Development of Prototype of Natural Language Answer Processor for e-Learning

2020 ◽  
pp. 448-459
Author(s):  
Dzhavdet Suleymanov ◽  
Nikolai Prokopyev
Keyword(s):  
Natural Language ◽  
E Learning

scholarly journals Technology Enhanced Learning Using Humanoid Robots

2021 ◽  
Vol 13 (2) ◽  
pp. 32
Author(s):  
Diego Reforgiato Recupero
Keyword(s):  
Natural Language ◽  
Sentiment Analysis ◽  
Architectural Design ◽  
Humanoid Robots ◽  
Human Robot Interaction ◽  
Technology Enhanced Learning ◽  
Rehabilitation Centers ◽  
Learning Domains ◽  
E Learning ◽  
Analysis Engine

In this paper we present a mixture of technologies tailored for e-learning related to the Deep Learning, Sentiment Analysis, and Semantic Web domains, which we have employed to show four different use cases that we have validated in the field of Human-Robot Interaction. The approach has been designed using Zora, a humanoid robot that can be easily extended with new software behaviors. The goal is to make the robot able to engage users through natural language for different tasks. Using our software the robot can (i) talk to the user and understand their sentiments through a dedicated Semantic Sentiment Analysis engine; (ii) answer to open-dialog natural language utterances by means of a Generative Conversational Agent; (iii) perform action commands leveraging a defined Robot Action ontology and open-dialog natural language utterances; and (iv) detect which objects the user is handing by using convolutional neural networks trained on a huge collection of annotated objects. Each module can be extended with more data and information and the overall architectural design is general, flexible, and scalable and can be expanded with other components, thus enriching the interaction with the human. Different applications within the e-learning domains are foreseen: The robot can either be a trainer and autonomously perform physical actions (e.g., in rehabilitation centers) or it can interact with the users (performing simple tests or even identifying emotions) according to the program developed by the teachers.

On the Generation of E-Learning Resources Using Business Process, Natural Language Processing, and Web Services

2021 ◽  
Vol 23 (2) ◽  
pp. 40-44
Author(s):  
Olivia Fragoso-Diaz ◽  
Vitervo Lopez Caballero ◽  
Juan Carlos Rojas-Perez ◽  
Rene Santaolaya-Salgado ◽  
Juan Gabriel Gonzalez-Serna
Keyword(s):  
Natural Language Processing ◽  
Web Services ◽  
Natural Language ◽  
Language Processing ◽  
Business Process ◽  
Learning Resources ◽  
E Learning

scholarly journals OBJECT ORIENTED METRIC BASED ANALYSIS OF TEXT TRANSMISSION IN E-LEARNING THROUGH NATURAL LANGUAGE STEGANOGRAPHY

2020 ◽  
Vol 4 (11) ◽  
pp. 68-74
Author(s):  
Soumendu Banerjee ◽  
Kh Amirul Islam ◽  
Sunil Karforma ◽  
Akash Nag
Keyword(s):  
Cost Effectiveness ◽  
Natural Language ◽  
Information And Communication Technology ◽  
Communication Technology ◽  
Object Oriented ◽  
Learning System ◽  
Proposed Model ◽  
Text Steganography ◽  
Information And Communication ◽  
E Learning

E-learning is an application of information and communication technology in the field of learning. Through steganography the e-learning institution can provide security to other participants of e-learning like teacher and learner. Here we use text steganography with modified SNOW algorithm while passing secret texts from the administrator to the learner in an e-learning system. In this paper, we calculate the object oriented metric based analysis of CK and MOOD metrics of our proposed model, which ensures the advantages of code redundancy, code reusability, and cost effectiveness and so on.

Understanding Romanian Texts by Using Gamification Methods

2019 ◽  
Vol 9 (1) ◽  
pp. 52-57
Author(s):  
Ștefania-Eliza Berghia ◽  
Bogdan Pahomi ◽  
Daniel Volovici
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Choice Theory ◽  
Social Choice Theory ◽  
Parts Of Speech ◽  
Automatic Translation ◽  
E Learning ◽  
Syntactic Function

AbstractIn recent years, there has been increasing interest in the field of natural language processing. Determining which syntactic function is right for a specific word is an important task in this field, being useful for a variety of applications like understanding texts, automatic translation and question-answering applications and even in e-learning systems. In the Romanian language, this is an even harder task because of the complexity of the grammar. The present paper falls within the field of “Natural Language Processing”, but it also blends with other concepts such as “Gamification”, “Social Choice Theory” and “Wisdom of the Crowd”. There are two main purposes for developing the application in this paper:a) For students to have at their disposal some support through which they can deepen their knowledge about the syntactic functions of the parts of speech, a knowledge that they have accumulated during the teaching hours at schoolb) For collecting data about how the students make their choices, how do they know which grammar role is correct for a specific word, these data being primordial for replicating the learning process

scholarly journals Evaluation of an international medical E-learning course with natural language processing and machine learning

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Aditya Borakati
Keyword(s):  
Machine Learning ◽  
Cohort Study ◽  
Natural Language Processing ◽  
Medical Students ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Small Scale ◽  
E Learning ◽  
The Impact

Abstract Background In the context of the ongoing pandemic, e-learning has become essential to maintain existing medical educational programmes. Evaluation of such courses has thus far been on a small scale at single institutions. Further, systematic appraisal of the large volume of qualitative feedback generated by massive online e-learning courses manually is time consuming. This study aimed to evaluate the impact of an e-learning course targeting medical students collaborating in an international cohort study, with semi-automated analysis of feedback using text mining and machine learning methods. Method This study was based on a multi-centre cohort study exploring gastrointestinal recovery following elective colorectal surgery. Collaborators were invited to complete a series of e-learning modules on key aspects of the study and complete a feedback questionnaire on the modules. Quantitative data were analysed using simple descriptive statistics. Qualitative data were analysed using text mining with most frequent words, sentiment analysis with the AFINN-111 and syuzhet lexicons and topic modelling using the Latent Dirichlet Allocation (LDA). Results One thousand six hundred and eleventh collaborators from 24 countries completed the e-learning course; 1396 (86.7%) were medical students; 1067 (66.2%) entered feedback. 1031 (96.6%) rated the quality of the course a 4/5 or higher (mean 4.56; SD 0.58). The mean sentiment score using the AFINN was + 1.54/5 (5: most positive; SD 1.19) and + 0.287/1 (1: most positive; SD 0.390) using syuzhet. LDA generated topics consolidated into the themes: (1) ease of use, (2) conciseness and (3) interactivity. Conclusions E-learning can have high user satisfaction for training investigators of clinical studies and medical students. Natural language processing may be beneficial in analysis of large scale educational courses.

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.

scholarly journals Evaluation of an International Medical E-Learning Course with Natural Language Processing and Machine Learning

2020 ◽  
Author(s):  
Aditya Borakati
Keyword(s):  
Machine Learning ◽  
Cohort Study ◽  
Natural Language Processing ◽  
Medical Students ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Small Scale ◽  
E Learning ◽  
The Impact

Abstract Background: In the context of the ongoing pandemic, e-learning has become essential to maintain existing medical educational programmes. Evaluation of such courses has thus far been on a small scale at single institutions. Further, systematic appraisal of the large volume of qualitative feedback generated by massive online e-learning courses manually is time consuming. This study aimed to evaluate the impact of an e-learning course targeting medical students collaborating in an international cohort study, with semi-automated analysis of feedback using text mining and machine learning methods.Method: This study was based on a multi-centre cohort study exploring gastrointestinal recovery following elective colorectal surgery. Collaborators were invited to complete a series of e-learning modules on key aspects of the study and complete a feedback questionnaire on the modules. Quantitative data were analysed using simple descriptive statistics. Qualitative data were analysed using text mining with most frequent words, sentiment analysis with the AFINN-111 and syuzhet lexicons and topic modelling using the Latent Dirichlet Allocation (LDA). Results: 1,611 collaborators from 24 countries completed the e-learning course; 1,396 (86.7%) were medical students; 1,067 (66.2%) entered feedback. 1,031 (96.6%) rated the quality of the course a 4/5 or higher (mean 4.56; SD 0.58). The mean sentiment score using the AFINN was +1.54/5 (5: most positive; SD 1.19) and +0.287/1 (1: most positive; SD 0.390) using syuzhet. LDA generated topics consolidated into the themes: (1) ease of use, (2) conciseness and (3) interactivity.Conclusions: E-learning can have high user satisfaction for training investigators of clinical studies and medical students. Natural language processing may be beneficial in analysis of large scale educational courses.

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