Silvia Gaftandzhieva

Biographic Data

ID8548446
NAMESilvia Gaftandzhieva
GIVEN NAMESSilvia
FAMILY NAMEGaftandzhieva
SIGNATUREGAFTANDZHIEVA S
AFFILIATIONSFaculty of Mathematics and Informatics, University of Plovdiv Paisii Hilendarski
VERIFIEDNo
TOTAL WORKS8
TOTAL CITATIONS0
AUTHOR COUNT8
EDITOR COUNT0
FIRST PUBLICATION YEAR2025
LATEST PUBLICATION YEAR2026
H-INDEX0

  • An innovative interdisciplinary approach to spatial modeling of student satisfaction from online courses

    Open AccessKostadin Yotov, Silvia Gaftandzhıeva et al.ARTICLEFrontiers in Education2026

    Introduction Student satisfaction is a critical metric for assessing the success of online courses. Understanding the factors influencing satisfaction allows educational institutions and faculty staff to take steps to improve the quality of training, which can lead to higher student engagement and retention and improved institutional reputation. Methods We examine the relationship between 213 students' satisfaction with the quality of an online c…

  • AI in education through the learners’ eyes: Practical experience, perceptions, and challenges

    Open AccessKostadin Yotov, Silvia Gaftandzhıeva et al.ARTICLEFrontiers in Education2026

    Introduction In order to remain competitive, higher education institutions strive to enhance the student experience by integrating modern technologies for both educational and administrative purposes. This paper presents the results of a study exploring students’ attitudes toward the use of artificial intelligence (AI) in higher education. Methods The data was collected through a survey conducted among 138 students, who responded to 50 questions …

  • Explainable artificial intelligence for educational decision-making: A LightGBM leaf-embedding neural blender for interpretable major course prediction using SHAP

    Open AccessEmi Kalita, Ashima Kukkar et al.ARTICLEFrontiers in Education2026

    Introduction Selecting an undergraduate major is a crucial academic decision that significantly influences students' long-term learning trajectories and career outcomes. Despite policies promoting flexible curriculum design and informed major–minor combinations, students often make suboptimal choices due to limited guidance and external influences. Methods To address this challenge, this study proposes a LightGBM Leaf-Embedding Neural Blender (LE…

  • Ex-Ada: A SHAP-based explainable AdaBoost framework for predicting at-risk students

    Open AccessEmrah Arslan, Silvia Gaftandzhıeva et al.ARTICLEFrontiers in Education2026

    Introduction Early identification of academically at-risk students remains a persistent challenge in higher education, largely due to the limited explainability and adaptability of existing predictive models. Although many early-warning systems rely on behavioral, assessment, or attendance data, their lack of transparent decision-making often reduces trust and limits their practical utility for educators. Methods To address this problem, this stu…

  • Analyzing dropout of students and an explainable prediction of academic performance utilizing artificial intelligence techniques

    Open AccessRanjit Paul, Sazol Sarker et al.ARTICLEFrontiers in Education2025

    Modern higher education institutions (HEIs) face significant challenges in identifying, students who are at risk of low academic performance, at an early stage, while maintaining educational quality, and improving graduation rates. Predicting student success and dropout is crucial for institutional decision-making, as it helps formulate effective strategies, allocate resources efficiently, and improve student support. This study explores machine …

  • Predicting student academic performance using Bi-LSTM: A deep learning framework with SHAP-based interpretability and statistical validation

    Open AccessEmi Kalita, Abdullah Mana Alfarwan et al.ARTICLEFrontiers in Education2025

    Introduction Educational Data Mining (EDM) involves analysing educational data to identify patterns and trends. By uncovering these insights, educators can better understand student learning, optimise teaching methods, and refine curriculum. One of the main tasks in educational data mining is predicting the student’s academic performance because it makes it possible to provide appropriate interventions supporting students’ achievements. Predictin…

  • A conceptual framework for multi-component summative assessment in an e-learning management system

    Open AccessStanka Hadzhikoleva, Emil Hadzhikolev et al.ARTICLEFrontiers in Education2025

    The article presents a conceptual framework for the design, implementation, and analysis of multi-component summative assessment systems in an electronic educational environment. A universal model is proposed, based on a four-level hierarchy – meta-meta-model, meta-model, model, and actual assessment system. Various structures and assessment components are examined, including Bloom’s taxonomy, higher- and lower-order thinking skills, theory and p…

  • LSTM-SHAP based academic performance prediction for disabled learners in virtual learning environments: A statistical analysis approach

    Open AccessEmi Kalita, Houssam El Aouifi et al.ARTICLESocial Network Analysis and Mining2025

    With the increasing use of Virtual Learning Environments (VLEs), student performance prediction has become a necessary task to better academic outcomes, especially for disabled students who experience specific constraints. Machine learning (ML) models like Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN) have shown high predictive accuracy. However, these models often lack interpretability and making it difficult for educators to…