Ontology and Learning Path Driven Social E-learning Platform

2016 ◽  
Author(s):  
Yin-An Chen ◽  
Chuan-Jun Su
Keyword(s):  
Learning Platform ◽  
Learning Path ◽  
E Learning

scholarly journals Reinforcement Learning Approach for Adaptive e-Learning Based on Multiple Learner Characteristics

2021 ◽  
Vol 4 (2) ◽  
pp. 55-76
Author(s):  
Dan Oyuga Anne ◽  
Elizaphan Maina
Keyword(s):  
Learning Style ◽  
Affective State ◽  
Learner Characteristics ◽  
Q Learning ◽  
Model Free ◽  
Learning Platform ◽  
Learning Path ◽  
Multiple Characteristics ◽  
E Learning ◽  
Extraction Model

We introduce a novel three stepwise model of adaptive e-learning using multiple learner characteristics. We design a model of a learner attributes enlisting the study domain, summary details of the student and the requirements of the student. We include the theories of learning style to categorize and identify specific individuals so as to improve their experience on the online learning platform and apply it in the model. The affective state extraction model which extracts learner emotions from text inputs during the platform interactions. We finally pass the system extracted information the adaptivity domain which uses the off-policy Q-learning model free algorithm (Jang et al., 2019) to structure the learning path into tutorials, lectures and workshops depending on predefined constraints of learning. Simulated results show better adaptivity incases of multiple characteristics as opposed to single learner characteristics. Further research to include more than three characteristics as in this research.

scholarly journals E-learning e futuri studenti in mobilità internazionale. Riflessioni su aspetti e potenzialità di un corso di lingua italiana

2021 ◽  
Vol 19 (2) ◽  
Author(s):  
Alessandra Pettinelli ◽  
Chiara Sola ◽  
Monique Cintra ◽  
Luca Avellini
Keyword(s):  
Cultural Knowledge ◽  
Course Structure ◽  
Structure Selection ◽  
Learning Platform ◽  
Learning Path ◽  
E Learning ◽  
Exchange Programme ◽  
The One ◽  
The University ◽  
Self Learning

The A1 online Italian course offered by the CLA (University Linguistic Centre) of the University of Perugia, is one of the results achieved during various research projects, which has contributed, on the one hand, to the internationalization of the Institution, on the other, to the enhancement of digital technologies providing future university mobility students with the opportunity to acquire linguistic-cultural knowledge, even before the beginning of their mobility exchange programme in Italy. The experience reported in this article reflects on an evolving work, describing its design phase – course structure, selection and creation of linguistic and didactic materials, tools available in the Moodle open-source learning platform – and its subsequent phases of course activation and verification. Throughout the entire project, we focused on two fundamental aspects: inspiring and maintaining student motivation in addition to constructing an assisted, and above all, interactive self-learning path.

Cloud-Based E-Learning Platform

2016 ◽  
Vol 15 (5) ◽  
pp. 109-130 ◽  
Author(s):  
Mohsen El-Shawarby
Keyword(s):  
Learning Platform ◽  
E Learning

scholarly journals Validation of a TAM Extension in Agriculture: Exploring the Determinants of Acceptance of an e-Learning Platform

2021 ◽  
Vol 11 (10) ◽  
pp. 4672
Author(s):  
Ivonne Angelica Castiblanco Jimenez ◽  
Laura Cristina Cepeda García ◽  
Federica Marcolin ◽  
Maria Grazia Violante ◽  
Enrico Vezzetti
Keyword(s):  
Technology Acceptance ◽  
Time Management ◽  
Agricultural Sector ◽  
Geographical Location ◽  
Ease Of Use ◽  
Content Quality ◽  
Learning Platform ◽  
Primary Determinant ◽  
E Learning ◽  
Acceptance Model

Supporting education and training initiatives has been identified as an effective way to address Sustainable Development Challenges. In this sense, e-learning stands out as one of the most viable alternatives considering its advantages in terms of resources, time management, and geographical location. Understanding the reasons that move users to adopt these technologies is critical for achieving the desired social objectives. The Technology Acceptance Model (TAM) provides valuable guidelines to identify the variables shaping users’ acceptance of innovations. The present study aims to validate a TAM extension designed for FARMER 4.0, an e-learning application in the agricultural sector. Findings suggest that content quality (CQ) is the primary determinant of farmers’ and agricultural entrepreneurs’ perception of the tool’s usefulness (PU). Furthermore, experience (EXP) and self-efficacy (SE) shape potential users’ perceptions about ease of use (PEOU). This study offers helpful insight into the design and development of e-learning applications in the farming sector and provides empirical evidence of TAM’s validity to assess technology acceptance.

scholarly journals Multi-Agent System Observer: Intelligent Support for Engaged E-Learning

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1370
Author(s):  
Igor Vuković ◽  
Kristijan Kuk ◽  
Petar Čisar ◽  
Miloš Banđur ◽  
Đoko Banđur ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Intelligent Support ◽  
Course Success ◽  
States Of Emergency ◽  
Learning Platform ◽  
Rapid Spread ◽  
E Learning ◽  
Multi Agent ◽  
Observer System

Moodle is a widely deployed distance learning platform that provides numerous opportunities to enhance the learning process. Moodle’s importance in maintaining the continuity of education in states of emergency and other circumstances has been particularly demonstrated in the context of the COVID-19 virus’ rapid spread. However, there is a problem with personalizing the learning and monitoring of students’ work. There is room for upgrading the system by applying data mining and different machine-learning methods. The multi-agent Observer system proposed in our paper supports students engaged in learning by monitoring their work and making suggestions based on the prediction of their final course success, using indicators of engagement and machine-learning algorithms. A novelty is that Observer collects data independently of the Moodle database, autonomously creates a training set, and learns from gathered data. Since the data are anonymized, researchers and lecturers can freely use them for purposes broader than that specified for Observer. The paper shows how the methodology, technologies, and techniques used in Observer provide an autonomous system of personalized assistance for students within Moodle platforms.

scholarly journals The Impact of Narrative Feedback, E-Learning Modules and Realistic Video and the Reduction of Misconception

2021 ◽  
Vol 11 (4) ◽  
pp. 158
Author(s):  
Abdul Halim ◽  
Elmi Mahzum ◽  
Muhammad Yacob ◽  
Irwandi Irwandi ◽  
Lilia Halim
Keyword(s):  
Biology Education ◽  
Free Fall ◽  
Percentage Reduction ◽  
Significant Treatment ◽  
Learning Modules ◽  
Physics Courses ◽  
Learning Platform ◽  
Quasi Experimental ◽  
E Learning ◽  
The Impact

Physics learning in universities utilized the Moodle-based e-learning media as an online learning platform. However, the effectiveness of remediating misconception using online media has not been widely researched. Therefore, this study was set to determine the level of misconception percentage reduction through the use of narrative feedback, the e-learning modules, and realistic video. The study was a quantitative approach with a quasi-experimental method involving 281 students who were taking basic physics courses in the Department of Physics, Chemistry, and Biology Education. The data collection used a three-tier diagnostic test based on e-learning at the beginning of the activity and after the treatment (posttest). The results of the data analysis with descriptive statistics show that the most significant treatment in reducing misconception percentage on the topic of free-fall motion was in the following order: narrative feedback, e-learning modules and realistic video. The misconception percentage reduction in the sub-concept of accelerated free- fall was effective for all types of the treatments.

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