AI-enabled adaptive learning systems: A systematic mapping of the literature

Kabudi, Tumaini; Pappas, Ilias O.; Olsen, Dag H.

doi:10.1016/j.caeai.2021.100017

Cited by 251 publications

(113 citation statements)

References 59 publications

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“…Some of the key ones include random forest (RF) [ 22 , 23 ], support vector machine (SVM), neural networks (NNs), K-nearest neighbor (KNN), and the Gaussian process (GP). Among these techniques, RF stands out owing to its distinctive adaptive learning and predictive modeling capability [ 1 , 24 , 25 ]. RF is suitable for adaptive regression-based learning and the effective classification of extensive data.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Boosted Regression Ensemble Combined with Hyperparameter Tuning for Optimal Adaptive Learning

Isabona

Imoize

Kim

2022

Sensors

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Over the past couple of decades, many telecommunication industries have passed through the different facets of the digital revolution by integrating artificial intelligence (AI) techniques into the way they run and define their processes. Relevant data acquisition, analysis, harnessing, and mining are now fully considered vital drivers for business growth in these industries. Machine learning, a subset of artificial intelligence (AI), can assist, particularly in learning patterns in big data chunks, intelligent extrapolative extraction of data and automatic decision-making in predictive learning. Firstly, in this paper, a detailed performance benchmarking of adaptive learning capacities of different key machine-learning-based regression models is provided for extrapolative analysis of throughput data acquired at the different user communication distances to the gNodeB transmitter in 5G new radio networks. Secondly, a random forest (RF)-based machine learning model combined with a least-squares boosting algorithm and Bayesian hyperparameter tuning method for further extrapolative analysis of the acquired throughput data is proposed. The proposed model is herein referred to as the RF-LS-BPT method. While the least-squares boosting algorithm is engaged to turn the possible RF weak learners to form stronger ones, resulting in a single strong prediction model, the Bayesian hyperparameter tuning automatically determines the best RF hyperparameter values, thereby enabling the proposed RF-LS-BPT model to obtain desired optimal prediction performance. The application of the proposed RF-LS-BPT method showed superior prediction accuracy over the ordinary random forest model and six other machine-learning-based regression models on the acquired throughput data. The coefficient of determination (Rsq) and mean absolute error (MAE) values obtained for the throughput prediction at different user locations using the proposed RF-LS-BPT method range from 0.9800 to 0.9999 and 0.42 to 4.24, respectively. The standard RF models attained 0.9644 to 0.9944 Rsq and 5.47 to 12.56 MAE values. The improved throughput prediction accuracy of the proposed RF-LS-BPT method demonstrates the significance of hyperparameter tuning/optimization in developing precise and reliable machine-learning-based regression models. The projected model would find valuable applications in throughput estimation and modeling in 5G and beyond 5G wireless communication systems.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Boosted Regression Ensemble Combined with Hyperparameter Tuning for Optimal Adaptive Learning

Isabona

Imoize

Kim

2022

Sensors

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“…In addition, the learning of corpus linguistics can help students understand the correct expression meaning and practical use of some words, and with the help of advanced computer technology, it helps students to realize the meaning of corpus linguistics words, sentence patterns, and other forms of language expression. It can be seen that the application value of corpus linguistics in foreign language teaching is relatively high [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Construction and Analysis of Intelligent English Teaching Model Assisted by Personalized Virtual Corpus by Big Data Analysis

Zhu¹,

Zhu

Tsai

2021

Mathematical Problems in Engineering

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At present, a new round of scientific and technological revolution and industrial transformation with information technology at its core are accelerating. At present, a new round of scientific and technological revolution and industrial transformation with information technology at its core is accelerating. The challenge of new economy and new industry has put forward new requirements for the training of talents in China. The challenges of new economy and new industry have put forward new requirements for the cultivation of engineering talents in China. Based on corpus, this study constructed a model of intelligent English teaching assisted by virtual corpus. The traditional teaching of college English reading is based on, around, and for texts. Using DDL model, teachers can break the limitation of textbooks. On the basis of analyzing the general idea of the text, they can search out massive real corpus related to the general idea of the text by searching the core words in the text, so as to provide extensive reading resources for students in the maximum range. At the same time, teachers can rely on the corpus to design different types of teaching activities, realize student-centered task-based, inquiry-based, and autonomous learning and cultivate students’ critical thinking ability, practical ability, and cross-cultural communication ability. This model breaks the limitation of “classroom + textbook,” realizes student-centered task-based, exploratory, and autonomous learning, trains interdisciplinary new engineering talents needed by emerging industries and new economy in the future, and promotes the sustainable development of English teaching. Corpus-data-driven college English teaching mode breaks the limitation of “classroom + textbook,” changes the traditional college English teaching mode, and realizes student-centered task-oriented, inquiry-based, and autonomous learning.

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“…Although scholars have outlined the importance of an evolved training and a more intense interchange among academia and industry for an efficient and frictionless adoption of innovation, extant literature offers little insight into the first theme and does not fully explore the latter. For example, Zawacki-Richter et al (2019) offered a systematic review on the applications of AI in higher education, emphasizing ethical and pedagogical perspectives, and the capability of AI-enabled learning systems to enable an adaptation of content to individual needs of learners was explored by Kabudi et al (2021). Both are very interesting themes, but a wider perspective would be valuable.…”

Section: Research Gapsmentioning

confidence: 99%

Digitization, Epistemic Proximity, and the Education System: Insights from a Bibliometric Analysis

Fiore

Florea

Kifor

et al. 2021

JRFM

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Advances in IoT, AI, Cyber-Physical Systems, Computational Intelligence, and Big Data Analytics require organizations and workforce to be able and willing to learn how to interact with digital technology. In organizations, coordination and cooperation between actors with expertise in business and technology is fundamental, but integration is hard without understanding the terminology and problems of the interlocutor. Epistemic proximity becomes prominent, underlining the importance of an education focused on flexibility, willingness to cope with the unknown, and interdisciplinarity. The main goal of this work is to provide a perspective on how the education system is evolving to support organizations in the digitization era through a quantitative analysis of literature. More than 170,000 papers were selected from the Scopus database, matching a wide set of keywords related with innovation, problem solving, and organizational change. Patterns in the co-occurrence of keywords were studied. In addition, similarities and differences in the distribution of relevant themes across disciplinary areas, as well as their evolution since 2000, were analyzed. Academic interest is found to be generally increasing over the years in all disciplines, although considerable fluctuations can be observed. This variation is found to be nonuniform in the macroareas.

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AI-enabled adaptive learning systems: A systematic mapping of the literature

Cited by 251 publications

References 59 publications

Machine Learning-Based Boosted Regression Ensemble Combined with Hyperparameter Tuning for Optimal Adaptive Learning

Machine Learning-Based Boosted Regression Ensemble Combined with Hyperparameter Tuning for Optimal Adaptive Learning

Construction and Analysis of Intelligent English Teaching Model Assisted by Personalized Virtual Corpus by Big Data Analysis

Digitization, Epistemic Proximity, and the Education System: Insights from a Bibliometric Analysis

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