arXiv subjects

Elizabeth B. Cloude

Publications and source records attributed to Elizabeth B. Cloude.

4 recordsLinked to original sources

arXiv2026

Fifteen Years of Learning Analytics Research: Topics, Trends, and Challenges

The learning analytics (LA) community has recently reached two important milestones: celebrating the 15th LAK conference and updating the 2011 definition of LA to reflect the 15 years of changes in the discipline. However, despite LA's growth, little is known about how research topics, funding, and collaboration, as well as the relationships among them, have developed within the community over time. This study addressed this gap by analyzing all 936 full and short papers published at LAK over a 15-year period using unsupervised machine learning, natural language processing, and network analytics. The analysis revealed a stable core of prolific authors alongside high turnover of newcomers, systematic links between funding sources and research directions, and six enduring topical centers that remain globally shared but vary in prominence across countries. These six topical centers, which encompass LA research, are: self-regulated learning, dashboards and theory, social learning, automated feedback, multimodal analytics, and outcome prediction. Our findings highlight key challenges for the future: widening participation, reducing dependency on a narrow set of funders, and ensuring that emerging research trajectories remain responsive to educational practice and societal needs.

cs.CY↗

arXiv2025

Evaluating the Impact of LLM-guided Reflection on Learning Outcomes with Interactive AI-Generated Educational Podcasts

This study examined whether embedding LLM-guided reflection prompts in an interactive AI-generated podcast improved learning and user experience compared to a version without prompts. Thirty-six undergraduates participated, and while learning outcomes were similar across conditions, reflection prompts reduced perceived attractiveness, highlighting a call for more research on reflective interactivity design.

cs.HC↗

arXiv2024

Evaluating the Impact of Data Augmentation on Predictive Model Performance

In supervised machine learning (SML) research, large training datasets are essential for valid results. However, obtaining primary data in learning analytics (LA) is challenging. Data augmentation can address this by expanding and diversifying data, though its use in LA remains underexplored. This paper systematically compares data augmentation techniques and their impact on prediction performance in a typical LA task: prediction of academic outcomes. Augmentation is demonstrated on four SML models, which we successfully replicated from a previous LAK study based on AUC values. Among 21 augmentation techniques, SMOTE-ENN sampling performed the best, improving the average AUC by 0.01 and approximately halving the training time compared to the baseline models. In addition, we compared 99 combinations of chaining 21 techniques, and found minor, although statistically significant, improvements across models when adding noise to SMOTE-ENN (+0.014). Notably, some augmentation techniques significantly lowered predictive performance or increased performance fluctuation related to random chance. This paper's contribution is twofold. Primarily, our empirical findings show that sampling techniques provide the most statistically reliable performance improvements for LA applications of SML, and are computationally more efficient than deep generation methods with complex hyperparameter settings. Second, the LA community may benefit from validating a recent study through independent replication.

cs.LG↗

arXiv2024

Comparison of Three Programming Error Measures for Explaining Variability in CS1 Grades

Programming courses can be challenging for first year university students, especially for those without prior coding experience. Students initially struggle with code syntax, but as more advanced topics are introduced across a semester, the difficulty in learning to program shifts to learning computational thinking (e.g., debugging strategies). This study examined the relationships between students' rate of programming errors and their grades on two exams. Using an online integrated development environment, data were collected from 280 students in a Java programming course. The course had two parts. The first focused on introductory procedural programming and culminated with exam 1, while the second part covered more complex topics and object-oriented programming and ended with exam 2. To measure students' programming abilities, 51095 code snapshots were collected from students while they completed assignments that were autograded based on unit tests. Compiler and runtime errors were extracted from the snapshots, and three measures -- Error Count, Error Quotient and Repeated Error Density -- were explored to identify the best measure explaining variability in exam grades. Models utilizing Error Quotient outperformed the models using the other two measures, in terms of the explained variability in grades and Bayesian Information Criterion. Compiler errors were significant predictors of exam 1 grades but not exam 2 grades; only runtime errors significantly predicted exam 2 grades. The findings indicate that leveraging Error Quotient with multiple error types (compiler and runtime) may be a better measure of students' introductory programming abilities, though still not explaining most of the observed variability.

cs.PL↗