This paper delves into statistical disparities between human-written and ChatGPT-generated texts, utilizing an analysis of Shannon's equitability values, and token frequency. Our findings indicate that Shannon's equitability can potentially be a differentiating factor between texts produced by humans and those generated by ChatGPT. Additionally,…

This study elaborates on the pivotal roles of mathematical and statistical models in data-driven predictions in an integrated STEM context using the case of Year 4 students: (?) "a descriptive means" to describe the features of trends and variability of data and (?) "an explanatory means" to explain causal relationships behind…

Research studies in Educational Data Mining (EDM) often involve several variables related to student learning activities. As such, it may be necessary to run multiple statistical tests simultaneously, thereby leading to the problem of multiple comparisons. The Benjamini-Hochberg (BH) procedure is commonly used in EDM research to address this…

Purpose: Analysis of connected speech in the field of adult neurogenic communication disorders is essential for research and clinical purposes, yet time and expertise are often cited as limiting factors. The purpose of this project was to create and evaluate an automated program to score and compute the measures from the Quantitative Production…

Learning curves are an important tool in cognitive diagnostics modeling to help assess how well students acquire new skills, and to refine and improve knowledge component models. Learning curves are typically obtained from a model estimated on real data obtained from a finite, and usually limited, sample of students. As a consequence, there is…

In order to effectively grade persuasive writing we must be able to reliably identify and extract extract argument structures. In order to do this we must classify arguments by their structural roles (e.g., major claim, claim, and premise). Current approaches to classification typically rely on statistical models with heavy feature-engineering or…

Students acquire knowledge as they interact with a variety of learning materials, such as video lectures, problems, and discussions. Modeling student knowledge at each point during their learning period and understanding the contribution of each learning material to student knowledge are essential for detecting students' knowledge gaps and…

This position paper examines the phenomenon of the McNamara Fallacy to analyse flawed conceptions of "success" in mathematics learning, normalised assessment structures and their implications for mathematics education. The established presence of the McNamara Fallacy and the ramifications of this statistical fallacy provide a foundation…

Over the past decade, machine learning has become an integral part of educational technologies. With more and more applications such as students' performance prediction, course recommendation, dropout prediction and knowledge tracing relying upon machine learning models, there is increasing evidence and concerns about bias and unfairness of these…

Longitudinal models provide researchers with a framework for investigating key aspects of change over time, but rarely is "time" itself modeled as a focal parameter of interest. Rather than treat time as purely an index of measurement occasions, the proposed Time to Criterion (T2C) growth model allows for modeling individual variability…

One crucial function of a classroom, and a school more generally, is to prepare students for future learning. Students should have the capacity to learn new information and to acquire new skills. This ability to "learn" is a core competency in our rapidly changing world. But how do we measure ability to learn? And how can we measure how…

The "2nd Annual WPI-UMASS-UPENN EDM Data Mining Challenge" required contestants to predict efficient testtaking based on log data. In this paper, we describe our theory-driven and psychometric modeling approach. For feature engineering, we employed the Log-Normal Response Time Model for estimating latent person speed, and the Generalized…

Student modeling is useful in educational research and technology development due to a capability to estimate latent student attributes. Widely used approaches, such as the Additive Factors Model (AFM), have shown satisfactory results, but they can only handle binary outcomes, which may yield potential information loss. In this work, we propose a…

The Bayesian information criterion (BIC) can be useful for model selection within multilevel modeling studies. However, the formula for BIC requires a value for N, which is unclear in multilevel models, since N is observed in at least two levels. The present study uses simulated data to evaluate the rate of false positives and power when using a…

Accurately predicting which students are best suited for graduate programs is beneficial to both students and colleges. In this paper, we propose a quantitative machine learning approach to predict an applicant's potential performance in the graduate program. Our work is based on a real world dataset consisting of MS in CS [Master of Science in…