Understanding socio-academic and economic factors influencing students' performance is crucial for effective educational interventions. This study employs several machine learning techniques and causal analysis to predict and elucidate the impacts of these factors on academic performance. We constructed a hypothetical causal graph and collected data from 1,050 student profiles. Following meticulous data cleaning and visualization, we analyze linear relationships through correlation and variable plots, and perform causal analysis on the hypothetical graph. Regression and classification models are applied for prediction, and unsupervised causality analysis using PC, GES, ICA-LiNGAM, and GRASP algorithms is conducted. Our regression analysis shows that Ridge Regression achieve a Mean Absolute Error (MAE) of 0.12 and a Mean Squared Error (MSE) of 0.024, indicating robustness, while classification models like Random Forest achieve nearly perfect F1-scores. The causal analysis shows significant direct and indirect effects of factors such as class attendance, study hours, and group study on CGPA. These insights are validated through unsupervised causality analysis. By integrating the best regression model into a web application, we are developing a practical tool for students and educators to enhance academic outcomes based on empirical evidence.

The software for clustering students according to their educational achievements using fuzzy logic was developed in Python using the Google Colab cloud service. In the process of analyzing educational data, the problems of Data Mining are solved, since only some characteristics of the educational process are obtained from a large sample of data. Data clustering was performed using the classic K-Means method, which is characterized by simplicity and high speed. Cluster analysis was performed in the space of two features using the machine learning library scikit-learn (Python). The obtained clusters are described by fuzzy triangular membership functions, which allowed to correctly determine the membership of each student to a certain cluster. Creation of fuzzy membership functions is done using the scikit-fuzzy library. The development of fuzzy functions of objects belonging to clusters is also useful for educational purposes, as it allows a better understanding of the principles of using fuzzy logic. As a result of processing test educational data using the developed software, correct results were obtained. It is shown that the use of fuzzy membership functions makes it possible to correctly determine the belonging of students to certain clusters, even if such clusters are not clearly separated. Due to this, it is possible to more accurately determine the recommended level of difficulty of tasks for each student, depending on his previous evaluations.

In recent years, there is a lot of interest in modeling students' digital traces in Learning Management System (LMS) to understand students' learning behavior patterns including aspects of meta-cognition and self-regulation, with the ultimate goal to turn those insights into actionable information to support students to improve their learning outcomes. In achieving this goal, however, there are two main issues that need to be addressed given the existing literature. Firstly, most of the current work is course-centered (i.e. models are built from data for a specific course) rather than student-centered; secondly, a vast majority of the models are correlational rather than causal. Those issues make it challenging to identify the most promising actionable factors for intervention at the student level where most of the campus-wide academic support is designed for. In this paper, we explored a student-centric analytical framework for LMS activity data that can provide not only correlational but causal insights mined from observational data. We demonstrated this approach using a dataset of 1651 computing major students at a public university in the US during one semester in the Fall of 2019. This dataset includes students' fine-grained LMS interaction logs and administrative data, e.g. demographics and academic performance. In addition, we expand the repository of LMS behavior indicators to include those that can characterize the time-of-the-day of login (e.g. chronotype). Our analysis showed that student login volume, compared with other login behavior indicators, is both strongly correlated and causally linked to student academic performance, especially among students with low academic performance. We envision that those insights will provide convincing evidence for college student support groups to launch student-centered and targeted interventions that are effective and scalable.

In this study, we examine a set of primary data collected from 484 students enrolled in a large public university in the Mid-Atlantic United States region during the early stages of the COVID-19 pandemic. The data, called Ties data, included students' demographic and support network information. The support network data comprised of information that highlighted the type of support, (i.e. emotional or educational; routine or intense). Using this data set, models for predicting students' academic achievement, quantified by their self-reported GPA, were created using Chi-Square Automatic Interaction Detection (CHAID), a decision tree algorithm, and cforest, a random forest algorithm that uses conditional inference trees. We compare the methods' accuracy and variation in the set of important variables suggested by each algorithm. Each algorithm found different variables important for different student demographics with some overlap. For White students, different types of educational support were important in predicting academic achievement, while for non-White students, different types of emotional support were important in predicting academic achievement. The presence of differing types of routine support were important in predicting academic achievement for cisgender women, while differing types of intense support were important in predicting academic achievement for cisgender men.

Children living in areas with high levels of air pollution have weaker spelling, reading and maths skills, a new study warns. They also have poorer levels of inhibitory control – the cognitive ability to stop an automatic thought, action or feeling, the study claims. The authors recruited pregnant women from three areas in New York City – Washington Heights, Central Harlem and the South Bronx. They recorded levels of exposure to a carcinogenic pollutant and followed up on their child's performance around a decade later. While the reason for the link remains unconfirmed, the researchers suggest that exposure to the pollutant may affect disrupt the development of the fetus in the womb. During the fetal period, the rapidly-developing brain is vulnerable to'neurotoxic insults', the researchers say, that may subsequently manifest'as adverse physical and mental health outcomes in childhood and adulthood'.

From doing Sudoku every morning to playing more chess to learning a musical instrument, lots of people try different ways to become smarter and improve their memory. Thirty-five years after a landmark memory training experiment in 1982, have scientists really found any foolproof way to make us more intelligent? In a new paper, researchers have looked through several cognitive training programmes and find they actually don't improve our general cognitive and academic skills. Writing for The Conversation, PhD Candidate Giovanni Sala and Professor Fernand Gobet from the University of Liverpool say the general public should be fully aware of the benefits - and limits - of training the brain. Music instruction does not seem to exert any true effect on skills outside of music.

A child's performance at school is widely considered to be a complex combination of inherited ability, the way they were brought up, the quality of teaching they received and a bit of luck. But a new study has suggested it may be possible to predict a person's academic achievement by looking at their DNA alone. Researchers have developed a new genetic scoring technique that explains almost 10 per cent of the differences between children's educational attainment by the age of 16-years-old. A DNA test could soon be used to predict how a child will do when they are at school after researchers found they can explain 10 per cent of a person's academic achievement by the age of 16-years-old by creating what is known as a polygenic score based on 74 genetic variants thought to play a role in educational performance The IQ test has long been dismissed as an inaccurate way to discern how intelligent a person really is - but now scientists may have found a better way. Researchers at the University of Warwick say MRI scans can measure human intelligence, and define exactly what it is.

The Institute of Education Sciences (IES) provides funding opportunities for researchers to bring their knowledge of learning, cognitive science, and technology to bear on education practice. This panel describes opportunities available through the National Center for Education Research and the National Center for Special Education Research.