Review
Agriculture, Dairy & Animal Science
Elizabeth C. Ragland, Scott Radcliffe, Elizabeth L. Karcher
Summary: Most student attrition in undergraduate education occurs early on, so it is important to interest and motivate students at an early stage. The demographics of animal science students have shifted, with many having minimal background in food-producing animals. This presents a unique challenge as students have different expectations and primarily focus on careers in veterinary medicine. Active learning classroom strategies, such as experiential learning and flipped classrooms, have been linked to increased knowledge. It is crucial to understand best practices in active learning and future research areas in animal science teaching programs.
JOURNAL OF ANIMAL SCIENCE
(2023)
Article
Chemistry, Multidisciplinary
Yong Han, Wenjun Wu, Lijun Zhang, Yu Liang
Summary: This study focuses on the online blended learning model of computer network experimentation, designing an online network experiment platform and management system to enable students to conduct remote computer network hardware experiments anytime and anywhere. By organizing experimental modules and knowledge points via the SPOC course concept, modularizing and fragmenting experiments, the utilization rate of teaching resources can be improved effectively.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Information Systems
Qing-xin Meng, Jian-wei Liu
Summary: This work focuses on dynamic regret for non-stationary online convex optimization with full information. State-of-the-art analysis shows that Implicit Online Mirror Descent (IOMD) combined with Hedge achieves an upper bound on dynamic regret, and Optimistic IOMD (OptIOMD) also has a small dynamic regret. To further reduce dynamic regret, we propose an algorithm named Hedge-OptIOMD and verify its advantages through numerical experiments.
INFORMATION SCIENCES
(2024)
Editorial Material
Biochemistry & Molecular Biology
Stefano Sandrone, Gregory Scott, William J. Anderson, Kiran Musunuru
Summary: The global pandemic of COVID-19 has pushed higher education towards remote learning, presenting an opportunity to embrace active learning that can improve student performance, narrow achievement gaps, and promote equity and inclusivity in STEM education.
Article
Engineering, Multidisciplinary
Mohammed A. M. Alhefnawi
Summary: The study found that both online handouts and active lecturing were effective in improving students' learning outcomes, with active lecturing significantly introducing more knowledge to students than online handouts.
AIN SHAMS ENGINEERING JOURNAL
(2021)
Article
Engineering, Electrical & Electronic
Boshuang Huang, Sudeep Salgia, Qing Zhao
Summary: This article studies online active learning for classifying streaming instances within the framework of statistical learning theory. By developing a disagreement-based online learning algorithm and establishing the tradeoff between label complexity and regret, an optimized algorithm is proposed.
IEEE TRANSACTIONS ON SIGNAL PROCESSING
(2022)
Article
Multidisciplinary Sciences
Nouf Al-Kahtani, Abdullah Almurayh, Arun Vijay Subbarayalu, Tunny Sebastian, Hend Alkahtani, Duaa Aljabri
Summary: This study investigated students' perception and learning experiences with blended and online courses during and after the COVID-19 lockdowns. The findings showed that students were highly satisfied with the blended learning format, particularly the Blended 0.75-course format during the lockdown. After the lockdown, students reported high satisfaction with courses delivered through 100% online learning mode.
Article
Psychology, Multidisciplinary
Christian Scheibenzuber, Sarah Hofer, Nicolae Nistor
Summary: In response to the Covid-19 pandemic, universities shifted to emergency online learning while academics sought ways to combat the infodemic of misinformation. A proposed educational sciences undergraduate online course addressing fake news illiteracy showed that students were particularly interested in online communication and feedback, leading to a significant decrease in fake news credibility and strong academic achievement. This study suggests that problem-based online courses can be effective in combating fake news illiteracy, even in emergency learning situations.
COMPUTERS IN HUMAN BEHAVIOR
(2021)
Editorial Material
Multidisciplinary Sciences
Aleksandra Piktus
Summary: Large language models are incorporating external tools like Wikipedia to enhance their accuracy in reasoning, which could lead to better fact-finding outcomes and online shopping experiences.
Article
Health Care Sciences & Services
Kah Meng Chong, Hsiang-Wen Yang, Hsien-Chin He, Wan-Ching Lien, Mei -Fen Yang, Chien-Yu Chi, Yen-Pin Chen, Chien-Hua Huang, Patrick Chow-In Ko
Summary: This study compared the educational outcomes of a remote practice blended learning (RBL) model with a conventional classroom-based learning (CBL) model for basic life support (BLS) CPR training. The results showed that remote self-directed deliberate practice was not inferior to classroom-based instructor-led method in terms of CPR performance, although it took more time to achieve the same effect.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2023)
Article
Education & Educational Research
Kamran Ali, E. S. A. Alhaija, Mahwish Raja, Daniel Zahra, Zoe L. Brookes, Ewen McColl, Sobia Zafar, Barbara Kirnbauer, Ahed M. Al Wahadni, Rami S. Al-Fodeh, Thikrayat Ghazi Bani-Hani, Saba O. Daher, Hasan O. Daher
Summary: This study explores the global trends in blended learning in undergraduate dental education during and after the COVID pandemic. Participants included dental faculty and undergraduate students from 80 dental institutions worldwide. The results highlight the need for faculty to engage more closely with students and adapt teaching practices to suit their learning needs. Recommendations for future teaching and learning strategies and assessments in the post-pandemic era were provided.
MEDICAL EDUCATION ONLINE
(2023)
Article
Psychology, Multidisciplinary
Xiaoxia Li, Wanxia Zhu
Summary: This study, based on the User Satisfaction and Technology Acceptance Integration Theory, analyzed the factors influencing college students' acceptance and satisfaction of online learning platforms and the differences in these factors between blended learning and online learning scenarios. The results showed that the quality of the online learning platform and information quality significantly influenced user satisfaction, which in turn affected usefulness and ease of use, and ultimately influenced attitude and intention. The comparison between the two groups revealed significant differences in the impact of information quality on information satisfaction and the impact of perceived usefulness on usage intention. In the online learning scenario, the endogenous latent variables of the model had higher explanatory power, indicating that learners rely more on the quality and relevant characteristics of the learning platform in the online learning scenario.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Yanfang Liu, Xiaocong Fan, Wenbin Li, Yang Gao
Summary: This study combines active query strategy and passive-aggressive update strategy, and proposes a novel online active learning algorithm for trapezoidal data streams. Experimental results confirm its effectiveness in learning from trapezoidal data streams.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Social Sciences, Interdisciplinary
Ashraf Ali, Raja Muhammad Ishtiaq Khan, Abdullah Alouraini
Summary: The COVID-19 pandemic has prompted educational institutions to adopt online and blended learning, which has become crucial for instruction and learning. This study investigated the impact of online and blended learning on grammatical knowledge and skill acquisition. 76 first-year medical learners participated in a 7-week experiment, with one group having face-to-face and online learning, and the other group having online learning only. Pre-test and post-test data showed that both approaches improved grammar performance, but the blended learning group outperformed the online learning group.
Article
Computer Science, Artificial Intelligence
Muhammad Kamran Janjua, Haseeb Shah, Martha White, Erfan Miahi, Marlos C. Machado, Adam White
Summary: This paper investigates the use of reinforcement-learning based prediction approaches for a real drinking-water treatment plant. The development of such a prediction system is critical for optimizing and automating water treatment. The paper discusses the predictability of the data, suitable neural network architectures, and how to overcome challenges such as partial observability and heterogeneity across sensors and operation modes.