22).SPSSV )، وتوصلت نتائج البحث إلى: فاعلية بيئة التعلم التکيفية في تنمية القابلية للاستخدام لدي التلاميذ المعاقين عقليا القابلين للتعلم. الکلمات المفتاحية: بيئة التعلم التکيفية، أسلوب التعلم (السمعي- البصري)، القابلية للاستخدام. Abstract: The current research aimed to design an adaptive learning environment according to the learning style (audio- visual) and measure its impact on enhancing Usability among Educable Mentally Retarded Students. for reaching this goal, a list of adaptive e-learning environment criteria was prepared in the current research. The research sample consisted of (12) students, and they were divided into two experimental groups according to the results of (VARK) scale introduced to them. the global experimental design 2x1 was used, and appropriate statistical treatment methods were applied using the spss program (v.22).The research results revealed the effectiveness of the adaptive learning environment according to Learning Style for Enhancing Usability among Educable Mentally retarded students. Keywords: Adaptive learning Environment, Learning style( audio- Visual), Usability.

A Review: Artificial Intelligent Approach for Enhancing Adaptability in an Adaptive E-Learning Environment

Adaptive e-learning the aim is to fill the gap between the pupil and the educator by discussing the needs and skills of individual learners. Artificial intelligence strategies that have the potential to simulate human decision-making processes are important around adaptive e-Learning. This paper explores the Artificial techniques; Fuzzy Logic, Neural Networks, Bayesian Networks and Genetic Algorithms, highlighting their contributions to the notion of the adaptability in the sense of Adaptive E-learning. The implementation of Artificial Neural Networks to resolve problems in the current Adaptive e-learning frameworks have been established.

A Review: Artificial Intelligent Approach for Enhancing Adaptability in an Adaptive E-Learning Environment

Adaptive e-learning the aim is to fill the gap between the pupil and the educator by discussing the needs and skills of individual learners. Artificial intelligence strategies that have the potential to simulate human decision-making processes are important around adaptive e-Learning. This paper explores the Artificial techniques; Fuzzy Logic, Neural Networks, Bayesian Networks and Genetic Algorithms, highlighting their contributions to the notion of the adaptability in the sense of Adaptive E-learning. The implementation of Artificial Neural Networks to resolve problems in the current Adaptive e-learning frameworks have been established.

Технология аналитики обучения в адаптивных средах электронного обучения

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.

Modeling adaptive E-Learning environment using facial expressions and fuzzy logic

The integration of two or more intelligent systems, such as deep neural networks and fuzzy techniques, results in so-called hybrid intelligent systems. These have recently attracted considerable attention owing to their broad success in several complex real-world applications. An interesting example is the development of adaptive computer-based learning environments, in which learner responses to certain questions are considered so that the next level of the learning process may be determined. However, these methods fail to capture the behavior and emotional expressions of the learner during the learning process. Capturing these could make the learning flow more adaptive, and could allow the learning environment to redirect learners to different learning paths based on their capabilities and interaction levels. In this regard, the contribution of the present study is threefold. First, it proposes an approach for modeling an intelligent adaptive e-learning environment by considering the integration of the learner responses to questions and their emotional states. In the proposed approach, a loosely coupled integration between a convolutional neural network (CNN) and a fuzzy system is adopted. The CNN is used to detect a learner’s facial expressions, and outperforms other CNN models on the same training benchmark. The fuzzy system is used to determine the next learning level based on the extracted facial expression states from the CNN and several response factors by the learner. Second, the study introduces methods whereby a group of facial expressions is aggregated into a single representative. Third, it introduces corpora for evaluating the performance of the proposed approach. The corpora of 12 learners contain 72 learning activities and 1735 data points of distinct emotional states. The experimental results using these corpora demonstrate that the proposed approach provides adaptive learning flows that match the learning capabilities of all learners in a group. Moreover, the approach allows decision makers to monitor the learning performance for each learner.

Designing an Adaptive E-learning Environment and its Effectiveness in Developing e-Lesson Design Skills for Teacher Students

Designing an Adaptive E-learning Environment and its Effectiveness in Developing e-Lesson Design Skills for Teacher Students