Student success rate is a significant indicator of the quality of the educational services offered at higher education institutions (HEIs). It allows students to make their plans to achieve the set goals and helps teachers to identify the at-risk students and make timely interventions. University decision-makers need reliable data on student success rates to formulate specific and coherent decisions to improve students’ academic performance. In recent years, EDM has become an effective tool for exploring data from student activities to predict their final grades. This study presents a case study for predicting the students’ final grades based on their activities in Moodle Learning Management System (LMS) and attendance in online lectures conducted via Zoom by applying statistical and machine learning techniques. The data set consists of the final grades for 105 students who study Object-Oriented Programming at the University of Plovdiv during the 2021–2022 year, data for their activities in the online course (7057 records), and attendance to lectures (738). The predictions are based on 46 attributes. The Chi-square test is utilized to assess the association between students’ final grades and event context (lectures, source code, exercise, and assignment) and the relationships between attendance at lectures and final results. The logistic regression model is utilized to assess the actual impact of event context on “Fail” students in a multivariate setup. Four machine learning algorithms (Random Forest, XGBoost, KNN, and SVM) are applied using 70% of training data and 30% of test data to predict the students’ final grades. Five-fold cross validation was also utilized. The results show correlations between the students’ final grades and their activity in the online course and between students’ final grades and attendance at lectures. All applied machine learning algorithms performed moderately well predicting the students’ final results, as the Random Forest algorithm obtained the highest prediction accuracy—78%. The findings of the study clearly show that the Random Forest algorithm may be used to predict which students will fail after eight weeks. Such data-driven predictions are significant for teachers and decision-makers and allow them to take measures to reduce the number of failed students and identify which types of learning resources or student activities are better predictors of the student’s academic performance.

An interactive analytical dashboard that analyzes students’ facial expressions during online lectures is crucial for digital learning platforms. This research addresses the need for educational institutions to analyze individual students’ emotions to improve teaching standards. Given the challenge of occluded facial data, we employ a regenerative Generative Adversarial Network (GAN) to reconstruct these occluded regions. Subsequently, the emotions of the students are predicted and analyzed using our proposed interactive dashboard, which incorporates additional inputs such as subject name and teaching faculty. The dashboard visualizes various charts and analytics to support informed decision-making. We validated our deep learning model using the CK+ dataset, achieving notable accuracy in classifying each type of emotion. Our results demonstrate that the model can effectively interpret student emotions, even in the presence of occlusions, thereby providing educators with precise, real-time emotional insights to tailor their teaching methodologies effectively.

Many educational institutions use a large number of information systems to automate their activities for different stakeholders’ groups – learning management systems, student diary, library system, digital repositories, management system, etc. This leads to a significant increase in the volume and variety of data that can be captured, stored, and harnessed to improve student learning and school effectiveness. Educational institutions are realising that, with the help of technology, they are collecting data that could be very valuable when properly analysed, aligned with learning outcomes, and integrated into a tighter feedback loop with stakeholders. In addition, the analysis of data can help their managers to take data-driven decision making at all levels of educational institutions. The paper presents a comprehensive approach to Learning Analytics in the field of Secondary Education from the perspective of all different stakeholders, which aims to improve its methods of approaching and analysing learning data. On the basis of a literature review in the field and an investigation of requirements for quality evaluation of learning in school education, the corresponding stakeholder groups are identified - students, teachers, class teachers, managers, parents, inspectors and 6 models (1 model per each stakeholder group) for data collection and personalized and meaningful analysis are proposed for the needs of Learning Analytics. Each model consists of measurable indicators allowing the relevant stakeholder to track data for students’ learning or training for different purposes, e.g. monitoring, analysis, forecast, intervention, recommendations, etc., but finally to improve the quality of learning and teaching processes. The proposed models are evaluated by the representatives of 4 stakeholder groups – students, teachers, class teachers, parents.

Although many teachers are sceptical and think that the use of social media can disengage students and lead to lower results, we have witnessed increasingly widespread use of social networks in the educational environment. In recent years, there is also a trend towards more formal acceptance of the role of these networks in higher education by participants in the educational process. The study presented in this paper is intended to seek clarity on the development of this trend in Bulgaria. On the one hand, it aims to assess the extent to which the use of social networking for educational purposes shall be formally accepted by the two main groups of participants in the educational process (students and teachers) and to what degree it is still informal. On the other hand, the study explores specific areas of the use of social networking in higher education, with an emphasis on their efficacy.

Globally, the number of online courses offered as electronic versions of conventional learning courses has increased annually over the past decade. A critical variable for measuring the success or failure of these courses is student satisfaction. This paper presents a study which aims to investigate the students’ satisfaction with the quality of an online course supporting training in Object-Oriented Programming performed in blended learning mode. The developed questionnaire allows students to assess the course content and design, course organization, communication and support, and evaluation. Based on the summarized result, recommendations for improving the course quality to meet students’ expectations and needs have been done.

This paper presents a study on known approaches for quality assurance of educational test and test items. On its basis a comprehensive approach to the quality assurance of online educational testing is proposed to address the needs of all stakeholders (authors of online tests, teachers, students, experts, quality managers, etc.). According to the proposed approach is developed an original software application Test Quality Evaluation (TQE) for the automation of the stakeholders’ activities for quality assurance of educational tests throughout the whole lifecycle. The application retrieves and provides analysis of data from online tests conducted and specially designed surveys for quality evaluation of educational tests by students and experts. It allows tracking and evaluating the quality of educational tests in real time and provides the related quantitative data in different levels of generalization – in the level of a separate educational test, of educational tests of an entire course, or educational tests of a subject area. The software application has been put under real-time testing for quality evaluation of educational tests, included in e-learning courses from different subject areas that prove its applicability.