A General Framework and Control Theoretic Approach for Adaptive Interactive Learning Environments

This study investigates the long-term impact of adaptive digital learning ecosystems on students' self-regulated learning (SRL) behaviors and academic persistence. Adaptive learning systems personalize the learning experience by adjusting content and feedback to meet individual students' needs, preferences, and performance. These systems enhance engagement, motivation, and learning outcomes through real-time adjustments and continuous feedback. The research aims to explore how adaptive learning systems influence SRL and academic persistence in university courses over time. Using a longitudinal quantitative design, the study tracks SRL behaviors and academic persistence at multiple points during the semester. Results show significant improvements in SRL behaviors such as goal setting, planning, self-monitoring, and reflection among students engaged with adaptive learning environments. These students exhibited greater autonomy, improved metacognitive awareness, and higher motivation. Additionally, students in adaptive systems demonstrated greater academic persistence, as indicated by more time spent on tasks, higher assignment completion rates, and sustained engagement. The findings suggest that adaptive learning platforms promote SRL and academic persistence by offering personalized, responsive learning experiences. Unlike static, non-adaptive environments, adaptive systems provide dynamic support, enhancing students' ability to regulate their learning and remain engaged despite challenges. The study concludes that adaptive learning systems are vital for long-term academic success, though further research is needed to assess the sustainability of these effects in various educational settings and among diverse student populations.

Identification of personal traits in adaptive learning environment: Systematic literature review

The aim of this study is to investigate the utility of gesture-based interaction in adaptive learning environments. Based on theories of embodied cognition, this study applied gesture-based technologies within an adaptive learning system. The learning system is continuously recalibrated in response to the attention level and the meditation level of learners. A total of 60 undergraduate and graduate students were recruited to participate in the experiment and were randomly divided into two groups. One group of learners were required to use their body movements to interact with the memory training game while the other group used a computer mouse. Brainwave detection devices were used on both groups to monitor their attention and mediation levels. The result shows that the learners in the gesture-based group significantly outperformed the learners in the mouse-based group.

In adaptive digital learning environments, it is essential to track learning trajectories. The Elo rating system, known for its computational simplicity, is frequently employed for this purpose. Current Elo-based systems cannot handle rapid changes in ability or are unable to balance accuracy and speed when updating player and item ratings. Changes in Elo ratings depend on the sensitivity parameter K. Using fixed K values necessitates a trade-off: larger values facilitate the tracking of evolving ability levels but introduce greater rating volatility. Smaller values yield more stable estimates, but are slower to reflect actual ability levels. Existing modifications of the Elo system, which diminish K as the number of responses increases, are inadequate in scenarios characterized by considerable ability fluctuation, a common occurrence in digital learning environments. To address this challenge, we introduce a novel approach for dynamically adjusting K values in response to observed trends in rating changes. This method increases K during noticeable upward or downward shifts in ratings and reduces it otherwise. We present a computationally efficient implementation of this idea and validate its superiority over existing K adjustment strategies through simulation studies. Additionally, we describe the implementation of this adaptive K model in a widely-used digital learning platform, Math Garden, which leverages both accuracy and response time in its assessments. By successfully integrating speed and precision, this innovative implementation enhances the effectiveness of digital adaptive learning environments.

EDFA: Ensemble deep CNN for assessing student's cognitive state in adaptive online learning environments

Studies show that an adaptive learning environment is needed by learners. Adaptive e-learning systems have been developed with personalized instructions based on student's knowledge level. Since each student has a unique cognitive and metacognitive level, we currently develop an adaptive Hypermedia Learning Environment (HLE) for Algorithm and Data Structure Course based on a metacognitive awareness level. HLE provides adaptive scaffolding as instructional interventions to facilitate students' aptitude in programming. Scaffolding can be determined by knowing student's metacognitive awareness level. In this research, we propose the use of Tahani Model of Fuzzy Database in web-based HLE to support the adaptive decision-making. Tahani is one of some feasible models in a fuzzy database that can categorize students' metacognitive awareness level. The produced fuzzy rules have been validated by metacognitive experts of educational psychology. A hundred students have been categorized into three groups (low, medium, high) and provided with appropriate scaffolding. Evaluation results verified the suitability of the proposed method and the student's scaffolding.

: In recent years, the integration of technology in education has sparked interest in enhancing learning experiences. Adaptive learning environments, which customize educational content and strategies to individual learners, offer a promising approach. Personalization within adaptive learning environments is crucial for addressing learner diversity and maximizing engagement and learning outcomes. This research project aims to explore the role of personalization in adaptive learning environments and its impact on the user experience and educational outcomes. By investigating the effectiveness of personalized interfaces and adaptive strategies, we seek to understand how these technologies can create more engaging and effective learning environments. The project objectives are threefold. Firstly, we aim to design and implement adaptive learning interfaces with personalized elements to customize the learning experience for each user. Secondly, we will evaluate the impact of personalization on user experience dimensions, including engagement, satisfaction, and motivation. Lastly, we will assess the effect of personalization on learning outcomes, such as knowledge acquisition and retention. This research contributes to the field of educational technology and user experience (UX) design. By examining the role of personalization in adaptive learning environments, we aim to inform the development of more effective and learner-centered educational tools and strategies.

Currently, the online learners are increasingly demanding more personalized learning since the web technology, and the learners have individual features of characteristics such as learning goals, experiences, interests, personality traits, learning styles, learning activities, and prior knowledge. A personalized learning process requires an adaptive learning system (ALS). In order to adapt, a learner model is required. Thus, modelling the learner model in an adaptive system environment is a key point to success in recommending the learner. The ontology-based approach was used to model the adaptive learning model in this research. Ontology is a graph structure that consists of a collection of contexts, relationships, and models which related to contexts. The ontology of the learner model enables to produce a description of learner’s properties which contains important information about domain knowledge, learning performance, interests, preference, goal, tasks, and personal traits. Keywords - Personalized Learning, Adaptive Learning System, Ontology, Learner Model

Despite the criticality of considering student academic motivation as it influences learning, research within the field of adaptive learning technologies for education has so far focused more on customising instruction to implement personalised learning, than investigating how personalised learning is associated with learners’ motivation. Given this, a robust instrument is required to gather information about student academic motivation within the context of adaptive learning technologies. This study sought to validate the Academic Motivation Toward Mathematics Survey (AMTMS) currently used to measure motivation based on self-determination theory in mathematics education at pre-tertiary levels (grades 11 and 12) in Asia. A total of 196 participants recruited via availability sampling took part in this study, after interacting with an in-house mathematics adaptive learning system within a tertiary educational institution. The validation was performed based on modern test theory given that it overcomes some limitations of classical factor analytic approaches. Results supported the factorial structure of the AMTMS but 12 of the original 21 items had to be rescored to establish ordered thresholds. Further, the bifactor equivalent solution suggested the possibility of reporting a singular motivation index comprising the five factors within the AMTMS. Along with the results, this study offers researchers a robust and validated instrument to measure motivation toward mathematics that can be used within an adaptive learning environment.

It is recognised that a strength of the computer science (CS) academic community has been its willingness to come to terms with the pressure on its curriculum and teaching methodology brought about by the “evolutionary” nature of the discipline (Tucker, 1996). This pressure is felt by academia in the increasing demand for just-in-time and life-long learning. Research has indicated that adaptive and interactive on-line CS learning environments play a useful role in the provision of life-long learning but these needs are not being fully met by current tools or COTS systems. This paper looks at the support of CS learning by the provision of new highly-integrated and context-aware tools.

Background and Aims: The deep blended learning paradigm offers students a more adaptable, customized, and interactive learning environment by fusing the benefits of contemporary online learning with conventional classroom teaching. This study aims to explore a deep blended learning model intended to improve chemistry students’ abilities for learning. Methodology: A comprehensive literature review and expert panel discussions were conducted simultaneously to formulate an initial list of experts’ analysis competency, A total of twenty-one experts were chosen based on their expertise in different disciplines to find out the problems faced by chemistry students in the process of deep learning. A graphical model was established using the Delphi method based on feedback from experts. The final nine experts' feedback was evaluated the blended learning model using the CIPP evaluation principles. Results: According to Bloom’s taxonomy of cognitive educational goals, it was found that students encounter issues such as difficulty in accurately memorizing chemical concepts, insufficient ability to summarize and generalize, a disconnect between practical application and theoretical principles, lack of practical operational skills, insufficient ability to analyze and interpret experimental data, difficulty in solving complex problems, and inadequate evaluation of experimental methods. To address these issues, this study developed a hybrid learning model that identifies effective solutions across six dimensions: knowledge, understanding, application, analysis, synthesis, and evaluation. For students, this model enhances their understanding and application of knowledge, and promotes self-directed learning, critical thinking, comprehensive innovation abilities, and active participation, thereby providing strong support for their comprehensive development and academic progress. Conclusion: The hybrid learning model developed in this study effectively addresses students' difficulties in mastering chemical concepts by improving their cognitive abilities at all levels of Bloom's taxonomy. This approach promotes deeper understanding, critical thinking, and practical skills, ultimately benefiting students' academic growth and development.

This research investigates the impact of integrating artificial intelligence (AI) and immersive technologies, such as virtual reality (VR) and augmented reality (AR), in software engineering education to promote continuous and adaptive learning. The purpose of this study was to explore how these technologies increase student engagement, personalize the learning experience, and enhance practical skills essential in software development. A mixed methods approach was used, combining a quantitative survey with qualitative interviews of 20 students and 5 educators actively engaged in AI and VR powered courses. Findings revealed that AI-based adaptive learning systems encourage personalized feedback and help address individual knowledge gaps, leading to increased engagement and retention. VR based simulations enable hands-on learning, which significantly improves skill acquisition in coding, debugging, and system analysis. Despite these benefits, challenges such as high costs, data privacy concerns, and limited infrastructure remain a barrier to widespread implementation. This study contributes original insights by identifying strategies to mitigate these challenges, including gradual adoption of technology, cloud-based solutions to reduce costs, prioritizing data security, and continuous educator training. The findings underscore the transformative potential of AI and immersive technologies in creating sustainable, accessible and adaptive learning environments and equipping students with the skills needed for the innovation driven software industry.

During the last decade, many governments and ed‐tech companies have demonstrated an increased interest in digital personalised learning, which resulted in a variety of often game‐like adaptive learning environments. However, there has been limited attention for the impact of these personalised learning technologies on children's learning efficiency. Does digital personalised learning, like popular claims insist, foster learning in young children? This study attempts to empirically validate the beneficial impact of adaptive learning technology by analysing log‐data from the Number Sense Game (NSG), an educational game that trains early numerical skills. In total, 81 children were randomly assigned to use either an adaptive or a non‐adaptive version of the NSG in six sessions in a three‐week period. Using a longitudinal item response model children's progress within and across sessions was modelled and compared between the two versions of the game. Regardless of the version of the NSG, children demonstrated progress within and across sessions. However, compared to the non‐adaptive NSG, the progress across sessions was stronger in the adaptive NSG. These results provide empirical evidence that adaptive learning environments can improve learning efficiency in young children. Practitioner notes What is already known about this topic Adaptive digital technologies are able to address students’ individual needs. Pretest posttest intervention research on the effectiveness of adaptive digital technologies revealed mixed results. What this paper adds Learning effectiveness is different from learning efficiency. Learning efficiency can be operationalised using log‐data to model students’ progress over time. Adaptive digital technologies increased students’ learning efficiency compared to non‐adaptive digital technologies. Implications for practice and/or policy Log‐data can be obtained unintrusively and provides fine‐grained information about students’ learning. Researchers are encouraged to investigate the effects of adaptive digital technologies in terms of learning effectiveness and learning efficiency.

Digital literacy is a critical competency in higher education, yet its multidimensional nature makes it difficult to assess and model using traditional approaches. This study proposes an AI-driven framework for mining digital literacy competency patterns within adaptive learning environments. Data from 230 undergraduate computer science students were collected through surveys, LMS behavioral logs, and performance records. A deep autoencoder was used for representation learning, followed by clustering algorithms (KMeans, DBSCAN) to identify homogeneous student profiles. Supervised models (Decision Tree, Random Forest) were then applied to predict engagement and performance. Finally, explainable AI techniques (SHAP, LIME) provided interpretable insights. Results revealed three distinct profiles—Technically Skilled but Critical-Weak, Balanced Digital Users, and Critical Creators-that significantly correlated with engagement and academic outcomes. Random Forest achieved the best performance (ROC-AUC $=0.86)$. SHAP analysis highlighted critical literacy and projective literacy as dominant predictors of success. These findings demonstrate how AI-based pattern mining can generate actionable insights for adaptive learning design, bridging computational methods and educational practice.

The rapid advancement with regard to Artificial Intelligence (AI) has transformed multiple educational domains, particularly in adaptive teaching and learning, where AI technologies play a crucial role in personalizing instruction and enhancing learning outcomes. Despite its increasing significance, there remains a lack of comprehensive bibliometric evidence that maps the intellectual structure, research patterns, and emerging trends in this field. This study addresses this gap by conducting a bibliometric analysis of AI-assisted adaptive teaching and learning to identify publication dynamics, research hotspots, and global collaboration networks. Data were collected from the Scopus database via advanced search strategies incorporating the keywords “Artificial Intelligence,” “Adaptive,” “Teaching,” and “Learning,” which yielded 866 relevant publications. The dataset was cleaned and harmonized using OpenRefine to ensure consistency, while statistical and graphical analyses were performed through the Scopus analyzer. Visualization of co-authorship, keyword co-occurrence, and thematic clustering was generated using VOSviewer to offer a deeper comprehension regarding the field’s conceptual and intellectual structures. The results reveal a significant growth trajectory of publications over the past decade, dominated by contributions from China, the United States, India, and the United Kingdom, highlighting the global nature of research in this area. Keyword co-occurrence analysis identified core themes, for example, intelligent tutoring systems, machine learning (ML), adaptive learning environments, and collaborative learning, with emerging interest in areas like deep learning, educational data mining, and personalized learning pathways. These research findings demonstrate that AI-assisted adaptive teaching and learning research is highly interdisciplinary, bridging education, computer science, and cognitive sciences, and is increasingly shaped by international collaborations. In conclusion, this study provides an overview of the knowledge structure in AI-driven adaptive education and offers valuable insights for scholars, practitioners, and policymakers. These insights help identify future research directions, strengthen cross-border partnerships, and advance innovative pedagogical frameworks that leverage AI for inclusive and effective learning.