A Review of Data Mining in Personalized Education: Current Trends and
Future Prospects
- URL: http://arxiv.org/abs/2402.17236v1
- Date: Tue, 27 Feb 2024 06:09:48 GMT
- Title: A Review of Data Mining in Personalized Education: Current Trends and
Future Prospects
- Authors: Zhang Xiong, Haoxuan Li, Zhuang Liu, Zhuofan Chen, Hao Zhou, Wenge
Rong, Yuanxin Ouyang
- Abstract summary: 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.
- Score: 30.033926908231297
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Personalized education, tailored to individual student needs, leverages
educational technology and artificial intelligence (AI) in the digital age to
enhance learning effectiveness. The integration of AI in educational platforms
provides insights into academic performance, learning preferences, and
behaviors, optimizing the personal learning process. Driven by data mining
techniques, it not only benefits students but also provides educators and
institutions with tools to craft customized learning experiences. To offer a
comprehensive review of recent advancements in personalized educational data
mining, this paper focuses on four primary scenarios: educational
recommendation, cognitive diagnosis, knowledge tracing, and learning analysis.
This paper presents a structured taxonomy for each area, compiles commonly used
datasets, and identifies future research directions, emphasizing the role of
data mining in enhancing personalized education and paving the way for future
exploration and innovation.
Related papers
- A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions [66.40362209055023]
This paper aims to provide a survey of current models for cognitive diagnosis, with more attention on new developments using machine learning-based methods.
By comparing the model structures, parameter estimation algorithms, model evaluation methods and applications, we provide a relatively comprehensive review of the recent trends in cognitive diagnosis models.
arXiv Detail & Related papers (2024-07-07T18:02:00Z) - 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) - 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) - Integrating AI and Learning Analytics for Data-Driven Pedagogical
Decisions and Personalized Interventions in Education [0.30723404270319693]
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.
arXiv Detail & Related papers (2023-12-15T06:00:26Z) - Artificial Intelligence-Enabled Intelligent Assistant for Personalized
and Adaptive Learning in Higher Education [0.2812395851874055]
This paper presents a novel framework, Artificial Intelligence-Enabled Intelligent Assistant (AIIA) for personalized and adaptive learning in higher education.
The AIIA system leverages advanced AI and Natural Language Processing (NLP) techniques to create an interactive and engaging learning platform.
arXiv Detail & Related papers (2023-09-19T19:31:15Z) - Privacy-Preserving Graph Machine Learning from Data to Computation: A
Survey [67.7834898542701]
We focus on reviewing privacy-preserving techniques of graph machine learning.
We first review methods for generating privacy-preserving graph data.
Then we describe methods for transmitting privacy-preserved information.
arXiv Detail & Related papers (2023-07-10T04:30:23Z) - Deep Active Learning for Computer Vision: Past and Future [50.19394935978135]
Despite its indispensable role for developing AI models, research on active learning is not as intensive as other research directions.
By addressing data automation challenges and coping with automated machine learning systems, active learning will facilitate democratization of AI technologies.
arXiv Detail & Related papers (2022-11-27T13:07:14Z) - Comparative Study of Learning Outcomes for Online Learning Platforms [47.5164159412965]
Personalization and active learning are key aspects to successful learning.
We run a comparative head-to-head study of learning outcomes for two popular online learning platforms.
arXiv Detail & Related papers (2021-04-15T20:40:24Z) - 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) - Ethical behavior in humans and machines -- Evaluating training data
quality for beneficial machine learning [0.0]
This study describes new dimensions of data quality for supervised machine learning applications.
The specific objective of this study is to describe how training data can be selected according to ethical assessments of the behavior it originates from.
arXiv Detail & Related papers (2020-08-26T09:48:38Z)
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.