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.

Sentiment Analysis to Evaluate Teaching Performance

2016 ◽  
Vol 7 (4) ◽  
pp. 86-107 ◽  
Author(s):  
Paola Adinolfi ◽  
Ernesto D'Avanzo ◽  
Miltiadis D. Lytras ◽  
Isabel Novo-Corti ◽  
Jose Picatoste
Keyword(s):  
Higher Education ◽  
Literature Review ◽  
Student Satisfaction ◽  
Sentiment Analysis ◽  
Learning Analytics ◽  
Teaching Performance ◽  
Learning Domains ◽  
E Learning ◽  
Specific Learning ◽  
Open Issues

The aim of this work is to review a specific learning analytics method - sentiment analysis - in the field of Higher Education, showing how it is employed to monitor student satisfaction on different platforms, and to propose an architecture of Sentiment Analysis for Higher Education purposes, which trace and unify what emerges from the literature review. First, a literature review is carried out, which proves the widespread and increasing interest of the communities, of both scholars and practitioners, in the use of sentiment analysis in the field of Higher Education. The analysis, focused on three different e-learning domains, identifies weaknesses and gaps, and in particular the lack of a unifying approach which is able to deal with the different domains. Secondly, a prototype architecture – LADEL (Learning Analytics Dashboard for E-Learning) - is introduced, which is able to deal with the different e-learning domains. Some preliminary experiments are carried out, highlighting some limitations and open issues, as stimulus to continue the development of the platform.

Perceived Innovative Teaching Procedures in Higher Education From Students' Perspectives From a Sentiment Analysis Approach

2018 ◽  
pp. 126-147 ◽  
Author(s):  
Ernesto D'Avanzo ◽  
Miltiadis Demetrios Lytras ◽  
Jose Picatoste ◽  
Isabel Novo-Corti ◽  
Paola Adinolfi
Keyword(s):  
Higher Education ◽  
Sentiment Analysis ◽  
Learning Analytics ◽  
Point Of View ◽  
Economic Activities ◽  
High Quality ◽  
Innovative Teaching ◽  
Learning Domains ◽  
E Learning ◽  
Core Issue

In the fourth revolution era talking innovation is not only necessary for improving and forwarding all social and economic activities, but also is it an exigence of those who are demanding high-quality goods or services. This is important for knowledge services, and becomes a core issue for educative issues designed for the millennials or digital natives. Sometimes, these exigencies are no shown directly, but it is possible assessing them by means of the analysis of sentiments, which reflects a range of feelings which are not clearly verbalized and even self-recognized. This paper analyzes sentiments for assessing the importance of innovative procedures in higher education, from the point of view of the students. A prototype architecture – LADEL (Learning Analytics Dashboard for E-Learning) - is introduced, for dealing with the diverse e-learning domains. Some experiments are conducted. This demonstrated the necessity of innovative procedures in higher education, since it is a widespread, multidisciplinary and transversal demand, even it is not always explicit.

scholarly journals ANALISIS SENTIMEN APLIKASI E-LEARNING SELAMA PANDEMI COVID-19 DENGAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DAN CONVOLUTIONAL NEURAL NETWORK

SEMINASTIKA ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 16-25
Author(s):  
Alek Sander Simbolon ◽  
Nina Ismaya Pangaribuan ◽  
Nenni Mona Aruan
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Natural Language ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Support Vector ◽  
E Learning ◽  
F Measure

Aplikasi e-learning dibutuhkan masyarakat dalam meningkatkan pendidikan di mana e-learning yang menjadi objek penelitian adalah Ruangguru dan Zenius karena memiliki jumlah pengguna lebih dari 16 juta dengan kepuasan pengguna lebih dari 8.5/10 dan lebih dari 1 juta kali di download di play store. Aplikasi tersebut memberikan ruang bagi pengguna aplikasi untuk mendapatkan tingkat kepuasan dari pengguna aplikasi. Sentiment analysis merupakan natural language preprocessing yang dapat digunakan dalam melakukan ekstraksi opini dari data berupa teks di mana tujuan penelitian ini melakukan evaluasi pada peningkatan hal positif dan memperbaiki hal yang negatif. Data ulasan yang diambil dari Twitter dan play store memiliki promosi dan giveaway yang akan berpengaruh pada pengolahan data dalam penentuan opini dan bukan opini. Penulis menggunakan metode lexicon based dalam memberikan label atau nilai sentiment pada setiap data. Pendekatan yang digunakan algoritma Support Vector Machine (SVM) dan Convolutional Neural Network (CNN) dalam melakukan klasifikasi terhadap data test yang di uji dari model yang telah dibangun. Berdasarkan hasil klasifikasi opini menjadi tiga kelas yaitu kelas positif, negatif, dan netral dari ulasan aplikasi Ruangguru dan Zenius. Dari nilai akurasi dan F-measure diperoleh bahwa klasifikasi yang terbaik adalah menggunakan algoritma CNN dengan akurasi dan F-measure memiliki nilai 86%.

A Review on Sentiment Classification: Natural Language Understanding

2019 ◽  
Vol 13 (1) ◽  
pp. 20-27 ◽  
Author(s):  
Srishty Jindal ◽  
Kamlesh Sharma
Keyword(s):  
Natural Language ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Natural Language Understanding ◽  
Business Analytics ◽  
Language Understanding ◽  
Text Data ◽  
Data Set ◽  
Market Positioning ◽  
Illegal Activities

Background: With the tremendous increase in the use of social networking sites for sharing the emotions, views, preferences etc. a huge volume of data and text is available on the internet, there comes the need for understanding the text and analysing the data to determine the exact intent behind the same for a greater good. This process of understanding the text and data involves loads of analytical methods, several phases and multiple techniques. Efficient use of these techniques is important for an effective and relevant understanding of the text/data. This analysis can in turn be very helpful in ecommerce for targeting audience, social media monitoring for anticipating the foul elements from society and take proactive actions to avoid unethical and illegal activities, business analytics, market positioning etc. Method: The goal is to understand the basic steps involved in analysing the text data which can be helpful in determining sentiments behind them. This review provides detailed description of steps involved in sentiment analysis with the recent research done. Patents related to sentiment analysis and classification are reviewed to throw some light in the work done related to the field. Results: Sentiment analysis determines the polarity behind the text data/review. This analysis helps in increasing the business revenue, e-health, or determining the behaviour of a person. Conclusion: This study helps in understanding the basic steps involved in natural language understanding. At each step there are multiple techniques that can be applied on data. Different classifiers provide variable accuracy depending upon the data set and classification technique used.

Humans and humanoids

2018 ◽  
Author(s):  
Giorgio Metta
Keyword(s):  
Humanoid Robot ◽  
Robot Vision ◽  
Physical Space ◽  
Humanoid Robots ◽  
Human Robot Interaction ◽  
Biologically Inspired ◽  
Robot Interaction ◽  
Humanoid Robotics ◽  
Biomimetic Robot ◽  
Compliant Actuation

This chapter outlines a number of research lines that, starting from the observation of nature, attempt to mimic human behavior in humanoid robots. Humanoid robotics is one of the most exciting proving grounds for the development of biologically inspired hardware and software—machines that try to recreate billions of years of evolution with some of the abilities and characteristics of living beings. Humanoids could be especially useful for their ability to “live” in human-populated environments, occupying the same physical space as people and using tools that have been designed for people. Natural human–robot interaction is also an important facet of humanoid research. Finally, learning and adapting from experience, the hallmark of human intelligence, may require some approximation to the human body in order to attain similar capacities to humans. This chapter focuses particularly on compliant actuation, soft robotics, biomimetic robot vision, robot touch, and brain-inspired motor control in the context of the iCub humanoid robot.

scholarly journals Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation

2021 ◽  
Vol 18 (12) ◽  
pp. 6408
Author(s):  
Mario Jojoa Acosta ◽  
Gema Castillo-Sánchez ◽  
Begonya Garcia-Zapirain ◽  
Isabel de la Torre Díez ◽  
Manuel Franco-Martín
Keyword(s):  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Language Processing ◽  
Health Care Professionals ◽  
Ground Truth ◽  
Relevant Information ◽  
Free Text

The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available—86 registers for the first and 68 for the second—transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages.

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 User-centred design of humanoid robots’ communication

2020 ◽  
Vol 12 (1) ◽  
pp. 58-73
Author(s):  
Sofia Thunberg ◽  
Tom Ziemke
Keyword(s):  
Mental Models ◽  
Participatory Design ◽  
Humanoid Robot ◽  
State Of The Art ◽  
Design Method ◽  
Humanoid Robots ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Design Concepts ◽  
Design Ideas

AbstractInteraction between humans and robots will benefit if people have at least a rough mental model of what a robot knows about the world and what it plans to do. But how do we design human-robot interactions to facilitate this? Previous research has shown that one can change people’s mental models of robots by manipulating the robots’ physical appearance. However, this has mostly not been done in a user-centred way, i.e. without a focus on what users need and want. Starting from theories of how humans form and adapt mental models of others, we investigated how the participatory design method, PICTIVE, can be used to generate design ideas about how a humanoid robot could communicate. Five participants went through three phases based on eight scenarios from the state-of-the-art tasks in the RoboCup@Home social robotics competition. The results indicate that participatory design can be a suitable method to generate design concepts for robots’ communication in human-robot interaction.

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