Adaptive Learning Experiences Research Articles

The Influence of Artificial Intelligence Tools on Student Performance in e-Learning Environments: Case Study

This study investigated the impact of AI-powered personalized learning tools on the academic performance and perceptions of pre-service student teachers enrolled in the Educational Technology course, a compulsory component of the Professional Postgraduate Diploma in Teaching program at Ajman University. A quasi-experimental design was employed with 55 students in the experimental group and 55 in the control group. The experimental group utilized AI-powered tools within the Moodle platform, while the control group received traditional instruction. Pre- and post-tests, along with a rubric-based assessment and a questionnaire, were administered to assess performance, knowledge retention, critical thinking, motivation, and engagement. Statistical analysis revealed significant differences between the groups, favoring the experimental group in terms of academic performance and knowledge retention. Additionally, students in the experimental group demonstrated higher levels of critical thinking, motivation, and engagement. These findings underscore the transformative potential of AI-powered personalized learning tools in teacher education. By integrating AI into educational practices, institutions can revolutionize e-learning by providing personalized instruction, intelligent tutoring, automated grading, and adaptive learning experiences. This can enhance student engagement, improve learning outcomes, and reduce the workload of educators. Ultimately, AI empowers institutions to create more effective and inclusive learning environments that cater to the diverse needs of students and prepare them for the future.

Unveiling the Dynamic Mechanisms of Generative AI in English Language Learning: A Hybrid Study Based on fsQCA and System Dynamics

The burgeoning development of generative artificial intelligence (GenAI) has unleashed transformative potential in reshaping English language education. However, the complex interplay of learner, technology, pedagogy, and contextual factors that shape the effectiveness of GenAI-assisted language learning remains underexplored. This study employed a novel mixed-methods approach, integrating qualitative comparative analysis (QCA) and system dynamics (SD) modeling, to unravel the multi-dimensional, dynamic mechanisms underlying the impact of GenAI on English learning outcomes in higher education. Leveraging a sample of 33 English classes at the Harbin Institute of Technology, the QCA results revealed four distinct configurational paths to high and low learning effectiveness, highlighting the necessary and sufficient conditions for optimal GenAI integration. The SD simulation further captured the emergent, nonlinear feedback processes among learner attributes, human–computer interaction, pedagogical practices, and ethical considerations, shedding light on the temporal evolution of the GenAI-empowered language-learning ecosystem. The findings contribute to the theoretical advancement of intelligent language education by constructing an integrative framework encompassing learner, technology, pedagogy, and context dimensions. Practical implications are generated to guide the responsible design, implementation, and optimization of GenAI in English language education, paving the way for learner-centric, adaptive learning experiences in the intelligence era.

Analyzing the Impact of Learning Technology on the Quality of Education in Schools: Potential and Risks

This research examines the impact of learning technologies on the quality of education in schools, focusing on the potential and associated risks. In the digital era, the integration of technology in the teaching and learning process is becoming increasingly important. This research method uses a qualitative approach with literature study techniques to analyze and understand the impact of learning technology on the quality of education. Learning technology contributes significantly to improving the quality of education in schools, both in terms of accessibility and quality of learning processes. Technology allows students to access a wide range of information sources and learning materials and supports more flexible and interactive learning. With educational apps, learning videos and online platforms, students can study independently outside of school hours and adapt learning to their own style and pace. This helps to create a more personalized and adaptive learning experience, which results in increased student motivation and understanding of the material. However, the application of technology in education also brings a number of risks that need to be taken seriously. One of the main risks is that students' dependence on technological devices can reduce their ability to think critically and solve problems independently. This implies that schools and governments need to develop policies that support the effective application of technology, including teacher training in integrating technology with the curriculum, provision of adequate infrastructure, and efforts to ensure equal access for all students.

Unlocking the power of immersive learning: The FAIRI instructional design proposition for adaptive immersive virtual reality

Immersive Virtual Reality (IVR) is a promising tool for providing immersive and adaptive learning experiences by simulating realistic work environments and gathering data on learner's interactions to offer personalized instructional content. However, IVR-supported instruction presents unique challenges due to its heightened immersion, including managing the learner's cognitive load and self-regulation. Incorporating adaptivity into IVR can mitigate these challenges by regulating the complexity of the virtual environment, reducing cognitive strain, and maintaining learner focus through real-time feedback for both learners and instructors. However, recent reviews highlight the underutilization of adaptivity within IVR research, making it difficult to inform the design of adaptive IVR instruction.This paper proposes FAIRI, an instructional design for adaptive IVR in domain-specific training. FAIRI synthesizes recent IVR research relevant to the design of adaptivity for IVR through a purposive literature review, guided by the instructional design elements of the Four-Component Instructional Design-model (4C/ID) and Intelligent Tutoring Systems (ITS). The instructional design elements of the 4C/ID-model and ITS are evaluated for their potential viability and further refined for adaptive IVR-supported instruction based on recent IVR studies. Two key design principles are identified for adaptive IVR: limiting IVR to tasks that benefit from its heightened realism and leveraging its adaptive features to manage the intensity of the experience. Additionally, the application of FAIRI's design principles and elements are demonstrated through a forklift operator training case in IVR. FAIRI aims to catalyse further research into adaptivity for IVR by proposing and aligning promising instructional design elements for adaptive IVR instruction.

The pedagogical insights of mathematics teachers integrating artificial intelligence in rural upper primary schools of Namibia

This paper presents a qualitative intervention study that explores the pedagogical experiences of mathematics teachers integrating Artificial Intelligence (AI) technologies into teaching mathematics in rural upper primary schools in Namibia. The Technological Pedagogical Content Knowledge (TPACK) framework, which intersects teachers’ understanding of technology integration with pedagogical techniques and subject matter knowledge, was adopted for the study. The research involved 13 mathematics teachers selected through purposive sampling. Data collection included two sharing circle discussions, lesson observations, and semi-structured interviews. A thematic analysis approach was employed to interpret the collected data. The findings reveal a diverse range of AI tools used by the selected teachers, such as Geogebra, Khan Academy, Virtual Tutors, and various problem-solving mobile applications, including Photomath, Mathway, and Microsoft Math Solver. These AI resources were noted for enhancing the visualisation of mathematical concepts, providing adaptive learning experiences, and promoting independent, paced learning that supports learners' learning styles. Teachers reported a marked improvement in learners’ comprehension and problem-solving abilities attributable to AI integration. Despite the benefits of AI in education, challenges remain, particularly in rural areas. Key issues include inadequate infrastructure, limited access to devices, and the need for ongoing teacher training in technological pedagogical content knowledge. Additionally, the lack of culturally relevant AI technologies hinders effective AI-supported learning in diverse contexts. Greater investment in localised AI educational tools is needed to achieve equitable and effective learning outcomes.

AI Tutors vs. Human Instructors: Perceptions of Higher Education Students in Hungary and Spain

Integrating AI-powered tutoring systems in higher education represents a significant advancement in educational technology, offering personalized and adaptive learning experiences. This study investigates the perceptions and expectations of higher education students in Hungary and Spain regarding AI tutors. Despite extensive research on the technological efficacy of AI systems, there is limited understanding of student attitudes in these specific cultural contexts. This research aims to fill this gap by exploring student expectations, satisfaction levels, and perceived benefits of AI tutors compared to human instructors. To achieve this, a validated questionnaire was administered to 184 higher education students from Hungary and Spain, capturing data on various dimensions of their expectations. The study's findings indicate that students appreciate the adaptability and continuous guidance provided by AI tutors, with Hungarian students showing higher overall expectations compared to their Spanish counterparts. These insights suggest that AI tutoring systems can enhance the learning experience by addressing individual student needs more effectively. The implications of this study are significant for higher education institutions seeking to integrate AI technologies.

Language learning technologies: A review of trends in the USA and globally

This review provides a succinct overview of the comprehensive review on language learning technologies, exploring key trends in both the United States and global contexts. The review delves into the transformative impact of technology on language education, examining emerging trends that shape the landscape of language learning methodologies. In recent years, language learning technologies have witnessed a remarkable evolution, revolutionizing traditional approaches to language education. This review critically analyzes trends in language learning technologies, focusing on their applications, effectiveness, and implications for language learners, with a specific emphasis on both the United States and global perspectives. The exploration of language learning technologies in the United States reveals a dynamic landscape characterized by innovative tools, platforms, and methodologies. From interactive language apps to virtual reality language immersion experiences, the USA has been at the forefront of adopting cutting-edge technologies to enhance language acquisition. The review assesses the success and challenges of these technologies in diverse educational settings, shedding light on their integration into formal education systems and informal learning environments. On a global scale, the review provides insights into how language learning technologies are shaping language education practices across different countries and regions. It examines the adoption of technology in diverse cultural and linguistic contexts, exploring the effectiveness of digital language learning resources in overcoming linguistic barriers and promoting multilingualism. Key trends identified in the review include the rise of artificial intelligence-driven language tutors, the gamification of language learning, and the increasing emphasis on personalized, adaptive learning experiences. Additionally, the review explores the impact of technology on fostering cultural competence and global communication skills, essential components of language proficiency in the interconnected world. The findings of this review contribute to a deeper understanding of the current state of language learning technologies, offering valuable insights for educators, policymakers, and researchers. By examining trends in the USA and globally, the review provides a comprehensive perspective on the role of technology in shaping the future of language education, paving the way for informed decisions and advancements in language learning methodologies.

Research on Cooperative Learning in College English Listening Course Empowered by Digital Intelligence

With the rapid development of information technology and the continuous integration of the global economy, the digital era has arrived, which has had a profound impact on educational models. The introduction of the digitally empowered cooperative learning model in college English listening course not only enriches teaching methods but also enhances students' independent learning abilities and teamwork skills. This research paper delves into the integration of digital intelligence in the enhancement of cooperative learning within college English listening course. The study aims to explore the impact of digital tools and technologies on the efficiency and effectiveness of English listening instruction, focusing on the collaborative learning process among students. The research underscores the trans-formative potential of digital intelligence in empowering cooperative learning in college English listening course. By leveraging the strengths of technology, educators can create more interactive, adaptive, and student-centered learning experiences that enhance language proficiency and prepare students for the demands of the digital age.

Digital Pedagogy Philosophy: Building Critical Awareness of The Role of Technology in Education

Technology integration in education raises the need for a philosophical foundation that guides the use of technology in learning contexts. The philosophy of digital pedagogy is an approach that provides an ethical basis and values that guide the application of technology in the educational process. This paper aims to explore the relationship between digital pedagogical philosophy, critical consciousness, and the role of technology in shaping contextual and adaptive learning experiences. This research uses a hermeneutic approach to describe the reality of Digital Pedagogical Philosophy in building critical awareness of the role of technology in education. This method involves elements of interpretation and description in analyzing and understanding the implications and significance of technology integration in the learning context. The philosophy of digital pedagogy provides a foundation for integrating technology into learning to support the formation of critical, creative, and responsible individuals. Critical awareness of the role of technology in education creates a deeper understanding of the challenges and opportunities faced in the digital environment. This approach can increase student interaction with course material, increase understanding of concepts, and promote critical thinking.

The Effect Of Adaptive Learning Experience (Adlx) On Mathematics Learning Outcomes Of High School Students In Grade Xi Based On Learning Motivation

This study aims to analyze the effect of the use of ADLX (Adaptive Learning Experience) on the mathematics learning outcomes of grade XI high school students by considering learning motivation. Using a quasi-experimental method with a non-equivalent control group design, this study involved several 11th grade high school students in X City who were divided into two groups: an experimental group using ADLX and a control group using conventional methods. The research instruments include mathematics learning outcome tests and learning motivation questionnaires validated by experts. The data were analyzed using the variance analysis technique (ANOVA) to test the hypothesis. The results showed significant differences in mathematics learning outcomes between the experimental and control groups, with the experimental group using ADLX showing better results. The data analysis also revealed that learning motivation interacted significantly with ADLX use, suggesting students with high learning motivation benefited more from ADLX use than students with low learning motivation. In general, students report that their learning motivation is influenced by the material's relevance to their daily lives and interactive and engaging teaching approaches. The use of ADLX, which involves hands-on experience and real-life examples, is considered effective in increasing understanding and motivation for learning math. This shows that the contextual learning approach is to improve students' understanding of mathematics. The conclusion of this study is that the use of ADLX can improve the mathematics learning outcomes of high school students in grade XI, especially for students with high learning motivation. These findings emphasize the importance of integrating adaptive technology in learning and the need for strategies to increase student learning motivation to achieve optimal learning outcomes.

Impact of Artificial Intelligence on Learning Management Systems: A Bibliometric Review

The field of artificial intelligence is drastically advancing. This study aims to provide an overview of the integration of artificial intelligence into learning management systems. This study followed a bibliometric review approach. Specifically, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, 256 documents from the Scopus and Web of Science (WoS) databases over the period of 2004–2023 were identified and examined. Besides an analysis of the documents within the existing literature, emerging themes and topics were identified, and directions and recommendations for future research are provided. Based on the outcomes, the use of artificial intelligence within learning management systems offers adaptive and personalized learning experiences, promotes active learning, and supports self-regulated learning in face-to-face, hybrid, and online learning environments. Additionally, learning management systems enriched with artificial intelligence can improve students’ learning outcomes, engagement, and motivation. Their ability to increase accessibility and ensure equal access to education by supporting open educational resources was evident. However, the need to develop effective design approaches, evaluation methods, and methodologies to successfully integrate them within classrooms emerged as an issue to be solved. Finally, the need to further explore education stakeholders’ artificial intelligence literacy also arose.

Automated English Teaching System Through Deep Belief Network for Human-Computer Interaction Experience

This paper presented the integration of Human-Computer Interaction (HCI) with the Automated Teaching Belief Network (ATBN) to enhance automated English teaching experiences. The proposed ATBN model implemented the Deep Belief Network for the estimation of the factors related to the HCI to promote the experience of the users. The ATBN model uses the deep learning model for the classification in English Teaching. Through the capabilities of deep learning and HCI principles, the ATBN system offers personalized and adaptive learning experiences tailored to individual student needs. The proposed ATBN model estimates the features in English teaching to improve the performance of the Students through HCI model. Simulation analysis expressed that proposed ATBN model improves the pre-test and post-test score by +15 for the English Teaching. The classification values are achieved with accuracy value of 94.8% with minimal loss of 0.12. The assessment of student performance through pre-test and post-test score is improved by 15 for the beginner, intermediate and advanced level. The findings expressed that proposed ATBN model achieves the higher teaching test performance for the HCI language level through the belief network those significantly improves the user experience.

The Role of Technology in Modern Language Education

This article explores the transformative role of technology in modern language education, highlighting the significant advancements over the past thirty years. It begins with an overview of traditional language teaching methods and their limitations, followed by a discussion on the integration of the Internet, which has revolutionized access to language learning resources and facilitated virtual classrooms. The article delves into the impact of Artificial Intelligence (AI), particularly its recent widespread adoption since 2022, in creating personalized and adaptive learning experiences. It also examines interactive and immersive technologies such as Virtual Reality (VR) and Augmented Reality (AR), which provide engaging, real-world practice scenarios. Additionally, the benefits and challenges of technology in language education are analyzed, supported by case studies and testimonials. The article concludes by exploring emerging trends and the need for a balanced approach between traditional methods and technological advancements to create effective and inclusive language learning environments.

Integration of Virtual Reality (VR) and Artificial Intelligence (AI) in Autism Therapy

This concept note explores the integration of Virtual Reality (VR) and Artificial Intelligence (AI) in autism therapy, aiming to enhance therapeutic outcomes for children with autism spectrum disorder (ASD). VR provides immersive, controlled environments for practicing social, cognitive, and motor skills, while AI offers personalized, adaptive learning experiences and real-time feedback. The synergy of these technologies promises innovative, effective interventions that cater to the unique needs of each child. This project proposes the development and evaluation of a VR-AI therapy platform, focusing on improving social interaction, communication, and daily living skills among young children with autism. The integration of Virtual Reality (VR) and Artificial Intelligence (AI) into autism therapy offers a transformative approach to enhancing treatment effectiveness and accessibility. Autism Spectrum Disorder (ASD) presents unique challenges in social interaction, communication, and behavior, requiring tailored therapeutic interventions. VR provides immersive environments where individuals with ASD can safely practice social scenarios, sensory processing, and daily living skills. AI, particularly through machine learning algorithms, can personalize these virtual experiences by adapting to the user's progress and specific needs, offering real-time feedback and data-driven insights for therapists. This concept note explores the synergistic potential of combining VR and AI in autism therapy. VR environments can be meticulously controlled and replicated, allowing for consistent therapeutic sessions. AI can analyze user interactions within these environments, providing granular data to refine treatment plans. The integration aims to make therapy more engaging, individualized, and scalable. This approach not only enhances the effectiveness of traditional therapies but also addresses limitations such as therapist availability and the need for generalized settings. Furthermore, this technology-driven paradigm can democratize access to high-quality autism therapy, particularly in underserved regions. The proposed integration of VR and AI heralds a new era in autism therapy, promising improved outcomes and greater inclusivity.

Implementation of an Artificial Intelligence Based Learning Management System for Adaptive Learning

In the world of modern education, the need for adaptive and personalized learning systems is becoming increasingly important to accommodate the unique learning needs of each individual. Implementing artificial intelligence (AI) in learning management systems offers great potential for optimizing adaptive learning experiences, where the system automatically adjusts learning content and approaches based on student profiles and responses. This research aims to develop and implement an artificial intelligence-based learning management system to personalize learning. The primary objective is to assess the effectiveness of the system in improving student engagement and learning outcomes and identify factors that influence its success. The method includes developing a system using machine learning algorithms for real-time analysis of student learning data. This research uses a mixed design that combines quantitative and qualitative data to analyze learning outcomes and understand user perceptions of the system. The research results show that this AI-based learning management system successfully increases student engagement and provides effective personalization of learning. Data shows improvements in grades and understanding of the material, especially in concepts students previously found difficult. Implementing an artificial intelligence-based learning management system has proven effective in supporting adaptive learning. This system improves learning outcomes and provides new insights into how AI technology can effectively integrate into educational contexts to support diverse and dynamic learning needs. The research also identified several challenges, including the need for more significant and varied training data and smoother integration with school curricula.

The transformative potential of Artificial Intelligence for Education

Education today faces a range of challenges, but also unique opportunities, vis-à-vis the evolution of digital technologies. Traditional teaching methods struggle to meet the diverse needs of students, as classrooms comprise learners at different levels. The Education AI project represents an initiative aimed at integrating Artificial Intelligence (AI) into the classroom to address the diversity of students and the challenges faced by traditional teaching methods. The project's core objective is to enhance student learning outcomes by providing an adaptive and personalised learning experience, thereby making education more equitable and effective. This paper evaluates the project's impact on student learning, teacher workload, and its broader applicability in education. The study's methodology combines quantitative and qualitative data, employing a mixed-methods approach to provide a comprehensive analysis of the Education AI system's effectiveness. Preliminary findings indicate a notable improvement in student performance, particularly among low and medium-performing students, with quantitative data revealing an average improvement of 23% in assessments for low-performing students. The system's user-friendly interface, gamified elements, and immediate feedback mechanisms have been well-received, with a majority of students reporting enhanced engagement and understanding of mathematical concepts. Teachers have also reported positive experiences, highlighting the system's role in reducing their workload and facilitating personalised and adaptive teaching and learning. Qualitative feedback from interviews with teachers and students provides further insights into the system's impact, emphasising its varied and engaging question types, adaptivity, and the shift from paper-based to digital learning. The feedback also reveals a desire for expansion to other subjects and enhancements in the system's analytics for more targeted support.The Education AI project demonstrates the transformative potential of AI in the classroom, with significant improvements in student performance and positive reception from both students and teachers. The study highlights the importance of addressing technical challenges and expanding the system's capabilities for broader application. The project offers valuable insights into the future of education, where technology and human expertise converge to create a more personalised, adaptive engaging, and effective learning experience for all students. This initiative, underpinned by the transformative potential of AI for education, aims and informing and guiding future innovations in educational technology.

AI-Driven Content Creation and Curation in Digital Marketing Education: Tools and Techniques

This research paper investigates the utilization of artificial intelligence (AI) technologies for content creation and curation within the realm of digital marketing education and its subsequent impact on student engagement and learning outcomes. As digital marketing continues to evolve rapidly, educators are increasingly turning to AI-driven tools to tailor content delivery to the individual needs and preferences of students. Drawing upon a comprehensive review of literature spanning AI, education, and digital marketing, this study establishes a theoretical framework to elucidate the mechanisms through which AI-driven content influences student learning experiences. Employing a mixed-methods research design, the study incorporates both quantitative surveys and qualitative interviews to gather insights from students and instructors alike. The results reveal compelling trends indicating that AI-driven content not only enhances student engagement but also contributes to improved learning outcomes by fostering deeper comprehension and retention of digital marketing concepts. However, amidst the myriad benefits, the study also uncovers challenges such as algorithm bias and technological limitations that warrant careful consideration. This paper underscores the critical importance of pedagogical innovation and the ethical use of AI technologies in digital marketing education. By harnessing the power of AI to deliver personalized, adaptive learning experiences, educators can empower students to thrive in an increasingly competitive and dynamic industry landscape. Ultimately, this research contributes to a deeper understanding of the transformative potential of AI in shaping the future of education and preparing students for success in the ever-evolving field of digital marketing.

Evaluation of Teaching Effectiveness Based on Bayesian Network Algorithm in Teaching and Learning Process in Higher Education Institutions

The Bayesian network algorithm plays a pivotal role in enhancing the teaching and learning process in higher education institutions by facilitating personalized and adaptive learning experiences. This algorithm leverages probabilistic graphical models to represent relationships among various educational variables, such as student performance, learning resources, and teaching methods. Additionally, Bayesian networks aid instructors in optimizing course design and instructional strategies by identifying areas for improvement and evaluating the effectiveness of different teaching approaches. This paper explores the application of Hidden Markov Chain Bayesian Networks (HMCBN) in analyzing teaching effectiveness within higher education institutions. Teaching effectiveness is a multifaceted concept influenced by various factors, including teaching strategies, student characteristics, and learning outcomes. Traditional statistical methods often struggle to capture the dynamic and interdependent nature of these factors. Through the construction of HMCBN models, this study investigates the probabilistic relationships between teaching strategies, student characteristics, learning outcomes, and teaching effectiveness. For teaching strategies, the probabilities [0.3, 0.5, 0.2] indicate the likelihood of each strategy being employed, suggesting that Strategy 2 is the most frequently utilized, followed by Strategy 1 and Strategy 3. Similarly, the probabilities [0.4, 0.3, 0.3] for student characteristics suggest a relatively balanced distribution among the three characteristic categories. In terms of learning outcomes, the conditional probabilities reflect the influence of both teaching strategies and student characteristics on the outcomes achieved. For each combination of teaching strategy and student characteristic, the probabilities [0.1, 0.3, 0.6], [0.4, 0.5, 0.1], [0.3, 0.4, 0.3], [0.2, 0.5, 0.3], [0.3, 0.4, 0.3], [0.4, 0.3, 0.3], [0.1, 0.7, 0.2], [0.4, 0.5, 0.1], and [0.5, 0.4, 0.1] represent the probabilities of achieving different learning outcomes given specific combinations of teaching strategies and student characteristics.

Exploring the Optimization Model of University English Blended Teaching Mode Combined with Deep Learning

With university English blended teaching mode combined with deep learning marks a significant advancement in language education. With integrating deep learning techniques into blended teaching approaches, educators can leverage vast amounts of educational data to enhance the effectiveness and personalization of English language instruction. Deep learning algorithms analyze student interactions, performance data, and learning preferences to tailor instructional content and activities to individual needs. This model enables educators to create dynamic and adaptive learning experiences that blend traditional classroom instruction with online resources and digital tools. The paper presents a comprehensive examination of blended teaching methodologies within the realm of English education, integrating principles of deep learning to enhance learning outcomes. Through a series of experiments and analyses, the study investigates the effectiveness of various teaching components, the utilization of online resources, and the application of diverse deep learning architectures. Findings underscore the significance of a balanced integration of traditional pedagogies and digital resources, with online materials emerging as a pivotal aspect of the teaching strategy. The study conducted experiments evaluating the effectiveness of blended teaching methodologies in English education, with numerical scores ranging from 4.2 to 9.0 across different parameters. Results indicated that experiments with higher scores for traditional teaching effectiveness (ranging from 6.5 to 9.2) and deep learning integration (ranging from 3.9 to 9.1) tended to yield superior overall learning outcomes (ranging from 4.3 to 9.0). Furthermore, online resources received consistently high weighted scores (ranging from 7.2 to 9.5), underscoring their significant contribution to the overall teaching strategy.

Decoding Student Success in Higher Education: A Comparative Study on Learning Strategies of Undergraduate and Graduate Students


 
 Learning management systems (LMS) provide a rich source of data about the engagement of students with courses and their materials that tends to be underutilized in practice. In this paper, we use data collected from the LMS to uncover learning strategies adopted by students and compare their effectiveness. Starting from a sample of over 11,000 enrollments at a Portuguese information management school, we extracted features indicative of self-regulated learning (SRL) behavior from the associated interactions. Then, we employed an unsupervised machine learning algorithm (k-means) to group students according to the similarity of their patterns of interaction. This process was conducted separately for undergraduate and graduate students. Our analysis uncovered five distinct learning strategy profiles at both the undergraduate and graduate levels: 1) active, prolonged and frequent engagement; 2) mildly frequent and task-focused engagement; 3) mildly frequent, mild activity in short sessions engagement; 4) likely procrastinators; and 5) inactive. Mapping strategies with the students' final grades, we found that students at both levels who accessed the LMS early and frequently had better outcomes. Conversely, students who exhibited procrastinating behavior had worse end-of-course grades. Interestingly, the relative effectiveness of the various learning strategies was consistent across instruction levels. Despite the LMS offering an incomplete and partial view of the learning processes students employ, these findings suggest potentially generalizable relationships between online student behaviors and learning outcomes. While further validation with new data is necessary, these connections between online behaviors and performance could guide the development of personalized, adaptive learning experiences.