Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

Alkhatlan, Ali; Kalita, Jugal

doi:10.48550/arxiv.1812.09628

Cited by 10 publications

(11 citation statements)

References 36 publications

(45 reference statements)

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“…These rules were learnt from several inputted features. These features belonged to two main categories: statistical, and psycholinguistic 7 . Their statistical features included unigram and lemma frequencies, word, stem and lemma length, probability of a word's character sequence, and lastly, number of synsets [105].…”

Section: Machine Learning Classifiersmentioning

confidence: 99%

“…CALL applications are subsequently ITSs that specialize in language learning and have been found to improve second language (L2) acquisition [134]. These applications include multiple designs, are based on differing pedagogical practices, and allow for varying degrees of learner-computer interaction [7].…”

Section: Improving Readabilitymentioning

confidence: 99%

“…The poor performance of POS-tags as a feature for LCP was recently verified by Desai et al[41] 7. HMC also utilized POS-tags as features.…”

mentioning

confidence: 93%

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Lexical Complexity Prediction: An Overview

2023

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The occurrence of unknown words in texts significantly hinders reading comprehension. To improve accessibility for specific target populations, computational modelling has been applied to identify complex words in texts and substitute them for simpler alternatives. In this paper, we present an overview of computational approaches to lexical complexity prediction focusing on the work carried out on English data. We survey relevant approaches to this problem which include traditional machine learning classifiers (e.g. SVMs, logistic regression) and deep neural networks as well as a variety of features, such as those inspired by literature in psycholinguistics as well as word frequency, word length, and many others. Furthermore, we introduce readers to past competitions and available datasets created on this topic. Finally, we include brief sections on applications of lexical complexity prediction, such as readability and text simplification, together with related studies on languages other than English.

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Section: Machine Learning Classifiersmentioning

confidence: 99%

Section: Improving Readabilitymentioning

confidence: 99%

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Lexical Complexity Prediction: An Overview

2023

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“…Personalized learning systems change course contents based on students' knowledge of a particular subject. The objective is that the AI technologies utilized by ITSs will facilitate training through machine learning and personalized instruction [38]. Machine learning depends on techniques that allow a computer system to learn from training data and make choices or forecasts automatically.…”

Section: Intelligent Tutoring Systems Techniques On Personalized Lear...mentioning

confidence: 99%

A review of the Development Trend of Personalized learning Technologies and its Applications

Ambele¹,

Kaijage²,

Dida³

et al. 2022

IJASRE

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Personalized learning tailors material and strategy to student requirements, interests, and goals in e-learning. These developments help educational institutions and other organizations to keep up with the fast pace of information technology, communications, and computing power. Studies show that self-adaptive learning and relevant learning information improve study efficiency.Compared to traditional teaching methods, the practice of online education is well in its infancy. On the other hand, the pedagogy and evaluation of students in online courses have a large gap that has to be filled, necessitating significant improvements in e-learning. We call this approach to education "personalized learning," which is a central focus of today's leading online education platforms. Several studies have been conducted on e-learning and personalized learning, but few investigated the development trend of personalized learning technologies and applications. Therefore this study examines the literature to close the gap and promote the development trend for personalized learning technologies and applications in higher education from 2010 to 2021 by analyzing related journal articles. The pivotal studies used inclusion criteria after a searchgenerated 372 complete research articles and reduced them to 146 publications based on their proposed learning domains and research themes. Through carefully reviewing current trends and successes in numerous aspects of personalized learning, this discussion analyzes prospective future research directions in the field of personalized learning.

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“…Intelligent Tutoring Systems (ITS) have gained popularity as learner-centric platforms that adapt instruction to individual student needs. A typical ITS consists of four components: domain model, tutoring model, student model, and interface [1,7]. ALIN (Assistive Learning Intelligence Navigator) is an example of an online ITS developed by EdTech.…”

Section: Introductionmentioning

confidence: 99%

Predicting Students' Progress in Intelligent Tutoring Systems

He,

Huang,

Yang

et al. 2023

Preprint

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Intelligent Tutoring Systems (ITS) are increasingly popular for online learning. These systems use adaptive algorithms to recommend relevant content based on students' profiles. However, instructors need to periodically assess students' performance to ensure learning outcomes and adjust strategies accordingly. Our objective is to predict students' progress in advance, enabling teachers to make quicker decisions and facilitating the iterative process of adaptive algorithms. For this study, we collected a dataset from ALIN, an online learning platform, consisting of over 5,000 students' learning records and test results. Using this dataset, we conducted experiments employing various machine learning algorithms. The results indicate that learning behavior contributes to improving forecast performance, while students' progress strongly correlates with their previous test results. Additionally, we discovered that students' progress can be indirectly predicted by forecasting their scores. Furthermore, by breaking down overall scores into several distinct components and predicting individual scores for each component, the accuracy of the forecasts can be improved.

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Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

Cited by 10 publications

References 36 publications

Lexical Complexity Prediction: An Overview

Lexical Complexity Prediction: An Overview

A review of the Development Trend of Personalized learning Technologies and its Applications

Predicting Students' Progress in Intelligent Tutoring Systems

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