Artificial Intelligence and Machine Learning Approaches in Digital Education: A Systematic Revision

Abstract

The use of artificial intelligence and machine learning techniques across all disciplines has exploded in the past few years, with the ever-growing size of data and the changing needs of higher education, such as digital education. Similarly, online educational information systems have a huge amount of data related to students in digital education. This educational data can be used with artificial intelligence and machine learning techniques to improve digital education. This study makes two main contributions. First, the study follows a repeatable and objective process of exploring the literature. Second, the study outlines and explains the literature’s themes related to the use of AI-based algorithms in digital education. The study findings present six themes related to the use of machines in digital education. The synthesized evidence in this study suggests that machine learning and deep learning algorithms are used in several themes of digital learning. These themes include using intelligent tutors, dropout predictions, performance predictions, adaptive and predictive learning and learning styles, analytics and group-based learning, and automation. artificial neural network and support vector machine algorithms appear to be utilized among all the identified themes, followed by random forest, decision tree, naive Bayes, and logistic regression algorithms.

Study on optimization of communication network for multi-unmanned aerial vehicles

“…For UAV deployment problems, many studies use artificial intelligence technology [29,30], such as clustering algorithms [31], deep learning [32,36], particle swarm optimization [33], reinforcement learning [19] and so on. Although the performance between the UAVs and users is improved to a certain extent, the promotion of the performance is limited.…”

“…For UAV deployment problems, many studies used artificial intelligence technology [29,30], such as clustering algorithms [31], deep learning (DL) [32], reinforcement learning (RL) [19] and so on. Zhang et al [33] adopted the coverage probability as an evaluation index to find the 3D position of UAVs using particle swarm optimization, and they achieved the optimization purpose of maximizing the coverage probability.…”

A Smart Testing Model Based on Mining Semantic Relations

“…Different research such as in [41] has confirmed the effectiveness of applying machine learning approaches in digital education for performing intelligent tutoring, raising prediction tasks' performance, working with learning styles, and automation in general. As mentioned in [42], although automated tests could be considered a vital advance in e-learning, however, to date, the use of automated methods for item development and test construction has been limited.…”

Metamorphosing traditional pedagogy: examining the transcendent influence of Alexa in catalyzing educational paradigm shifts within rural Indian communities

“…Inadequate facilities, a scarcity of resources, and a shortage of trained teachers are only some of the problems affecting rural India's educational system (Roy, 2020). The advent of Alexa as an educational assistant provides an opportunity to overcome these challenges by providing learners in remote areas with a low-cost, engaging medium that can enhance standard teaching methods (Munir et al, 2022). Students can participate in collaborative projects, develop their critical thinking abilities, and gain access to a wide range of information and resources by using Alexa.…”