Educational data mining to predict students' academic performance: A survey study

S Batool, J Rashid, MW Nisar, J Kim, HY Kwon… - Education and …, 2023‏ - Springer
Educational data mining is an emerging interdisciplinary research area involving both
education and informatics. It has become an imperative research area due to many …

[HTML][HTML] Infrastructure elements for smart campuses: A bibliometric analysis

T Omotayo, A Moghayedi, B Awuzie, S Ajayi - Sustainability, 2021‏ - mdpi.com
Sustainable development can be attained at a microlevel and having smart campuses
around the world presents an opportunity to achieve city-wide smartness. In the process of …

Modeling inter-station relationships with attentive temporal graph convolutional network for air quality prediction

C Wang, Y Zhu, T Zang, H Liu, J Yu - … conference on web search and data …, 2021‏ - dl.acm.org
Air pollution is an important environmental issue of increasing concern, which impacts
human health. Accurate air quality prediction is crucial for avoiding people suffering from …

Identifying Student Behavior in Smart Classrooms: A Systematic Literature Map** and Taxonomies

LG Eich, R Francisco, JLV Barbosa - International Journal of …, 2024‏ - Taylor & Francis
The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) in educational
settings has revolutionized the traditional teaching-learning environment, giving rise to the …

Jointly modeling spatio–temporal dependencies and daily flow correlations for crowd flow prediction

T Zang, Y Zhu, Y Xu, J Yu - … on Knowledge Discovery from Data (TKDD), 2021‏ - dl.acm.org
Crowd flow prediction is a vital problem for an intelligent transportation system construction
in a smart city. It plays a crucial role in traffic management and behavioral analysis, thus it …

Enhancing user interest modeling with knowledge-enriched itemsets for sequential recommendation

C Wang, Y Zhu, H Liu, W Ma, T Zang, J Yu - Proceedings of the 30th …, 2021‏ - dl.acm.org
Sequential recommendation which aims to predict a user's next interaction based on his/her
previous behaviors, has attracted great attention. Recent studies mainly employ deep …

Exploiting explicit item–item correlations from knowledge graphs for enhanced sequential recommendation

Y Zhang, Y Shi, D Yang, X Gu - Information Systems, 2025‏ - Elsevier
In recent years, the research of employing knowledge graphs (KGs) in sequential
recommendation (SR) has received a lot of attention, since the side information extracted …

Incorporating heterogeneous user behaviors and social influences for predictive analysis

H Liu, Y Zhu, C Wang, J Ding, J Yu… - IEEE Transactions on …, 2022‏ - ieeexplore.ieee.org
Behavior prediction based on historical behavioral data have practical real-world
significance. It has been applied in recommendation, predicting academic performance, etc …

Multifaceted Relation-aware Meta-learning with Dual Customization for User Cold-start Recommendation

C Wang, Y Zhu, H Liu, T Zang, K Wang… - ACM Transactions on …, 2023‏ - dl.acm.org
User cold-start scenarios pose great challenges to recommendation systems in accurately
capturing user preferences with sparse interaction records. Besides incorporating auxiliary …

[PDF][PDF] Prediction of On-time Student Graduation with Deep Learning Method.

NV Darenoh, FA Bachtiar… - Journal of ICT …, 2024‏ - pdfs.semanticscholar.org
Universities have an important role in providing quality education to their students so they
can build a foundation for their future. However, a problem that often arises is that the …