Online Quantitative Research Methodology: Reflections on Good Practices and Future Perspectives

Limone, Pierpaolo; Toto, Giusi Antonia; Guarini, Piergiorgio; Furia, Marco di

doi:10.1007/978-3-031-10461-9_45

Cited by 3 publications

(5 citation statements)

References 30 publications

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“…To collect the data, the research team used a survey based on a questionnaire, which is a quantitative method of investigating social problems [72,73]. The sequence of the research stages was the following: formulating the questions of the questionnaire, establishing the population to be investigated, of the method of data collection, and the design of the working tool, followed by collecting the information, analyzing the answers, and writing the research report.…”

Section: Methodsmentioning

confidence: 99%

Transition from Online to Face-to-Face Education after COVID-19: The Benefits of Online Education from Students’ Perspective

et al. 2022

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The COVID-19 pandemic has left a mark on education as it had been known before. Beyond his, attention needs to be paid to the transition back to face-to-face learning and its implications. Within this context of change, the present research focuses on identifying the students’ preferred aspects of online education during the pandemic, namely learning, teaching, assessment, and interaction with peers and teachers, in order to improve face-to-face education by contextually adapting it to their needs. A survey was used on a representative sample of students from Politehnica University of Timisoara, Romania, who were required to indicate their preferred form of education and the most beneficial one for their professional development. The results indicate the targeted students’ several preferences, such as teachers’ support with electronic educational resources, the use of online educational platforms to access resources and take tests, the easier and individualized communication with teachers, as well as peer connectivity in common projects. These findings identify practical suggestions that lead to a balance between face-to-face and digital education, probably under the form of blended learning, which could be considered by the main stakeholders in order to have a future sustainable education.

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Section: Methodsmentioning

confidence: 99%

Transition from Online to Face-to-Face Education after COVID-19: The Benefits of Online Education from Students’ Perspective

et al. 2022

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“…The research team collected the data using a questionnaire-based survey, a quantitative tool for researching social problems [22]. The study subjects were 148 students from all departments, and they were willing to be involved in the study.…”

Section: Methodsmentioning

confidence: 99%

From Online to In-Person: Indonesian Students’ Insights on Education Post-pandemic

Novita,

Megawati,

Agustina

et al. 2023

Advances in Social Science, Education and Humanities Research

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As the COVID-19 outbreak starts to subside, Indonesian schools have started to reopen their campuses for students. Regarding this, consideration must be given to the change from online to face-to-face learning and its effects. The current study focuses on the learning and teaching that students liked best during the pandemic. This will help improve face-to-face education by making it more relevant to their needs. A survey was given to a random group of students at a private university in East Java, Indonesia, and they were asked what kind of education they liked. The results show that the students asked about their preferences wanted different things -for example, using online learning environments to access resources and take exams, getting better interaction, and having their teachers help them use electronic learning tools. For this, the primary parties in education could consider these findings to create a balance between face-to-face and online learning, most likely in the form of blended learning to create a viable educational system for the future.

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“…Figure 10 also shows that labels 0 and 25 have been predicted more confidently by all the models, because these are the majority classes. 5), (25) and predicted labels: (0), ( 5), (25) (b) true labels: (19), (25) and predicted labels: ( 19), ( 25); (c) true labels: (7), (21) and predicted labels: ( 7), (21); and (d) true labels: (0), ( 21), (25) and predicted labels: (0), ( 21), (25).…”

Section: Visualization Of Correct Classificationsmentioning

confidence: 99%

“…In the recent past, with rapid technological improvement and a wide range of applications, Artificial Intelligence has become increasingly prevalent due to its robust applicability in situations that cannot be solved well by humans or traditional computing structures. Artificial intelligence with deep learning and machine learning is having a significant impact in all industries, including agriculture [6][7][8], medical diagnostics [9], education [10], autonomous vehicles [11], voice assistants [12], and many more.…”

Section: Introductionmentioning

confidence: 99%

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An Artificial Intelligence-Based Stacked Ensemble Approach for Prediction of Protein Subcellular Localization in Confocal Microscopy Images

Aggarwal

Gupta

et al. 2023

Sustainability

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Predicting subcellular protein localization has become a popular topic due to its utility in understanding disease mechanisms and developing innovative drugs. With the rapid advancement of automated microscopic imaging technology, approaches using bio-images for protein subcellular localization have gained a lot of interest. The Human Protein Atlas (HPA) project is a macro-initiative that aims to map the human proteome utilizing antibody-based proteomics and related c. Millions of images have been tagged with single or multiple labels in the HPA database. However, fewer techniques for predicting the location of proteins have been devised, with the majority of them relying on automatic single-label classification. As a result, there is a need for an automatic and sustainable system capable of multi-label classification of the HPA database. Deep learning presents a potential option for automatic labeling of protein’s subcellular localization, given the vast image number generated by high-content microscopy and the fact that manual labeling is both time-consuming and error-prone. Hence, this research aims to use an ensemble technique for the improvement in the performance of existing state-of-art convolutional neural networks and pretrained models were applied; finally, a stacked ensemble-based deep learning model was presented, which delivers a more reliable and robust classifier. The F1-score, precision, and recall have been used for the evaluation of the proposed model’s efficiency. In addition, a comparison of existing deep learning approaches has been conducted with respect to the proposed method. The results show the proposed ensemble strategy performed exponentially well on the multi-label classification of Human Protein Atlas images, with recall, precision, and F1-score of 0.70, 0.72, and 0.71, respectively.

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Online Quantitative Research Methodology: Reflections on Good Practices and Future Perspectives

Cited by 3 publications

References 30 publications

Transition from Online to Face-to-Face Education after COVID-19: The Benefits of Online Education from Students’ Perspective

Transition from Online to Face-to-Face Education after COVID-19: The Benefits of Online Education from Students’ Perspective

From Online to In-Person: Indonesian Students’ Insights on Education Post-pandemic

An Artificial Intelligence-Based Stacked Ensemble Approach for Prediction of Protein Subcellular Localization in Confocal Microscopy Images

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