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Artificial Intelligence in Online Higher Educational Data Mining

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 74986

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Dr. Hüseyin Kusetogullari

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Department of Computer Science, Blekinge Institute of Technology, SE-371 41 Karlskrona, Sweden
Interests: image processing; computer vision; evolutionary algorithms; artificial intelligence; handwritten document analysis; deep learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chih-Hsiung Tu

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Guest Editor

Department of Educational Specialties, College of Education, Northern Arizona University (NAU), Flagstaff, AZ 86011, USA
Interests: online teaching; blended teaching; socio-cultural leanring; mobile learning

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Special Issue on “Artificial Intelligence in Online Higher Educational Data Mining”.

Online higher educational data have become increasingly important since the COVID-19 began, affecting the many systems in our daily life, and education systems in particular. Educational data mining using artificial intelligence methods has become an increasingly important topic of interest in the research community. Moreover, the amount of higher educational data generated and recorded has been rapidly growing, and extracting meaningful patterns and information from large educational data has become critical to improve the quality of online education. Artificial intelligence frameworks play a key role in resolving complex problems for online higher education. Artificial intelligence methods employ one or more techniques such as neural networks, probabilistic learning methods, deep learning, and evolutionary algorithms. In addition, a hybrid model which is an integration of multiple learning techniques can be developed and employed to resolve a problem. This Special Issue offers an opportunity for researchers to contribute on both theoretical and application aspects of artificial intelligence in online educational data mining.

The purpose of this Special Issue is to investigate and examine theory and application of artificial intelligence methods for online educational data mining applications. We invite researchers to contribute their original research and review papers that will motivate and support ongoing research on the application of artificial intelligence frameworks to solve online higher educational data mining problems. Topics of interest include but are not limited to:

Identification of student behavioral patterns
Early identification of at-risk students
Intelligent online learning systems
Prediction of student learning outcomes
Cognitive models of learning
Student support and online learning recommendations
Modelling and developing students’ online education systems
Self-adaptive learning
Hybrid learning systems
Deep learning frameworks and applications in online education systems
Evolutionary methods and applications in online education systems
Computer vision-based intelligent systems for online learning systems
Automatic assessment
Smart classes
Interpretable and explainable smart educational systems

Dr. Hüseyin Kusetogullari
Prof. Dr. Chih-Hsiung Tu
Guest Editors

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Keywords

identification of student behavioral patterns
early identification of at-risk students
intelligent online learning systems
prediction of student learning outcomes
cognitive models of learning
student support and online learning recommendations
modelling and developing students’ online education systems
self-adaptive learning
hybrid learning systems
deep learning frameworks and applications in online education systems
evolutionary methods and applications in online education systems
computer vision-based intelligent systems for online learning systems
automatic assessment
smart classes
interpretable and explainable smart educational systems

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Published Papers (13 papers)

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Research

19 pages, 528 KiB

Open AccessArticle

Predicting Students’ Outcome in an Introductory Programming Course: Leveraging the Student Background

by Jacqueline Köhler, Luciano Hidalgo and José Luis Jara

Appl. Sci. 2023, 13(21), 11994; https://doi.org/10.3390/app132111994 - 3 Nov 2023

Cited by 1 | Viewed by 1066

Abstract

For a lot of beginners, learning to program is challenging; similarly, for teachers, it is difficult to draw on students’ prior knowledge to help the process because it is not quite obvious which abilities are significant for developing programming skills. This paper seeks to shed some light on the subject by identifying which previously recorded variables have the strongest correlation with passing an introductory programming course. To do this, a data set was collected including data from four cohorts of students who attended an introductory programming course, common to all Engineering programmes at a Chilean university. With this data set, several classifiers were built, using different Machine Learning methods, to determine whether students pass or fail the course. In addition, models were trained on subsets of students by programme duration and engineering specialisation. An accuracy of 68% was achieved, but the analysis by specialisation shows that both accuracy and the significant variables vary depending on the programme. The fact that classification methods select different predictors depending on the specialisation suggests that there is a variety of factors that affect a student’s ability to succeed in a programming course, such as overall academic performance, language proficiency, and mathematical and scientific skills. Full article

(This article belongs to the Special Issue Artificial Intelligence in Online Higher Educational Data Mining)

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22 pages, 3984 KiB

Open AccessArticle

Research on Recognition and Analysis of Teacher–Student Behavior Based on a Blended Synchronous Classroom

by Taojie Xu, Wei Deng, Si Zhang, Yantao Wei and Qingtang Liu

Appl. Sci. 2023, 13(6), 3432; https://doi.org/10.3390/app13063432 - 8 Mar 2023

Cited by 6 | Viewed by 2426

Abstract

Due to the impact of the COVID-19 pandemic, many students are unable to attend face-to-face courses, Therefore, in this case, distance education should be promoted to replace face-to-face education. However, because of the imbalance of education in different regions, such as the imbalance of education resources between rural and urban areas, the quality of distance education may not be guaranteed. Therefore, in China and some regions, there have been efforts made to carry out blended synchronous classroom attempts. In hybrid synchronous classroom situations, teachers’ workloads have increased, and it is difficult to fully understand students’ learning efficiency and class participation. We use deep learning to identify the behaviors of teachers and students in a blended synchronous classroom-based situation, aiming to automate the analysis of classroom videos, which can help teachers in classroom reflection and summary in a blended synchronous classroom or face-to-face classroom. In the behavior recognition of students and teachers, we combine the head, hand, and body posture information of teachers and students and add the feature pyramid (FPN) and convolutional block attention module (CBAM) for comparative experiments. Finally, S–T (student–teacher) analysis and engagement analysis were carried out on the identification results. Full article

(This article belongs to the Special Issue Artificial Intelligence in Online Higher Educational Data Mining)

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14 pages, 2424 KiB

Open AccessArticle

Identification of Student Behavioral Patterns in Higher Education Using K-Means Clustering and Support Vector Machine

by Nur Izzati Mohd Talib, Nazatul Aini Abd Majid and Shahnorbanun Sahran

Appl. Sci. 2023, 13(5), 3267; https://doi.org/10.3390/app13053267 - 3 Mar 2023

Cited by 5 | Viewed by 2999

Abstract

In many academic fields, predicting student academic success using data mining techniques has long been a major research issue. Monitoring students in higher education institutions (HEIs) and having the ability to predict student performance is important to improve academic quality. The objective of the study is to (1) identify features that form clusters that have holistic characteristics and (2) develop and validate a prediction model for each of the clusters to predict student performance holistically. For this study, both classification and clustering methods will be used using Support Vector Machine (SVM) and K-means clustering. Three clusters were identified using K-means clustering. Based on the learning program outcome feature, there are primarily three types of students: low, average, and high performance. The prediction model with the new labels obtained from the clusters also gained higher accuracy when compared to the student dataset with labels using their semester grade. Full article

(This article belongs to the Special Issue Artificial Intelligence in Online Higher Educational Data Mining)

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12 pages, 2682 KiB

Open AccessArticle

The Use of Artificial Intelligence (AI) in Online Learning and Distance Education Processes: A Systematic Review of Empirical Studies

by Murat Ertan Dogan, Tulay Goru Dogan and Aras Bozkurt

Appl. Sci. 2023, 13(5), 3056; https://doi.org/10.3390/app13053056 - 27 Feb 2023

Cited by 94 | Viewed by 32521

Abstract

Artificial intelligence (AI) technologies are used in many dimensions of our lives, including education. Motivated by the increasing use of AI technologies and the current state of the art, this study examines research on AI from the perspective of online distance education. Following a systematic review protocol and using data mining and analytics approaches, the study examines a total of 276 publications. Accordingly, time trend analysis increases steadily with a peak in recent years, and China, India, and the United States are the leading countries in research on AI in online learning and distance education. Computer science and engineering are the research areas that make the most of the contribution, followed by social sciences. t-SNE analysis reveals three dominant clusters showing thematic tendencies, which are as follows: (1) how AI technologies are used in online teaching and learning processes, (2) how algorithms are used for the recognition, identification, and prediction of students’ behaviors, and (3) adaptive and personalized learning empowered through artificial intelligence technologies. Additionally, the text mining and social network analysis identified three broad research themes, which are (1) educational data mining, learning analytics, and artificial intelligence for adaptive and personalized learning; (2) algorithmic online educational spaces, ethics, and human agency; and (3) online learning through detection, identification, recognition, and prediction. Full article

(This article belongs to the Special Issue Artificial Intelligence in Online Higher Educational Data Mining)

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22 pages, 4267 KiB

Open AccessArticle

A Domain-Driven Framework to Analyze Learning Dynamics in MOOCs through Event Abstraction

by Luciano Hidalgo and Jorge Munoz-Gama

Appl. Sci. 2023, 13(5), 3039; https://doi.org/10.3390/app13053039 - 27 Feb 2023

Cited by 3 | Viewed by 2797

Abstract

Interest in studying Massive Online Open Courses (MOOC) learners’ sessions has grown as a result of the retention and completion issues that these courses present. Applying process mining to study this phenomenon is difficult due to the freedom of navigation that these courses give their students. The goal of this research is to provide a domain-driven top-down method that enables educators who are unfamiliar with data and process analytics to search for a set of preset high-level concepts in their own MOOC data, hence simplifying the use of typical process mining techniques. This is accomplished by defining a three-stage process that generates a low-level event log from a minimum data model and then abstracts it to a high-level event log with seven possible learning dynamics that a student may perform in a session. By examining the actions of students who successfully completed a Coursera introductory programming course, the framework was tested. As a consequence, patterns in the repetition of content and assessments were described; it was discovered that students’ willingness to evaluate themselves increases as they advance through the course; and four distinct session types were characterized via clustering. This study shows the potential of employing event abstraction strategies to gain relevant insights from educational data. Full article

(This article belongs to the Special Issue Artificial Intelligence in Online Higher Educational Data Mining)

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15 pages, 3260 KiB

Open AccessArticle

A Two-Phase Ensemble-Based Method for Predicting Learners’ Grade in MOOCs

by Warunya Wunnasri, Pakarat Musikawan and Chakchai So-In

Appl. Sci. 2023, 13(3), 1492; https://doi.org/10.3390/app13031492 - 23 Jan 2023

Cited by 3 | Viewed by 1989

Abstract

MOOCs are online learning environments which many students use, but the success rate of online learning is low. Machine learning can be used to predict learning success based on how people learn in MOOCs. Predicting the learning performance can promote learning through various methods, such as identifying low-performance students or by grouping students together. Recent machine learning has enabled the development of predictive models, and the ensemble method can assist in reducing the variance and bias errors associated with single-machine learning. This study uses a two-phase classification model with an ensemble technique to predict the learners’ grades. In the first phase, binary classification is used, and the non-majority class is then sent to the second phase, which is multi-class classification. The new features are computed based on the distance from the class’s center. The distance between the data and the center of an overlapping cluster is calculated using silhouette score-based feature selection. Lastly, Bayesian optimization boosts the performance by fine tuning the optimal parameter set. Using data from the HMPC- and the CNPC datasets, the experiment results demonstrate that the proposed design, the two-phase ensemble-based method, outperforms the state-of-the-art machine learning algorithms. Full article

(This article belongs to the Special Issue Artificial Intelligence in Online Higher Educational Data Mining)

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14 pages, 2930 KiB

Open AccessArticle

All-Year Dropout Prediction Modeling and Analysis for University Students

by Zihan Song, Sang-Ha Sung, Do-Myung Park and Byung-Kwon Park

Appl. Sci. 2023, 13(2), 1143; https://doi.org/10.3390/app13021143 - 14 Jan 2023

Cited by 14 | Viewed by 7958

Abstract

The core of dropout prediction lies in the selection of predictive models and feature tables. Machine learning models have been shown to predict student dropouts accurately. Because students may drop out of school in any semester, the student history data recorded in the academic management system would have a different length. The different length of student history data poses a challenge for generating feature tables. Most current studies predict student dropouts in the first academic year and therefore avoid discussing this issue. The central assumption of these studies is that more than 50% of dropouts will leave school in the first academic year. However, in our study, we found the distribution of dropouts is evenly distributed in all academic years based on the dataset from a Korean university. This result suggests that Korean students’ data characteristics included in our dataset may differ from those of other developed countries. More specifically, the result that dropouts are evenly distributed throughout the academic years indicates the importance of a dropout prediction for the students in any academic year. Based on this, we explore the universal feature tables applicable to dropout prediction for university students in any academic year. We design several feature tables and compare the performance of six machine learning models on these feature tables. We find that the mean value-based feature table exhibits better generalization, and the model based on the gradient boosting technique performs better than other models. This result reveals the importance of students’ historical information in predicting dropout. Full article

(This article belongs to the Special Issue Artificial Intelligence in Online Higher Educational Data Mining)

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22 pages, 3272 KiB

Open AccessArticle

Clustering Analysis for Classifying Student Academic Performance in Higher Education

by Ahmad Fikri Mohamed Nafuri, Nor Samsiah Sani, Nur Fatin Aqilah Zainudin, Abdul Hadi Abd Rahman and Mohd Aliff

Appl. Sci. 2022, 12(19), 9467; https://doi.org/10.3390/app12199467 - 21 Sep 2022

Cited by 16 | Viewed by 7163

Abstract

There are three income categories for Malaysians: the top 20% (T20), the middle 40% (M40), and the bottom 40% (B40). The government has extended B40′s access to higher education to eliminate socioeconomic disparities and improve their lives. The number of students enrolled in bachelor’s degree programmes at universities has risen annually. However, not all students who enrolled graduated. Machine learning approaches have been widely used and improved in education. However, research studies related to unsupervised learning in education are generally lacking. Therefore, this study proposes a clustering-based approach for classifying B40 students based on their performance in higher education institutions (HEIs). This study developed three unsupervised models (k-means, BIRCH, and DBSCAN) based on the data of B40 students. Several data pre-processing tasks and feature selection have been conducted on the raw dataset to ensure the quality of the training data. Each model is optimized using different tuning parameters. The observational results have shown that the optimized k-means on Model B (KMoB) achieved the highest performance among all the models. KMoB produced five clusters of B40 students based on their performance. With KMoB, this study may assist the government in reducing HEI drop-out rates, increasing graduation rates, and eventually boosting students’ socioeconomic status. Full article

(This article belongs to the Special Issue Artificial Intelligence in Online Higher Educational Data Mining)

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17 pages, 2789 KiB

Open AccessArticle

An Optimized CNN Model for Engagement Recognition in an E-Learning Environment

by Yan Hu, Zeting Jiang and Kaicheng Zhu

Appl. Sci. 2022, 12(16), 8007; https://doi.org/10.3390/app12168007 - 10 Aug 2022

Cited by 17 | Viewed by 2954

Abstract

In the wake of the restrictions imposed on social interactions due to the COVID-19 pandemic, traditional classroom education was replaced by distance education in many universities. Under the changed circumstances, students are required to learn more independently. The challenge for teachers has been to duly ascertain students’ learning efficiency and engagement during online lectures. This paper proposes an optimized lightweight convolutional neural network (CNN) model for engagement recognition within a distance-learning setup through facial expressions. The ShuffleNet v2 architecture was selected, as this model can easily adapt to mobile platforms and deliver outstanding performance compared to other lightweight models. The proposed model was trained, tested, evaluated and compared with other CNN models. The results of our experiment showed that an optimized model based on the ShuffleNet v2 architecture with a change of activation function and the introduction of an attention mechanism provides the best performance concerning engagement recognition. Further, our proposed model outperforms many existing works in engagement recognition on the same database. Finally, this model is suitable for student engagement recognition for distance learning on mobile platforms. Full article

(This article belongs to the Special Issue Artificial Intelligence in Online Higher Educational Data Mining)

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15 pages, 5640 KiB

Open AccessArticle

Deep Neural Network-Based Prediction and Early Warning of Student Grades and Recommendations for Similar Learning Approaches

by Tao Tao, Chen Sun, Zhaoyang Wu, Jian Yang and Jing Wang

Appl. Sci. 2022, 12(15), 7733; https://doi.org/10.3390/app12157733 - 1 Aug 2022

Cited by 14 | Viewed by 2982

Abstract

Studies reported that if teachers can accurately predict students’ follow-up learning effects via data mining and other means, as per their current performances, and explore the difficulty level of students’ mastery of future-related courses in advance, it will help improve students’ scores in future exams. Although educational data mining and learning analytics have experienced an increase in exploration and use, they are still difficult to precisely define. The usage of deep learning methods to predict academic performances and recommend optimal learning methods has not received considerable attention from researchers. This study aims to predict unknown course grades based on students’ previous learning situations and use clustering algorithms to identify similar learning situations, thereby improving students’ academic performance. In this study, the methods of linear regression, random forest, back-propagation neural network, and deep neural network are compared; the prediction and early warning of students’ academic performances based on deep neural network are proposed, in addition to the improved K-nearest neighbor clustering based on association rules (Pearson correlation coefficient). The algorithm performs a similar category clustering for early-warning students. Using the mean square error, standard deviation, mean absolute percentage error, and prediction of ups-and-downs accuracy as evaluation indicators, the proposed method achieves a steady improvement of 20% in the prediction of ups-and-downs accuracy, and demonstrates improved prediction results when compared under similar conditions. Full article

(This article belongs to the Special Issue Artificial Intelligence in Online Higher Educational Data Mining)

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18 pages, 875 KiB

Open AccessArticle

Multimodal Classification of Teaching Activities from University Lecture Recordings

by Oscar Sapena and Eva Onaindia

Appl. Sci. 2022, 12(9), 4785; https://doi.org/10.3390/app12094785 - 9 May 2022

Viewed by 2347

Abstract

The way of understanding online higher education has greatly changed due to the worldwide pandemic situation. Teaching is undertaken remotely, and the faculty incorporate lecture audio recordings as part of the teaching material. This new online teaching–learning setting has largely impacted university classes. While online teaching technology that enriches virtual classrooms has been abundant over the past two years, the same has not occurred in supporting students during online learning. To overcome this limitation, our aim is to work toward enabling students to easily access the piece of the lesson recording in which the teacher explains a theoretical concept, solves an exercise, or comments on organizational issues of the course. To that end, we present a multimodal classification algorithm that identifies the type of activity that is being carried out at any time of the lesson by using a transformer-based language model that exploits features from the audio file and from the automated lecture transcription. The experimental results will show that some academic activities are more easily identifiable with the audio signal while resorting to the text transcription is needed to identify others. All in all, our contribution aims to recognize the academic activities of a teacher during a lesson. Full article

(This article belongs to the Special Issue Artificial Intelligence in Online Higher Educational Data Mining)

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18 pages, 3556 KiB

Open AccessArticle

Research on Emotion Recognition for Online Learning in a Novel Computing Model

by Mengnan Chen, Lun Xie, Chiqin Li and Zhiliang Wang

Appl. Sci. 2022, 12(9), 4236; https://doi.org/10.3390/app12094236 - 22 Apr 2022

Cited by 9 | Viewed by 2619

Abstract

The recognition of human emotions is expected to completely change the mode of human-computer interaction. In emotion recognition research, we need to focus on accuracy and real-time performance in order to apply emotional recognition based on physiological signals to solve practical problems. Considering the timeliness dimension of emotion recognition, we propose a terminal-edge-cloud system architecture. Compared to traditional sentiment computing architectures, the proposed architecture in this paper reduces the average time consumption by 15% when running the same affective computing process. Proposed Joint Mutual Information (JMI) based feature extraction affective computing model, and we conducted extensive experiments on the AMIGOS dataset. Through experimental comparison, this feature extraction network has obvious advantages over the commonly used methods. The model performs sentiment classification, and the average accuracy of valence and arousal is 71% and 81.8%, compared with recent similar sentiment classifier research, the average accuracy is improved by 0.85%. In addition, we set up an experiment with 30 people in an online learning scenario to validate the computing system and algorithm model. The result proved that the accuracy and real-time recognition were satisfactory, and improved the online learning real-time emotional interaction experience. Full article

(This article belongs to the Special Issue Artificial Intelligence in Online Higher Educational Data Mining)

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20 pages, 810 KiB

Open AccessArticle

A Predictive Model for Student Achievement Using Spiking Neural Networks Based on Educational Data

by Chuang Liu, Haojie Wang, Yingkui Du and Zhonghu Yuan

Appl. Sci. 2022, 12(8), 3841; https://doi.org/10.3390/app12083841 - 11 Apr 2022

Cited by 10 | Viewed by 2692

Abstract

Student achievement prediction is one of the most important research directions in educational data mining. Student achievement directly reflects students’ course mastery and lecturers’ teaching level. Especially for the achievement prediction of college students, it not only plays an early warning and timely correction role for students and teachers, but also provides a method for university decision-makers to evaluate the quality of courses. Based on the existing research and experimental results, this paper proposes a student achievement prediction model based on evolutionary spiking neural network. On the basis of fully analyzing the relationship between course attributes and student attributes, a student achievement prediction model based on spiking neural network is established. The evolutionary membrane algorithm is introduced to learn hyperparameters of the model, so as to improve the accuracy of the model in predicting student achievement. Finally, the proposed model is used to predict student achievement on two benchmark student datasets, and the performance of the prediction model proposed in this paper is analyzed by comparing with other experimental algorithms. The experimental results show that the model based on spiking neural network can effectively improve the prediction accuracy of student achievement. Full article

(This article belongs to the Special Issue Artificial Intelligence in Online Higher Educational Data Mining)

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