Machine learning (ML) models are nowadays used in complex applications in various domains such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may someti ...

The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations

Artificial intelligence education (AIEd) is defined in the field of education as the utilization of artificial intelligence. There are currently many AIEd‐driven applications in schools and universities. This paper applies an artificial intelligence module combined with the knowledge recommendation to the system and develops an online English teaching system in comparison with the common teaching auxiliary system. The method of English teaching is useful in investigating the potential internal connections between evaluation outcomes and various factors. This article develops deep learning‐assisted online intelligent English teaching system that utilizes to create a modern tool platform to help students improve their English language teaching efficiency in line with their mastery of knowledge and personality. The decision tree algorithm and neural networks have been used and to generate an English teaching assessment implementation model based on decision tree technologies. It provides valuable data from extensive information, summarizes rules and data, and helps teachers to improve their education and the English scores of students. This system reflects the thinking of the artificial intelligence expert system. Test application demonstrates that the system can help students improve their learning efficiency and will make learning content more relevant. Besides, the system provides an example model with similar methods and has a referential definition.

Design of online intelligent English teaching platform based on artificial intelligence techniques

Abstract Over the last decade, there has been great research interest in the application of artificial intelligence (AI) in various fields, such as medicine, finance, and law. Recently, there has been a research focus on the application of AI in education, where it has great potential. Therefore, a systematic review of the literature on AI in education is therefore necessary. This article considers its usage and applications in Latin American higher education institutions. After identifying the studies dedicated to educational innovations brought about by the application of AI techniques, this review examines AI applications in three educational processes: learning, teaching, and administration. Each study is analyzed for the AI techniques used, such as machine learning, deep learning, and natural language processing, the AI tools and algorithms that are applied, and the main education topic. The results reveal that the main AI applications in education are: predictive modelling, intelligent analytics, assistive technology, automatic content analysis, and image analytics. It is further demonstrated that AI applications help to address important education issues (e.g., detecting students at risk of dropping out) and thereby contribute to ensuring quality education. Finally, the article presents the lessons learned from the review concerning the application of AI technologies in higher education in the Latin American context. 

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Artificial intelligence applications in Latin American higher education: a systematic review

Since the education sector is associated with highly dynamic business environments which are controlled and maintained by information systems, recent technological advancements and the increasing pace of adopting artificial intelligence (AI) technologies constitute a need to identify and analyze the issues regarding their implementation in education sector. However, a study of the contemporary literature reveled that relatively little research has been undertaken in this area. To fill this void, we have identified the benefits and challenges of implementing artificial intelligence in the education sector, preceded by a short discussion on the concepts of AI and its evolution over time. Moreover, we have also reviewed modern AI technologies for learners and educators, currently available on the software market, evaluating their usefulness. Last but not least, we have developed a strategy implementation model, described by a five-stage, generic process, along with the corresponding configuration guide. To verify and validate their design, we separately developed three implementation strategies for three different higher education organizations. We believe that the obtained results will contribute to better understanding the specificities of AI systems, services and tools, and afterwards pave a smooth way in their implementation.

Artificial Intelligence Technologies in Education: Benefits, Challenges and Strategies of Implementation

As the amounts of online books are exponentially increasing due to COVID-19 pandemic, finding relevant books from a vast e-book space becomes a tremendous challenge for online users Personal recommendation systems have been emerged to conduct effective search which mine related books based on user rating and interest Most of these existing systems are user-based ratings where content-based and collaborativebased learning methods are used These systems' irrationality is their rating technique, which counts the users who have already been unsubscribed from the services and no longer rate books This paper proposed an effective system for recommending books for online users that rated a book using the clustering method and then found a similarity of that book to suggest a new book The proposed system used the K-means Cosine Distance function to measure distance and Cosine Similarity function to find Similarity between the book clusters Sensitivity, Specificity, and F Score were calculated for ten different datasets The average Specificity was higher than sensitivity, which means that the classifier could re-move boring books from the reader's list Besides, a receiver operating characteristic curve was plotted to find a graphical view of the classifiers' accuracy Most of the datasets were close to the ideal diagonal classifier line and far from the worst classifier line The result concludes that recommendations, based on a particular book, are more accurately effective than a user-based recommendation system © 2021, International Journal of Advanced Computer Science and Applications All Rights Recerved

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Personalized Book Recommendation System using Machine Learning Algorithm

Decisions made at the strategic level of Higher Educational Institutions (HEIs) affect policies, strategies, and actions that the institutions make as a whole. Decision’s structures at HEIs are depicted in this paper and their effectiveness in supporting the institutions’ governance. The disengagement of the stakeholders and the lack of using efficient computational algorithms lead to 1) the decision process takes longer; 2) the “whole picture” is not involved along with all data necessary; and 3) small academic impact is produced by the decision, among others. Machine learning is an emerging field of artificial intelligence that using various algorithms analyzes information and provides a richer understanding of the data contained in a specific context. Based on the author’s previous works, we focus on supporting decision-making at a strategic level, being deans’ concerns the preeminent mission to bolster. In this paper, three supervised classification algorithms are deployed to predict graduation rates from real data about undergraduate engineering students in South America. The analysis of receiver operating characteristic (ROC) curve and accuracy are executed as measures of effectiveness to compare and evaluate decision tree, logistic regression, and random forest, where this last one demonstrates the best outcomes.

/pdf/usage-of-machine-learning-for-strategic-decision-making-at-4ibbjibcu3.pdf

Usage of Machine Learning for Strategic Decision Making at Higher Educational Institutions

ABSTRACT Remote-sensing images comprise massive amount of spatial and semantic data that can be employed for several applications. Presently, deep learning (DL) models for RS image processing become a familiar research area. Due to the advancements of recent satellite imaging sensors , the issue of huge amount of data processing becomes a challenging problem. To accomplish this, deep transfer learning (DTL) models are developed to resolve the semantic gap among various datasets This study develops a new DTL-based fusion model for environmental remote-sensing image classification, called DTLF-ERSIC technique. The proposed technique focuses on the design of fusion model to combine multiple feature vectors and thereby attains maximum classification performance. The DTLF-ERSIC technique incorporates the entropy-based fusion of three feature extraction techniques, namely, Discrete Local Binary Pattern (DLBP), Residual Network (ResNet50), and EfficientNet models. Besides, a rain optimization algorithm (ROA) with fuzzy rule-based classifier (FRC) is applied to predict the class labels of the test RS images and shows the novelty of the work. A comprehensive experimental analysis of the DTLF-ERSIC technique takes place on benchmark dataset and examined the results in terms of different performance measures. The simulation results reported the supremacy of the DTLF-ERSIC technique over the recent state-of-art techniques.

https://www.tandfonline.com/doi/pdf/10.1080/22797254.2021.2017799?needAccess=true

Deep Transfer Learning based Fusion Model for Environmental Remote Sensing Image Classification Model

The empirical evaluation of the success of a participant is critical for a thorough assessment of sporting events. Evaluating students' efficiency or scripting in sports is limited, even if skilled experts do it. In this paper, support vector machine-assisted sports training (SVMST) has been proposed to evaluate student sports efficiency. Sports training prototypes are based on different criteria that participate in the matches, traditional game statistics, person quality measures, and opposing data. The success of students is divided into two grades: moderate and large. The primarily supervised learning-based classification method is used to create a template for identifying student sports training efficiency. SVM implements learning methods, data collection methods, effective model assessment methods, and particular difficulties in predicting sports performance. The experimental results show SVMST to high student performance of 98.7%, a low error rate of 9.8%, enhanced assessment ratio of 97.6%, training outcome of 95.6%, and an efficiency ratio of 96.8%.

Vicente Gacia-Diaz

Papers

Usage of Machine Learning for Strategic Decision Making at Higher Educational Institutions

Deep Transfer Learning based Fusion Model for Environmental Remote Sensing Image Classification Model

Evaluating the Efficiency of Student Sports Training Based on Supervised Learning

Securing the Software-Defined Perimeter Framework with Automated Security Configuration Deployment Systems