Integrating AI and Learning Analytics for Data-Driven Pedagogical
Decisions and Personalized Interventions in Education
- URL: http://arxiv.org/abs/2312.09548v1
- Date: Fri, 15 Dec 2023 06:00:26 GMT
- Title: Integrating AI and Learning Analytics for Data-Driven Pedagogical
Decisions and Personalized Interventions in Education
- Authors: Ramteja Sajja, Yusuf Sermet, David Cwiertny, Ibrahim Demir
- Abstract summary: This research study delves into the conceptualization, development, and deployment of an innovative learning analytics tool.
This tool is designed to quantify student engagement, map learning progression, and evaluate the efficacy of diverse instructional strategies.
- Score: 0.30723404270319693
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Revolutionising Role-Playing Games with ChatGPT [0.0]
The aim of the study was to analyse the impact of AI-based simulations on students' learning experience.
Based on Vygotsky's sociocultural theory, ChatGPT was used to give students a deeper understanding of strategic decision-making processes.
arXiv Detail & Related papers (2024-07-02T08:21:40Z) - The Perceived Learning Behaviors and Assessment Techniques of First-Year Students in Computer Science: An Empirical Study [0.0]
Students believe that in-person instruction is the most effective way to learn.
For evaluation methods, there is a preference for practical and written examinations.
arXiv Detail & Related papers (2024-05-10T08:45:32Z) - Large Language Models for Education: A Survey and Outlook [69.02214694865229]
We systematically review the technological advancements in each perspective, organize related datasets and benchmarks, and identify the risks and challenges associated with deploying LLMs in education.
Our survey aims to provide a comprehensive technological picture for educators, researchers, and policymakers to harness the power of LLMs to revolutionize educational practices and foster a more effective personalized learning environment.
arXiv Detail & Related papers (2024-03-26T21:04:29Z) - Evaluating and Optimizing Educational Content with Large Language Model Judgments [52.33701672559594]
We use Language Models (LMs) as educational experts to assess the impact of various instructions on learning outcomes.
We introduce an instruction optimization approach in which one LM generates instructional materials using the judgments of another LM as a reward function.
Human teachers' evaluations of these LM-generated worksheets show a significant alignment between the LM judgments and human teacher preferences.
arXiv Detail & Related papers (2024-03-05T09:09:15Z) - A Review of Data Mining in Personalized Education: Current Trends and
Future Prospects [30.033926908231297]
This paper focuses on four scenarios: educational recommendation, cognitive diagnosis, knowledge tracing, and learning analysis.
The integration of AI in educational platforms provides insights into academic performance, learning preferences, and behaviors, optimizing the personal learning process.
arXiv Detail & Related papers (2024-02-27T06:09:48Z) - A Comprehensive Exploration of Personalized Learning in Smart Education:
From Student Modeling to Personalized Recommendations [19.064610936977402]
China, the United States, the European Union, and others have put forward the importance of personalized learning.
This review provides a comprehensive analysis of the current situation of personalized learning and its key role in education.
arXiv Detail & Related papers (2024-01-15T08:49:25Z) - Towards Goal-oriented Intelligent Tutoring Systems in Online Education [69.06930979754627]
We propose a new task, named Goal-oriented Intelligent Tutoring Systems (GITS)
GITS aims to enable the student's mastery of a designated concept by strategically planning a customized sequence of exercises and assessment.
We propose a novel graph-based reinforcement learning framework, named Planning-Assessment-Interaction (PAI)
arXiv Detail & Related papers (2023-12-03T12:37:16Z) - Tool Learning with Foundation Models [114.2581831746077]
With the advent of foundation models, AI systems have the potential to be equally adept in tool use as humans.
Despite its immense potential, there is still a lack of a comprehensive understanding of key challenges, opportunities, and future endeavors in this field.
arXiv Detail & Related papers (2023-04-17T15:16:10Z) - Latent Properties of Lifelong Learning Systems [59.50307752165016]
We introduce an algorithm-agnostic explainable surrogate-modeling approach to estimate latent properties of lifelong learning algorithms.
We validate the approach for estimating these properties via experiments on synthetic data.
arXiv Detail & Related papers (2022-07-28T20:58:13Z) - Desperately seeking the impact of learning analytics in education at
scale: Marrying data analysis with teaching and learning [0.0]
Learning analytics (LA) is argued to be able to improve learning outcomes, learner support and teaching.
There is still little empirical evidence of impact on practice that shows the effectiveness of LA in education settings.
We argue that in order to increase the impact of data-driven decision-making aimed at students' improved learning at scale, we need to better understand educators' needs.
arXiv Detail & Related papers (2021-05-14T07:33:17Z) - Personalized Education in the AI Era: What to Expect Next? [76.37000521334585]
The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to meet her desired goal.
In recent years, the boost of artificial intelligence (AI) and machine learning (ML) has unfolded novel perspectives to enhance personalized education.
arXiv Detail & Related papers (2021-01-19T12:23:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.