Educational Data Mining with Learning Analytics and Unsupervised Algorithms: Analysis and Diagnosis in Basic Education

Torcate, Arianne Sarmento; Rodrigues, Cleyton Mário de Oliveira

doi:10.1109/laclo54177.2021.00014

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…One notable study [27] investigates the integration of artificial intelligence and machine learning in LA, emphasizing their role in providing personalized feedback and adaptive learning experiences. Another study [28] focuses on the latest trends and challenges in educational data mining, providing insights into state-of-the-art intelligent methodologies. Additionally, Casalino et al's research [14] explores the use of deep learning techniques, such as neural networks, in education analytics, highlighting their ability to model complex relationships within educational data.…”

Section: G Intelligent Methodologies In Education Analyticsmentioning

confidence: 99%

“…This field has experienced substantial growth due to advancements in technology, which enable the collection and analysis of large-scale data. The primary goal of LA is to prioritize students and utilize their digital traces of interaction to understand their cognitive patterns and behavioral choices [13]. By comprehending and utilizing this data, educators can customize instruction and interventions to meet the unique needs of individual students, resulting in improved learning outcomes.…”

Section: B Learning Analytics In Educationmentioning

confidence: 99%

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Multi-Modal LA in Personalized Education Using Deep Reinforcement Learning Based Approach

Sharif,

Uckelmann

2024

IEEE Access

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The demand for personalized learning experiences and effective analytics in education has significantly increased. The integration of technology in education has brought about significant changes in teaching and learning practices. In the era of digital technology, the integration of education technology in the classroom has led to a change in teaching methods and learning strategies. In this paper, we introduce the KNIGHT (AI in Education at Hochschule für Technik (HFT) Stuttgart) framework, which is a holistic solution designed to tackle the complex issue of personalized education in a digital era. The paper explores the application of multimodal data integration, the novel application of deep reinforcement learning to education analytics, and the ethical consideration of privacy-preserving personalized feedback. The proposed framework's efficacy is substantiated through a case study, demonstrating its potential to revolutionize personalized education. This paper provides a comprehensive overview of the current discourse, providing valuable insights for educators, policymakers, and researchers into the multifaceted landscape of modern education, contributing to ongoing discussions and advancements in educational technology.

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Section: G Intelligent Methodologies In Education Analyticsmentioning

confidence: 99%

Section: B Learning Analytics In Educationmentioning

confidence: 99%

Multi-Modal LA in Personalized Education Using Deep Reinforcement Learning Based Approach

Sharif,

Uckelmann

2024

IEEE Access

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“…To prevent the job from becoming infeasible for humans, it is crucial to have resources, computational tools, and algorithms that can spot patterns, trends, or regularities in the data. 4 For accomplishing this task, the use of Green-IoT, AI and 5G or 6G technologies is inevitable and requires sophisticated IT platforms. In technical and engineering disciplines, the concept of Outcome Based Educa-tion has received significant attention from all stakeholders but due to large volume of data aggregation and processing, the higher education institutes are facing acute difficulties in its implementation in true spirit.…”

Section: Tionmentioning

confidence: 99%

Potentializing Green-IoT, AI and 6G Technologies in Computational Thinking

Abosaq

2023

Yanbu Journal of Engineering and Science

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Computational thinking is a problem-solving approach that involves breaking down complex problems into smaller, more manageable parts and identifying patterns and relationships between different components whereas Green-IoT and Artificial Intelligence (AI) in 6G environment are important approaches for potentializing an effective Computational Thinking solution. AI approaches involve developing algorithms that can learn from data, make predictions and decisions, and perform complex tasks without explicit programming. When it comes to Green-IoT, both computational thinking and Artificial Intelligence can be used to analyze and interpret the massive amounts of data generated by connected devices using 6G communication networks. This can lead to more intelligent and efficient use of resources, as well as improved automation of various processes. The combination of Green-IoT, AI and 6G Technologies in computational thinking has the potential to transform various industries and improve the quality of life for individuals is expected to lead to new applications and services that are more intelligent, efficient, and personalized. However, it is important to consider the ethical implications of using these technologies and to ensure that they are used in a responsible and transparent manner. This research presents a review on computational thinking, particularly, we ﬁrst give an overview of essential parameters through Green-IoT enabled sensor technologies under 5G, 6G environment and Artificial Intelligence. We then present a basic architecture for IoT and AI based computational thinking system considering key requirements to potentialize social applications discussing their strengths and weaknesses in the context of framework for community services. Finally, we explored various security threats for Artificial Intelligence based architecture and their solutions with a comprehensive methodology to design robust and resilient computational thinking services system needed in the context of social networking and related issues.

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A critical review of data mining in education on the levels and aspects of education

Fuseini,

Missah

2024

QEA

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Purpose This systematic literature review aims to identify the pattern of data mining (DM) research by looking at the levels and aspects of education. Design/methodology/approach This paper reviews 113 conference and research papers from well-known publishers of educational data mining (EDM) and learning analytics-related research using a recognized literature review in computer science by Carrera-Rivera et al. (2022a). Two major stages, planning and conducting the review, were used. The databases of Elsevier, Springer, IEEE, SAI, Hindawi, MDPI, Wiley, Emerald and Sage were searched to retrieve EDM papers from the period 2017 to 2023. The papers retrieved were then filtered based on the application of DM to the three educational levels – basic, pre-tertiary and tertiary education. Findings EDM is concentrated on higher education. Basic education is not given the needed attention in EDM. This does not enhance inclusivity and equity. Learner performance is given much attention. Resource availability and teaching and learning are not given the needed attention. Research limitations/implications This review is limited to only EDM. Literature from the year 2017 to 2023 is covered. Other aspects of DM and other relevant literature published in EDM outside the research period are not considered. Practical implications As the current trend of EDM shows an increase in zeal, future research in EDM should concentrate on the lower levels of education to identify the challenges of basic education which serves as the core of education. This will enable addressing the challenges of education at an early stage and facilitate getting a quality education at all levels of education. Appropriate EDM techniques for mining the data at this level should be the focus of the research. Specifically, techniques that can cater for the variation in learner abilities and the appropriate identification of learner needs should be considered. Social implications Content sequencing is necessary in facilitating an easy understanding of concepts. Curriculum design from basic to higher education dwells much on this. Identifying the challenge of learning at the early stages will facilitate efficient learning. At the basic level of learning, data on learning should be collected by educational institutions just as it is done at the tertiary level. This will enable EDM to accurately identify the challenges and appropriate solutions to educational problems. Resource availability is a catalyst for effective teaching and learning. The attributes of a learner will enable knowing the true nature of the learner to determine the prospects of the learner. Originality/value This research has not been published in any journal. The information presented is the original knowledge of the authors. However, a pre-print of the work is in Research Square.

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Educational Data Mining with Learning Analytics and Unsupervised Algorithms: Analysis and Diagnosis in Basic Education

Cited by 4 publications

References 23 publications

Multi-Modal LA in Personalized Education Using Deep Reinforcement Learning Based Approach

Multi-Modal LA in Personalized Education Using Deep Reinforcement Learning Based Approach

Potentializing Green-IoT, AI and 6G Technologies in Computational Thinking

A critical review of data mining in education on the levels and aspects of education

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