Search arXivSearch

arXiv · 2407.20234

Exploring Factors Affecting Student Learning Satisfaction during COVID-19 in South Korea

Abstract

Understanding students' preferences and learning satisfaction during COVID-19 has focused on learning attributes such as self-efficacy, performance, and engagement. Although existing efforts have constructed statistical models capable of accurately identifying significant factors impacting learning satisfaction, they do not necessarily explain the complex relationships among these factors in depth. This study aimed to understand several facets related to student learning preferences and satisfaction during the pandemic such as individual learner characteristics, instructional design elements and social and environmental factors. Responses from 302 students from Sungkyunkwan University, South Korea were collected between 2021 and 2022. Information gathered included their gender, study major, satisfaction and motivation levels when learning, perceived performance, emotional state and learning environment. Wilcoxon Rank sum test and Explainable Boosting Machine (EBM) were performed to determine significant differences in specific cohorts. The two core findings of the study are as follows:1) Using Wilcoxon Rank Sum test, we can attest with 95% confidence that students who took offline classes had significantly higher learning satisfaction, among other attributes, than those who took online classes, as with STEM versus HASS students; 2) An explainable boosting machine (EBM) model fitted to 95.08% accuracy determined the top five factors affecting students' learning satisfaction as their perceived performance, their perception on participating in class activities, their study majors, their ability to conduct discussions in class and the study space availability at home. Positive perceived performance and ability to discuss with classmates had a positive impact on learning satisfaction, while negative perception on class activities participation had a negative impact on learning satisfaction.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jiwon Han, Chaeeun Ryu, Gayathri Nadarajan. 2024-07-12. Exploring Factors Affecting Student Learning Satisfaction during COVID-19 in South Korea. https://doi.org/10.1145/3678392.3678406

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

A Brief AI Literacy Intervention Does Not Significantly Reduce Over-Reliance and Increases Under-Reliance on ChatGPT: A Randomized Study

In this study, we examined whether a brief AI literacy intervention influences high school students' reliance on recommendations from large language models (LLMs). In a randomized experiment, students were assigned to either a control group receiving a brief introduction to LLMs or an intervention group receiving additional information about how LLMs work, their limitations, and effective usage strategies. Participants then solved eight math puzzles with ChatGPT's advice, which was incorrect in half of the trials. Results indicated widespread over-reliance, with incorrect recommendations adopted in 52.1% of the trials. The intervention did not significantly reduce over-reliance. Instead, it led to an increase in under-reliance, as students were more likely to reject correct recommendations. These findings provide preliminary evidence that brief text-based interventions may be ineffective in fostering appropriate reliance. More comprehensive and interactive approaches may be required to meaningfully influence students' real-world reliance on LLMs.

cs.CY

Your Programming Students' Cognition with ChatGPT: Higher Performance, Lower Retention, and Reduced Ownership

Generative AI can improve students' programming performance, but successful task completion may not reflect what they retain. We examined performance, retention, cognitive load, and ownership in a controlled between-subjects experiment with 59 undergraduate computer science students, 55 were retained for analysis. Participants completed three introductory C programming tasks with access to ChatGPT-4.5 or conventional web search without generative AI. We measured task performance, self-reported mental effort and difficulty, pupillary responses, heart rate variability, and ownership, and assessed cued recall immediately and 48 hours later. ChatGPT-assisted students achieved higher coding scores (89% vs. 69%) but lower recall scores immediately (41% vs. 53%) and after 48 hours (39% vs. 52%). There was no significant difference in the loss of recall information over 48 hours between the groups. Self-reported mental effort increased less across tasks in the ChatGPT condition (Holm-adjusted p = .047), and students attributed less of the submitted code to themselves (45% vs. 81%). Confirmatory physiological tests did not detect significant differences in trajectories between conditions; substantial data loss limits their interpretation. These findings reveal a gap between assisted task performance and subsequent recall and sense of ownership in this setting. They motivate the need for assessment practices and AI learning tools that require students to explain, retrieve, and contribute to the work they submit as active participants in their education.

cs.CY

Open Platform Field Experiments: Expanding the Design Space of Experimental Research on Social Media

Despite a growing demand for causal evidence about social media, independent researchers remain severely constrained in their ability to conduct experiments directly on online platforms. To cope, multiple methodological workarounds have emerged - from controlled surveys and simulations to client-side overlays and platform partnerships - each requiring distinct trade-offs between desirable experimental properties. The recent emergence of open social media platforms offers a qualitatively different methodological opportunity. Here we propose a design space of social media experimentation and discuss Open Platform Field Experiments (OPFEs). OPFEs represent a distinct class of experimental approaches that enable independent researchers to directly intervene on functional platform components - such as clients, recommendation systems, and moderation services - within live social media environments. Through a comparative analysis of experimental archetypes, we show that OPFEs occupy a previously unexplored region of the design space. We then bridge theory and practice by characterizing the architectural and governance elements that enable OPFEs, mapping them onto Bluesky and the AT Protocol, and illustrating the end-to-end lifecycle of a complete OPFE design. Overall, this work establishes OPFEs as a practical methodological paradigm for independent, transparent, and ecologically grounded experimentation on open social media.

cs.CY