The effects of cognitive- and affective-based trust on students' knowledge sharing and learning performance during the Covid-19 pandemic

This study examined the effects of cognitive and affective-based trust on knowledge sharing among students, which influences learning performance during the COVID-19 pandemic. A survey was conducted with 730 participants, and analysis was carried out using structural equation modeling (SEM) based on the uses and gratifications (U&G) theory. The results showed that cognitive and affective trust significantly affects students' knowledge sharing behavior on Facebook, which further influences learning performance. This study also showed that social media had become a tool for social interaction and learning, which is crucial to students during the COVID-19 pandemic.

Exploring the effect of improved learning performance: A mobile augmented reality learning system.

Students are commonly in a high cognitive load state when they encounter sophisticated knowledge. Whether the novel augmented reality (AR) technology can be utilized in an online learning course to explain complicated scientific concepts in a more understandable manner to students during the COVID-19 period is an unaddressed issue. This study aims to investigate the influences of reducing the physical touch or face-to-face teaching/learning practices via using mobile augmented reality learning systems (MARLS) on students' perceived learning effectiveness. The information feedback viewpoint, flow theory, and cognitive load theory are integrated to examine the effects of the information feedback of MARLS on students' learning effectiveness. This study recruited 204 participants from ten universities to complete a learning task via a MARLS and fill out a questionnaire to collect data for the proposed research model. The empirical results revealed information feedback positively and significantly affected flow experience, perceived learning effectiveness, and continued intention. The improved learning performance of learners was positively related to their continued intention. Also, the extraneous cognitive load negatively and significantly moderated the relationship between information feedback and perceived learning effectiveness. This study proposes meaningful implications and suggestions for future research based on the findings of this experiment.

Fuzzy logic controller development for prediction of student performance based on activity parameters

Owing to the COVID-19 pandemic, most of the academic education has suddenly shifted from traditional teaching methods to advanced technological methods on the internet. Many teachers encountered difficulties in successfully evaluating and monitoring their students. We address these challenges and propose a fuzzy logic-based controller that can assists teachers during classes and support allocation of appropriate resources to students. The purpose of the controller is to provide early warning about students who have performed poorly in the initial part of the course assessment. The controller makes predictions based on 5 input parameters which, by applying statistical tools, have been proven to accurately reflect the students' achievements. The model was tested on a group of 50 students and the results indicate 82% prediction accuracy. There is a possibility for additional improvements related to the built-in parameters, both in terms of their selection and in terms of their number.

An Optimized Ensemble Model for COVID Detection

The entire world has gone through a pandemic situation due to the spread of novel corona virus. In this paper, the authors have proposed an ensemble learning model for the classification of the subjects to be infected by coronavirus. For this purpose, five types of symptoms are considered. The dataset contains 2889 samples with six attributes and is collected from the Kaggle database. Three different types of classifiers such as Support vector machine (SVM), Gradient boosting, and extreme gradient boosting (XGBoost) are considered for classification purposes. For improving the learning strategy and performance of the proposed models subjected to accuracy, the learning rates are varied for each node of the tree-based ensemble classifiers. Also, the hyperparameters of the XGBoost model are optimized by applying the Bayesian optimization (BO) technique. The best accuracy in SVM classifier is found as 91.69%. 96.58% accuracy is obtained in the modified gradient boosting model. The optimized XGBoost model is providing 100% accuracy which is better than other. © 2022 IEEE.

How does indoor physical environment differentially affect learning performance in various classroom types?

Classroom indoor physical environment (CIPE) crucially impacts learning performance (LP). Along with the extended school hours caused by COVID-19, an investigation was conducted at Zhejiang Sci-Tech University to explore the effect of CIPE on LP in different classroom types under natural working condition of transitional seasons. Based on a six-day physical environment measurement and learning performance test, then five CIPE parameters and three LP indicators of four learning abilities were obtained. Through the statistical analysis, the results demonstrated that all CIPE parameters had some correlation or influence on LP, briefly, (1) Low carbon dioxide concentration (CCD, below 700 ppm) was a positive significant factor for all learning abilities, and relative humidity (RH) was a negative factor for comprehension memory ability (CMA) (significant) and logic deduction ability (LDA) (general) to varying degrees, with center illumination (Ic) being a positive significant factor for CMA only. (2) Deeper abilities, like CMA and LDA, were more susceptible to air temperature (Ta) and RH, with the former being positive and the latter being negative. (3) Compared to other types, LP in compact classrooms was more vulnerable to CIPE parameters, such as the positive influence of Ta and CCD, due to the greater variation in CIPE. The findings revealed the differential relationships between the CIPE and LP in various classroom types, guiding classroom design that couples the dual optimization of CIPE and LP. Limitations remain, however, and need to be supplemented by more future research, e.g. year-round experiments and medical instrumentation assistance. © 2023 Elsevier Ltd

Gamification tailored for novelty effect in distance learning during COVID-19

The pandemic led to an increase of online teaching tools use. One such tool, which might have helped students to stay engaged despite the distance, is gamification. However, gamification is often criticized due to a novelty effect. Yet, others state novelty is a natural part of gamification. Therefore, we investigated whether gamification novelty effect brings incremental value in comparison to other novelties in a course. We created achievement- and socialization-based gamification connected to coursework and practice test. We then measured students' behavioral engagement and performance in a quasi-experiment. On the one hand, results show ICT students engaged and performed moderately better in a gamified condition than in control over time. On the other hand, BA course results show no difference between gamified and practice test condition and their novelty effect. We conclude an external gamification system yields better results than a classical design but does not exceed practice tests effect. Copyright © 2023 Kratochvil, Vaculik and Macak.

Optimal Machine Learning Driven Sentiment Analysis on COVID-19 Twitter Data

The outbreak of the pandemic, caused by Coronavirus Disease 2019 (COVID-19), has affected the daily activities of people across the globe. During COVID-19 outbreak and the successive lockdowns, Twitter was heavily used and the number of tweets regarding COVID-19 increased tremendously. Several studies used Sentiment Analysis (SA) to analyze the emotions expressed through tweets upon COVID-19. Therefore, in current study, a new Artificial Bee Colony (ABC) with Machine Learning-driven SA (ABCML-SA) model is developed for conducting Sentiment Analysis of COVID-19 Twitter data. The prime focus of the presented ABCML-SA model is to recognize the sentiments expressed in tweets made upon COVID-19. It involves data pre-processing at the initial stage followed by n-gram based feature extraction to derive the feature vectors. For identification and classification of the sentiments, the Support Vector Machine (SVM) model is exploited. At last, the ABC algorithm is applied to fine tune the parameters involved in SVM. To demonstrate the improved performance of the proposed ABCML-SA model, a sequence of simulations was conducted. The comparative assessment results confirmed the effectual performance of the proposed ABCML-SA model over other approaches. © 2023 Tech Science Press. All rights reserved.

Performance Review of Ensemble Learning Method Use in COVID-19 Case Detection

COVID-19 cases attract most computer science researchers. There are two popular learning approaches: Machine Learning (ML) and Deep Learning (DL). The approach was applied as a computer-based COVID-19 diagnosis. Most researchers prefer ensemble learning used to assist the process. The technique has various features and performance results. Based on the survey, there are several efforts to improve performance better. This review describes a brief of the ensemble approach. The ensemble applies to image classification. The application employs X-Ray and Computerized Tomography (CT) images. The technique should consider various ensemble strategies. As supportive evidence, a brief description of each method is presented in the table. This study shows all ensemble methods demonstrate to improve prediction results. The stacking ensemble becomes a method that achieves the highest performance. © 2022 IEEE.

Detection of Corona Virus from Chest X-Rays Using CNN Algorithm

COVID19's global epidemic has wreaked havoc on our lives in every aspect. Healthcare systems, to be more specific, was pushed beyond their limits. Artificial intelligence developments have paved the way for the creation of complicated applications that can meet a wide range of requirements. Precision in clinical practice is necessary. In this study, machine learning-based deep learning models that were customized and pretrained were used. Convolutional Neural Networks that's utilized from detected COVID-19 respiratory pneumonia complications. Then more number of COVID-19 patients' radiographs pictures were collected locally. In Data was also used from three publicly available datasets. There are four options for evaluating performance. The public dataset was utilized first for training and testing. Second, data from both the local and national levels]. A variety of public sources were used to train and test the models. Because all diagnostic procedures have little retrieved data at the moment, medical conciliation should examine the likelihood of incorporating X-rays into illness diagnosis based on the data, while all research-based X-ray is carried out. It is possible to approach the problem from various angle. © 2022 IEEE.

Online E-learning Mobile Application for Self-learning

This research work is focused on the accessible Mobile user application developments to facilitate student and faculty communication through native android applications. Covid'19 and this pandemic brings E-learning systems as majority education levels. Mobile technology has efficient learning systems in many countries like the United States where the students use google paths such as the classroom to extend learning effectively. Limitations in existing apps are that students are not appreciated and monitored for self learning, schedule sharing is only at the end of the course and also knowing the mapping concepts of teaching pedagogy is also less approachable. To overcome these problems mobile technology support is proposed in this work with these three modules such as i) Authentication self learning and performance (ASLP) - Authentication for right users along with improvement of monitoring self learning to analyze performance ii) Syllable Schedule (SS) - prior scheduling on syllable and organization of time table matching based on outcome iii) Authorized facilitator (AF) - Set permission based on designation such that facilitator communicates based on needs. To achieve the above highlighted terms Google API is applied by peer reviews and interactions enabled such that efficient mobile applications development is proved. Also the ion hierarchy is improved when setting the interaction module implies less complexity, less storage. Thus the scope of research work is to use classroom overall performance, interaction, development process to upgrade the results of students. Education level is also enhanced through online E-learning mobile technology (OELMT) that has native applications to develop students' knowledge in a better way. © 2022 IEEE.

Satisfaction with virtual simulation learning and academic performance in the context of COVID-19 in nursing students at a public university-Peru

Universities worldwide had to close their doors due to the Covid-19 health emergency, and from face-To-face classes go to the virtual modality, being the theoretical/practical nursing career to carry out the virtual simulation, through their homes. The objective of the research was to describe the satisfaction of virtual simulation learning and academic performance, in the context of Covid-19, in nursing students at a Public University-2021. The survey was applied to 186 students of the Faculty of Nursing, of the semesters: III, IV, VII and VIII of the Public University of Ica-Peru. It was a mixed approach study, applying a 15-question Likert scale questionnaire, presenting the physical format. It is concluded that young students are satisfied with virtual simulation learning and it is not related to the grades obtained by a group in their academic performance. © 2022 ACM.

Learning Performance Indicators a Statistical Analysis on the Subject of Natural Sciences During the COVID-19 Pandemic at the Tulcán District

Learning systems during the COVID-19 period has been modified in terms of methodology strategies as well as teachers' remote teaching emergency approach at primary education and higher education institutions. As a consequence, educators had to limitedly teach the basics from prioritized academic curriculums during the health emergency. Natural Sciences was not an exception, and the majority of educators in this field of study have notably identified low-academic performance during the COVID-19 pandemic. In Ecuador, learning expected results was obtained through the evaluation of performance indicators, so in this research project a statistical analysis was performed using scores for these indicators obtained from Middle School samples of students of the Carchi province, with the aim of identifying significantly affected population strata by the application of remote learning and characteristics leading to low-academic performance. Data gathered was statistically evaluated and the test was calibrated using the Item Response Theory;significative difference among variables and performance indicators were analyzed via students' scores using ANOVA, Pairwise T-Tests, and T-tests. Difference tests were carried out using the weighted score of each student for each indicator as continuous variables and the categorical variables were the internet availability, students' residence location and quintile they belong to. Results proved that there exist significant differences in the student scores depending on the internet availability and the zone where they live, where the academic performance was significantly higher on those students that had stable internet connection in their homes and resided in urban zones during the pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Analysis of Students' Online Learning Engagement during the COVID-19 Pandemic: A Case Study of a SPOC-Based Geography Education Undergraduate Course

With the long-lasting impact of the COVID-19 pandemic, online learning has gradually become one of the mainstream learning methods in Chinese universities. The effectiveness of online learning is significantly influenced by learning engagement, and studies into this topic can help learners by providing them with process-based learning support and focused teaching interventions. Based on the online learning environment, this research constructs an online learning engagement analysis model. Additionally, this study explores the relationship between students' online learning engagement and their online learning performance by taking the Secondary School Geography Curriculum Standards and Textbooks Research, a small-scale private online course (SPOC) of the geography education undergraduate course at Nanjing Normal University, as an example. The findings are as follows: In the cognitive engagement dimension, only "analyze” is significantly positively correlated with learning performance;in the behavioral engagement dimension, the "number of question and answer (Q&A) topic posts,” the "replies to others,” and the "teachers' replies” are all significantly positively correlated with learning performance. In terms of the emotional engagement dimension, "curiosity” and "pleasure” are positively correlated with learning performance;as for the social engagement dimension, "point centrality” and "intermediary centrality” are positively correlated with learning performance. The findings of this case study reveal that the student's engagement in higher-order cognitive learning is obviously insufficient. Students' online learning performance can be enhanced both by behavioral engagement in knowledge reprocessing and positive emotional engagement. Further research should be focused on finding ways to increase students' enthusiasm for social engagement. © 2023 by the authors.

SA-FGDEM: A Self-adaptive E-Learning Performance Prediction Model

With the rapid development of computer computing power and the severe challenges brought by the COVID-19, e-learning, as the optimal solution for most students and other learner groups, plays an extremely important role in maintaining the normal operation of educational institutions. As the user community continues to expand, it has become increasingly important to guarantee the quality of teaching and learning. One way to ensure the quality of online education is to construct e-learning behavior data to build learning performance predictors. Still, most studies have ignored the intrinsic correlation between e-learning behaviors. Therefore, this study proposes an adaptive feature fusion-based e-learning performance prediction model (SA-FGDEM) relying on the theoretical model of learning behav-ior classification. The experimental results show that the feature space mined by fine-grained differential evolution algorithm and the adaptive feature fusion combined with differential evolution algorithm can support e-learning performance prediction more effectively and is better than the benchmark method. © 2022 IEEE.

Performance of Machine Learning Methods Using Tweets

Public opinions shared in common platforms like Twitter, Facebook, Instagram, etc. act as the sources of information for experts. Transportation and analysis of such data is very important and difficult due to data regulations and its structure. The pre-processing approaches and word-based dictionaries are used to understand the unprocessed data and make possible the opinions/tweets to be analyzed. Machine learning algorithms learn from past experience and use a variety of statistical, probabilistic and optimization algorithms to detect useful patterns from unstructured data sets. Our study aims to compare the performance of classification algorithms to predict individuals with COVID-19(+ ) or COVID-19(−) using the emotions among the tweets by text mining procedures. Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes (NB), Decision Trees (DT), Random Forest (RF), Artificial Neural Networks (ANN), Gradient Boost (GBM) and XGradient algorithms were used to extract the accuracy of model performance of each model for the detection and identification of the disease related to the COVID-19 virus, which has been on the agenda recently. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Examining the effects of academic motivation and online learning on Malaysian tertiary students' psychological well-being and perceived learning performance

This study investigated how online learning, mandated for tertiary education over one year in Malaysia since March 2020 due to the COVID-19 pandemic, impacted students' perceived learning performance and psychological well-being. This study focused on the relationship between academic motivation, psychological engagement (enthusiasm, perseverance, and reconciliation), and their impact on perceived learning performance and psychological well-being, with psychological engagement acting as a mediator. This study collected survey responses from 288 students at 49 higher learning institutions in Malaysia using purposive sampling in March 2022. The results revealed that intrinsic motivation is the sole predictor of enthusiasm engagement. Intrinsic and extrinsic motivations jointly influence perseverance engagement, while reconciliation is significantly affected by all three types of motivations. The mediation analysis results suggest that enthusiasm engagement mediates the relationship between intrinsic motivation and two outcome variables. Furthermore, perseverance engagement mediates the relationship between both intrinsic and extrinsic motivations with the two outcome variables. In contrast, reconciliation serves as a mediator for the relationship between amotivation and learning performance, as well as the relationship between extrinsic motivation and both learning performance and psychological well-being. Overall, the study highlights the importance of academic motivation and psychological engagement in online learning in tertiary education. © 2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.

Feature Extraction Based Face Mask Detection

Face masks are becoming an essential part of every person's life to protect against COVID-19, air pollution, etc. In public venues like airports, hospitals, malls, and many other locations, face masks are now required. This research introduces a unique method for face mask identification that combines deep learning and machine learning. Thepade Sorted 10-ary Block Trucation Code with RGB colour plane, Thepade Sorted Block Truncation Code with LUV colour plane, Local Binary Pattern method, and Gray Level Co-occurrence Matrics are used for feature extraction. Deep learning techniques are used to train these retrieved characteristics. Accuracy, precision, and f1-score are the performance measures used to evaluate performance. © 2022 IEEE.

Integrated Social Cognitive Theory with Learning Input Factors: The Effects of Problem-Solving Skills and Critical Thinking Skills on Learning Performance Sustainability

E-learning is expected to become a common teaching and learning approach in educational institutions in the near future;thus, the success of e-learning initiatives must be ensured in order to make this a sustainable mode of learning. In order to improve students' learning performance through the use of e-learning in Saudi Arabia's higher education, it was the objective of this paper to examine the relationships between social cognitive theory and learning input factors and the reflective thinking and inquiry learning style as well as the indirect effects of student problem-solving and critical thinking skills. As a result, this study thoroughly assessed the social cognitive theory that is currently in use, along with learning input components and situational factors that should be carefully taken into account while introducing an online education system into Saudi Arabia's top universities as a way of ensuring learning sustainability. As a result, 294 university students completed a questionnaire that served as the initial dataset for the research study, and the proposed conceptual model was comprehensively assessed using SEM. The research results demonstrated that the inquiry style of learning and reflective thinking have always had a significant impact on the social involvement, human engagement, social power, social identity, and social support. Similar findings were obtained regarding the impact of problem-solving and critical thinking skills on the inquiry-based learning approach and reflective thinking. Thus, students' ability to learn in Saudi Arabia's higher education is greatly influenced by their ability to solve problems and think critically. Therefore, it is almost certain that this research study will aid university policy makers in their decision on whether to fully deploy an online learning system as a way of ensuring learning sustainability at educational institutions throughout Saudi Arabia.

Spatial-Slice Feature Learning Using Visual Transformer and Essential Slices Selection Module for COVID-19 Detection of CT Scans in the Wild

Computed tomography (CT) imaging could be convenient for diagnosing various diseases. However, the CT images could be diverse since their resolution and number of slices are determined by the machine and its settings. Conventional deep learning models are hard to tickle such diverse data since the essential requirement of the deep neural network is the consistent shape of the input data in each dimension. A way to overcome this issue is based on the slice-level classifier and aggregating the predictions for each slice to make the final result. However, it lacks slice-wise feature learning, leading to suppressed performance. This paper proposes an effective spatial-slice feature learning (SSFL) to tickle this issue for COVID-19 symptom classification. First, the semantic feature embedding of each slice for a CT scan is extracted by a conventional 2D convolutional neural network (CNN) and followed by using the visual Transformer-based sub-network to deal with feature learning between slices, leading to joint feature representation. Then, an essential slices set algorithm is proposed to automatically select a subset of the CT scan, which could effectively remove the uncertain slices as well as improve the performance of our SSFL. Comprehensive experiments reveal that the proposed SSFL method shows not only excellent performance but also achieves stable detection results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Andragogical principles in a gamification concept: how does it work for adult learners in an online class?

Purpose: A lack of research has reported how playful gamification is applied to adult learners as an idea of andragogical instruction. Thus, this study aims to identify how the concept of gamification was used for adult learners in an online class during the COVID-19 pandemic and its impact on learning performance and motivation with the guidance of Knowles' andragogical principle. Design/methodology/approach: The study applied an explanatory sequential mixed method in collecting the data. Assessments' scores during the experimental research and questionnaires were used as the quantitative data. For the qualitative data, personal semi-structured interviews were employed. Findings: The findings indicate that gamification raises student enthusiasm and interest and improves learning outcomes. Students who previously lacked attentiveness to the online class are now waiting for game quiz activities during the class meeting. Furthermore, the experimental groups reported statistically improved assessments compared to their counterparts. Indeed, some recommended other courses with whole activities of gamification and discussion rather than listening to talks. Originality/value: For its implications, this study has enriched the literature on gamification implementation for adult learners. Regarding its originality, it has discussed an old issue of Knowles' andragogical principle from the novelty angle of gamification. © 2022, Emerald Publishing Limited.