A Virtual Tutor for Significant Learning within e-Learning 2.0 Environments

2013 ◽  
Vol 4 (3) ◽  
pp. 31-41
Author(s):  
Orlando De Pietro ◽  
Giovanni Frontera
Keyword(s):  
Natural Language ◽  
Knowledge Base ◽  
Monitoring System ◽  
Search Engine ◽  
Intelligent Agent ◽  
Technical Failure ◽  
Adaptive Search ◽  
Learning Platform ◽  
E Learning ◽  
External Sources

The paper reports of an automatic application solution that allows the realization of significant learning within e-learning 2.0 environments. In particular, it is presented an intelligent agent which supports tutoring activities within an e-Learning platform. The agent interacts with the users in natural language; it integrates an adaptive search engine to do automated researches based on external sources of the knowledge base of the platform, and a monitoring system to report of any technical failure.

Authentic and Situated Learning with the Use of an Adaptive Search Engine and a QR-Code in Mobile Mode

2013 ◽  
Vol 4 (3) ◽  
pp. 19-30
Author(s):  
Orlando De Pietro
Keyword(s):  
Learning Environment ◽  
Knowledge Base ◽  
Search Engine ◽  
Situated Learning ◽  
Authentic Learning ◽  
Virtual Learning Environment ◽  
Qr Code ◽  
Adaptive Search ◽  
Learning Platform ◽  
E Learning

The paper presents a technique of interrogation, in mobile technology, of a knowledge base contained in an e-Learning platform. The query is done through the integrated use of an adaptive search engine (ASE) and QR codes. The QR-Code are used to label objects in real environments of cultural matter (eg: museums, art galleries, archaeological sites, etc.). These objects can then be analyzed in more detail during a real learning activity (situated learning and authentic learning). The knowledge base of the virtual learning environment (VLE), is interrogated through a mobile device (smartphone or tablet) which, through an appropriate decoder software, interprets the instructions contained in a QR label previously positioned on the object actually observed by the learner (eg, during a visit to a museum). ASE combines to the data contained in the QR-Code those relating to the user who performs the query (previously recognized via the log-in on the e-Learning platform), therefore, extracts information about the observed object, adapting to the profile of the user-learner. With the help of these tools the learner can expand his real learning experience, while interacting with a virtual learning environment. In this way, situated and authentic learning activities can be upgraded and virtualized, maturing a significative educational experience with the use of intuitive and user friendly digital tools.

Knowledge tracing with an intelligent agent, in an e-learning platform

2018 ◽  
Vol 24 (1) ◽  
pp. 711-741 ◽  
Author(s):  
Amal Trifa ◽  
Aroua Hedhili ◽  
Wided Lejouad Chaari
Keyword(s):  
Intelligent Agent ◽  
Learning Platform ◽  
Knowledge Tracing ◽  
E Learning

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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