Research

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23 pages, 8449 KiB

Open AccessFeature PaperArticle

Hybrid Feature Extraction Model to Categorize Student Attention Pattern and Its Relationship with Learning

by Sujan Poudyal, Mahnas J. Mohammadi-Aragh and John E. Ball

Electronics 2022, 11(9), 1476; https://doi.org/10.3390/electronics11091476 - 05 May 2022

Viewed by 1819

The increase of instructional technology, e-learning resources, and online courses has created opportunities for data mining and learning analytics in the pedagogical domain. A large amount of data is obtained from this domain that can be analyzed and interpreted so that educators can [...] Read more.

The increase of instructional technology, e-learning resources, and online courses has created opportunities for data mining and learning analytics in the pedagogical domain. A large amount of data is obtained from this domain that can be analyzed and interpreted so that educators can understand students’ attention. In a classroom where students have their own computers in front of them, it is important for instructors to understand whether students are paying attention. We collected on- and off-task data to analyze the attention behaviors of students. Educational data mining extracts hidden information from educational records, and we are using it to classify student attention patterns. A hybrid method is used to combine various techniques like classifications, regressions, or feature extraction. In our work, we combined two feature extraction techniques: principal component analysis and linear discriminant analysis. Extracted features are used by a linear and kernel support vector machine (SVM) to classify attention patterns. Classification results are compared with linear and kernel SVM. Our hybrid method achieved the best results in terms of accuracy, precision, recall, F1, and kappa. Also, we correlated attention with learning. Here, learning corresponds to tests and a final course grade. For determining the correlation between grades and attention, Pearson’s correlation coefficient and p-value were used. Full article

(This article belongs to the Special Issue Machine Learning in Educational Data Mining)

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21 pages, 3481 KiB

Open AccessArticle

Prediction of Student Academic Performance Using a Hybrid 2D CNN Model

by Sujan Poudyal, Mahnas J. Mohammadi-Aragh and John E. Ball

Electronics 2022, 11(7), 1005; https://doi.org/10.3390/electronics11071005 - 24 Mar 2022

Cited by 28 | Viewed by 4432

Abstract

Opportunities to apply data mining techniques to analyze educational data and improve learning are increasing. A multitude of data are being produced by institutional technology, e-learning resources, and online and virtual courses. These data could be used by educators to analyze and understand [...] Read more.

Opportunities to apply data mining techniques to analyze educational data and improve learning are increasing. A multitude of data are being produced by institutional technology, e-learning resources, and online and virtual courses. These data could be used by educators to analyze and understand the learning behaviors of students. The obtained data are raw data that must be analyzed, requiring educational data mining to predict useful information about students, such as academic performance, among other things. Many researchers have used traditional machine learning to predict the academic performance of students, and very little research has been conducted on the architecture of convolutional neural networks (CNNs) in the context of the pedagogical domain. We built a hybrid 2D CNN model by combining two different 2D CNN models to predict academic performance. Our sample comprised 1D data, so we transformed it to 2D image data to test the performance of our hybrid model. We compared the performance of our model with that of different traditional baseline models. Our model outperformed baseline models, such as k-nearest neighbor, naïve Bayes, decision trees, and logistic regression, in terms of accuracy. Full article

(This article belongs to the Special Issue Machine Learning in Educational Data Mining)

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11 pages, 1484 KiB

Open AccessArticle

Relation between Student Engagement and Demographic Characteristics in Distance Learning Using Association Rules

by Moohanad Jawthari and Veronika Stoffa

Electronics 2022, 11(5), 724; https://doi.org/10.3390/electronics11050724 - 26 Feb 2022

Cited by 6 | Viewed by 2405

Abstract

Distance learning has made learning possible for those who cannot attend traditional courses, especially in pandemic periods. This type of learning, however, faces a challenge in keeping students engaged and interested. Furthermore, it is important to identify students who are in need of [...] Read more.

Distance learning has made learning possible for those who cannot attend traditional courses, especially in pandemic periods. This type of learning, however, faces a challenge in keeping students engaged and interested. Furthermore, it is important to identify students who are in need of help to ensure that their progress does not deteriorate. First, the research identifies students’ engagement based on their behaviors in Virtual Learning Environment (VLE) and their performances in assessments. This research goal is to investigate the association/relationship between demographic characteristics and engagement level. It identifies less engaged students by using an unsupervised clustering model based on VLE interactions and assessments of submission-derived features. According to results, the two-level clustering model outperforms other models in regard to cluster separation using silhouette coefficient. Apriori algorithm is utilized to obtain a set of rules that connect demographic features to student engagement. Results show gender, highest education, studied credits, and number of previous attempts have positive correlation with engagement level in distance-based learning. Full article

(This article belongs to the Special Issue Machine Learning in Educational Data Mining)

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23 pages, 4669 KiB

Open AccessArticle

Predicting Students at Risk of Dropout in Technical Course Using LMS Logs

by Mariela Mizota Tamada, Rafael Giusti and José Francisco de Magalhães Netto

Electronics 2022, 11(3), 468; https://doi.org/10.3390/electronics11030468 - 05 Feb 2022

Cited by 12 | Viewed by 2793

Abstract

Educational data mining is a process that aims at discovering patterns that provide insight into teaching and learning processes. This work uses Machine Learning techniques to create a student performance prediction model, using academic data and records from a Learning Management System, that [...] Read more.

Educational data mining is a process that aims at discovering patterns that provide insight into teaching and learning processes. This work uses Machine Learning techniques to create a student performance prediction model, using academic data and records from a Learning Management System, that correlates with success or failure in completing the course. Six algorithms were employed, with models trained at three different stages of their two-year course completion. We tested the models with records of 394 students from 3 courses. Random Forest provided the best results with 84.47% on the F1 score in our experiments, followed by Decision Tree obtaining similar results in the first subjects. We also employ clustering techniques and find different behavior groups with a strong correlation to performance. This work contributes to predicting students at risk of dropping out, offers insight into understanding student behavior, and provides a support mechanism for academic managers to take corrective and preventive actions on this problem. Full article

(This article belongs to the Special Issue Machine Learning in Educational Data Mining)

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16 pages, 1746 KiB

Open AccessArticle

Using Advanced Learning Technologies with University Students: An Analysis with Machine Learning Techniques

by María Consuelo Sáiz-Manzanares, Raúl Marticorena-Sánchez and Javier Ochoa-Orihuel

Electronics 2021, 10(21), 2620; https://doi.org/10.3390/electronics10212620 - 27 Oct 2021

Cited by 5 | Viewed by 2545

Abstract

The use of advanced learning technologies (ALT) techniques in learning management systems (LMS) allows teachers to enhance self-regulated learning and to carry out the personalized monitoring of their students throughout the teaching–learning process. However, the application of educational data mining (EDM) techniques, such [...] Read more.

The use of advanced learning technologies (ALT) techniques in learning management systems (LMS) allows teachers to enhance self-regulated learning and to carry out the personalized monitoring of their students throughout the teaching–learning process. However, the application of educational data mining (EDM) techniques, such as supervised and unsupervised machine learning, is required to interpret the results of the tracking logs in LMS. The objectives of this work were (1) to determine which of the ALT resources would be the best predictor and the best classifier of learning outcomes, behaviours in LMS, and student satisfaction with teaching; (2) to determine whether the groupings found in the clusters coincide with the students’ group of origin. We worked with a sample of third-year students completing Health Sciences degrees. The results indicate that the combination of ALT resources used predict 31% of learning outcomes, behaviours in the LMS, and student satisfaction. In addition, student access to automatic feedback was the best classifier. Finally, the degree of relationship between the source group and the found cluster was medium (C = 0.61). It is necessary to include ALT resources and the greater automation of EDM techniques in the LMS to facilitate their use by teachers. Full article

(This article belongs to the Special Issue Machine Learning in Educational Data Mining)

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11 pages, 5101 KiB

Open AccessFeature PaperArticle

Early Dropout Prediction in MOOCs through Supervised Learning and Hyperparameter Optimization

by Theodor Panagiotakopoulos, Sotiris Kotsiantis, Georgios Kostopoulos, Omiros Iatrellis and Achilles Kameas

Electronics 2021, 10(14), 1701; https://doi.org/10.3390/electronics10141701 - 16 Jul 2021

Cited by 18 | Viewed by 3307

Abstract

Over recent years, massive open online courses (MOOCs) have gained increasing popularity in the field of online education. Students with different needs and learning specificities are able to attend a wide range of specialized online courses offered by universities and educational institutions. As [...] Read more.

Over recent years, massive open online courses (MOOCs) have gained increasing popularity in the field of online education. Students with different needs and learning specificities are able to attend a wide range of specialized online courses offered by universities and educational institutions. As a result, large amounts of data regarding students’ demographic characteristics, activity patterns, and learning performances are generated and stored in institutional repositories on a daily basis. Unfortunately, a key issue in MOOCs is low completion rates, which directly affect student success. Therefore, it is of utmost importance for educational institutions and faculty members to find more effective practices and reduce non-completer ratios. In this context, the main purpose of the present study is to employ a plethora of state-of-the-art supervised machine learning algorithms for predicting student dropout in a MOOC for smart city professionals at an early stage. The experimental results show that accuracy exceeds 96% based on data collected during the first week of the course, thus enabling effective intervention strategies and support actions. Full article

(This article belongs to the Special Issue Machine Learning in Educational Data Mining)

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15 pages, 2355 KiB

Open AccessArticle

Futuristic Trends and Innovations for Examining the Performance of Course Learning Outcomes Using the Rasch Analytical Model

by Moustafa M. Nasralla, Basiem Al-Shattarat, Dhafer J. Almakhles, Abdelhakim Abdelhadi and Eman S. Abowardah

Electronics 2021, 10(6), 727; https://doi.org/10.3390/electronics10060727 - 19 Mar 2021

Cited by 11 | Viewed by 2205

Abstract

The literature on engineering education research highlights the relevance of evaluating course learning outcomes (CLOs). However, generic and reliable mechanisms for evaluating CLOs remain challenges. The purpose of this project was to accurately assess the efficacy of the learning and teaching techniques through [...] Read more.

The literature on engineering education research highlights the relevance of evaluating course learning outcomes (CLOs). However, generic and reliable mechanisms for evaluating CLOs remain challenges. The purpose of this project was to accurately assess the efficacy of the learning and teaching techniques through analysing the CLOs’ performance by using an advanced analytical model (i.e., the Rasch model) in the context of engineering and business education. This model produced an association pattern between the students and the overall achieved CLO performance. The sample in this project comprised students who are enrolled in some nominated engineering and business courses over one academic year at Prince Sultan University, Saudi Arabia. This sample considered several types of assessment, such as direct assessments (e.g., quizzes, assignments, projects, and examination) and indirect assessments (e.g., surveys). The current research illustrates that the Rasch model for measurement can categorise grades according to course expectations and standards in a more accurate manner, thus differentiating students by their extent of educational knowledge. The results from this project will guide the educator to track and monitor the CLOs’ performance, which is identified in every course to estimate the students’ knowledge, skills, and competence levels, which will be collected from the predefined sample by the end of each semester. The Rasch measurement model’s proposed approach can adequately assess the learning outcomes. Full article

(This article belongs to the Special Issue Machine Learning in Educational Data Mining)

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Review

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21 pages, 913 KiB

Open AccessReview

Recommendation Systems for Education: Systematic Review

by María Cora Urdaneta-Ponte, Amaia Mendez-Zorrilla and Ibon Oleagordia-Ruiz

Electronics 2021, 10(14), 1611; https://doi.org/10.3390/electronics10141611 - 06 Jul 2021

Cited by 53 | Viewed by 8679

Abstract

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of [...] Read more.

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented. Full article

(This article belongs to the Special Issue Machine Learning in Educational Data Mining)

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