Understanding adoption of artificial intelligence-enabled language e-learning system: an empirical study of UTAUT model

2022 ◽  
Vol 16 (1) ◽  
pp. 74
Author(s):  
Hao Chu Lin ◽  
Han Yang ◽  
Chih Feng Ho
Keyword(s):  
Artificial Intelligence ◽  
Empirical Study ◽  
Learning System ◽  
Utaut Model ◽  
E Learning

Swarm-Based Techniques in E-Learning

2011 ◽  
pp. 199-212
Author(s):  
Sergio Gutiérrez ◽  
Abelardo Pardo
Keyword(s):  
Artificial Intelligence ◽  
Swarm Intelligence ◽  
Social Insects ◽  
Learning System ◽  
The Internet ◽  
Artificial Intelligence Technique ◽  
E Learning ◽  
Weak Points ◽  
Intelligence Technique

This chapter provides an overview of the use of swarm-intelligence techniques in the field of e-learning. Swarm intelligence is an artificial intelligence technique inspired by the behavior of social insects. Taking into account that the Internet connects a high number of users with a negligible delay, some of those techniques can be combined with sociology concepts and applied to e-learning. The chapter analyzes several of such applications and exposes their strong and weak points. The authors hope that understanding the concepts used in the applications described in the chapter will not only inform researchers about an emerging trend, but also provide with interesting ideas that can be applied and combined with any e-learning system.

Specifying the personalization reasoning mechanism for an intelligent medical e-learning system on Atheromatosis: An empirical study

2008 ◽  
Vol 2 (3) ◽  
pp. 179-190 ◽  
Author(s):  
Katerina Kabassi ◽  
Maria Virvou ◽  
George A. Tsihrintzis ◽  
Yiannis Vlachos ◽  
Despina Perrea
Keyword(s):  
Empirical Study ◽  
Learning System ◽  
E Learning ◽  
Reasoning Mechanism

scholarly journals Using Voice Recognition in E-Learning System to Reduce Educational Inequality During Covid-19

2021 ◽  
Vol 11 (04) ◽  
pp. 01-16
Author(s):  
Vladimir Tregubov
Keyword(s):  
Artificial Intelligence ◽  
Educational Inequality ◽  
Voice Recognition ◽  
Traditional Education ◽  
Educational Process ◽  
Learning System ◽  
Educational Institutions ◽  
Inequality In Education ◽  
E Learning ◽  
Online Format

During the COVID -19 pandemic, educational institutions around the world faced problems that have to do with the frustration of students for whom traditional education has been replaced by the online format. Students are experiencing technical difficulties in the digitalization of education. International monitoring of education systems has shown that quite a few countries were ready to move to distance learning, both for technical and economic reasons. The covid pandemic has caused an increase in educational inequality. Elearning systems were expected to reduce inequality in education, but empirical research has shown that learning in this format not only does not reduce, but can increase inequality, increasing the gap in educational outcomes between students with different socioeconomic status. The article describes applications of using voice recognition technology based on artificial intelligence which, by our opinion, may reduce educational inequality during covid-19. We presented a comparative analysis of existing examples artificial intelligence in the educational process. Artificial intelligence uses in specialized software it makes educational process more convenient for both the students and the teachers. There is a description of an application “Academic phrase bank" developed by author. The application consists of two specialising actions for Google assistant. The application allows to increase academic vocabulary, train of creating grammatically correct academic expressions, and memorize templates of academic phrases. In active mode, this application helps to create correct phrases of academic English and improve the abilities of understanding English speech.

scholarly journals An E-learning Acceptance Evaluation Through UTAUT Model in a Postgraduate Program

2019 ◽  
Vol 16 ◽  
Author(s):  
Ridwan Mahande ◽  
Jasruddin Malago
Keyword(s):  
Social Influence ◽  
Behavioral Intention ◽  
Hypothesis Test ◽  
Learning System ◽  
Utaut Model ◽  
Ex Post ◽  
Ex Post Facto ◽  
E Learning ◽  
Facilitating Conditions ◽  
Ex Post Facto Study

This study aims to evaluate e-learning acceptance through the UTAUT model by showing the contributing variables to the acceptance of e-learning in a Postgraduate Program at Universitas Negeri Makassar, Indonesia. This study was an ex post facto study with 170 samples distributed proportionally. The data were collected through a questionnaire that was developed from UTAUT model variables and indicators. The data collected were analyzed by path. The results of the e-learning acceptance evaluation based on the hypothesis test showed that facilitating conditions, behavioral intention, effort expectancy, performance expectancy, and social influence significantly and positively affected behavioral intention. Facilitating conditions and behavioral intention significantly and positively affected the e-learning acceptance. Variables that greatly contributed to the higher or lower e-learning acceptance were facilitating conditions and behavioral intention. Facilitating conditions were strongly affected by the students’ knowledge and internet speed. Meanwhile, the behavioral intention was strongly influenced by the level of students’ belief in the future of e-learning and students’ eagerness that e-learning be integrated in every subject. Nevertheless, social influence variables need more attention for implementing a better and sustainable e-learning system.

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