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Educational Data Mining and Technology
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Educational Data Mining and Technology

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 39507

Special Issue Editors

Assoc. Prof. Michael A. Cowling

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Guest Editor
College of Information and Communication Technology (ICT), Centre for Research in Equity & Advancement of Teaching & Education (CREATE), School of Engineering and Technology, Tertiary Education Division, Central Queensland University, Brisbane, QLD 4000, Australia
Interests: educational technology; mixed reality; technology-enhanced learning
Special Issues, Collections and Topics in MDPI journals
Dr. Meena Jha

E-Mail Website
Guest Editor
College of Information and Communication Technology (ICT), Centre for Intelligent Systems, School of Engineering and Technology, Central Queensland University, Sydney, NSW 2000, Australia
Interests: virtual reality in education, big data and artificial intelligence; deep learning; expert systems; business intelligence; real time analytics, legacy system modernization and Internet of Things (IoT)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As a field, big data can be applied to many areas, from business to finance to health and medical applications. One field that has attracted significant interest in recent years in this broad scope of big data is the area of education. Indeed, big data in education has evolved from pure educational data mining, and now has the ability to support students and policy makers while making decisions to improve the educational processes and learning outcomes.

Increasingly, students’ digital footprints are being used to support learning processes, because as more and more education moves online, a larger corpus of data is being produced about students and how they learn, and many academics feel they can leverage this data to improve learning outcomes. The emergence of big data in educational contexts also has led to intelligent data-driven approaches to support decision-makers. Learning behavior within an institution is predominantly captured by the learning management system (LMS); however, there are many varieties of innovations in the digital learning environment. In particular, a collection of students’ digital footprints will lead to structured, unstructured, and multi-modal data in volumes which cannot be stored and analyzed using traditional education systems.

Therefore, a challenge in this area, like in many others, is understanding how this data can be effectively mined and categorized to produce a dataset that is well suited to big data applications and learning analytics. A broad range of data mining techniques can be utilized for big data in education. Accessing, analyzing, and using big data in the educational context is still new and full of challenges. The sheer number of input variables, plus the vagaries of how these map to learning outcomes, can make this particularly challenging for education, especially as new technologies such as mixed reality and robotics are quickly adding more and more data sources to the mix.

This Special Issue will aim to address this gap in the current literature, and seeks contributions from learned academics in the field on their approaches to the educational data mining of learning data, and how it is subsequently used for big data applications and the generation of learning analytics. Papers on new data sources produced by technology are also welcome, as are papers that focus on education and the relationship it has with cloud computing or big data. As Guest Editors, we look forward greatly to reading your contributions, and to assembling a Special Issue that pushes forward the field of educational data mining and technology.

Assoc. Prof. Michael A. Cowling
Dr. Meena Jha
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. Big Data and Cognitive Computing is an international peer-reviewed open access monthly 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 1800 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

  • educational technology
  • data mining
  • learning analytics
  • big data
  • learning management systems

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Published Papers (6 papers)

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Research

16 pages, 9862 KiB  
Article
On Developing Generic Models for Predicting Student Outcomes in Educational Data Mining
by Gomathy Ramaswami, Teo Susnjak and Anuradha Mathrani
Big Data Cogn. Comput. 2022, 6(1), 6; https://doi.org/10.3390/bdcc6010006 - 7 Jan 2022
Cited by 23 | Viewed by 5188
Abstract
Poor academic performance of students is a concern in the educational sector, especially if it leads to students being unable to meet minimum course requirements. However, with timely prediction of students’ performance, educators can detect at-risk students, thereby enabling early interventions for supporting [...] Read more.
Poor academic performance of students is a concern in the educational sector, especially if it leads to students being unable to meet minimum course requirements. However, with timely prediction of students’ performance, educators can detect at-risk students, thereby enabling early interventions for supporting these students in overcoming their learning difficulties. However, the majority of studies have taken the approach of developing individual models that target a single course while developing prediction models. These models are tailored to specific attributes of each course amongst a very diverse set of possibilities. While this approach can yield accurate models in some instances, this strategy is associated with limitations. In many cases, overfitting can take place when course data is small or when new courses are devised. Additionally, maintaining a large suite of models per course is a significant overhead. This issue can be tackled by developing a generic and course-agnostic predictive model that captures more abstract patterns and is able to operate across all courses, irrespective of their differences. This study demonstrates how a generic predictive model can be developed that identifies at-risk students across a wide variety of courses. Experiments were conducted using a range of algorithms, with the generic model producing an effective accuracy. The findings showed that the CatBoost algorithm performed the best on our dataset across the F-measure, ROC (receiver operating characteristic) curve and AUC scores; therefore, it is an excellent candidate algorithm for providing solutions on this domain given its capabilities to seamlessly handle categorical and missing data, which is frequently a feature in educational datasets. Full article
(This article belongs to the Special Issue Educational Data Mining and Technology)
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17 pages, 1862 KiB  
Article
Clustering Algorithm to Measure Student Assessment Accuracy: A Double Study
by Sónia Rolland Sobral and Catarina Félix de Oliveira
Big Data Cogn. Comput. 2021, 5(4), 81; https://doi.org/10.3390/bdcc5040081 - 18 Dec 2021
Cited by 3 | Viewed by 3560
Abstract
Self-assessment is one of the strategies used in active teaching to engage students in the entire learning process, in the form of self-regulated academic learning. This study aims to assess the possibility of including self-evaluation in the student’s final grade, not just as [...] Read more.
Self-assessment is one of the strategies used in active teaching to engage students in the entire learning process, in the form of self-regulated academic learning. This study aims to assess the possibility of including self-evaluation in the student’s final grade, not just as a self-assessment that allows students to predict the grade obtained but also as something to weigh on the final grade. Two different curricular units are used, both from the first year of graduation, one from the international relations course (N = 29) and the other from the computer science and computer engineering courses (N = 50). Students were asked to self-assess at each of the two evaluation moments of each unit, after submitting their work/test and after knowing the correct answers. This study uses statistical analysis as well as a clustering algorithm (K-means) on the data to try to gain deeper knowledge and visual insights into the data and the patterns among them. It was verified that there are no differences between the obtained grade and the thought grade by gender and age variables, but a direct correlation was found between the thought grade averages and the grade level. The difference is less accentuated at the second moment of evaluation—which suggests that an improvement in the self-assessment skill occurs from the first to the second evaluation moment. Full article
(This article belongs to the Special Issue Educational Data Mining and Technology)
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17 pages, 1558 KiB  
Article
Customized Rule-Based Model to Identify At-Risk Students and Propose Rational Remedial Actions
by Balqis Albreiki, Tetiana Habuza, Zaid Shuqfa, Mohamed Adel Serhani, Nazar Zaki and Saad Harous
Big Data Cogn. Comput. 2021, 5(4), 71; https://doi.org/10.3390/bdcc5040071 - 29 Nov 2021
Cited by 12 | Viewed by 4989
Abstract
Detecting at-risk students provides advanced benefits for improving student retention rates, effective enrollment management, alumni engagement, targeted marketing improvement, and institutional effectiveness advancement. One of the success factors of educational institutes is based on accurate and timely identification and prioritization of the students [...] Read more.
Detecting at-risk students provides advanced benefits for improving student retention rates, effective enrollment management, alumni engagement, targeted marketing improvement, and institutional effectiveness advancement. One of the success factors of educational institutes is based on accurate and timely identification and prioritization of the students requiring assistance. The main objective of this paper is to detect at-risk students as early as possible in order to take appropriate correction measures taking into consideration the most important and influential attributes in students’ data. This paper emphasizes the use of a customized rule-based system (RBS) to identify and visualize at-risk students in early stages throughout the course delivery using the Risk Flag (RF). Moreover, it can serve as a warning tool for instructors to identify those students that may struggle to grasp learning outcomes. The module allows the instructor to have a dashboard that graphically depicts the students’ performance in different coursework components. The at-risk student will be distinguished (flagged), and remedial actions will be communicated to the student, instructor, and stakeholders. The system suggests remedial actions based on the severity of the case and the time the student is flagged. It is expected to improve students’ achievement and success, and it could also have positive impacts on under-performing students, educators, and academic institutions in general. Full article
(This article belongs to the Special Issue Educational Data Mining and Technology)
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33 pages, 643 KiB  
Article
How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review
by Catarina Félix de Oliveira, Sónia Rolland Sobral, Maria João Ferreira and Fernando Moreira
Big Data Cogn. Comput. 2021, 5(4), 64; https://doi.org/10.3390/bdcc5040064 - 4 Nov 2021
Cited by 55 | Viewed by 10872
Abstract
Retention and dropout of higher education students is a subject that must be analysed carefully. Learning analytics can be used to help prevent failure cases. The purpose of this paper is to analyse the scientific production in this area in higher education in [...] Read more.
Retention and dropout of higher education students is a subject that must be analysed carefully. Learning analytics can be used to help prevent failure cases. The purpose of this paper is to analyse the scientific production in this area in higher education in journals indexed in Clarivate Analytics’ Web of Science and Elsevier’s Scopus. We use a bibliometric and systematic study to obtain deep knowledge of the referred scientific production. The information gathered allows us to perceive where, how, and in what ways learning analytics has been used in the latest years. By analysing studies performed all over the world, we identify what kinds of data and techniques are used to approach the subject. We propose a feature classification into several categories and subcategories, regarding student and external features. Student features can be seen as personal or academic data, while external factors include information about the university, environment, and support offered to the students. To approach the problems, authors successfully use data mining applied to the identified educational data. We also identify some other concerns, such as privacy issues, that need to be considered in the studies. Full article
(This article belongs to the Special Issue Educational Data Mining and Technology)
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12 pages, 546 KiB  
Article
Without Data Quality, There Is No Data Migration
by Otmane Azeroual and Meena Jha
Big Data Cogn. Comput. 2021, 5(2), 24; https://doi.org/10.3390/bdcc5020024 - 18 May 2021
Cited by 10 | Viewed by 6438
Abstract
Data migration is required to run data-intensive applications. Legacy data storage systems are not capable of accommodating the changing nature of data. In many companies, data migration projects fail because their importance and complexity are not taken seriously enough. Data migration strategies include [...] Read more.
Data migration is required to run data-intensive applications. Legacy data storage systems are not capable of accommodating the changing nature of data. In many companies, data migration projects fail because their importance and complexity are not taken seriously enough. Data migration strategies include storage migration, database migration, application migration, and business process migration. Regardless of which migration strategy a company chooses, there should always be a stronger focus on data cleansing. On the one hand, complete, correct, and clean data not only reduce the cost, complexity, and risk of the changeover, it also means a good basis for quick and strategic company decisions and is therefore an essential basis for today’s dynamic business processes. Data quality is an important issue for companies looking for data migration these days and should not be overlooked. In order to determine the relationship between data quality and data migration, an empirical study with 25 large German and Swiss companies was carried out to find out the importance of data quality in companies for data migration. In this paper, we present our findings regarding how data quality plays an important role in a data migration plans and must not be ignored. Without acceptable data quality, data migration is impossible. Full article
(This article belongs to the Special Issue Educational Data Mining and Technology)
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11 pages, 503 KiB  
Article
Treatment of Bad Big Data in Research Data Management (RDM) Systems
by Otmane Azeroual
Big Data Cogn. Comput. 2020, 4(4), 29; https://doi.org/10.3390/bdcc4040029 - 18 Oct 2020
Cited by 2 | Viewed by 5585
Abstract
Databases such as research data management systems (RDMS) provide the research data in which information is to be searched for. They provide techniques with which even large amounts of data can be evaluated efficiently. This includes the management of research data and the [...] Read more.
Databases such as research data management systems (RDMS) provide the research data in which information is to be searched for. They provide techniques with which even large amounts of data can be evaluated efficiently. This includes the management of research data and the optimization of access to this data, especially if it cannot be fully loaded into the main memory. They also provide methods for grouping and sorting and optimize requests that are made to them so that they can be processed efficiently even when accessing large amounts of data. Research data offer one thing above all: the opportunity to generate valuable knowledge. The quality of research data is of primary importance for this. Only flawless research data can deliver reliable, beneficial results and enable sound decision-making. Correct, complete and up-to-date research data are therefore essential for successful operational processes. Wrong decisions and inefficiencies in day-to-day operations are only the tip of the iceberg, since the problems with poor data quality span various areas and weaken entire university processes. Therefore, this paper addresses the problems of data quality in the context of RDMS and tries to shed light on the solution for ensuring data quality and to show a way to fix the dirty research data that arise during its integration before it has a negative impact on business success. Full article
(This article belongs to the Special Issue Educational Data Mining and Technology)
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