Challenges of Hybrid Flexible (HyFlex) Learning on the Example of a University of Applied Sciences

Due to the global coronavirus pandemic, it became increasingly necessary to rearrange the teaching process at all school levels. Higher education institutions all over the world have been facing the challenge since 2020, to find blended teaching formats and activities to provide higher education without compromising the quality of education, but at the same time mitigating health risks. This article deals with the HyFlex learning model. The aim of this paper is to identify problems that may arise when implementing HyFlex teaching and learning in higher education. Identifying problems also provides an opportunity to offer solutions to these problems and to introduce possible solutions more widely. In order to answer the research question an online survey was conducted in spring 2021 (n = 570). The survey consisted of both closed and open questions. The fact that Estonia was one of those countries, where periods of F2F classes during the first and second waves of the COVID-19 pandemic were possible, speaks in favor of conducting the research in Estonia. In conclusion, most of the students (75%) participating in the survey were rather positive, rating the learning experience to be good or even excellent. However, some problems were pointed out too: difficulties in concentrating, decrease of learning motivation/self-discipline, lack of depth in learning, and insufficient self-directed learning skills;followed by communication barriers and problems related to digital competencies and skills for both teachers and students. Based on the above, almost a quarter of the respondents found that the volume of learning increased. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A New Hybrid Artificial Intelligence Model for Diseases Identification

Artificial intelligence is increasingly applied in many fields, specially in medicine to assist patients and physicians. Growing datasets provide a sound basis to adapt machine learning methods to identify and detect some diseases. These later, are often very similar which make difficult their identification by chest X-ray images. In this paper, we introduce a diagnostic AI model that allow to separate, diagnose and classify three various diseases: tuberculosis, covid19 and Pneumonia. The proposed model is based on a combination of Deep Learning using the deep SqueezeNet model and Machine Learning: SVM, KNN, Logistic Regression, decision tree and Naive Bayes. The model is applied to a chest X-ray dataset containing images for each type of disease. To train and test our model, we split the image dataset into two training and test subsets in order to differentiate between different disease types. The accuracy show clearly that our model provides better results of diagnosis and identification. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Explaining variation in teachers' use of ict: A learning analytics approach

Significant attention has been paid to the use of ICT by teachers, especially during the COVID-19 health crisis. This usage has mostly been captured through self-reported survey measurements. Learning analytics can complement such findings, by using log data to document precisely how long teachers use ICT, and what ICT behaviors they perform online. Using log data of 800 teachers, the present study documents their use of ICT in mathematics on a digital learning platform used across Luxembourg during COVID-19 remote education. Our findings confirm the large differences between teachers' use of ICT found in previous research, measured here through the time spent active on the platform. The types of ICT behaviors teachers engage with online, measured via the SAMR model, explain most of this variation. Specifically, more time on the platform is associated with activities that create a meaningful learning experience, and redefined tasks that could engage students as active learners. Experience with the technology, and participation in incentive events and teacher training, explain another significant part of this variation. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Learning gain rather than learning loss during COVID-19: A proposal for reframing the narrative

"Learning loss" has become the new buzzword in education during the COVID-19 era. Learning loss may be real in certain academic subjects (e.g. mathematics and reading) for certain students, as indicated by standardized test scores. However, it only tells a partial story. The other part of the story actually indicates different kinds of learning gain that might have occurred for children experiencing non-conventional learning opportunities during the COVID-19 pandemic. Thus, the authors caution against subscribing to a learning-loss narrative, a deficits-based perspective, which can lead one to lose sight of children's potential learning gains that are not necessarily assessed or recognized. Against this backdrop, the authors offer four recommendations: (1) reframing the concept of "learning loss" to "learning gain";(2) applying a strengths-based model rather than a deficits-based model for understanding student learning;(3) investing in the development of the whole child;and (4) ensuring that we focus on young children's socio-emotional well-being (e.g. relationship-building) and not solely on the cognitive domains. [ FROM AUTHOR] Copyright of Contemporary Issues in Early Childhood is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Epidemiological Modeling with Sir and Arima Dynamic Models and Machine Learning to Predict the Future Behavior of Covid-19 in Panama

Mathematical models SIR and ARIMA were used, within an epidemiological approach, to adjust them to the COVID-19 pandemic data in Panama to establish a scientific criterion for taking decisions for the effects control that this pandemic has brought. Based on the predictions made from the adjustments of these models, it was concluded that they can be adjusted correctly to the data, allowing to make short-term predictions in a satisfactory way, however, if a more accurate model were to be carried out, independent variables could be included, besides time, such as mobility restrictions. This work lays down the foundations for future investigations of epidemiological models in Panama due to its exposition of mathematical model's comparison used to analyze the behavior of the COVID-19 Pandemic. Jupyter Notebook, GitHub, Machine Learning libraries and mathematical software such as Wolfram Mathematica were used. Adjustment of data was performed through statistical techniques and, for this prediction, statistical software Minitab and E-Views were also used. © 2022 IEEE.

IMPLEMENTATION OF MACHINE LEARNING ALGORITHM FOR COVID19 DETECTION USING X-RAY AND VOICE DATASET

Ever since the deadly corona virus came into existence the life of the people has been shattered both in terms of health and economic crisis. Even today its various variants are creating havoc among the people. The traditional way of testing the disease is time consuming and is also not cost efficient due to the requirement of PEP kits. In this paper various Machine Learning (ML) techniques have been implemented based on the cough samples and chest x-ray images of the individuals. A hybrid model with GUI interface is designed to predict covid-19 and to perform the comparative analysis of sequential model and ResNet50 for image dataset, CNN with hyper parameter tuning and CNN for voice dataset. From the experimental analysis, ResNet50 performed better when compared to sequential model for image dataset and CNN with hyper parameter tuning performed better when compared with CNN model for voice dataset. © 2022 IEEE.

On Modeling LMS Users' Quality of Interaction Using Temporal Convolutional Neural Networks

Learning Management Systems (LMSs) have been widely employed following the Covid-19 pandemic. The user modeling of LMS including educators and learners is a point of interest for Higher Education Institutions (HEI), stakeholders and system users. In this work user's engagement with LMS is modeled using the Quality of Interaction (QoI) indicator under a combined approach of blended and collaborative learning. The present research extends the previous work of ‘Fuzzy QoI' and ‘DeepLMS' to develop a generalized model that substitutes the fuzzy logic system with a deep learning model. In this line, Temporal Convolutional Neural Networks (T-CNN) were used to predict QoI, achieving MAE (0.027), RMSE (0.066) and R2 (0.698). The feedback received from the T-CNN model provides insights to educators and stakeholders in order to enhance the pedagogical experience. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Hybrid Approach for Detection of Fake News in Sinhala Text

The impact of technology on people's lives has grown continuously. The consumption of online news is one of the important trends as the share of population with internet access grows rapidly over time. Global statistics have shown that the internet and social media usage has an increasing trend. Recent developments like the Covid 19 pandemic have amplified this trend even more. However, the credibility of online news is a very critical issue to consider since it directly impacts the society and the people's mindsets. Majority of users tend to instinctively believe what they encounter and come into conclusions based upon them. It is essential that the consumers have an understanding or prior knowledge regarding the news and its source before coming into conclusions. This research proposes a hybrid model to predict the accuracy of a particular news article in Sinhala text. The model combines the general news content based analysis techniques using machine learning/ deep learning classifiers with social network related features of the news source to make predictions. A scoring mechanism is utilized to provide an overall score to a given news item where two independent scores- Accuracy Score (by analyzing the news content) and Credibility Score (by a scoring mechanism on social network features of the news source) are combined. The hybrid model containing the Passive Aggressive Classifier has shown the highest accuracy of 88%. Also, the models containing deep neural netWorks has shown accuracy around 75-80%. These results highlight that the proposed method could efficiently serve as a Fake News Detection mechanism for news content in Sinhala Language. Also, since there's no publicly available dataset for Fake News detection in Sinhala, the datasets produced in this work could also be considered as a contribution from this research. © 2022 IEEE.

D-Dimer Elevation Matters to Predict COVID-19 Severity: A Machine Learning Approach

Although previous studies using limited data have documented an association of D-dimer levels with COVID-19 severity, the role of D-dimer in the progression of COVID-19 remains unclear and requires further investigation using data from larger cohorts. We used traditional statistical modeling and machine learning methods to examine critical factors influencing the D-dimer elevation and to characterize associated risk factors of D-dimer elevation over the course of inpatient admission. We identified 20 important features to predict D-dimer levels, some of which could be used to predict and prevent the D-dimer elevation. Laboratory monitoring of D-dimer level and its risk factors at early stage can mitigate severe or death cases in COVID-19. © 2022 IEEE.

Covid-19: Why Virtual Learning Becomes a Pedagogical Acceptance Model in Africa

The outbreak of coronavirus pandemic has led to different regulations and changed the usual way of doing things. Considering the level of technology in Africa society before the outbreak of the epidemic many activities, including classroom teaching and learning were affected. This paper examines virtual learning as an unavoidable pedagogical model for learning during the COVID-19 pandemic. A cross-sectional study was conducted by adopting the Technology Acceptance Model. Data were obtained from an online survey of 543 respondents and analyzed. Regression of Partial Least Squares (PLS) was used for modeling and hypothesis testing. The results revealed that perceived usefulness, perceived ease of use, regulatory compliance, and implementation context significantly affect educators' and learners' attitudes towards adopting virtual learning for learning. Subsequently, regulatory compliance had the most substantial influence on educators' and learners' attitudes towards adopting virtual learning for learning during the COVID-19 outbreak. This study established that the adoption of virtual learning has enhanced learning during the coronavirus pandemic lockdown, and the process would also continue after the pandemic. Virtual learning has provided the classroom experience for learners and educators. © 2022. All Rights Reserved.

Making it Work in the Virtual Capstone Climate and Beyond: Project-based Perspectives Across a Variety of Programs and Universities

With all the changes in the educational landscape due to COVID-19, capstone design courses have been uniquely affected. With several transitions to virtual course delivery and/or hybrid models of learning, capstone faculty are now challenged with helping students meet project objectives and deliverables, fostering student team cohesion, and managing sponsor expectations in virtual settings, all while fulfilling the course learning outcomes. While there have been countless programs, communities, and support systems implemented to guide the transition to online teaching, initially there was very little available to systematically understand and support the capstone and PBL community. The objectives of this work are twofold. The first aim is to outline the challenges faced by capstone faculty due to transitions to primarily remote capstone offerings, particularly within the areas of managing sponsorship, completing projects, and producing the associated final project deliverables. The second objective is to open a dialogue to chronicle concerns, gather input, and share best practices across the broader capstone community. The overarching goal is to help overcome -and even rise to- these challenges. This research was conducted by capstone faculty at four different universities. The first phase of this initiative involved research to identify the issues and practices in the existing literature, especially relevant to virtual capstone offerings. The second phase of this research involved a survey of capstone faculty on this topic to reinforce and/or supplement the literature findings as the virtual circumstances evolved. To understand the acute challenges, the survey noted above was conducted with the broader capstone community to include a diversity of faculty associated with capstone at a variety of institutions. This included capstone directors, coordinators, instructors, and advisors. The third phase gathered information through a panel organized and conducted by the authors at the most recent ASEE conference while dealing with societal and academic COVID-19 restrictions. The ASEE panel served as a platform to bring together the capstone community for ongoing dialogue, supplying additional solution recommendations. Results from this research coupled with literature findings indicated the commonality of challenges faced by capstone programs regardless of timing, engineering major, program profile, or type of institution. Among the survey results were the following: (1) Due to COVID-19 conditions, 44% of the respondents reported complete cancellation of this event while 56% reported conducting some form of virtual exposition. The work represented in this paper supports an intention to be agile enough to adapt to any situation along this continuum - and likewise be posed to adjust when our capstone programs must react to emerging circumstances in the future. © American Society for Engineering Education, 2021