Integrating AI and Learning Analytics for Data-Driven Pedagogical Decisions and Personalized Interventions in Education

Ramteja Sajja; Yusuf Sermet; David Cwiertny; Ibrahim Demir

doi:10.48550/arxiv.2312.09548

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Integrating AI and Learning Analytics for Data-Driven Pedagogical Decisions and Personalized Interventions in Education

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Integrating AI and Learning Analytics for Data-Driven Pedagogical Decisions and Personalized Interventions in Education

Ramteja Sajja, Yusuf Sermet, David Cwiertny and Ibrahim Demir

arXiv.org

Cornell University

12/15/2023

DOI: 10.48550/arxiv.2312.09548

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Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

This research study delves into the conceptualization, development, and deployment of an innovative learning analytics tool, leveraging the capabilities of OpenAI's GPT-4 model. This tool is designed to quantify student engagement, map learning progression, and evaluate the efficacy of diverse instructional strategies within an educational context. Through the analysis of various critical data points such as students' stress levels, curiosity, confusion, agitation, topic preferences, and study methods, the tool offers a rich, multi-dimensional view of the learning environment. Furthermore, it employs Bloom's taxonomy as a framework to gauge the cognitive levels addressed by students' questions, thereby elucidating their learning progression. The information gathered from these measurements can empower educators by providing valuable insights to enhance teaching methodologies, pinpoint potential areas for improvement, and craft personalized interventions for individual students. The study articulates the design intricacies, implementation strategy, and thorough evaluation of the learning analytics tool, underscoring its prospective contributions to enhancing educational outcomes and bolstering student success. Moreover, the practicalities of integrating the tool within existing educational platforms and the requisite robust, secure, and scalable technical infrastructure are addressed. This research opens avenues for harnessing AI's potential in shaping the future of education, facilitating data-driven pedagogical decisions, and ultimately fostering a more conducive, personalized learning environment.

Computer Science - Artificial Intelligence

Computer Science - Computers and Society

Computer Science - Human-Computer Interaction

Details

Title: Subtitle: Integrating AI and Learning Analytics for Data-Driven Pedagogical Decisions and Personalized Interventions in Education
Creators: Ramteja Sajja
Yusuf Sermet
David Cwiertny
Ibrahim Demir
Resource Type: Preprint
Publication Details: arXiv.org
DOI: 10.48550/arxiv.2312.09548
eISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 12/15/2023
Academic Unit: Electrical and Computer Engineering; Center for Health Effects of Environmental Contamination; Civil and Environmental Engineering; IIHR--Hydroscience and Engineering; Injury Prevention Research Center; Public Policy Center (Archive); Chemistry; Chemical and Biochemical Engineering
Record Identifier: 9984532056802771

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