When building statistical models in educational research or predictive analytics, one of the most important considerations is ensuring the model generalizes well to new data. This blog will focus on two fundamental concepts: cross-validation and overfitting, and how they relate to ensuring model robustness. What is Overfitting? Overfitting occurs when your model fits not just […]
In this blog post, we’ll explore some key metrics for evaluating regressors, including linear correlation, Spearman’s rho, mean absolute error (MAE), root mean squared error (RMSE), and information criteria like BIC and AIC. These metrics are vital in understanding the performance of regression models, especially in education and other data-driven fields. Linear Correlation: How Do […]
In machine learning and educational research, choosing the right metric to evaluate a classifier’s performance is critical. While many people are familiar with accuracy, there are other diagnostic metrics that provide deeper insights into how well a model is performing. In this blog, we’ll dive into two metrics: Accuracy and Cohen’s Kappa, explaining why accuracy […]
If you’re someone just starting out or considering a career in digital marketing, you’ll find countless resources. You could turn to platforms like YouTube, Udemy, or any other e-learning site and search for courses on Google Ads, SEO, Facebook Ads, Programmatic Advertising, Growth Marketing, and so on. You’ll likely be overwhelmed. So, let me walk […]
In any classification task, we are predicting a label, which is the categorical outcome we want to forecast, such as whether a student will pass a course. While knowing whether the prediction is correct is important, it is even more valuable to understand how confident the model is in its prediction. For example, suppose a […]
Artificial Intelligence (AI) is reshaping various fields, including education, but its complexity often leads to questions about trust, fairness, and transparency. In response, two crucial concepts have emerged: Explainable AI (xAI) and Interpretable AI. These terms may sound similar, but they offer distinct affordances and are critical in educational contexts where understanding predictions can drive […]
Classification is a type of machine learning task where you predict a category (or label) rather than a continuous number. For example, classifying whether an email is spam or not is a classification task, because the output is categorical (spam or not spam), not a continuous number. Neural Networks: The Building Blocks Neural networks are a […]
In educational data mining, prediction models play a critical role in analyzing student data to infer specific outcomes, known as predicted variables, from a combination of other features, referred to as predictor variables. These models are applied in various contexts: sometimes to forecast future performance, and at other times to gain insights into current states […]
Prediction models are powerful tools in educational data mining, designed to infer a specific aspect of data (known as the predicted variable) from a combination of other aspects (predictor variables). These models can be used in various contexts, from forecasting future events to making inferences about the present. Predicting the Future: A Challenging Task One […]
In today’s data-driven world, the field of education has not been left behind. Two key communities have been at the forefront of harnessing the potential of “big data” in education are: the Educational Data Mining Society (EDM) and the Society of Learning Analytics Research (SoLAR). These groups have been instrumental in advancing our understanding of […]
