Solving a math word problem (MWP) requires understanding the mathematical components of the problem and an ability to decode the text. For some students, lower reading comprehension skills may make engagement with the mathematical content more difficult. Readability formulas (e.g., Flesch Reading Ease) are frequently used to assess reading…

Solving math word problems (MWPs) involves uncovering logical relationships among quantities in natural language descriptions of math problems. Recent studies have demonstrated that the contrastive learning framework can assist models in identifying semantically similar examples while distinguishing between different mathematical logics. This…

Understanding a student's problem-solving strategy can have a significant impact on effective math learning using Intelligent Tutoring Systems (ITSs) and Adaptive Instructional Systems (AISs). For instance, the ITS/AIS can better personalize itself to correct specific misconceptions that are indicated by incorrect strategies, specific problems can…

The increasing use of complex machine learning models in education has led to concerns about their interpretability, which in turn has spurred interest in developing explainability techniques that are both faithful to the model's inner workings and intelligible to human end-users. In this paper, we describe a novel approach to creating a…

Collaborative Problem Solving (CPS) is a vital 21st-century skill that integrates social and cognitive processes to achieve shared goals. Despite its importance, understanding how communication dynamics shape individual learning outcomes in CPS tasks remains a challenge, particularly in virtual settings. To address this gap, this study analyzes…

We presented two empirical studies to assess the efficacy of two Deep Reinforcement Learning (DRL) frameworks on two distinct Intelligent Tutoring Systems (ITSs) to exploring the impact of Worked Example (WE) and Problem Solving (PS) on student learning. The first study was conducted on a probability tutor where we applied a classic DRL to induce…

Open-ended learning environments (OELEs) support constructivist approaches to STEM learning. This promotes student engagement and facilitates a deeper understanding of STEM topics. Despite their benefits, OELEs can present significant challenges for novice learners. Recent studies have revealed the complex relationship between students' affective…

A key challenge in e-learning environments like Intelligent Tutoring Systems (ITSs) is to induce effective pedagogical policies efficiently. While Deep Reinforcement Learning (DRL) often suffers from "sample inefficiency" and "reward function" design difficulty, Apprenticeship Learning (AL) algorithms can overcome them.…

Predicting student problem-solving strategies is a complex problem but one that can significantly impact automated instruction systems since they can adapt or personalize the system to suit the learner. While for small datasets, learning experts may be able to manually analyze data to infer student strategies, for large datasets, this approach is…

Promptly and properly addressing students' help requests during office hours is a critical challenge for large CS courses. With a large amount of help requests, instructors often find themselves facing a long office hours queue and need to decide who to help next. Most instructors typically select the earliest arrival students (FCFS), while some…

The gold-standard evaluation of an educational technology product is a randomized study comparing students randomized to use a computer-based learning platform (CBLP) to students assigned to a "business as usual" condition, such as pencil-and-paper work, and estimating average treatment effects. However, not everyone uses the same CBLP…

Various similarity measures for source code have been proposed, many rely on edit- or tree-distance. To support a lecturer in quickly assessing live or online exercises with respect to "approaches taken by the students," we compare source code on a more abstract, semantic level. Even if novice student's solutions follow the same idea,…

We investigate encoder-decoder GRU networks with attention mechanism for solving a diverse array of elementary math problems with mathematical symbolic structures. We quantitatively measure performances of recurrent models on a given question type using a test set of unseen problems with a binary scoring and partial credit system. From our…

Peer tutoring can improve learning by prompting learners to reflect. To assess whether peer interactions are conducive to learning and provide peer tutoring support accordingly, what tutorial dialog types relate to student learning most? Advancements in collaborative learning analytics allow for merging machine learning-based dialog act…

Over the years, researchers have studied novice programming behaviors when doing assignments and projects to identify struggling students. Much of these efforts focused on using student programming and interaction features to predict student success at a course level. While these methods are effective at early detection of struggling students in…