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%.
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.