This paper presents a tool for creating student models in logistic regression. Creating student models has typically been done by expert selection of the appropriate terms, beginning with models as simple as IRT or AFM but more recently with highly complex models like BestLR. While alternative methods exist to select the appropriate predictors for…

Automated Essay Scoring (AES) tools aim to improve the efficiency and consistency of essay scoring by using machine learning algorithms. In the existing research work on this topic, most researchers agree that human-automated score agreement remains the benchmark for assessing the accuracy of machine-generated scores. To measure the performance of…

Automatic short answer grading is an important research direction in the exploration of how to use artificial intelligence (AI)-based tools to improve education. Current state-of-the-art approaches use neural language models to create vectorized representations of students responses, followed by classifiers to predict the score. However, these…

Automated scoring of student responses to open-ended questions, including short-answer questions, has great potential to scale to a large number of responses. Recent approaches for automated scoring rely on supervised learning, i.e., training classifiers or fine-tuning language models on a small number of responses with human-provided score…

Recent research seeks to develop more comprehensive learner models for adaptive learning software. For example, models of reading comprehension built using data from students' use of adaptive instructional software for mathematics have recently been developed. These models aim to deliver experiences that consider factors related to learning beyond…

In studies that generate course recommendations based on similarity, the typical enrollment data used for model training consists only of one record per student-course pair. In this study, we explore and quantify the additional signal present in course transaction data, which includes a more granular account of student administrative interactions…

Automatically identifying struggling students learning to program can assist teachers in providing timely and focused help. This work presents a new deep-learning language model for predicting "bug-fix-time", the expected duration between when a software bug occurs and the time it will be fixed by the student. Such information can guide…

What can eye movements reveal about reading, a complex skill ubiquitous in everyday life? Research suggests that gaze can reflect short-term comprehension for facts, but it is unknown whether it can measure long-term, deep comprehension. We tracked gaze while 147 participants read long, connected, informative texts and completed assessments of…

In this paper, we propose a novel approach to combine domain modelling and student modelling techniques in a single, automated pipeline which does not require expert knowledge and can be used to predict future student performance. Domain modelling techniques map questions to concepts and student modelling techniques generate a mastery score for a…

Academic advising plays an important role in students' decision-making in higher education. Data-driven methods provide useful recommendations to students to help them with degree completion. Several course recommendation models have been proposed in the literature to recommend courses for the next semester. One aspect of the data that has yet to…

The assessment of program functionality can generally be accomplished with straight-forward unit tests. However, assessing the design quality of a program is a much more difficult and nuanced problem. Design quality is an important consideration since it affects the readability and maintainability of programs. Assessing design quality and giving…

In educational applications, "Knowledge Tracing" (KT) has been widely studied for decades as it is considered a fundamental task towards adaptive online learning. Among proposed KT methods, Deep Knowledge Tracing (DKT) and its variants are by far the most effective ones due to the high flexibility of the neural network. However, DKT…

Classifying educational forum posts is a longstanding task in the research of Learning Analytics and Educational Data Mining. Though this task has been tackled by applying both traditional Machine Learning (ML) approaches (e.g., Logistics Regression and Random Forest) and up-to-date Deep Learning (DL) approaches, there lacks a systematic…

How can we test whether state-of-the-art generative models, such as Blender and GPT-3, are good AI teachers, capable of replying to a student in an educational dialogue? Designing an AI teacher test is challenging: although evaluation methods are much-needed, there is no off-the-shelf solution to measuring pedagogical ability. This paper reports…

For the development of successful human-agent dialogue-based tutoring systems, it is essential to understand what makes a human-human tutorial dialogue successful. While there has been much research on dialogue-based intelligent tutoring systems, there have been comparatively fewer studies on analyzing large-scale datasets of human-human online…