Intelligent Learning Management Research Articles

Intelligent Learning Management Systems (ILMS): Implications for teaching and learning

Intelligent learning management systems (ILMS) are transforming education by integrating artificial intelligence (AI) and machine learning. These systems automate administrative tasks, offer personalized learning experiences, and boost student engagement. This article explores the benefits and challenges of ILMS, highlighting how AI can predict learner behavior, customize educational content, and optimize curriculum management. It also delves into the ethical considerations of AI in education, such as data privacy and algorithmic bias. Finally, the article provides insights into the impact of ILMS on teaching and learning and concludes with a discussion of the future of ILMS.

Research on the Application of Artificial Intelligence in The Teaching Reform of Ideological and Political Courses in Universities

The rapid development of artificial intelligence (AI) technology has brought new opportunities and challenges to the field of education. As an important link to cultivate students' comprehensive quality and socialist core values, it is necessary to carry out continuous teaching reform and innovation. Based on the idea of AI-enabled ideological and political course teaching reform, this study aims to explore how to use artificial intelligence technology to improve the teaching effect and learning experience of ideological and political course. This study first analyzes the application status of artificial intelligence technology in education, and then discusses the application potential of artificial intelligence in ideological and political courses. Then, it puts forward the teaching reform strategy of ideological and political course based on artificial intelligence, including the use of virtual reality technology, the application of intelligent auxiliary teaching tools to enhance personalized teaching, and the construction of intelligent learning management system. Finally, through case analysis, this paper discusses the effect of ideological and political teaching reform in colleges and universities. The results show that the application of artificial intelligence technology can effectively improve the teaching effect and learning experience of ideological and political courses, and provide new ideas and methods for the teaching reform of ideological and political courses in colleges and universities.

Empowerment of Artificial Intelligence in Teaching Reform of Ideological and Political Courses in Universities

The rapid development of artificial intelligence (AI) technology has brought new opportunities and challenges to the field of education. As an important link in cultivating students’ comprehensive quality and socialist core values, it is necessary to carry out continuous teaching reform and innovation in ideological and political courses in colleges and universities. Based on the concept of AI empowering the teaching reform of ideological and political courses, this study aims to explore how to use artificial intelligence technology to improve the teaching effect and learning experience of ideological and political courses. The research first analyzes the application status of artificial intelligence technology in education, and then discusses the application potential of artificial intelligence in ideological and political courses. Subsequently, the teaching reform strategy of ideological and political courses based on artificial intelligence is proposed, including the use of virtual reality technology, the application of intelligent auxiliary teaching tools to enhance personalized teaching, and the construction of an intelligent learning management system. Lastly, a case analysis is conducted to explore the implementation effect of the teaching reform of ideological and political courses in universities. The results showed that the application of artificial intelligence technology can effectively improve the teaching effect and learning experience of ideological and political courses, and provide new ideas and methods for the teaching reform of ideological and political courses in universities.

Constructing hands-on distance labs: the development and implementation of an Intelligent Learning Management System (ILMS-d) in undergraduate IoT courses

ABSTRACT Engineering education emphasizes experiential learning and laboratory experience, an approach which has faced significant challenges during the COVID-19 pandemic. The inability to conduct hands-on laboratory experiments in engineering courses can significantly impede the student's learning experience, as well as their acquisition and retention of knowledge. Hence, it is imperative that practical hands-on laboratory experiments be incorporated as a critical component of distance learning tools. While researchers have proposed multiple methods, the provision of practical hands-on laboratory experience without limitations of time or place remains a challenging issue. This study aimed to address this issue by developing an Intelligent Learning Management System (ILMS), the ILMS-d, to construct a hands-on distance laboratory for engineering courses. We investigated the effect of implementing the ILMS-d in IoT courses on students’ learning achievement and engineering problem-solving skills, and we assessed their acceptance of the proposed system. We found a significant positive effect on the learning achievement and engineering problem-solving skills of high, medium, and low achievers. In addition, students at all achievement levels responded positively to the usefulness and ease of use of the ILMS-d. Future research should incorporate different AI functions into the ILMS-d to construct more adaptive hands-on distance laboratories for students.

Establish the Individual Learning Space and Create the Wisdom Learning Environment

Students’ first-hand experience in intelligent learning, such as discovery, conception, selection, induction, evaluation, etc., is the prerequisite for students to achieve intellectual development. Due to the differences in learners’ cognitive basis and learning styles, students’ learning processes are inevitably personalized and dynamic. Building a personal learning space and establish an intelligent learning context for students is the key to ensuring students go through the learning process smoothly. The personal learning space meets the requirements for dynamic learning paths, resourceful teaching supplies, intelligent learning management, and other intelligent conditions. This article analyzed the structure and function of personal learning space to explore’ intelligent learning further.

Intelligent Learning Management by Means of Multi-sensory Feedback

Along with new manufacturing paradigms such as Industry 4.0, based on cyber-physical systems or ubiquitous manufacturing and the rapid development of underlying technologies, the importance of lifelong learning as an integrated part of the overall activities within manufacturing companies increases. Herewith, a holistic learning culture and modern learning environments are required. To allow the learner to independently acquire knowledge and skills in a learning factory an intelligent learning management system and extensive feedback information to the learner are needed. A central approach is to pursue the new interactive knowledge transfer through the multi-sensory approach combined with innovative feedback processes, enabling a learning process with all human senses.

Use of Semi-Formal and Formal Methods in Requirement Engineering of ILMS

A systematic approach in requirement engineering helps in discovering and understanding the requirements at different levels of abstraction as well as makes the requirements traceable and verifiable early in the project. This paper presents a methodology for requirement engineering by using a combination of formal and semiformal techniques to model software requirements for analysis and verification. The proposed framework incorporates requirement-analysis and requirement-verification models, which are guided by the Domain Model represented by a Conceptual Class diagram and the defined Ontology. The proposed approach is particularly useful when requirements are defined in hierarchical levels of abstraction and traceability between a requirement at a detailed level and its correspondence at an abstract level is required. The proposed framework also allows verification of the accordance of the requirements with respect to the Domain Model. The requirement analysis model at each level of abstraction is represented by a combination of three different modeling techniques: Conceptual Graph, Use-Case diagram and VDM-SL specification. A verification model to ensure traceability between the requirements at different abstraction levels, using a three-dimensional schema, is also proposed. The Requirements of an Intelligent Learning Management System are used as an example to illustrate the proposal.

Intelligent Learning Management Systems

Virtual Learning Environments have been the center of attention in the last few decades and help educators tremendously with providing students with educational resources. Since artificial intelligence was used for educational proposes, learning management system developers showed much interest in making their products smarter and more intelligent. Nevertheless, the questions of what an intelligent learning management system (ILSM) is and which tools and features are needed to make such system intelligent, are not clearly answered, therefore educational institutes do not have a proper tool to decide upon the degree of intelligence they need for their LMSs. This paper proposes a prevalent, thorough definition of “Intelligent Learning Management Systems”, and the design of a fuzzy model to measure the intelligence of these systems. In order to devise a comprehensive definition of an Intelligent Learning Management System, experts from around the world were consulted. Following that, different proposed Intelligent Learning Management Systems were studied, and forty-one features and tools were found and analyzed. After the analysis, experts' opinions were taken into account to rank these features. The paper proposes thirteen most significant features and tools as criteria to be used in fuzzy analytic hierarchy process (AHP) as a fuzzy model to measure the intelligence of Learning Management System.

Design of an Intelligent Agent for Personalization of Moodle’s Contents

This paper presents the architecture of an Intelligent Learning Management System (ILMS) applied to Moodle. Specifically, we present the design and implementation of an agent that select the teaching strategy according to the student's learning style. The chosen teaching strategy is used to filter the learning objects that are displayed to the students.