Adaptive Learning Management Research Articles

AI, Personalized Education, and Challenges

Artificial Intelligence (AI) is gaining traction in education, with potential applications that range from personalized learning to automated administrative tasks. However, the integration of AI into educational systems is not without its challenges. This paper explores both the opportunities and obstacles that AI presents in the field of education, particularly through the use of Intelligent Tutoring Systems (ITS) and Adaptive Learning Management Systems (ALMS). These technologies aim to tailor learning experiences to individual students by analyzing data and adjusting content to suit their needs. While this personalized approach could enhance student engagement and comprehension, it relies on vast amounts of data, raising concerns about privacy and the potential misuse of personal information. Moreover, AI’s impact on education extends to supporting educators by providing insights into student performance and automating routine tasks. However, the effectiveness of AI systems in this regard remains questionable, particularly when considering the limitations in current AI models and the challenges of integrating them into existing educational frameworks. The risk of algorithmic bias is also a critical issue, as AI systems can inadvertently reinforce inequalities present in the data they are trained on, leading to unfair or discriminatory outcomes. Additionally, while AI promises to streamline certain aspects of education, there are concerns that over-reliance on technology could depersonalize the learning process. Human educators play a crucial role in fostering not only intellectual growth but also emotional and social development—elements that AI systems are currently unable to replicate. This paper argues that while AI holds significant promise in education, its deployment must be carefully managed, with attention to ethical considerations, equity in access, and the preservation of the human elements essential to effective learning. Addressing these challenges is key to ensuring that AI contributes meaningfully to education rather than exacerbating existing issues.

AI-powered Leadership in Moroccan Organizations: An Integrative Literature Review

This integrative literature review (ILR) examines how we can exploit AI to enhance organizational leadership in Morocco, where there is a notable lack of awareness among leaders and IT policymakers about the revolutionary potential of AI-powered leadership. This ILR aims to address this knowledge gap by looking at the application and impact of strategic analytics and adaptive machine learning management in critical industries such as banking, healthcare, tourism, manufacturing, and logistics. These AI-driven techniques get value for their ability to alter leadership by improving Moroccan decision-makers capacities in driving competitiveness and organizational sustainability. The review thoroughly illuminates the power of AI-driven leadership, which Strategic Analytics and Adaptive Machine Learning Management trigger. The study's conceptual framework revolves around four key pillars: AI, strategic analytics, leadership, and adaptive machine learning management. This ILR employs inclusion and exclusion criteria, data extraction, data analysis and synthesis, and conceptual mapping to ensure the rigor and comprehensiveness of the research material. The findings of this ILR highlight the significance of implementing strategic analytics and adaptive machine learning management into Morocco's leadership practices. They illuminate the need to develop a strong leadership pipeline, conduct collaborative research, establish policy frameworks, safeguard data, foster innovation in management, and train the workforce. This review offers suggestions for forthcoming policies and practices, emphasizing the application of adaptive machine learning management and strategic analytics. These recommendations can significantly influence the practice of AI-powered leadership in Moroccan organizations and offer valuable insights for future research.

Implementation Of The Use Of Artificial Intelligence In Higher Education

Higher education is currently undergoing significant digital transformation, with artificial intelligence (AI) emerging as a key innovation shaping this change. This research elaborates on the implementation and impact of AI in the context of higher education. The discussion encompasses the influence of AI on the learning process, improvements in administrative efficiency, and shifts in the education paradigm. The research also explores specific AI applications, such as adaptive learning, sentiment analysis, and data management, which substantially enrich student learning experiences and support decision-making in higher education institutions. While the benefits are substantial, the research also highlights challenges and issues that may arise, including privacy concerns, data security, and the integration of technology into academic curricula. In conclusion, the implementation of artificial intelligence in higher education has the potential to be a cornerstone in shaping students who are prepared for the demands of knowledge-based societies and contributing to the development of a more adaptive and innovative education system.

Using Machine Learning Techniques to Enhance Adaptive Learning Management System in The Case of Kenyan Universities.

Using Machine Learning Techniques to Enhance Adaptive Learning Management System in The Case of Kenyan Universities.

Toward an artificial intelligence-based decision framework for developing adaptive e-learning systems to impact learners’ emotions

ABSTRACT Learners’ emotional states might change during the learning process, and unpredictable variations of a person’s emotions raise the demand for regular assessment of feelings during learning. In this paper, an AI-based decision framework is proposed and implemented for e-learning systems that identify suitable micro-brake activities based on the learner’s emotional state through an evolutionary genetic algorithm to change learner’s mood and increase learning performance. This proposed framework was tested using a case study of English as a second language learner during one semester. The students were divided into two groups of participants (each group containing twenty students, forming a total of 40 students). The results of this study demonstrated the importance of learners’ emotions in their learning performance and proved the effectiveness of our proposed framework and the success of the recommended micro-break activities chosen based on learners’ emotions and preferences. The findings of this study have important practical implications in designing adaptive e-learning systems and learning management systems such as Moodle. They also contribute to theoretical implications in the field of AI and learner emotions by suggesting a novel approach to identifying, categorizing, and offering a learning path that can cater to the needs of individual learners.

Strengthening COVID-19 pandemic response coordination through public health emergency operations centres (PHEOC) in Africa: Review of a multi-faceted knowledge management and sharing approach, 2020-2021.

The coronavirus disease 2019 (COVID-19) pandemic disrupted health security program implementation and incremental gains achieved after the West African Ebola outbreak in 2016 across Africa. Following cancellation of in-person events, a multi-faceted intervention program was established in May 2020 by Africa Centres for Disease Control and Prevention (Africa CDC), the World Health Organisation, and partners to strengthen national COVID-19 response coordination through public health emergency operations centres (PHEOC) utilizing continuous learning, mentorship, and networking. We present the lessons learned and reflection points. A multi-partner program coordination group was established to facilitate interventions' delivery including webinars and virtual community of practice (COP). We retrieved data from Africa CDC's program repository, synthesised major findings and describe these per thematic area. The virtual COP recorded 1,968 members and approximately 300 engagements in its initial three months. Fifty-six webinar sessions were held, providing 97 cumulative learning hours to 12,715 unique participants. Zoom data showed a return rate of 85%; 67% of webinar attendees were from Africa, and about 26 interactions occurred between participants and facilitators per session. Of 4,084 (44%) participants responding to post-session surveys, over 95% rated the topics as being relevant to their work and contributing to improving their understanding of PHEOC operationalisation. In addition, 95% agreed that the simplicity of the training delivery encouraged a greater number of public health staff to participate and spread lessons from it to their own networks. This just-in-time, progressively adaptive multi-faceted learning and knowledge management approach in Africa, with a consequential global audience at the peak of the COVID-19 pandemic, served its intended audience, had a high number of participants from Africa and received greatly satisfactory feedback.

The Effects and Effectiveness of An Adaptive E-Learning System on The Learning Process and Performance of Students

Students acquire learning material in different ways. Some prefer to read, some prefer to listen, others use the third type of sense. Traditional teaching uses only one of these teaching strategies since it is impossible to use all of them in the classroom. However, these days, adaptive e-learning systems enable learning material to be customized to the individual needs of learners. For the purpose of this paper, the researchers designed a model of the adaptive learning management system and implemented it in Moodle. The system was evaluated on 228 students. The incorporation of learning styles in Moodle is based on the VAK learning style model. The authors analysed the effects and effectiveness of an adaptive e-learning system. It was discovered that there are significant differences in learning effectiveness, satisfaction and motivation when students use an adaptive e-learning module in comparison to a standard e-learning module. Moreover, we investigated the durability of knowledge acquired with an adaptive e-learning system by comparing the performance of students not only after the completion of the course but also a month after the course. The results of the research confirmed the authors’ expectations and showed that an adaptive e-learning system can increase students’ learning results. So far, to our knowledge, no study has evaluated the performance between a control and experiment group a few months after the completion of the course, i.e. by analysing the durability of knowledge acquired through an adaptive e-learning system. Moreover, the motivation of students to continue using an adaptive e-learning system hasn’t been analysed until now.

Retraction Note to: Real-time personalization and recommendation in Adaptive Learning Management System

Retraction Note to: Real-time personalization and recommendation in Adaptive Learning Management System

THE DEVELOPMENT OF MATHEMATICAL THINKING THROUGH ADAPTIVE EDUCATION: THE CASE OF A LINEAR ALGEBRA COURSE

The one-size-fits-all model is widely used in teaching and learning processes around the world. This model does not recognize students’ learning conditions and preferences. As an alternative, the adaptive education model has been developing in recent years. In mathematics, the current state of this adaptive education model is oriented to the learning of objects and algorithms and not to the development of mathematical thinking. To overcome this shortcoming, the design-based research approach was used to create and implement an adaptive learning management system framed in a module of a linear algebra course, in which adaptation was done based on modes of thinking (SIERPINSKA, 2000). The results of this implementation show that people manifest diverse modes of thinking when conceiving mathematical objects and that these modes affect the way they learn. These results also highlight some existing limitations for adaptive educational processes such as these to generate greater impacts.

Formation of Individual Educational Trajectory in Adaptive Learning Management Systems

Purpose of the study. The purpose of this article is to consider the theoretical and practical aspects of designing adaptive training courses aimed at creating an individual educational trajectory, taking into account various mechanisms of adaptation.Materials and methods. This study includes a review of bibliographic sources on the formation of an individual learning path and the implementation of adaptive learning in e-learning courses. It also includes constructing of a structural model of an adaptive learning management system with a description of possible adaptation mechanisms that act as parameters for the formation of a personalized learning path, and a description of an example of the implementation of an adaptive course in the LMS of the Bashkir State Agrarian University.Results. An analysis of factors that influence the choice of a professional educational trajectory is presented. At the same time, different levels of formation of factors are described, for instance, such groups as macrofactors, mesofactors and microfactors are distinguished and characterized. The concept of a flexible adaptive learning model is considered and possible resources (mechanisms) for its adaptation are analyzed. With regard to the management model of the formation of an individual trajectory of training, such parameters as the form of training, the content of training, training methods, the organization of e-learning in the electronic training course, the model of a student, and the trajectory of training are distinguished as adaptation resources. A structural model of an adaptive learning management system is being made, including the following series of structural elements: block of educational content; block of the knowledge control, skills and abilities; communicative block; reflection block; personal identification unit; navigation system; learning path control module. Various approaches to the concept of a learner’s model are also considered (in particular, scalar, overlay and cognitive models).To test the adaptive learning model, an electronic course in the discipline “Informatics and Information Technologies” was designed and implemented in the e-learning management system of the Bashkir State Agrarian University on the LMS Moodle platform. The e-course is used in the educational process for full-time and parttime education, as well as a form of support for distance learning during periods of self-isolation. A comparative analysis of the progress of students who study by using linear and adaptive versions of the e-course is carried out. Students studying with the adaptive version of the e-course showed better learning outcomes. From this, it is concluded that the adaptive course allowed students to better adapt to the conditions of forced distance learning in the new reality.Conclusion. An individual educational trajectory is one of the effective ways of realizing the professional and educational potential of an individual and is based on certain forms, methods, technologies and learning mechanisms. Modern e-learning technologies provide vast opportunities for implementing the concept of personalized flexible learning, in particular, using adaptive training courses. In this case, both the parameters of the learning management environment and the characteristics of the students can act as a resource (parameter) of adaptation.

Adaptive learning management expert system with evolving knowledge base and enhanced learnability

There exist numerous resources online to gain the desired level of knowledge on any topic. However, this complicates the process of selecting the most appropriate resources. Every learner differs in terms of their learning speed, proficiency, and preferred mode of learning. This paper develops an adaptive learning management system to tackle this challenge. It creates a customized course for every student based on their level of knowledge, preferred mode of learning and continuously updates the course based on their learning speed. The material is filtered from a knowledge base that is dynamically updated using web scraping and ranked using feedback from students on the relevance and quality of each material. The model is tested in two phases: the content generation algorithm and the learnability of the system as a whole. The evaluation is done both quantitatively and qualitatively and validated with statistical analysis. Real-time testing of the system shows state-of-the-art performance.

USING CLOUD PLATFORMS TO BUILD DISTRIBUTED LEARNING MANAGEMENT SYSTEMS

Distributed systems have problems with downtime, data loss during malfunctions, scalability and efficient use of computing resources. At the same time in the learning and training process, the use of a distributed system has the advantage of data processing: storage of information about students, construction of training courses, verification of passed material, etc. The problems of scaling and efficient use of resources in distributed learning management systems are investigated in this research. Cloud platforms for hosting the system, such as Amazon Web Services, Microsoft Azure, Google Cloud Platform and DigitalOcean are reviewed. Problems and features of a scalability in cloud computing are discussed. Methods, scaling and load balancing algorithms for the efficient use of computing resources are proposed. According to the list of advantages, the DigitalOcean platform was selected for the investigation. DigitalOcean provides cloud servers that can be used for quick creation of the new virtual machines for the projects. These servers allow to fully control the web hosting environment at the same time that the user pays only for the resources used. The main goal of DigitalOcean is to use a solid-state drive (SSD) to create a user-friendly platform that will allow clients to migrate projects to and from the cloud, increasing productivity with high speed and efficiency. As a result of analyzing information on existing technologies, approaches and methods for using cloud platforms in distributed systems, they have been applied to develop a solution to reduce downtime for a distributed adaptive Learning Management System (LMS). It is concluded that the use of cloud platforms for the construction of distributed LMS a practice that allows to use only the required amount of computing capacity. It is proven, that the implementation of the proposed solution into the work of adaptive LMS will improve its efficiency by reducing the time of the content delivering.

Design and Development of Adaptive Online Learning Management System for Harmony

Design and Development of Adaptive Online Learning Management System for Harmony

Students’ use of learning management systems and desired e-learning experiences: are they ready for next generation digital learning environments?

ABSTRACT Despite continual upgrades, students tend to use the administrative functions of learning management systems more frequently than their learning applications. As learning management systems morph into next generation digital learning environments that are capable of supporting user accessibility through content creation and curation, integrated systems interoperability, personalised adaptive learning, collaborative learning, and analytics-driven performance management, students’ readiness to engage with these system functionalities needs to be considered. Through a survey of 262 Fine Arts majors, this study examines how students use learning management systems and if their desired e-learning experiences correspond to the functional dimensions of next generation digital learning environments. The results show that when students frequently use learning management systems for content learning and discussion, they also desire to engage in student-centred e-learning activities and to use e-learning systems that support content curation, private group management, and mobile interoperability. These findings suggest that students’ current experiences shape how they desire to use e-learning systems. To foster student-centred digital learning experiences, both infrastructural upgrades and faculty preparation need to be considered in tandem. The implications of these findings for higher education institutions that are seeking to implement next generation digital learning environments are discussed.

RETRACTED ARTICLE: Real-time personalization and recommendation in Adaptive Learning Management System

Various e-learning environments have been developed to provide sufficient materials for learners and thereby guide them to gain knowledge in any domain. Even though there were several factors that determine the motivation of the student, the skill set possessed by them is definitely an important factor. Hence in our proposed work, the behavioural and educational skill of the learners is tested by a skill test and learning content is provided only based on their skill test evaluation reports. The entire process is done by real time personalization based Adaptive Learning Management System and Personalized Page Rank algorithm. Finally, the Navies’ Bayes classifier was employed to classify the learners based on the performance over the skill test. Learners on their own can express both the optimistic and adverse skills that considerably impact the adaptive learning in e-learning environment. High skilled categorized learners are offered with advanced, intermediate learners with moderate and beginner or slow learners are provided with basic level of study content. The results are analysed based on their skills, individual's method of learning and time constraints.

Complexity Leadership Theory and its contribution to decentralized water management analysis and practice

Water management systems in the world have evolved considreably in the last three decades from the traditional "prediction and control"(Phl-Wostl, 2006. p49) approach to more integrative approaches.(Biswas,2004,@004, p24). Practioners in the discipline may find inspiration from a number of leadership approaches in the literature that include transformational, intgrative, collabroative and entrepreneurial leadership, inter alia(Meijerink, S.2011). Another recent and interesting addition to leaadership literature is found in Complexity Leadership Theory(CLT) as developed by UHl-Bien, Marion and Bill McKelvey(@007). It is premised on on seeking understanding on how organisations respond to pressures in the modern knowledge era, in the contemporary and current work environment as well as in evolving circumstances. The model draws from complexity science, which is the " study of the behaviour of large collections of ....simple, interacting units endowed with the potential to evolve with time"(Coveney, 2003, p.1058 cited in Uhl-Bien et al 2007, p.299). In this respect the central tenet behind Complexity leadership theory is the use of the concept of complex adaptive systems(CAS) where issues of leadership , which is understood as the capacity to influence others, are seen as not as positions of authority but roles, are entangled between members of an organisation and "can be enacted within everyinteraction between members"(Lichenstein and Plowman 2009, p.618). In unpacking the conceptual framework behind CLT, there a reflection on the dynamic relationship between the bureacratic, administrative functions of the organisations and the emergent, informal dynamics of complex adaptive systems(CAS).The current state of water management, given the competing demands of the biosphere and the socio-sphere are amenable to be relooked at using the CLT lens. The concept of adaptive water management of water resources(Pahl-Wostl, 2006,p.49) is reviewed in realtion to CLT.
 Key words: complexity theory, complex adaptive systems, creativity, learning and adaptive water management
 

Conceptual design of the new generation adaptive learning management system

Taking into account that each new day an amount of daily processed data grows exponentially, it is obvious that knowledge society needs flexible, effective and high performing tools to retrieve, learn and apply necessary information. Learning management systems become more and more intelligent and adaptive. Learning analytics instruments give course developers a possibility to assess and analyze learners’ activities and behavior patterns within e-learning environment, allowing to propose them personalized self-paced learning path and types of learning objects. This paper discusses challenges in development of adaptive learning management system and outlines its prospective models and properties.

Smart adaptive learning management application

Smart adaptive learning management application

Fuzzy based approach for adaptivity evaluation of web based open source Learning Management Systems

Adaptive Learning Management System is a concept that has been recently explored to meet the individual needs of learners. Research studies regarding learning environments’, adaptation and personalization indicate that they play a key role to inspire, motivate and engage learners. The Learning Management Systems (LMSs) in this regard are currently facing the challenge of adapting the entire learning procedure according to the individual learning needs. Presently, there are numerous open source, custom developed, and commercial LMSs available. This paper presents an evaluation of web based open source LMSs since they can be easily supplemented and integrated with other software solutions than commercial and custom developed systems. The aim of this evaluation is to find a LMS platform that is most appropriate and has the potential for augmenting to an adaptive one. Initially, the evaluation is based on Elimination by Aspects approach for the qualification and selection of LMSs. Afterwards, the selected nine LMSs were evaluated using Linear Weighted approach (LWA) and fuzzy logic approach. The LWA was applied by assigning performance ratings to the overall categorical as well as adaptivity features. Finally the fuzzy logic was applied to the assigned ratings of each feature for the fuzzification of crisp set values. The results indicate that Moodle outperforms other platforms in overall functionally as well as in adaptation category.

Study of learner Behavior and Learning Styles on the Adaptive Learning Management System Manhali: Results and Analysis According to Gender and Academic Performance

Study of learner Behavior and Learning Styles on the Adaptive Learning Management System Manhali: Results and Analysis According to Gender and Academic Performance