In recent years, many researchers have been engaged in the development of educational computer games; however, previous studies have indicated that, without supportive models that take individual ...students' learning needs or difficulties into consideration, students might only show temporary interest during the learning process, and their learning performance is often not as good as expected. Learning styles have been recognized as being an important human factor affecting students' learning performance. Previous studies have shown that, by taking learning styles into account, learning systems can be of greater benefit to students owing to the provision of personalized learning content presentation that matches the information perceiving and processing styles of individuals. In this paper, a personalized game-based learning approach is proposed based on the sequential/global dimension of the learning style proposed by Felder and Silverman. To evaluate the effectiveness of the proposed approach, a role-playing game has been implemented based on the approach; moreover, an experiment has been conducted on an elementary school natural science course. From the experimental results, it is found that the personalized educational computer game not only promotes learning motivation, but also improves the learning achievements of the students.
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BFBNIB, DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NMLJ, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ, ZRSKP
The aim of this article is to review the state of the art of research and theory development on student learning patterns in higher education and beyond. First, the learning patterns perspective and ...the theoretical framework are introduced. Second, research published since 2004 on student learning patterns is systematically identified and reviewed. This part includes two main sections. In the first section, new evidence on internal and external relationships of learning patterns is reviewed. Four themes are covered here: the dimensionality and the internal relationships of learning patterns and relationships of learning patterns with personal, contextual, and outcome variables. In the second section, new directions in learning patterns research are examined. These include studies on learning patterns in new international contexts and populations, longitudinal development of learning patterns over time, methodological advances in learning patterns research, and studies on fostering the quality of students' learning patterns. Next, relationships with adjacent theories on student learning are discussed, the learning patterns perspective is critically examined, and pathways are derived to move the model forward. Finally, future conceptual and methodological directions for learning patterns research are derived.
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Summary
The new generation of artificial intelligence (AI), called AI 2.0, has recently become a research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and electric power ...system (Smart EEPS). In AI 2.0, machine learning (ML) forms a typical representative algorithm category used to achieve predictions and judgments by analyzing and learning from massive amounts of historical and synthetic data to help people make optimal decisions. ML has preliminarily been applied to the Smart Grid (SG) and Energy Internet (EI) fields, which are important Smart EEPS representatives. AI 2.0, especially ML, is undergoing a critical period of rapid development worldwide and will play an essential role in Smart EEPS. In this context, this study, combined with the emerging SG and EI technologies, takes the typical representative of AI 2.0—ML—as the research objective and reviews its research status in the operation, optimization, control, dispatching, and management of SG and EI. The paper focuses on introducing and summarizing the mainstream uses of seven representative ML methods, including reinforcement learning, deep learning, transfer learning, parallel learning, hybrid learning, adversarial learning, and ensemble learning, in the SG and EI fields. In this survey, we begin with an introduction to these seven types of ML methods and then systematically review their applications in Smart EEPS. Finally, we discuss ML development under the big data thinking and offer a prospect for the future development of AI 2.0 and ML in Smart EEPS. We conduct this survey intended to arouse the interest and excitement of experts and scholars in the EEPS industry and to look ahead to efforts that jointly promote the rapid development of AI 2.0 in the Smart EEPS field.
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Although many educational researchers were pioneers in the integration of technology into teaching and learning prior to 2000, institutions started extensively adopting technology in their courses ...around this period. However, the adoption process was slow and mainly followed the traditional mode of teaching in the formal university learning environment. The COVID-19 pandemic’s disruption “forced” everyone to use technology for teaching and learning purposes, supporting synchronous and/or asynchronous teaching and learning processes. This book aims not only to present successful practice examples from before or during the COVID-19 pandemic, but also to provide useful information to university teachers, assisting them in further understanding the higher education context, demands and challenges of digital education. Including evidence from the current higher education landscape from all over the world and discussing various frameworks allows institutions and policymakers to take decisions about the future digital education transformation, while teachers and educational researchers can find examples of how various digital learning tools (i.e., virtual simulations and e-portfolios) are integrated into teaching and learning processes in various environment (i.e., online, and blended learning). Considering experiences prior to the COVID-19 pandemic alongside the opportunities and challenges brought about by the pandemic, this book can support the higher education sector in considering curriculum reformations and introducing innovative teaching and learning approaches to meet the Industrial 4.0 revolution.
Structural Deep Clustering Network Bo, Deyu; Wang, Xiao; Shi, Chuan ...
Proceedings of The Web Conference 2020,
04/2020
Conference Proceeding
Open access
Clustering is a fundamental task in data analysis. Recently, deep clustering, which derives inspiration primarily from deep learning approaches, achieves state-of-the-art performance and has ...attracted considerable attention. Current deep clustering methods usually boost the clustering results by means of the powerful representation ability of deep learning, e.g., autoencoder, suggesting that learning an effective representation for clustering is a crucial requirement. The strength of deep clustering methods is to extract the useful representations from the data itself, rather than the structure of data, which receives scarce attention in representation learning. Motivated by the great success of Graph Convolutional Network (GCN) in encoding the graph structure, we propose a Structural Deep Clustering Network (SDCN) to integrate the structural information into deep clustering. Specifically, we design a delivery operator to transfer the representations learned by autoencoder to the corresponding GCN layer, and a dual self-supervised mechanism to unify these two different deep neural architectures and guide the update of the whole model. In this way, the multiple structures of data, from low-order to high-order, are naturally combined with the multiple representations learned by autoencoder. Furthermore, we theoretically analyze the delivery operator, i.e., with the delivery operator, GCN improves the autoencoder-specific representation as a high-order graph regularization constraint and autoencoder helps alleviate the over-smoothing problem in GCN. Through comprehensive experiments, we demonstrate that our propose model can consistently perform better over the state-of-the-art techniques.
Meta-Learning in Neural Networks: A Survey Hospedales, Timothy; Antoniou, Antreas; Micaelli, Paul ...
IEEE transactions on pattern analysis and machine intelligence,
09/2022, Volume:
44, Issue:
9
Journal Article
Peer reviewed
Open access
The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent years. Contrary to conventional approaches to AI where tasks are solved from scratch using a fixed ...learning algorithm, meta-learning aims to improve the learning algorithm itself, given the experience of multiple learning episodes. This paradigm provides an opportunity to tackle many conventional challenges of deep learning, including data and computation bottlenecks, as well as generalization. This survey describes the contemporary meta-learning landscape. We first discuss definitions of meta-learning and position it with respect to related fields, such as transfer learning and hyperparameter optimization. We then propose a new taxonomy that provides a more comprehensive breakdown of the space of meta-learning methods today. We survey promising applications and successes of meta-learning such as few-shot learning and reinforcement learning. Finally, we discuss outstanding challenges and promising areas for future research.
Since the advent of new technology for learning, innovative language instructors have been constantly seeking new pedagogy to match the potential of technology-enhanced instruction. While previous ...studies have supported the adoption of technologies to facilitate language teaching and learning, research into enhancing English as a foreign language (EFL) learners' oral proficiency by creating an online learning community in a flipped classroom remains insufficient. Therefore, the current study examined the impact of an online learning community in a flipped classroom, specifically via mobile platforms, on EFL learners' oral proficiency and student perceptions. Fifty English-majored sophomores enrolled in two oral training classes at a four-year comprehensive university in central Taiwan participated in this study. A mixed method was employed to analyze multiple sources of data, including pre- and post-tests on oral reading and comprehension questions, a ＂Community of Inquiry＂ (CoI) questionnaire, and semi-structured focus-group interviews. The results from multiple sources indicated that the online learning community not only facilitated meaningful and positive collaboration but also significantly improved the participants' oral proficiency, thus leading to more active engagement in highly interactive learning activities, such as storytelling, dialogue collaboration, class discussion, and group presentations.
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Online learning is the fastest growing segment in U.S. higher education and is increasingly adopted in public and private not-for-profit institutions. While the impact of online learning on ...educational outcomes is becoming more clear, the literature on its connection with student engagement is sparse. Student engagement measures identify key aspects of the learning process that can improve learning and outcomes like retention and achievement. The few studies investigating the link between online learning and student engagement found positive benefits for online learners compared to face-to-face learners in terms of perceived academic challenge, learning gains, satisfaction, and better study habits. On the other hand, face-to-face learners reported higher levels of environment support, collaborative learning, and faculty interaction. However, these studies did not effectively account for the differences in background characteristics like age, time spent working or caring for dependents, and enrollment status. Further, they did not consider the increasingly large population of students who enroll in both online and face-to-face courses. In our study, we used propensity score matching on the 2015 National Survey of Student Engagement data to account for the disparities in these groups' demographics variables. After matching, we found that some of the previous literature's differences diminish or disappear entirely. This suggests differences in supportive environments and learning strategies have more to do with online student characteristics than learning mode. However, online learning still falls well below other modes in terms of collaborative learning and interaction with faculty.
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Self-supervised learning has gained popularity because of its ability to avoid the cost of annotating large-scale datasets. It is capable of adopting self-defined pseudolabels as supervision and use ...the learned representations for several downstream tasks. Specifically, contrastive learning has recently become a dominant component in self-supervised learning for computer vision, natural language processing (NLP), and other domains. It aims at embedding augmented versions of the same sample close to each other while trying to push away embeddings from different samples. This paper provides an extensive review of self-supervised methods that follow the contrastive approach. The work explains commonly used pretext tasks in a contrastive learning setup, followed by different architectures that have been proposed so far. Next, we present a performance comparison of different methods for multiple downstream tasks such as image classification, object detection, and action recognition. Finally, we conclude with the limitations of the current methods and the need for further techniques and future directions to make meaningful progress.
The Odia language is one of the many regional languages spoken in India. It is the official language of Odisha, a State in eastern India. The Odia language carries a 1500-year-old history and ...worldwide is spoken by more than 50 million people. The Odia digits are complex due to the presence of many curves in each character. Handwritten scripts are even more complex due to free-style writing. However, the development of an innovative machine learning model is essential because Odia scripts consist of a huge number of historical documents of more than 1000 years old. A robust automation method will help in converting historical documents into digital form and will help to preserve the documents. This will solve a big problem in society. This work experiments with handwritten Odia numerals by implementing two different classifiers. The first one is the implementation of a Convolutional Neural Network (CNN) and the second experiment implements a Support Vector Machine (SVM). Finally, results from both experiments have been compared. The dataset has been generated through software by writing the digits on MS Paint. Both CNN and SVM models have been implemented through Python programming to recognize the inputs into a particular class. Both training and testing of the models have been done using this dataset. The accuracy from the CNN Model is obtained to be 94.999% which is ≈95% and for SVM, the model accuracy is 86%. Comparing both results, it is concluded that the CNN model is comparatively better than the SVM classifier in the case of the proposed work.
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