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Determining the factors that have an influence on software systems development and deployment project outcomes has been the focus of extensive and ongoing research for more than 30 years. We provide here a survey of the research literature…

Faculty hiring shapes the flow of ideas, resources, and opportunities in academia, influencing not only individual career trajectories but also broader patterns of institutional prestige and scientific progress. While traditional studies…

The aim of this study was to predict university students' learning performance using different sources of data from an Intelligent Tutoring System. We collected and preprocessed data from 40 students from different multimodal sources:…

When using machine learning for imbalanced binary classification problems, it is common to subsample the majority class to create a (more) balanced training dataset. This biases the model's predictions because the model learns from data…

Detecting abnormal behaviors of students in time and providing personalized intervention and guidance at the early stage is important in educational management. Academic performance prediction is an important building block to enabling this…

Blended learning has become a dominant educational model in higher education in the UK and worldwide, particularly after the COVID-19 pandemic. This is further enriched with accompanying pedagogical changes, such as strengthened…

Blended courses that mix in-person instruction with online platforms are increasingly popular in secondary education. These tools record a rich amount of data on students' study habits and social interactions. Prior research has shown that…

The Science4cast 2021 competition focuses on predicting future edges in an evolving semantic network, where each vertex represents an artificial intelligence concept, and an edge between a pair of vertices denotes that the two concepts have…

Cascading failure studies help assess and enhance the robustness of power systems against severe power outages. Onset time is a critical parameter in the analysis and management of power system stability and reliability, representing the…

A growing attention in the empirical literature has been paid to the incidence of climate shocks and change in migration decisions. Previous literature leads to different results and uses a multitude of traditional empirical approaches.…

The most fundamental problem in statistical causality is determining causal relationships from limited data. Probability trees, which combine prior causal structures with Bayesian updates, have been suggested as a possible solution. In this…

There has been an increasing trend of females performing better than males academically across the mathematical engineering courses. To confirm this assumption, final marks of two independent samples of students from Calculus courses across…

Understanding how "black-box" models arrive at their predictions has sparked significant interest from both within and outside the AI community. Our work focuses on doing this by generating local explanations about individual predictions…

This work develops formal statistical inference procedures for machine learning ensemble methods. Ensemble methods based on bootstrapping, such as bagging and random forests, have improved the predictive accuracy of individual trees, but…

Sepsis, a critical condition from the body's response to infection, poses a major global health crisis affecting all age groups. Timely detection and intervention are crucial for reducing healthcare expenses and improving patient outcomes.…

Tree-boosting is a widely used machine learning technique for tabular data. However, its out-of-sample accuracy is critically dependent on multiple hyperparameters. In this article, we empirically compare several popular methods for…

We describe our experience in developing a predictive model that placed high position in the BigDeal Challenge 2022, an energy competition of load and peak forecasting. We present a novel procedure for feature engineering and feature…

We propose a novel tree-based ensemble method named Selective Cascade of Residual ExtraTrees (SCORE). SCORE draws inspiration from representation learning, incorporates regularized regression with variable selection features, and utilizes…

Ethnic achievement gaps are often explained in terms of student and school factors. The decomposition of these gaps into their within- and between-school components has therefore been applied as a strategy to quantify the overall influence…

We present a boosting-based method to learn additive Structural Equation Models (SEMs) from observational data, with a focus on the theoretical aspects of determining the causal order among variables. We introduce a family of score…