A Systematic Review on Big Data Analytics Frameworks for Higher Education - Tools and Algorithms

Otoo-Arthur, David; Zyl, Terence L. van

doi:10.1145/3377817.3377836

Cited by 8 publications

(2 citation statements)

References 35 publications

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“…For example, educators upload material to deliver digital course materials to their students and student access these materials for learning, students attempt the LMS based tests related to a speci c concept or students submits the assessment documents on LMS. Big data analytics applies set of analytical techniques to extract useful information and provide insight from big educational data related to students' learning behaviours, assessment scores, student learning styles, student logging in information, time spend on a task/module, assessment submission patterns, most visited page/content, completing a task or module or posting details about extracurricular activities (44) (45,46). Big data analytics allows to identify the real learning pattern of the students more accurately than the traditional practices.…”

Section: Big Data Lms and Big Data Analyticsmentioning

confidence: 99%

Designing and Evaluating a Big Data Analytics Approach for predicting students’ success factors

Fahd

Miah

2022

Preprint

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Reducing student attrition in tertiary education plays a significant role in the core mission and financial well-being of an educational institution. The availability of big data source from Learning Management System (LMS) can be analysed to help with the attrition issues. This study aims to use an integrated Design science research (DSR) methodology to develop and evaluate a Big Data Analytical Solution (BDAS) embedded in Educational Decision Support System as an educational artefact. The BDAS as DSR artefact harnesses the outcome of the application of Artificial Intelligence(AI) based approaches on the dataset collected from student interaction with LMS to train a predictive model to predict potential students at risk. Identifying students at risk helps to take timely intervention in the learning process to improve student academic progress to increase the retention rate. To evaluate the performance of the predictive model, we compare the accuracy of the collection of representational Artificial Intelligence algorithms in the literature. The BDAS aims not to replace any existing practice but to support educators to implement a variety of pedagogical practices to improve students’ academic performance.

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Section: Big Data Lms and Big Data Analyticsmentioning

confidence: 99%

Designing and Evaluating a Big Data Analytics Approach for predicting students’ success factors

Fahd

Miah

2022

Preprint

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“…Another definition characterizes it as "the use of intelligent data, learner-generated data, and analytical models to uncover insights, social connections, and predict and provide guidance for learning." The educational community presents diverse perspectives; some researchers view LA as an alternative approach for collecting student-generated data to facilitate personalized learning experiences [25,26], while others emphasize identifying trends from students' learning activities for future instructional design decisions [27,28]. Long and Siemens [29] propose a comprehensive standpoint, asserting that LA serves as both a tool for gathering statistical educational data and a mechanism for leveraging this data to enhance learning and the surrounding educational community.…”

Section: Introductionmentioning

confidence: 99%

Big Data Analytics in Higher Education: A New Adaptive Learning Analytics Model Integrating Traditional Approaches

Bellaj,

Ben Dahmane,

Boudra

et al. 2024

Int. J. Interact. Mob. Technol.

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Despite the explosion of interest in adaptive learning and learning analytics (LA) for higher education (HE), there has been relatively little research integrating educational approaches’ indicators to build an adaptive learning analytics model. Adaptive learning analytics (ALA) models have grown in favor of HE due to their claims of enhancing student learning outcomes, providing personalized learning paths, and allowing students to interact with course material at their own pace. With focus on using data to personalize the learning experience and the environment in which the experience of learning occurs, LA centers on enhancing education through meticulous data analysis, while big data (BD) in education addresses the overarching challenges and opportunities arising from extensive and varied datasets. These concepts are interconnected, where LA and ALA specifically apply BD principles within the educational context. In this paper, we explain some BD concepts used in HE, define the essential perceptions related to LA, and analyze educational approaches to define the fundamental implications. Besides, we try to connect some LA model with educational approaches based on the big educational data collected in order to establish an efficient educational model. We include all steps cited before we try to build an ALA model in HE that resolves the limitations of the oldest models, thus improving the learner’s learning process by adding and treating additional indicators.

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A Scalable Heterogeneous Big Data Framework for e-Learning Systems

Otoo-Arthur

Zyl

2020

2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD)

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A Systematic Review on Big Data Analytics Frameworks for Higher Education - Tools and Algorithms

Cited by 8 publications

References 35 publications

Designing and Evaluating a Big Data Analytics Approach for predicting students’ success factors

Designing and Evaluating a Big Data Analytics Approach for predicting students’ success factors

Big Data Analytics in Higher Education: A New Adaptive Learning Analytics Model Integrating Traditional Approaches

A Scalable Heterogeneous Big Data Framework for e-Learning Systems

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