Large language models are increasingly used to generate programming practice, yet their cognitive demands for CS1 learners remain underexplored. We investigate student-perceived cognitive load on GPT-generated Python exercises using a custom intelligent tutoring system that captures fine-grained keystrokes and offers in-exercise AI feedback. Nineteen undergraduates completed nine exercises spanning loops, recursion, and classes and objects, yielding 171 submissions and 83,377 keystrokes. We triangulate self-reports with keystroke dynamics, code quality metrics, and knowledge component (KC) mastery. Perceived load largely tracked labeled difficulty and was inversely related to satisfaction. Keystroke features were the strongest correlates of load, with more typing bursts and longer inter-keystroke latencies accompanying higher demand; among code metrics, the SLoC-LLoC differential showed the highest association with demand, aligning higher structural complexity with greater mental effort. Lower mastery of core knowledge components—especially loops plus string and list operations—coincided with higher load. Students rated instant AI feedback helpful for debugging and best practices, though at times redundant or inconsistent with instructions. To estimate load automatically, we trained models on observed traces. An Extra Trees regressor predicted the primary load measure Demanding with RMSE 0.81, and a three-level classifier achieved 86 % accuracy with weighted F1 0.85 on held-out students. These results suggest actionable design levers for LLM-based tutors: calibrate task complexity, scaffold high-strain concepts, and deliver concise, context-aligned feedback while using keystroke- and code-based signals for real-time load monitoring. The code and data are available: https://github.com/DSAatUSU/LLMCognitiveLoadProgramming.

Online coding platforms (OCPs) often offer a limited selection of exercises, which can restrict the scope of Computer Science (CS) education. This study investigates the capabilities of Large Language Models (LLMs), particularly GPT-4 Turbo, in broadening this scope by autonomously generating Python programming exercises. These exercises are tailored to the CS1 curriculum-an introductory course in computer science. Utilizing curriculum-driven prompt engineering, we developed a dataset of 11,700 exercises, characterized by a variety of cate-gories, types, and difficulty levels. These exercises are distributed across 78 unique topics, which were derived from the CS1 course catalogs of leading universities and supplemented with online educational resources. To evaluate the effectiveness of GPT-4 Turbo in generating CSI Python programming exercises, we conducted a user study involving both students and instruc-tors. The study focused on several metrics: exercise quality, curriculum relevance, understandability, appropriate difficulty level, and the generation of useful hints. Our findings indicate that GPT-4 Turbo can produce high-quality, educationally effective programming exercises at scale, provided that the prompts are systematically crafted. Based on insights from the user study, adjustments to prompt design are recommended to optimize exercise generation. Our research concludes that GPT-4 Turbo can be seamlessly integrated into AI-driven OCPs, offering a scalable, cost and time-effective method to enhance CS edu-cation. This is achieved through targeted prompt engineering and thorough data preprocessing to mitigate inconsistencies. The code is available online: https://github.com/DSAatUSU/GPT_CS1400_Exercise_Generation

Keystroke data in programming reveals intricate patterns that reflect the behavior of programmers. These patterns hold promise for predicting grades and other applications, providing insights into the skills of both proficient and less proficient programmers. Analyzing these patterns can yield tailored feedback for students who need support, enabling effective interventions. Our study utilizes a keystroke dataset from the CS1 (Introduction to Computer Science) course at Utah State University. We developed novel features by combining elements like key presses, timestamps, source locations, and programming terminology, drawing on prior research, our insights, and an analysis of programming behavior. An ensemble-based feature selection method identifies key features, which are then used in hyperparameter optimization and grade prediction with six classification and three regression algorithms. We categorized grades into three levels: Low, Average, and High. Despite challenges such as class imbalance, plagiarism, limited data per assignment, and the ceiling effect, we attained a notable weighted F1 score of 78%. We also introduce an ensemble classification strategy, merging Isolation Forest outlier detection with a refined Random Forest classifier, achieving 80% accuracy on our test set. Additionally, we provide a detailed interpretation of our features, supported by results and a case study of our dataset. This research aims to enhance computer science education at the undergraduate level, focusing on improving its overall quality. Code and data are available https://github.com/DSAatUSU/Student-Coding-Behavior.git.

Online social networks are the breeding grounds for user connections, fostering information exchange, communication, content sharing, and community building. However, the dissolution of these digital relationships, often a less-explored facet, complements the studies of tie formation and maintenance. A comprehensive grasp of these connections, encompassing their inception, unraveling, and the potential foresight of disconnections, offers invaluable insights into network dynamics and the progression of interpersonal bonds. Yet, the investigation of broken ties faces a substantial challenge: the paucity of longitudinal and detailed data. To bridge this gap, this paper curates an expansive dataset, spanning over 120,000 Twitter users tracked across 15 weeks with weekly snapshots. Armed with this dataset, we embark on an extensive exploration of Twitter links, delving into five distinct categories within the Twitter social graph. These categories encompass structural features like centrality, content-related aspects, including post polarity, user profile attributes like verified status, egocentric network elements such as reciprocity, and dense user representations typified by node2vec. Subsequently, we conduct a thorough analysis of these diverse features to unveil meaningful patterns.

In recent years, Massive Open Online Courses (MOOCs) have gained significant traction as a rapidly growing phenomenon in online learning. Unlike traditional classrooms, MOOCs offer a unique opportunity to cater to a diverse audience from different backgrounds and geographical locations. Renowned universities and MOOC-specific providers, such as Coursera, offer MOOC courses on various subjects. Automated assessment tasks like grade and early dropout predictions are necessary due to the high enrollment and limited direct interaction between teachers and learners. However, current automated assessment approaches overlook the structural links between different entities involved in the downstream tasks, such as the students and courses. Our hypothesis suggests that these structural relationships, manifested through an interaction graph, contain valuable information that can enhance the performance of the task at hand. To validate this, we construct a unique knowledge graph for a large MOOC dataset, which will be publicly available to the research community. Furthermore, we utilize graph embedding techniques to extract latent structural information encoded in the interactions between entities in the dataset. These techniques do not require ground truth labels and can be utilized for various tasks. Finally, by combining entity-specific features, behavioral features, and extracted structural features, we enhance the performance of predictive machine learning models in student assignment grade prediction. Our experiments demonstrate that structural features can significantly improve the predictive performance of downstream assessment tasks. The code and data are available in https://github.com/DSAatUSU/MOOPer_grade_prediction

The central idea of individual fairness is based on an auspicious yet intuitive assertion: similar individuals should be treated similarly. Nevertheless, the fulfillment of individual fairness is hindered by three major obstacles. First, one needs to determine individuals who should receive similar treatment. Second, seamlessly formulating the notion of individual fairness in an ML learning process is another challenge. Third, effectively evaluating the notion of similar treatment in probabilistic classifiers is another challenge. To overcome these challenges, we propose a novel framework called FairMatch. Our proposed framework offers a new approach to pairing similar and dissimilar individuals using a causal analysis method called propensity score matching. Moreover, we formulate individual fairness as a representation learning problem where we incorporate similar and dissimilar pairs in a triplet-based loss function. Eventually, we devise a novel metric to evaluate individual fairness that captures the notion of similar treatment in probabilistic classifiers in a better way. Experimental results on four real-world datasets verify the superiority of FairMatch to existing solutions where we demonstrate it can deliver fairer decisions without scarifying the predictive performance.