scholarly journals Student Learning Style Extraction from On-Campus Learning Context Data

2017 ◽  
Vol 104 ◽  
pp. 272-278 ◽  
Author(s):  
Janis Bicans ◽  
Janis Grundspenkis
Author(s):  
Xiaoran Fu ◽  
K. Lokesh Krishna ◽  
R. Sabitha

Artificial Intelligence (AI) assisted educational institutions extensively utilize electronic learning context to guarantee improved teaching and learning experiences accompanied by educational activities. E-learning or online learning plays a significant role in Chinese higher education. There is a challenge to implement e-learning in China’s higher education to improve course resources, student learning style prediction, teaching quality, and service support. Hence in this paper, Artificial Intelligence based Efficient E-learning Framework (AI-EELF) has been proposed to overcome the challenges faced by China’s higher education while implementing e-learning modules. The collected student data can be efficiently utilized and exploited to progress in an adaptive learning environment. The proposed AI-EELF method introduces multiple learning models to enhance teaching quality and predict the student learning style. The experimental results show that the proposed AI-EELF achieves high performance, prediction ratio in determining students’ learning style and improves teaching quality compared to other existing methods.


2020 ◽  
Author(s):  
Javad Hashemi ◽  
Sachin Kholamkar ◽  
Naveen Chandrashekar ◽  
Edward Anderson

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0

Transnational higher education is a multinational growth strategy requiring a foreign direct investment to establish a university or a campus in a new country and, if possible, to use articulation agreements with credible partners to increase domestic enrolment. Due to the potential international student learning style differences, we hypothesized there may be difficulties teaching Information Communication Technology (ICT) courses in transnational strategies due to the student origin or domestic campus location. The purpose of this study was to examine if student learning was effective within ICT graduate courses at an accredited sub-Saharan Africa-based university implementing the transnational education strategy. We found student learning was effective, but paradoxically, some factors indicated unusual results. Learning impact was higher when students disregarded the learning objectives, which we were able to explain theoretically. Conversely, learning impact was higher for many students who avoided tutoring, which we also rationalized.


2014 ◽  
Vol 20 (10) ◽  
pp. 1936-1940 ◽  
Author(s):  
Adhistya Erna Permanasari ◽  
Indriana Hidayah ◽  
Sapta Nugraha

2012 ◽  
Vol 11 (1) ◽  
pp. 108-119 ◽  
Author(s):  
ChengTu Hsieh ◽  
Melissa Mache ◽  
Duane Knudson

2020 ◽  
pp. 65-70
Author(s):  
Rayung Wulan ◽  
Achmad Sarwandianto ◽  
Nur Alamsyah ◽  
Aulia Ar Rakhman Awaludin

This Android-based expert system application was created to assist teachers in analysing student learning styles and identification of student learning styles that are easy to transfer knowledge in schools. The instrument used was the results of a questionnaire to measure learning style variables and student assessment variables in receiving mathematics lessons. The Android-based expert system application was designed based on student learning style questionnaire, the questionnaire was validated, and internal consistency reliability, set the instrument items and then collected in the rules and decision trees. The results of the questionnaire were taken from 6 elementary schools in Surakarta. The inference method used in this calculation is the forward chaining method, looking at the results of the decision tree as outlined in the expert system application. The Android-based expert system application is very effective and efficient in analysing student learning styles.


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