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

• A Comprehensive Review of AI-based Intelligent Tutoring Systems: Applications and Challenges [0.4369550829556578]
  We use a systematic literature review method to analyze numerous qualified studies published from 2010 to 2025.<n>The results reveal a complex landscape regarding the effectiveness of ITS, highlighting both advancements and persistent challenges.
  arXiv  Detail & Related papers  (2025-07-25T01:43:07Z)
• On the development of an AI performance and behavioural measures for teaching and classroom management [29.68201271068342]
  This paper presents a two-year research project focused on developing AI-driven measures to analyze classroom dynamics.<n>Key outcomes include a curated audio-visual dataset, novel behavioral measures, and a proof-of-concept teaching review dashboard.
  arXiv  Detail & Related papers  (2025-06-11T04:52:50Z)
• Analyzing the Impact of AI Tools on Student Study Habits and Academic Performance [0.0]
  The research focuses on how AI tools can support personalized learning, adaptive test adjustments, and provide real-time classroom analysis. Student feedback revealed strong support for these features, and the study found a significant reduction in study hours alongside an increase in GPA. Despite these benefits, challenges such as over-reliance on AI and difficulties in integrating AI with traditional teaching methods were also identified.
  arXiv  Detail & Related papers  (2024-12-03T04:51:57Z)
• Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset [94.13848736705575]
  We introduce Facial Identity Unlearning Benchmark (FIUBench), a novel VLM unlearning benchmark designed to robustly evaluate the effectiveness of unlearning algorithms. We apply a two-stage evaluation pipeline that is designed to precisely control the sources of information and their exposure levels. Through the evaluation of four baseline VLM unlearning algorithms within FIUBench, we find that all methods remain limited in their unlearning performance.
  arXiv  Detail & Related papers  (2024-11-05T23:26:10Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• The Rise of Artificial Intelligence in Educational Measurement: Opportunities and Ethical Challenges [2.569083526579529]
  AI in education raises ethical concerns regarding validity, reliability, transparency, fairness, and equity. Various stakeholders, including educators, policymakers, and organizations, have developed guidelines to ensure ethical AI use in education. In this paper, a diverse group of AIME members examines the ethical implications of AI-powered tools in educational measurement.
  arXiv  Detail & Related papers  (2024-06-27T05:28:40Z)
• Qualitative and quantitative analysis of student's perceptions in the use of generative AI in educational environments [0.0]
  The effective integration of generative artificial intelligence in education is a fundamental aspect to prepare future generations. The objective of this study is to analyze from a quantitative and qualitative point of view the perception of controlled student-IA interaction within the classroom.
  arXiv  Detail & Related papers  (2024-05-22T09:56:05Z)
• 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)
• AI-assisted Learning for Electronic Engineering Courses in High Education [2.67766280323297]
  The study includes the evaluation and reflection of different stakeholders: students, lecturers, and engineers. The findings of this study shed light on the benefits and limitations of ChatGPT as an AI tool, paving the way for innovative learning approaches in technical disciplines.
  arXiv  Detail & Related papers  (2023-11-02T07:48:10Z)
• Exploring Federated Unlearning: Analysis, Comparison, and Insights [101.64910079905566]
  federated unlearning enables the selective removal of data from models trained in federated systems. This paper examines existing federated unlearning approaches, examining their algorithmic efficiency, impact on model accuracy, and effectiveness in preserving privacy. We propose the OpenFederatedUnlearning framework, a unified benchmark for evaluating federated unlearning methods.
  arXiv  Detail & Related papers  (2023-10-30T01:34:33Z)
• 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)
• 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.