Background: Learning Management Systems (LMS) represent one of the main technology to support learning in HE institutions. However, every educational institution differs in its experience with the usage of these systems. South East European University’s LMS experience is longer than a decade. From last year SEE – University is adopting Google Classroom (GC) as an LMS solution. Objectives: Identifying factors which encourage LMS activities, with special emphasis on SEEU, might be of crucial importance for Higher Education academic leaders as well as software developers who design tools related to fostering LMS. Methods/Approach: This paper introduces new approach of investigating the usage of LMS, i.e. identifying the determinants of increasing usage of LMS activities, by conducting empirical analysis for the case of SEEU. We apply appropriate estimation technique such as OLS methodology. Results: Using SEEU Usage Google Classroom Report & Analysis Data for spring semester (2016–2017) and winter semester (2017–2018) - SUGCR dataset 2017, we argue that (i) LMS activities are affected by demographic characteristics and (ii) the students’ LMS usage is affected by level and resources of instructors’ LMS usage. Conclusions: The empirical results show positive relationship between student and instructors’ LMS usage.
Digital transformation is the process of consuming digital technologies that drives business improvements and customer experience. Artificial intelligence plays a crucial role in businesses' digital transformation agendas. Technologies and algorithms are an important perspective in the implementation of chatbots. AI chatbots use natural language processing technology and offer solutions for modernizing traditional business processes. The key advantage of the implementation of chatbots in different domains is the impact by improving customers' experience and reducing costs. Chatbots are pieces of software that simulate human conversation through voice commands and/or text chats, intending to offer companies an approach on how to use this software to revolutionize their businesses. The aim of this paper is the analysis of the evaluation criteria; the study of insights into how chatbots can be implemented in the domains of banking, e-commerce, tourism, and call centres; and the discussion of some benefits and challenges of chatbots in driving the digital transformation of businesses.
Background: Text classification is a very important task in information retrieval. Its objective is to classify new text documents in a set of predefined classes, using different supervised algorithms. Objectives: We focus on the text classification for Albanian news articles using two approaches. Methods/Approach: In the first approach, the words in a collection are considered as independent components, allocating to each of them a conforming vector in the vector’s space. Here we utilized nine classifiers from the scikit-learn package, training the classifiers with part of news articles (80%) and testing the accuracy with the remaining part of these articles. In the second approach, the text classification treats words based on their semantic and syntactic word similarities, supposing a word is formed by n-grams of characters. In this case, we have used the fastText, a hierarchical classifier, that considers local word order, as well as sub-word information. We have measured the accuracy for each classifier separately. We have also analyzed the training and testing time. Results: Our results show that the bag of words model does better than fastText when testing the classification process for not a large dataset of text. FastText shows better performance when classifying multi-label text. Conclusions: News articles can serve to create a benchmark for testing classification algorithms of Albanian texts. The best results are achieved with a bag of words model, with an accuracy of 94%.
Authorship Analysis (AA) is a natural language processing field that examines the previous works of writers to identify the author of a text based on its features. Studies in authorship analysis include authorship identification, authorship profiling, and authorship verification. Due to its relevance, to many applications in this field attention has been paid. It is widely used in the attribution of historical literature. Other applications include legal linguistics, criminal law, forensic investigations, and computer forensics. This paper aims to provide an overview of the work done and the techniques applied in the authorship analysis domain. The examination of recent developments in this field is the principal focus. Many different criteria can be used to define a writer’s style. This paper investigates stylometric features in different author-related tasks, including lexical, syntactic, semantic, structural, and content-specific ones. A lot of classification methods have been applied to authorship analysis tasks. We examine many research studies that use different machine learning and deep learning techniques. As a means of pointing the direction for future studies, we present the most relevant methods recently proposed. The reviewed studies include documents of different types and different languages. In summary, due to the fact that each natural language has its own set of features, there is no standard technique generically applicable for solving the AA problem.
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