LAISES – Learning AI Integrating System for Elementary Students

Nurzaman, Adam Fahsyah; Tedja, Riyanti Teresa; Kusuma, Laksamana; Kurniawan, Yohannes; Bhutkar, Ganesh; Johan, Johan

doi:10.1145/3474906.3474913

Cited by 2 publications

(4 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, four databases were systematically searched and the reference lists of relevant studies were screened, ultimately resulting in the 48 studies involved in this review. The following references were the studies involved: [5], [6], [8], [10], [12], [14], [15], [16], [17], [18], [29], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [46], [47], [48], [49], [50], [51], [52], [53], [55], [61], [62], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79].…”

Section: Discussionmentioning

confidence: 99%

“…-Simulation (S): these researchers evaluate the proposed approach through simulations. Notable examples include those in [5], [35], and [46]. -Theoretical (T): the authors present a new framework or approach where there are no experiments or empirical evaluations.…”

Section: What Evaluation Methods Are Used To Determine the Performanc...mentioning

confidence: 99%

Section: A What Platforms Are Frequently Investigated In Personalised...mentioning

confidence: 99%

“…PAL platforms based on LS have also been applied to various platforms such as the ITS [33], [34]; the Conversational Intelligent Tutoring System (CITS) [17] and online mobile applications [35], [36]. In [35], the proposed approach enabled elementary students to learn materials in an online mobile application and adjust the delivery method according to their preferred LS. Other platforms found through the review contain a virtual laboratory; [37] proposed a complete personalised learning solution for LS identification, learning performance assessment and adaptive learning content delivery for virtual hands-on laboratory-based education solutions.…”

Section: A What Platforms Are Frequently Investigated In Personalised...mentioning

confidence: 99%

See 3 more Smart Citations

Personalized Adaptive Learning Technologies Based on Machine Learning Techniques to Identify Learning Styles: A Systematic Literature Review

2023

View full text Add to dashboard Cite

Artificial intelligence (AI) approaches have been used in personalised adaptive education systems to overcome the limitations of statically determined learning styles (LSs). These approaches utilise algorithms from machine learning (ML) to tackle the challenge of personalising e-learning by mapping students' behavioural attributes to a particular LS automatically and dynamically to optimise the individual learning process. Motivated by the many influential studies in this field and the current developments in ML and AI, a comprehensive systematic literature review was conducted from 2015 to 2022. Influential scientific literature was analysed to identify the emerging trends and gaps in the literature in terms of LS models and possible ML techniques employed for personalised adaptive learning platforms. The outcomes of this paper include a review and analysis of the current trends of this emerging field in terms of the applications and developments in using ML approaches to implement more intelligent and adaptive e-learning environments to detect learners' LSs automatically for enhancing learning. In addition, the following issues were also investigated: the platforms that stimulated research; identifying LS models utilised in e-learning; the evaluation methods used; and the learning supports provided. The results indicated an increasing interest in using artificial neural network approaches to identify LSs. However, limited work has been conducted on the comparison of deep learning methods in this context. The findings suggest the need to consider and stimulate further empirical investigation in documenting the adoption and comparison of deep learning algorithms in classifying LSs to provide higher adaptability.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: What Evaluation Methods Are Used To Determine the Performanc...mentioning

confidence: 99%

Section: A What Platforms Are Frequently Investigated In Personalised...mentioning

confidence: 99%

Section: A What Platforms Are Frequently Investigated In Personalised...mentioning

confidence: 99%

See 2 more Smart Citations

Personalized Adaptive Learning Technologies Based on Machine Learning Techniques to Identify Learning Styles: A Systematic Literature Review

2023

View full text Add to dashboard Cite

show abstract

What is needed to build a personalized recommender system for K-12 students’ E-Learning? Recommendations for future systems and a conceptual framework

et al. 2022

View full text Add to dashboard Cite

Online learning has significantly expanded along with the spread of the coronavirus disease (COVID-19). Personalization becomes an essential component of learning systems due to students’ different learning styles and abilities. Recommending materials that meet the needs and are tailored to learners’ styles and abilities is necessary to ensure a personalized learning system. The study conducted a systematic literature review (SLR) of papers on recommendation systems for e-learning in the K12 setting published between 2017 and 2021 and aims to identify the most important component of a personalized recommender system for school students’ e-learning. Recommendations for later studies were proposed based on the identified components, namely a personalized conceptual framework for providing materials to school students. The proposed framework comprised four stages: student profiling, material collection, material filtering, and validation.

show abstract

LAISES – Learning AI Integrating System for Elementary Students

Cited by 2 publications

References 1 publication

Personalized Adaptive Learning Technologies Based on Machine Learning Techniques to Identify Learning Styles: A Systematic Literature Review

Personalized Adaptive Learning Technologies Based on Machine Learning Techniques to Identify Learning Styles: A Systematic Literature Review

What is needed to build a personalized recommender system for K-12 students’ E-Learning? Recommendations for future systems and a conceptual framework

Contact Info

Product

Resources

About