IMPORTANCE AND OPPORTUNITIES OF SENTIMENT ANALYSIS IN DEVELOPING E-LEARNING SYSTEMS THROUGH SOCIAL MEDIA

The means of communication and interaction have benefited from incredible changes over the past decade, Social Media increasingly replacing traditional environments. Considering the collaborative nature of the learning sector and consequently the importance of communication and interaction within it, we intuitively realize that Social Media represents the future of educational systems, the research in this field pointing towards the integration of e-Learning with Social Media. However, in order to deliver efficient educational systems, it is not enough to identify the technological means that are conducive to their development, but is also important to shape these means depending on the needs of the target group. When we discuss about Social Media and learning, it is noticed that individuals are the direct beneficiaries and the main force of these environments. Therefore, it is important to understand their behavior, their needs and wants. Analyzing students\u27 attitudes, identifying their positive or negative reactions, or even the refined emotions they have towards learning, can be an extremely difficult task due to their diversity in countless ways. In this regard, an increasingly used tool whose accuracy cannot be challenged is the Sentiment Analysis. The inherent nature of Social Media tools offers multiple areas of application of Sentiment Analysis. Therefore, this paper will discuss the importance of Sentiment Analysis towards e-Learning development through Social Media, considering current evidence. Secondly, the paper aims to identify the opportunities offered by Social Media with regards to Sentiment Analysis implementation and how feedback on educational data can be collected via such online environments to help improve educational processes in an e-Learning context

IMPORTANCE AND OPPORTUNITIES OF SENTIMENT ANALYSIS IN DEVELOPING E-LEARNING SYSTEMS THROUGH SOCIAL MEDIA

The means of communication and interaction have benefited from incredible changes over the past decade, Social Media increasingly replacing traditional environments. Considering the collaborative nature of the learning sector and consequently the importance of communication and interaction within it, we intuitively realize that Social Media represents the future of educational systems, the research in this field pointing towards the integration of e-Learning with Social Media. However, in order to deliver efficient educational systems, it is not enough to identify the technological means that are conducive to their development, but is also important to shape these means depending on the needs of the target group. When we discuss about Social Media and learning, it is noticed that individuals are the direct beneficiaries and the main force of these environments. Therefore, it is important to understand their behavior, their needs and wants. Analyzing students\u27 attitudes, identifying their positive or negative reactions, or even the refined emotions they have towards learning, can be an extremely difficult task due to their diversity in countless ways. In this regard, an increasingly used tool whose accuracy cannot be challenged is the Sentiment Analysis. The inherent nature of Social Media tools offers multiple areas of application of Sentiment Analysis. Therefore, this paper will discuss the importance of Sentiment Analysis towards e-Learning development through Social Media, considering current evidence. Secondly, the paper aims to identify the opportunities offered by Social Media with regards to Sentiment Analysis implementation and how feedback on educational data can be collected via such online environments to help improve educational processes in an e-Learning context

Automated vs Manual Content Analysis – A Retrospective Look

Content Analysis, which is a part of qualitative analysis, has mainly been studied in scientific articles from health and medicine domains. With the emerge of social networks, there are new opportunities for content analysis, which can be used to analyse user generated content, from various sources. Nevertheless, the companies are investing millions of dollars in content analysis, which is often known as sentiment analysis. The discussion in this article helps to understand the main concepts of content analysis for those interested in the domain of qualitative analysis, with the help of automated and manual qualitative research. The overall conclusion is that automated qualitative analysis is dependent on how accurate is the tool used and this feature can be checked with the help of manual qualitative analysis.JEL Codes - O33; O3

CHALLENGES IN HIGHER ONLINE EDUCATION: DISCOURAGING FRAUDULENT ATTEMPTS ON ONLINE EXAMS

It has been more than a year since the coronavirus pandemic pushed higher education even more towards an online format, along with many of its key-activities involved. When it comes to transitioning from conventional face-to-face examination to fully online assessment, the use of e-learning tools such as Moodle may bring multiple benefits but they could also raise a lot of concerns. One of the main concerns refers to content leakage, which involves the unauthorized distribution of the exam subjects, such as question banks, or sharing the quiz attempts with colleagues. When this happens, it can hinder the integrity of the online exams and their unique content, and of course, it will impact grades. There could be various causes for content leaks, such as lack of supervision or maybe settings incorrectly applied to quizzes. However, these could be some of the contributing factors that are enabling students to cheat. In light of the above, the aim of this paper is straightforward: to identify and outline the most important and feasible key-measures that could be adopted in order to detect and prevent or (at least substantially) decrease cheating during online exams. As we will further see, the real challenge appears when it comes to tracking down and grasping cheat scenarios. Fortunately, in this approach, we can mix the facilities provided by technologies used in online classes

Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI Scans

CT scanners that are commonly-used in hospitals nowadays produce low-resolution images, up to 512 pixels in size. One pixel in the image corresponds to a one millimeter piece of tissue. In order to accurately segment tumors and make treatment plans, doctors need CT scans of higher resolution. The same problem appears in MRI. In this paper, we propose an approach for the single-image super-resolution of 3D CT or MRI scans. Our method is based on deep convolutional neural networks (CNNs) composed of 10 convolutional layers and an intermediate upscaling layer that is placed after the first 6 convolutional layers. Our first CNN, which increases the resolution on two axes (width and height), is followed by a second CNN, which increases the resolution on the third axis (depth). Different from other methods, we compute the loss with respect to the ground-truth high-resolution output right after the upscaling layer, in addition to computing the loss after the last convolutional layer. The intermediate loss forces our network to produce a better output, closer to the ground-truth. A widely-used approach to obtain sharp results is to add Gaussian blur using a fixed standard deviation. In order to avoid overfitting to a fixed standard deviation, we apply Gaussian smoothing with various standard deviations, unlike other approaches. We evaluate our method in the context of 2D and 3D super-resolution of CT and MRI scans from two databases, comparing it to relevant related works from the literature and baselines based on various interpolation schemes, using 2x and 4x scaling factors. The empirical results show that our approach attains superior results to all other methods. Moreover, our human annotation study reveals that both doctors and regular annotators chose our method in favor of Lanczos interpolation in 97.55% cases for 2x upscaling factor and in 96.69% cases for 4x upscaling factor.Comment: Accepted in IEEE Acces

European Efficiency or Inefficiency in Economic Growth Through Digital Transformation

The current global changes bring to the fore the importance of the innovation and digital transformation for economic development. Under the previous assumption, an objective evaluation of the economic growth discrepancies, considering the digitalization process, is required. The main goal of the present research is to analyse the economic growth of the European countries, based to the digitalization process, by using an input-output method. Under these circumstances, a Data Envelopment Analysis (DEA) was performed, considering the digitalization dimensions of DESI Index as input and the economic growth (annual %) as output. Based on the proposed model, the results highlighted the bidirectional relationship between economic growth and digitalization. Consistent with the research results, the European countries can be divided in two main categories: the efficient and the inefficient. On one hand, we can find the relatively efficient European states in terms of achieving the economic growth through digitalization (Ireland, Romania, Croatia and Greece). On the other hand, there is a numerous list of the inefficient ones, including important countries like Finland, Germany or France. Obviously, a remarkable aspect related to their situation is that, considering the national available inputs, an output maximization will be possible. According to the proposed model, the efficient countries can serve as peers or optimal benchmarks for solving the issue of relative inefficiency, by adapting and implementing their good practices