Traditional Learning Paradigms Research Articles

Graph neural networks for multi-view learning: a taxonomic review

With the explosive growth of user-generated content, multi-view learning has become a rapidly growing direction in pattern recognition and data analysis areas. Due to the significant application value of multi-view learning, there has been a continuous emergence of research based on machine learning methods and traditional deep learning paradigms. The core challenge in multi-view learning lies in harnessing both consistent and complementary information to forge a unified, comprehensive representation. However, many multi-view learning tasks are based on graph-structured data, making existing methods unable to effectively mine the information contained in the input multiple data sources. Recently, graph neural networks (GNN) techniques have been widely utilized to deal with non-Euclidean data, such as graphs or manifolds. Thus, it is essential to combine the advantages of the powerful learning capability of GNN models and multi-view data. In this paper, we aim to provide a comprehensive survey of recent research works on GNN-based multi-view learning. In detail, we first provide a taxonomy of GNN-based multi-view learning methods according to the input form of models: multi-relation, multi-attribute and mixed. Then, we introduce the applications of multi-view learning, including recommendation systems, computer vision and so on. Moreover, several public datasets and open-source codes are introduced for implementation. Finally, we analyze the challenges of applying GNN models on various multi-view learning tasks and state new future directions in this field.

A benchmark for Rey-Osterrieth complex figure test automatic scoring

The Rey–Osterrieth complex figure (ROCF) test is a neuropsychological task that can be useful for early detection of cognitive decline in the elderly population. Several computer vision systems have been proposed to automate this complex analysis task, but the lack of public benchmarks does not allow a fair comparison of these systems. To advance in that direction, we present a benchmarking framework for the automatic scoring of the ROCF test that provides: the ROCFD528 dataset, which is the first open dataset of ROCF line drawings; and experimental results obtained by several modern deep learning models, which can be used as a baseline for comparing new proposals. We evaluate different state-of-the-art convolutional neural networks (CNNs) under traditional and transfer learning paradigms. Experimental quantitative results (MAE = 3.448) indicate that a CNN specifically designed for sketches outperforms other state of the art CNN architectures when the number of examples available is limited. This benchmark can also be a paradigmatic example within the broad field of machine learning for the development of efficient and robust models for analyzing line drawings and sketches not only in classification but also in regression tasks.

The Impact and Acceptance of Gamification by Learners in a Digital Literacy Course at the Undergraduate Level: Randomized Controlled Trial.

In recent years, the integration of technology in education has revolutionized traditional learning paradigms. Digital literacy, a crucial skill in the 21st century, has become a vital aspect of modern education, enabling students to navigate, critically assess, and effectively use digital tools. As educators strive to boost engagement and learning outcomes, gamification has appeared as an auspicious pedagogical approach. By applying game mechanics to nongame contexts, gamification seeks to create a more immersive and digital learning experience. This research paper aims to investigate the impact and acceptance of gamification by learners in a digital literacy course at the undergraduate level. In a pre-post intervention study, 168 undergraduate students were randomly assigned either to the experimental group (gamification based) or control group (conventional) learning condition. Both groups of participants learned the same topics in digital literacy. Empirical findings showed that participants from the experimental group had better academic performance in digital literacy than those who were not exposed to the game-based learning environment. The participants' prior experience with gamification was not found to be a significant predictor of their acceptance of gamification in a digital literacy course. The study provides evidence supporting the potential benefits of gamification in enhancing digital literacy education and opens the door for further exploration and implementation of gamified learning approaches in higher education settings.

Incomplete label distribution learning via label correlation decomposition

Label distribution learning (LDL) has garnered increased attention in recent studies on label ambiguity. However, collecting complete annotations for LDL tasks is often time-consuming and labor-intensive compared to traditional learning paradigms. Therefore, designing effective incomplete LDL algorithms is crucial to broaden LDL’s application scope. In this paper, we propose a novel LDL algorithm, called Incomplete Label Distribution Learning via Label Correlation Decomposition (IncomLDL-LCD), which simultaneously learns label distributions and recovers missing description degrees of labels through label correlations. Specifically, we decompose the label correlation into sparse local label correlation and low-rank global label correlation using a soft-thresholding operator and a singular value thresholding operator, respectively. The former is utilized to capture the related label subsets necessary for reconstructing each possible label, while the latter focuses on extracting the coarse-grained semantic concepts from all labels and exploring the groupings of labels. Additionally, we develop an alternating solution with the accelerated proximal gradient descent method for optimization. Extensive experiments on 16 real-world data sets with varying degrees of missing annotations validate that our algorithm effectively handles incomplete LDL tasks and outperforms state-of-the-art algorithms.

Comparing Realities: A Study on the Impact of Virtual Reality versus Paper-Based Reading on Higher Education Students' Comprehension Skills

Virtual Reality (VR) stands at the forefront of transformative educational technologies in the 21st century, promising to redefine traditional learning paradigms. This research delves into the impact of VR on higher education students' Reading Comprehension (RC) skills, contrasting it with conventional paper-based reading environments. By conducting a comparative analysis of RC skills among students engaged in reading a selected narrative through VR glasses and those using a paper-based format, this study offers valuable insights into the efficacy and challenges of VR in enhancing reading comprehension. A cohort of 98 undergraduate students participated in this study, which employed a mixed-methods research approach to scrutinize the differences in RC outcomes between the experimental (VR) and control (paper-based) groups. The findings reveal a notable disparity in RC scores, with the control group outperforming their VR counterparts, highlighting the need for further investigation into the obstacles faced by students when reading in a VR setting. Qualitative analysis sheds light on the underlying reasons for the diminished RC levels observed in the VR group, providing a nuanced understanding of the interaction between medium and comprehension. The implications of this research are far-reaching, offering crucial guidance to educational technologists, curriculum designers, policy makers, and educators contemplating the integration of VR into educational frameworks. By identifying the constraints and potential of VR in reading instruction, this study paves the way for optimizing VR-based learning environments to foster higher levels of reading comprehension among students.

Scalability and Robustness of Federated Learning Systems: Challenges and Solutions

Federated Learning (FL) revolutionizes traditional machine learning paradigms by enabling collaborative model training across decentralized devices while preserving data privacy. However, its scalability and robustness remain formidable challenges. This paper delves into the complexities of scaling FL systems and enhancing their resilience in dynamic environments. We analyze scalability hurdles stemming from communication overhead, resource constraints, and diverse client populations. Additionally, we scrutinize robustness challenges posed by non-IID data distributions, heterogeneity, and adversarial threats. Proposing novel solutions, including communication-efficient aggregation techniques, adaptive client sampling strategies, and robust aggregation mechanisms, we aim to advance the scalability and robustness of FL systems. Empirical evaluations and case studies underscore the efficacy of these solutions across various applications. Our work outlines future research directions, emphasizing standardization efforts and ethical considerations, to propel the adoption of FL in real-world scenarios.. Keywords: Keywords: Federated Learning, Scalability, Robustness, Privacy Preservation, Communication Efficiency, Heterogeneity, Adversarial Attacks, Differential Privacy.

StudyNotion - An Edtech Website

In an era defined by rapid technological advancement, education stands as a crucial arena ripe for transformation. Our Edtech website emerges as a beacon of innovation, aiming to revolutionize traditional learning paradigms through the seamless integration of cutting-edge technology. With a multifaceted approach, our platform offers a comprehensive suite of tools and resources tailored to meet the diverse needs of students, educators, and institutions alike. At the heart of our platform lies a commitment to accessibility and inclusivity, ensuring that learning opportunities transcend geographical boundaries and socioeconomic barriers. Through interactive modules, engaging content, and personalized learning pathways, we empower learners to cultivate their intellectual curiosity and unlock their full potential. For educators, our platform serves as a dynamic hub for professional development, fostering collaboration, and knowledge sharing within a vibrant community of practice. From virtual classrooms to collaborative project management tools, we provide the scaffolding necessary to facilitate effective teaching in the digital age.Furthermore, our platform harnesses the power of data analytics and artificial intelligence to deliver actionable insights and personalized recommendations, facilitating data-driven decision-making and enhancing learning outcomes. In essence, our Edtech website represents more than just a digital learning platform; it embodies a vision for the future of education—one where technology serves as a catalyst for empowerment, innovation, and lifelong learning. Join us on this transformative journey as we redefine the possibilities of education in the 21st century and beyond. This abstract succinctly captures the essence of the Edtech website, highlighting its goals, features, and the transformative potential it holds for education.The abstract for the Edtech Website project encapsulates the endeavor to create an innovative educational platform tailored for diverse learners. This project aims to develop a user-friendly website offering interactive learning resources, personalized study tools, and collaborative features. Key objectives include providing seamless navigation, curating diverse educational content, implementing adaptive learning algorithms, and fostering real-time collaboration among students and educators. Emphasizing scalability, compatibility, and security, the website seeks to empower learners globally with accessible, engaging, and personalized learning experiences, ultimately fostering academic excellence and lifelong learning opportunities.

Online Education Big Data Management and Mining Based on Intelligent Technology

The advent of online education platforms has revolutionized traditional learning paradigms, providing unprecedented access to educational resources worldwide. However, the vast amounts of data generated by these platforms present significant challenges in terms of effective management and utilization. This paper proposes a comprehensive approach to address these challenges through the integration of big data management and mining techniques, supported by intelligent technologies. By harnessing the power of advanced data management strategies and cutting-edge machine learning algorithms, our approach aims to unlock valuable insights from online education data, thereby enhancing the learning experience and improving educational results. This paper presents a systematic framework for online education big data management and mining, encompassing data collection, storage, preprocessing, analysis, and visualization. Furthermore, we discuss the role of intelligent technologies such as artificial intelligence, natural language processing, and predictive analytics in optimizing the process of data mining and knowledge discovery. Through case studies and experimental evaluations, we demonstrate the effectiveness and applicability of our approach in real-world online education scenarios. Our research contributes to the growing body of knowledge in the field of educational data mining and underscores the potential of intelligent technology in shaping the future of online learning.

Advancing Frontiers: An In-Depth Analysis of Reinforcement Learning Applications in Recommendation System Technologies

This article commences by elucidating the concept and algorithms underpinning reinforcement learning (RL), laying the foundation for a comprehensive understanding of RL's principles. It then draws a detailed comparison between RL and traditional machine learning paradigms, using illustrative examples to highlight the distinctive methodologies and outcomes of these approaches. This comparison not only clarifies the unique attributes of RL but also contextualizes its position within the broader landscape of machine learning techniques. Subsequently, the focus shifts to recommendation systems (RS), where both the conceptual framework and algorithmic foundations are thoroughly examined. This exploration is not limited to the technical mechanics of RS but extends to an appraisal of their diverse applications, showcasing how these systems have become integral in various domains, from e-commerce to content curation. The core of the discussion then converges on the application of RL within the realm of Chinese RS. This section delves into how RL's dynamic learning capabilities and adaptability enhance the functionality and effectiveness of RS, particularly in the context of the unique market dynamics and user behaviors observed in China. The synergy between RL and RS in this context is dissected, offering insights into how RL-driven RS can lead to more personalized, context-aware, and efficient user experiences.

Comprehensive Introduction and Analysis of the UCB Algorithm in Multi-Armed Bandit Problems

This article commences with a review of the fundamentals of reinforcement learning, encompassing a detailed overview of its key models and algorithms. It then juxtaposes these concepts with traditional machine learning paradigms, offering a comparative analysis. Subsequently, the focus shifts to the background and developmental landscape of recommendation systems. It systematically categorizes and describes the commonly employed recommendation algorithms. The core of the discussion centers on the utilization of reinforcement learning in recommendation systems. Beginning with practical case studies, the article delves into the strategies for integrating reinforcement learning with recommendation systems, addressing current challenges and envisaging future directions for development. Insightful reflections and comparisons between reinforcement learning and traditional machine learning are also provided, elucidating the differences and applicable scenarios for each approach. In essence, this article serves as an extensive guide to the intricacies of reinforcement learning and recommendation systems. It aims to equip readers with the knowledge required to understand and effectively apply these technologies. As technological advancement and research progress, it is anticipated that reinforcement learning will increasingly infiltrate the operations of recommendation systems, offering more personalized services and enhanced user experiences.

Towards Cognition-Aligned Visual Language Models via Zero-Shot Instance Retrieval

The pursuit of Artificial Intelligence (AI) that emulates human cognitive processes is a cornerstone of ethical AI development, ensuring that emerging technologies can seamlessly integrate into societal frameworks requiring nuanced understanding and decision-making. Zero-Shot Instance Retrieval (ZSIR) stands at the forefront of this endeavour, potentially providing a robust platform for AI systems, particularly large visual language models, to demonstrate and refine cognition-aligned learning without the need for direct experience. In this paper, we critically evaluate current cognition alignment methodologies within traditional zero-shot learning paradigms using visual attributes and word embedding generated by large AI models. We propose a unified similarity function that quantifies the cognitive alignment level, bridging the gap between AI processes and human-like understanding. Through extensive experimentation, our findings illustrate that this similarity function can effectively mirror the visual–semantic gap, steering the model towards enhanced performance in Zero-Shot Instance Retrieval. Our models achieve state-of-the-art performance on both the SUN (92.8% and 82.2%) and CUB datasets (59.92% and 48.82%) for bi-directional image-attribute retrieval accuracy. This work not only benchmarks the cognition alignment of AI but also sets a new precedent for the development of visual language models attuned to the complexities of human cognition.

A TEE-Based Federated Privacy Protection Method: Proposal and Implementation

With the continuous enhancement of privacy protection globally, there is a problem for the traditional machine learning paradigm, which is that training data cannot be obtained from a single place. Federated learning is considered a viable technique for preserving privacy that can train deep models with decentralized data. Aiming at two-party vertical federated learning, and at common attack problems such as model inversion, gradient leakage, and data theft, we provide a formal definition of Intel SGX’s trusted computing base, remote attestation, integrity verification, and encrypted storage, and propose a general federated learning privacy enhancement algorithm in the scenario of a malicious adversary model, and we extend this method to support horizontal federated learning, secure outsourced computation, etc. Furthermore, the method is developed in a Fedlearner framework of open-sourced machine learning to achieve privacy protection of the training data and model without any modification to the existing neural network and algorithm running on the framework. The experimental results show that this scheme substantially improves on the existing schemes in terms of training efficiency, without losing model accuracy.

Challenges to Utilization of Online Learning: A Study on First-Year Nursing Students at Selected KMTC Campuses

Technological advancements play a pivotal role in reshaping traditional learning paradigms. The COVID-19 pandemic disrupted teaching in Kenya’s learning institutions, especially in medical schools, like Kenya medical training college (KMTC). Due to safety, face-to-face classes had to be suspended. Online learning tools played a crucial role during the pandemic; but in developing countries like Kenya, technological, educational/literacy background, and socioeconomic challenges existed that could be a hindrance to the adoption and utilization of online learning process. The purpose of this study was to identify challenges to online learning from the perspective of student nurses (March and September 2020 classes) at Kenya Medical Training College. The study objectives were to assess student-related, technological and institutional challenges to the utilization of online learning. A descriptive, cross-sectional design was utilised in the study. Structured questionnaires were used to collect data from sampled March and September 2020 class student nurses who undertook online classes. The respondents were 272 first-year diploma student nurses from March and September 2020 classes sampled from eight selected campuses in the Western, Nyanza, and North Rift regions of Kenya. Collected data was analysed using the Statistical Package for Social Sciences (SPSS) version 20 computer software. Descriptive statistics included proportions, and frequencies, while results were presented in the form of graphs and tables. Ethical issues were observed by ensuring confidentiality and anonymity, and permission was sought prior to data collection. Study findings revealed the existence of challenges to the utilization of online learning by students at KMTC. Student-related challenges included knowledge gap on the use of internet infrastructure (38.8%), not owning E-learning devices (9.6%), and inadequate utilization of online learning at home setting due to disruptions, limited space, and competing tasks (88.2%). On technological challenges, 70% of respondents owned gadgets that were incompatible with the media that was being utilised for e-learning, hence couldn’t log in for the sessions. Frequent internet instability was experienced by 54.4% of students. 58.8% stated having slow internet connectivity that affected timely logging in for sessions. On institutional challenges, 55.5% of students stated inadequate preparation for online learning by the institutions, and lack of orientation. 92.6% of respondents indicated preference for face-to-face mode of learning to E-learning, citing reasons like poor communication between students and lecturers, interruption of online classes due to poor network, inconsistent data bundles, and lack of instant feedback and supervision by lecturers. In conclusion, great importance was attached to needs assessment survey for students and lecturers on all campuses to identify these challenges to enhance the adoption of the teaching model

ASYNCHRONOUS AND SYNCHRONOUS СONTENT OF BLENDED LEARNING FOR PROSPECTIVE ENGLISH UNIVERSITY TEACHERS

The architecture of blended learning (BL) in the training of prospective English teachers in higher education establishments was illuminated, particularly emphasizing the synchronous and asynchronous content and learning modes, which are an integral basis for the development of their professionally oriented communicative competence (POCC). A multi-faceted methodological approach – encompassing analysis, synthesis, comparison, and generalization – served as the analytical lens for scrutinizing the extant scientific literature, undertaking categorical evaluations of core concepts, and delineating their interrelations. BL was positioned as a purposeful process of dissemination and assimilation of knowledge, enhancing an array of speech fluency, linguistic understanding, sociocultural awareness, educational and strategic competences, cognitive activities of Master’s degree students. It offers a nuanced hybridization of distance (remote) and traditional (conventional) learning paradigms, effectively enriching the learning process while enabling their independent (self-study) learning. A functional scheme of the content area of BL covers three generic notions and is considered as a multifarious interactive space that serves as a catalyst for educational synergy, fostering holistic interactions between students, teachers, and the curriculum, where two subcategories of asynchronous learning mode are distinguished: collaborative (group-based) and independent (individual-based). The research introduced a pedagogical blueprint encompassing a repertoire of synchronous and asynchronous learning modes that can be configured in the following varieties: synchronous-group, asynchronous-group, synchronous-individual, asynchronousindividual, virtual-synchronous and face-to-face-synchronous.

Challenges and Opportunity of Online Education for Home Science : A Comprehensive Review

Online education has become more prevalent across various fields of study, including Home Science. It is changing the existing traditional learning paradigms and offering educators, students, and institutions both challenges and opportunities. There are various challenges in online Home Science education. One of the major concerns is the practical nature of Home Science, which traditionally involves hands-on skills. Transitioning these hands-on activities to a virtual context poses a significant challenge. To ensure that students can effectively acquire and apply practical skills through online learning platforms is still an ongoing concern for educators. Traditional assessment methods that require in-person demonstrations and evaluations are demanding to develop innovative and reliable methods for assessing practical skills online. In the absence of face-to-face encounters, it might be difficult to nurture soft skills like effective communication, interpersonal skills, and empathy, which are essential to home science. Home Science education offers a range of opportunities. The COVID-19 epidemic enhanced the acceptance of online learning, notably in the field of home science. The use of digital tools by educators and institutions to maintain learning continuity helped them quickly adjust to remote instruction. Online platforms also support cross-cultural communication between students and teachers, encouraging the sharing of ideas and different points of view. Innovative technologies can be integrated into the digital environment, including virtual reality and augmented reality, which can improve the learning process by offering immersive simulations of real-world home science activities. Thus, this comprehensive review paper emphasizes the changing environment of online home science education. It highlights the difficulties in developing and evaluating practical abilities as well as the value of developing soft skills in a virtual world. However, it also acknowledges the advantages that come with online learning, such as improved resource accessibility, multicultural collaboration, and modern technology. Home Science has the ability to furnish people with empower life skills of individuals and foster domestic well-being. Thus, this comprehensive review paper emphasizes the changing environment of online home science education. It highlights the difficulties in developing and evaluating practical abilities as well as the value of developing soft skills in a virtual world. However, it also acknowledges the advantages that come with online learning, such as improved resource accessibility, multicultural collaboration, and modern technology. Home Science has the ability to furnish people with empower life skills of individuals and foster domestic well-being.

Deep graph tensor learning for temporal link prediction

Knowledge discovery on dynamic graphs has received much attention in recent years. As a key task of dynamic graph research, the goal of temporal link prediction is to accurately predict the time-varying links in dynamic networks. Uncertainty in link emergence is a major challenge in this research, as it is not easy to learn stable and reliable link-level feature representations, which are usually readily available on static graphs. In order to adapt to the ever-changing graph structure, this paper proposes to construct a deep graph tensor learning model, which can capture the contextual characteristics of graph evolution from both the graph structure (spatial) mode and the link sequence (temporal) mode. Therefore, compared to link prediction on static graphs, temporal link prediction can benefit more from the link-level embedding representations coupled with spatio-temporal features. The experimental results on seven public dynamic graph datasets show that the prediction accuracy obtained by the new model is overall better than competing models such as GC-LSTM, EvolveGCN, and HTGN. In the meantime, as a result of getting rid of the traditional RNN learning paradigm, the new model is also significantly better than the traditional temporal graph learning model in terms of training efficiency.

Improving Student Performance through Interactive Webinars

Abstract— In the rapidly evolving landscape of education, the integration of technology has proven to be instrumental in reshaping traditional learning paradigms. This paper delves into the realm of improving student’s performance by harnessing the potential of interactive webinars as on innovative educational tool. The paper aims to investigate the impact of interactive webinars on student engagement, knowledge retention and overall academic achievement in the course. The findings of the study provide empirical evidence that interactive webinars positively influence student performance. The analysis demonstrates that students who actively participate in interactive webinars exhibit higher levels of comprehension, engagement and retention compared to conventional classroom instructions. The students’ engagements in the webinars are monitored by MOODLE activities, question answer sessions and their quick notes. The comparative graph of pre-test and post-test clearly describe that, there is improvement and shifting of students from CD, DD & FF grade to AA, AB, BB & BC grade. Employing a mixed-methods approach, the research draws upon quantitative data from pre and post webinar assessment, as well as qualitative insights obtained from student surveys and interviews. These methods enable a comprehensive evaluation of the effectiveness of interactive webinars in comparison to traditional instruction methods. In addition to that incorporating industry resource persons into educational activities like interactive webinars can significantly enhance the learning experience of the students. Industry professionals bring real-worlds insights, practical knowledge and helping bridge the gap between academic theory and practical application. Keywords— Interactive Webinar, Industry Resource Person, Student Performance, Industry Practices.

Practical Research on Flipped Classroom Teaching Model in College English Based on Production-Oriented Approach

In the context of integrating technology into higher education and the widespread acceptance of a learning-centered philosophy, flipped learning (FL) is gaining popularity as a flexible new approach. To overcome the problem of input-output separation in the traditional text-centered teaching and learning paradigm in tertiary education in China, a flipped classroom teaching model based on the theory of the Production Oriented Approach (POA) was proposed and implemented for a vocational English course. Mixed research methods, including testing, questionnaire surveys, and interviews, were used to validate the effect of the model. Data analysis shows a notable enhancement in the mastery of new target language items among the experimental class that adopted the flipped classroom teaching model based on the POA. Moreover, this practice also has changed their learning behaviors both in and after class, increasing their time investment in learning and improving their performance and engagement in class, thus enhancing their autonomy in language learning. This study empirically supports the flipped classroom teaching model based on the POA. It aids in transforming traditional input-driven teaching into output-driven teaching and serves as a teaching reference for practitioner teachers.

Student Satisfaction Assessment Study of E-Learning Users: User Satisfaction with IT (USIT)

E-Learning has changed the traditional learning paradigm that is limited to a certain physical space and time. Through eLearning, students can access various educational contents, such as learning modules, learning videos, interactive exercises, and discussion forums, which can be accessed anytime and anywhere by using electronic devices such as computers, laptops, or smartphones.User Satisfaction with IT (USIT) was developed by DeLone and McLean in the context of Information Systems Evaluation in 1992. DeLone and McLean proposed a framework for measuring the success of information systems based on several dimensions including user satisfaction. This USIT framework has undergone development and modification from time to time by researchers and practitioners in the field of Information Systems Evaluation. This study aims to determine the level of satisfaction on the online simulation using the USIT model which is serious in user satisfaction which consists of variables in USIT.This study shows the level of satisfaction of students as e-learning users. It can be concluded that the majority of students are satisfied with their experience using the e-learning platform. This can be seen from the level of positive responses to questions relating to user satisfaction.

Disrupting the Traditional Learning Paradigm: Place-Based Learning as Transformational Space

Place-based learning may be one of the disruptive innovations called for by Randy Bass in his keynote address, Architecture of the Unexpected: Beyond the Learning Paradigm. Place-based learning goes beyond the learning paradigm by challenging students to partner with community members in a specific place, to learn through interacting with the people and spaces, and to think like agents of change. Working as co-designers, faculty, students, and community stakeholders become partners to promote learning and transformation of individuals and places. Using place-based learning in the university setting facilitates the application of global, generalizable knowledge to the specifics of local people and places, creating hope for solutions that address the locali realities of the world’s wicked challenges.