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Applications of Data Mining Algorithms and Big Data Analytics in...
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Applications of Data Mining Algorithms and Big Data Analytics in Education

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 13239

Special Issue Editors

Prof. Dr. Elizabeth Archer

E-Mail Website
Guest Editor
Institutional Research, University of the Western Cape, Bellville 7530, Western Cape, South Africa
Interests: learning analytics; online proctoring; student success; application of success modelling; higher education
Dr. Angelo Fynn

E-Mail Website
Guest Editor
Department of Institutional Research and Business Intelligence, University of South Africa, Pretoria, South Africa
Interests: student success; higher education; open distance learning

Special Issue Information

Dear Colleagues,

The application of educational analytics and data mining has increased alongside institutions’ adoption of online and blended learning. Assessment and learning analytics have become integral to improving student success by facilitating understanding of improving success and optimising learning and testing environments. The massification of education globally has increased the demand for accurate, timely information and provides vital insights that allow for early intervention, development of new pedagogies, optimisation of existing algorithms to better reflect the application context, and streamlining of processes aimed at student success.

This Special Issue focuses on applying data mining algorithms and big data analytics within educational settings. While generally neglected in the field, the application of big data includes the application by end users, typically educators and student support staff. It includes developing personalised recommendations and visualisations of data to improve student performance and provide personalised feedback at scale. We therefore invite submissions focusing on developing and applying models within institutional contexts in addition to critical reviews of widely applied algorithms and intervention as well as pedagogical outcomes based on the application of learning analytics. While developing algorithms, analytics and big data mining are essential; student success can only be improved by applying these in the educational setting. This Special Issue thus has a dual focus on algorithmic approaches and implementation.

Prof. Dr. Elizabeth Archer
Dr. Angelo Fynn
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • learning analytics
  • assessment analytics
  • application of modelling
  • educational application of analytics
  • big data in education
  • algorithmic applications in education

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Published Papers (4 papers)

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Research

23 pages, 793 KiB  
Article
An Analysis of PISA 2018 Mathematics Assessment for Asia-Pacific Countries Using Educational Data Mining
by Ezgi Gülenç Bayirli, Atabey Kaygun and Ersoy Öz
Mathematics 2023, 11(6), 1318; https://doi.org/10.3390/math11061318 - 9 Mar 2023
Cited by 6 | Viewed by 3616
Abstract
The purpose of this paper is to determine the variables of high importance affecting the mathematics achievement of the students of 12 Asia-Pacific countries participating in the Program for International Student Assessment (PISA) 2018. For this purpose, we used random forest (RF), logistic [...] Read more.
The purpose of this paper is to determine the variables of high importance affecting the mathematics achievement of the students of 12 Asia-Pacific countries participating in the Program for International Student Assessment (PISA) 2018. For this purpose, we used random forest (RF), logistic regression (LR) and support vector machine (SVM) models to classify student achievement in mathematics. The variables affecting the student achievement in mathematics were examined by the feature importance method. We observed that the variables with the highest importance for all of the 12 Asia-Pacific countries we considered are the educational status of the parents, having access to educational resources, age, the time allocated to weekly lessons, and the age of starting kindergarten. Then we applied two different clustering analysis by using the variable importance values and socio-economic variables of these countries. We observed that Korea, Japan and Taipei form one group of Asia-Pacific countries, while Thailand, China, Indonesia, and Malaysia form another meaningful group in both clustering analyses. The results we obtained strongly suggest that there is a quantifiable relationship between the educational attainment and socio-economic levels of these 12 Asia-Pacific countries. Full article
(This article belongs to the Special Issue Applications of Data Mining Algorithms and Big Data Analytics in Education)
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17 pages, 2859 KiB  
Article
Comparing Friends and Peer Tutors Amidst COVID-19 Using Social Network Analysis
by Nurul Zahirah Abd Rahim, Nurun Najwa Bahari, Nur Syaza Mohd Azzimi, Zamira Hasanah Zamzuri, Hafizah Bahaludin, Nurul Farahain Mohammad and Fatimah Abdul Razak
Mathematics 2023, 11(4), 1053; https://doi.org/10.3390/math11041053 - 20 Feb 2023
Cited by 1 | Viewed by 2321
Abstract
COVID-19 has drastically changed the teaching patterns of higher education from face-to-face to online learning, and it has also affected students’ engagement socially and academically. Understanding the nature of students’ engagement during online learning can help in identifying related issues so that various [...] Read more.
COVID-19 has drastically changed the teaching patterns of higher education from face-to-face to online learning, and it has also affected students’ engagement socially and academically. Understanding the nature of students’ engagement during online learning can help in identifying related issues so that various initiatives can be implemented in adapting to this situation. In this study, social network analysis is conducted to gain insights on students’ engagement during COVID-19. Directed and weighted networks were used to visualize and analyze friendship as well as peer tutor networks obtained from online questionnaires answered by all students in the class. Contrasting friends and peer tutors reveals some hidden interactions between students and shines some light on dynamics of the online learning community. The results indicate that, popular and important peer tutors may not be high achievers and thus possibly contributing to the spread of misinformation in the online learning community. By comparing weighted indegree and betweenness centrality values, we suggest approaches to cultivate a healthy online learning community. This study highlights the use of social network analysis to assist and monitor students’ engagement and further formulate strategies in order to make the class a conducive online learning community, particularly in the advent of online learning in higher education institutions. Full article
(This article belongs to the Special Issue Applications of Data Mining Algorithms and Big Data Analytics in Education)
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20 pages, 4207 KiB  
Article
Framework for Classroom Student Grading with Open-Ended Questions: A Text-Mining Approach
by Valter Martins Vairinhos, Luís Agonia Pereira, Florinda Matos, Helena Nunes, Carmen Patino and Purificación Galindo-Villardón
Mathematics 2022, 10(21), 4152; https://doi.org/10.3390/math10214152 - 6 Nov 2022
Cited by 1 | Viewed by 2575
Abstract
The purpose of this paper is to present a framework based on text-mining techniques to support teachers in their tasks of grading texts, compositions, or essays, which form the answers to open-ended questions (OEQ). The approach assumes that OEQ must be used as [...] Read more.
The purpose of this paper is to present a framework based on text-mining techniques to support teachers in their tasks of grading texts, compositions, or essays, which form the answers to open-ended questions (OEQ). The approach assumes that OEQ must be used as a learning and evaluation instrument with increasing frequency. Given the time-consuming grading process for those questions, their large-scale use is only possible when computational tools can help the teacher. This work assumes that the grading decision is entirely a teacher’s task responsibility, not the result of an automatic grading process. In this context, the teacher is the author of questions to be included in the tests, administration and results assessment, the entire cycle for this process being noticeably short: a few days at most. An attempt is made to address this problem. The method is entirely exploratory, descriptive and data-driven, the only data assumed as inputs being the texts of essays and compositions created by the students when answering OEQ for a single test on a specific occasion. Typically, the process involves exceedingly small data volumes measured by the power of current home computers, but big data when compared with human capabilities. The general idea is to use software to extract useful features from texts, perform lengthy and complex statistical analyses and present the results to the teacher, who, it is believed, will combine this information with his or her knowledge and experience to make decisions on mark allocation. A generic path model is formulated to represent that specific context and the kind of decisions and tasks a teacher should perform, the estimated results being synthesised using graphic displays. The method is illustrated by analysing three corpora of 126 texts originating in three different real learning contexts, time periods, educational levels and disciplines. Full article
(This article belongs to the Special Issue Applications of Data Mining Algorithms and Big Data Analytics in Education)
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19 pages, 471 KiB  
Article
A Method for Predicting the Academic Performances of College Students Based on Education System Data
by Chuang Liu, Haojie Wang and Zhonghu Yuan
Mathematics 2022, 10(20), 3737; https://doi.org/10.3390/math10203737 - 11 Oct 2022
Cited by 14 | Viewed by 3696
Abstract
With the development of university campus informatization, effective information mined from fragmented data can greatly improve the management levels of universities and the quality of student training. Academic performances are important in campus life and learning and are important indicators reflecting school administration, [...] Read more.
With the development of university campus informatization, effective information mined from fragmented data can greatly improve the management levels of universities and the quality of student training. Academic performances are important in campus life and learning and are important indicators reflecting school administration, teaching level, and learning abilities. As the number of college students increases each year, the quality of teaching in colleges and universities is receiving widespread attention. Academic performances measure the learning ‘effects’ of college students and evaluate the educational levels of colleges and universities. Existing studies related to academic performance prediction often only use a single data source, and their prediction accuracies are often not ideal. In this research, the academic performances of students will be predicted using a feedforward spike neural network trained on data collected from an educational administration system and an online learning platform. Finally, the performance of the proposed prediction model was validated by predicting student achievements on a real dataset (involving a university in Shenyang). The experimental results show that the proposed model can effectively improve the prediction accuracies of student achievements, and its prediction accuracy could reach 70.8%. Using artificial intelligence technology to deeply analyze the behavioral patterns of students and clarify the deep-level impact mechanisms of the academic performances of students can help college educators manage students in a timely and targeted manner, and formulate effective learning supervision plans. Full article
(This article belongs to the Special Issue Applications of Data Mining Algorithms and Big Data Analytics in Education)
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