Predicting Students Performance Using Educational Data Mining and Learning Analytics: A Systematic Literature Review

Tremendous proliferation in data generation in the past few years has paved the way for new research and the development of new and improved techniques and algorithms in different fields of science and education. Initially terms like educational data mining emerged as a branch of data mining borrowing techniques from its ancestor. The challenges brought about by this large and heterogeneous data are diverse and needs a greater serious technical treatment. New and emerging fields like learning analytics have been introduced to manage the complexities of this data deluge. Learning analytics deals with data in the context of learner and the learning environment to improve the overall learning experience. The ultimate aim of the field is to make use of the data about learners and their environments to gain insights into the learning process using some of the well-known techniques and algorithms from the fields of data mining and machine learning. The process involves collecting, analysis of data and reporting the results to understand and optimize the learning experience. The fields of data mining and academic analytics closely related to learning analytics. Systematic Literature Review (SLR) is a robust, organized and rigorous literature review and reporting process aimed at identifying, collecting and synthesizing the relevant literature on a research question according to specified criteria. The process is more unbiased and balanced by systematic sequence of steps. This paper presents a systematic literature review by first developing the systematic literature review protocol and then discussing the main findings of the literature review by especially focusing on the applications and uses of machine learning and data mining techniques in the domain of learning analytics. Index Terms—Systematic Literature Review (SLR), Learning Analytics (LA), Big Data, Educational Data Mining (EDM), Machine Learning (ML).

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The Pedagogy Optimization with Educational Data Mining and Learning Analytics for E-Learning System - A Review of the Literature Review

2018 Third International Conference on Informatics and Computing (ICIC) ◽

10.1109/iac.2018.8780514 ◽

2018 ◽

Cited By ~ 1

Author(s):

Tuti Purwoningsih ◽

Harry B. Santoso ◽

Yugo K. Isal ◽

Zainal A. Hasibuan

Keyword(s):

Data Mining ◽

Literature Review ◽

Learning Analytics ◽

Educational Data Mining ◽

Learning System ◽

Review Of The Literature ◽

System A ◽

E Learning

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Artificial Neural Networks for Educational Data Mining in Higher Education: A Systematic Literature Review

Applied Artificial Intelligence ◽

10.1080/08839514.2021.1922847 ◽

2021 ◽

Vol 35 (13) ◽

pp. 983-1021

Author(s):

Emmanuel Okewu ◽

Phillip Adewole ◽

Sanjay Misra ◽

Rytis Maskeliunas ◽

Robertas Damasevicius

Keyword(s):

Higher Education ◽

Data Mining ◽

Neural Networks ◽

Artificial Neural Networks ◽

Literature Review ◽

Systematic Literature Review ◽

Educational Data Mining ◽

Artificial Neural

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Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies

Global Business Review ◽

10.1177/0972150920984857 ◽

2021 ◽

pp. 097215092098485

Author(s):

Sonika Gupta ◽

Sushil Kumar Mehta

Keyword(s):

Machine Learning ◽

Data Mining ◽

Literature Review ◽

Systematic Literature Review ◽

Classification Accuracy ◽

Meta Analysis ◽

Financial Statement ◽

Research Articles ◽

Financial Statement Fraud ◽

Data Mining Techniques

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.

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Multimodal Learning Analytics and Education Data Mining: using computational technologies to measure complex learning tasks

Journal of Learning Analytics ◽

10.18608/jla.2016.32.11 ◽

2016 ◽

Vol 3 (2) ◽

pp. 220-238 ◽

Cited By ~ 97

Author(s):

Paulo Blikstein ◽

Marcelo Worsley

Keyword(s):

Data Mining ◽

Learning Analytics ◽

Online Courses ◽

Educational Data Mining ◽

Multimodal Learning ◽

Learning Tasks ◽

Education Data ◽

Multimodal Learning Analytics ◽

High Degree ◽

Learning Analysis

New high-frequency multimodal data collection technologies and machine learning analysis techniques could offer new insights into learning, especially when students have the opportunity to generate unique, personalized artifacts, such as computer programs, robots, and solutions engineering challenges. To date most of the work on learning analytics and educational data mining has been focused on online courses and cognitive tutors, both of which provide a high degree of structure to the tasks, and are restricted to interactions that occur in front of a computer screen. In this paper, we argue that multimodal learning analytics can offer new insights into students’ learning trajectories in more complex and open-ended learning environments. We present several examples of this work and its educational application.

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