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Adaptive digital learning courseware is becoming part of the instructor tool kit to support student performance and ultimately reduce DFWI rates. However, past studies of the effectiveness of adaptive digital learning platforms in elevating student performance on summative assessment have shown promising yet at times mixed reviews (e.g. Yarnall et al., 2016). This case study integrates adaptive digital learning to address the challenge of promoting reading and concept application outside of class and analyzes its impacts on students’ engagement in class, perceived learning, and performance on summative assessment. Such an analysis, which considers mediating factors not previously analyzed together in adaptive digital learning studies, such as individual rather than aggregate performance, digital learning platform design differences, resiliency factors, and in-class activities, is an important step in clarifying some of the previously mixed results. Drawing on data collected in two sections of the same general education social science course taught by the same instructor in the same semester, this study illustrates the varying potential of adaptive digital learning to increase student confidence in the material and how it can translate into increased student performance if aligned and coupled in certain ways with in-class active learning. This study also provides evidence that illustrates how digital learning that is designed for greater degrees of editability by faculty can maximize learning benefits for students.

Adaptive digital learning courseware is becoming part of the instructor tool kit to support student performance and ultimately reduce DFWI rates. However, past studies of the effectiveness of adaptive digital learning platforms in elevating student performance on summative assessment have shown promising yet at times mixed reviews (e.g. Yarnall et al., 2016). This case study integrates adaptive digital learning to address the challenge of promoting reading and concept application outside of class and analyzes its impacts on students’ engagement in class, perceived learning, and performance on summative assessment. Such an analysis, which considers mediating factors not previously analyzed together in adaptive digital learning studies, such as individual rather than aggregate performance, digital learning platform design differences, resiliency factors, and in-class activities, is an important step in clarifying some of the previously mixed results. Drawing on data collected in two sections of the same general education social science course taught by the same instructor in the same semester, this study illustrates the varying potential of adaptive digital learning to increase student confidence in the material and how it can translate into increased student performance if aligned and coupled in certain ways with in-class active learning. This study also provides evidence that illustrates how digital learning that is designed for greater degrees of editability by faculty can maximize learning benefits for students.

Adaptive digital learning courseware is becoming part of the instructor tool kit to support student performance and ultimately reduce DFWI rates. However, past studies of the effectiveness of adaptive digital learning platforms in elevating student performance on summative assessment have shown promising yet at times mixed reviews (e.g. Yarnall et al., 2016). This case study integrates adaptive digital learning to address the challenge of promoting reading and concept application outside of class and analyzes its impacts on students’ engagement in class, perceived learning, and performance on summative assessment. Such an analysis, which considers mediating factors not previously analyzed together in adaptive digital learning studies, such as individual rather than aggregate performance, digital learning platform design differences, resiliency factors, and in-class activities, is an important step in clarifying some of the previously mixed results. Drawing on data collected in two sections of the same general education social science course taught by the same instructor in the same semester, this study illustrates the varying potential of adaptive digital learning to increase student confidence in the material and how it can translate into increased student performance if aligned and coupled in certain ways with in-class active learning. This study also provides evidence that illustrates how digital learning that is designed for greater degrees of editability by faculty can maximize learning benefits for students.

Personalized learning has the potential to transfer the focus of higher education from teacher-centered to learner-centered environments. The purpose of this integrative literature review was to provide an overview of personalized learning theory, learning technology that supports the personalization of higher education, current practices, as well as case studies of implementing technology models to support personalized learning. The review results revealed the following: three technological models that support personalized learning within blended learning environments in higher education, an increase in personalized learning implementation in higher education with the support of the referenced technology models and platforms, and a lack of data-driven and independent research studies that investigate the effectiveness and impact of the personalized learning and technology models on student learning. The article informs educators and higher education administrators of the emerging models, platforms, and related opportunities to implement personalized learning in higher education settings. The review discusses the barriers, challenges, and theoretical and practical implications of implementing a personalized learning approach in higher education. Finally, recommendations for future research are discussed.

The present survey examines the intersection of methodological advancements and technological innovations in e-learning, emphasizing their transformative impact on modern education. It systematically explores instructional design frameworks, adaptive learning systems, immersive technologies, and data-driven analytics, highlighting their role in fostering personalized, scalable, and inclusive learning environments. Through the integration of pedagogical theories with advanced tools like artificial intelligence (AI), augmented reality (AR), virtual reality (VR), and mixed reality (MR), this study demonstrates how e-learning systems enhance engagement, retention, and accessibility. The survey addresses critical challenges such as the digital divide, data privacy, and resistance to adoption, offering evidence-based strategies to mitigate these issues. It underscores the importance of bridging equity gaps while maintaining scalability and sustainability, particularly in underserved regions. By synthesizing state-of-the-art research and practical applications, this work provides actionable insights into the future of e-learning, advocating for a balanced approach to innovation that aligns technological capabilities with the diverse needs of global learners. The findings contribute to the broader discourse on sustainable, inclusive, and effective digital education ecosystems.

Cancer histology reflects underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes. In this study, we show a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how these survival convolutional neural networks (SCNNs) can integrate information from both histology images and genomic biomarkers into a single unified framework to predict time-to-event outcomes and show prediction accuracy that surpasses the current clinical paradigm for predicting the overall survival of patients diagnosed with glioma. We use statistical sampling techniques to address challenges in learning survival from histology images, including tumor heterogeneity and the need for large training cohorts. We also provide insights into the prediction mechanisms of SCNNs, using heat map visualization to show that SCNNs recognize important structures, like microvascular proliferation, that are related to prognosis and that are used by pathologists in grading. These results highlight the emerging role of deep learning in precision medicine and suggest an expanding utility for computational analysis of histology in the future practice of pathology.

This paper elaborates on the large number of Uncertainty Quantification (UQ) techniques that have been proposed to enhance the reliability and the fidelity of Digital Twins that are increasingly finding applications in domains like Robotics and Smart Manufacturing. Digital twins are virtual duplicates or virtual models of a physical asset; they use advanced techniques such as data analytics and simulation-driven methods. However, the development and use of these advanced systems are plagued by a host of uncertainties, which are mainly introduced from sensor noise, intermittent connectivity, biases from data processing, and model abstractions and simulation stochasticity. Such uncertainties can be quantified by methods such as frequentist statistics, interval analysis, Bayesian inference, and random sampling. The mapping is important in gaining insights into these UQ methods and their associated advantages and limitations and the mitigation guidelines are to be used throughout the Digital Twin pipeline. UQ at its core involves real-time adaptive control in dynamically changing environments that leverage state awareness towards responsive action within predictive control models and feedback systems. In addition, machine learning algorithms support the ability to make better decisions from the identification of patterns in historical data to make plans for responsive trajectories of robots. UQ further allows the collaboration of human and machine, giving early warnings on anomalies and risks that enhance visibility which further fosters coordination and communication during disruptive situations. Robust development of digital twins for robotics and manufacturing relies on integrated UQ practices. The current review provides best practices, insights, and guidelines on the application of UQ across modeling, control strategies, and collaborative workflows aimed at delivering actionable and reliable insights from digital twin simulations, analytics, and decision support.

The increasing prevalence of Artificial Intelligence (AI) in higher education underscores the necessity to explore its implications on ethical, social, and educational dynamics within the sector. This study aims to comprehensively investigate the impact of AI on higher education in Saudi Arabia, delving into stakeholders’ attitudes, perceptions, and expectations regarding its implementation. The research hones in on key facets of AI in higher education, encompassing its influence on teaching and learning, ethical and social implications, and the anticipated role of AI in the future. Employing a quantitative approach through an online survey questionnaire ( N  = 1113), this study reveals positive attitudes toward AI in higher education. Stakeholders recognize its potential to enhance teaching and learning, streamline administration, and foster innovation. Emphasis is placed on ethical considerations and guidelines for AI implementation, highlighting the imperative need to address issues such as privacy, security, and bias. Participants envision a future characterized by personalized learning experiences, ethically integrated AI, collaboration, and ongoing support for lifelong learning. Furthermore, the results illuminate the intricate interplay between AI usage, purposes, difficulties, and their impact on attitudes, perceptions, and future implications. Accordingly, the research underscores the necessity for a comprehensive understanding of AI integration, considering not only its technical aspects but also the ethical, social, and educational dimensions. By acknowledging the role of AI uses, AI usage purposes, and addressing associated difficulties, educational stakeholders can work towards harnessing the benefits of AI while ensuring responsible and effective implementation in teaching and learning contexts.

Educational games are becoming increasingly prevalent. Recently, adaptive game-based learning to accommodate diverse learners has received considerable attention. The current study aims to explore the effect of adaptive scaffolding from a multidimensional engagement perspective. A total of 61 students from a Taiwan secondary school studied Newton’s laws in a computer-based interactive game environment. The students were assigned to either a fixed scaffolding group or an adaptive scaffolding group to test the effect of adaptive scaffolding. The findings suggest that adaptive scaffolding can have a significant effect on students’ learning performance and engagement. The study offers significant implications for the design and integration of adaptive scaffold to promote learning engagement in digital game-based learning.

Field learning is considered an essential part of geoscience education, yet there are many practical challenges that limit broad and equitable access to field learning. Virtual field experiences can now help to broaden access to field learning. This work details the learning design and technical components of immersive, interactive virtual field trips (iVFTs). iVFTs take the established idea of a computer-based virtual field trip and add adaptive feedback and richer interactivity, which allow for active and more scientifically authentic learning experiences. By using adaptive learning technology, iVFTs can respond intelligently and automatically to student actions, guiding students through exploration, discovery, and analysis. We present evidence for the effectiveness of iVFTs in achieving learning objectives in both high school and undergraduate settings. Students from both samples showed large and statistically significant gains in content knowledge. Normalized gains on a six-item knowledge survey were .90 and .96, respectively (p < .001). This result broadly demonstrates the value of our iVFT designs. Follow-up research should rigorously study the effects of adaptive feedback, interactivity, and the other distinctive features of iVFTs on student learning.