An APP-Based E-Learning Platform for Artificial Intelligence Cross-Domain Application Practices

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.

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

Electronics ◽

10.3390/electronics10121370 ◽

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.

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

Education Sciences ◽

10.3390/educsci11040158 ◽

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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Recommender E-Learning platform using sentiment analysis aggregation

Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications ◽

10.1145/3419604.3419784 ◽

2020 ◽

Author(s):

Jamal Mawane ◽

Abdelwahab Naji ◽

Mohamed Ramdani

Keyword(s):

Sentiment Analysis ◽

Learning Platform ◽

E Learning

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Evaluating the Usability of an E-Learning Platform within Higher Education from a Student Perspective

Proceedings of the 2019 3rd International Conference on Education and E-Learning ◽

10.1145/3371647.3371661 ◽

2019 ◽

Cited By ~ 1

Author(s):

Khaled Abuhlfaia ◽

Ed de Quincey

Keyword(s):

Higher Education ◽

Student Perspective ◽

Learning Platform ◽

E Learning

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Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data

Future Internet ◽

10.3390/fi13050124 ◽

2021 ◽

Vol 13 (5) ◽

pp. 124

Author(s):

Jiseong Son ◽

Chul-Su Lim ◽

Hyoung-Seop Shim ◽

Ji-Sun Kang

Keyword(s):

Artificial Intelligence ◽

Domain Knowledge ◽

Open Data ◽

Heterogeneous Data ◽

Big Data Analysis ◽

Knowledge Graph ◽

Cross Domain ◽

Disaster Data ◽

Knowledge Graphs ◽

Open Datasets

Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.

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