An Effective Pedagogy Toolkit for Learning in an Intelligent Virtual Environment

2021 ◽  
pp. 825-836
Author(s):  
R. S. Kamath ◽  
R. K. Kamat
Keyword(s):  
Natural Language ◽  
Virtual Environment ◽  
Language Processing ◽  
Virtual Learning Environment ◽  
Traditional Learning ◽  
Effective Learning ◽  
Effective Pedagogy ◽  
Pedagogic Tool ◽  
Virtual Setting ◽  
Quality Learning

Throughout the progression of the pedagogy, educators are striving hard to bring up the systems centering on effective learning. This omnipresent trend has led to the ontogeny of innovative and culmination of many congregation technologies such as virtual reality (VR), artificial intelligence (AI), and natural language processing (NLP) ensuing as intelligent virtual learning environment (IVLE). Technology-enhanced encyclopedism can facilitate pupils with influential and high-quality learning experiences as compared to the traditional learning approach. This chapter portrays learning in intelligent virtual environment as an effective pedagogy approach. The pedagogic tool developed by the authors captures text written in English as an input and creates the envisioned virtual setting. The ability of natural language interface (NLI) for VR-based learning systems is the most significant attainment of the present work, which brings a novel perspective in the field pedagogy.

scholarly journals Design, Development, and Usability of a Virtual Environment on Moral, Social, and Emotional Leaning

2020 ◽  
Vol 10 (2) ◽  
pp. 50-65
Author(s):  
Samiullah Paracha ◽  
Lynne Hall ◽  
Kathy Clawson ◽  
Nicole Mitsche
Keyword(s):  
Virtual Environment ◽  
Design Process ◽  
Collaborative Design ◽  
Learning Experience ◽  
Ethical Reasoning ◽  
Virtual Learning Environment ◽  
Design Development ◽  
Effective Learning ◽  
Social And Emotional ◽  
Sensitive Issues

Virtual environments have the potential to be an important teaching tool for emotionally sensitive issues capable of producing a sense of presence, perspective-taking and introspection in users in a risk-free, rapid feedback experience. In designing such experiences, it is essential that users are regularly engaged in a collaborative design process. However, engaging in design, development, and evaluation can in itself provide a learning experience. Here, the authors present an approach to engaging children in the design, development and evaluation of a virtual learning environment, specifically a serious game, focused on inculcating empathy, ethical reasoning, and reflection for coping with bullying. It was demonstrated that children's involvement not only contributed to an improved virtual environment, but significantly engaging in the design process provided children with a novel and effective learning opportunity.

scholarly journals Language modeling and bidirectional coders representations: an overview of key technologies

Informatics ◽  
2021 ◽  
Vol 17 (4) ◽  
pp. 61-72
Author(s):  
D. I. Kachkou
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Text Processing ◽  
Language Model ◽  
Language Modeling ◽  
Attention Mechanism ◽  
Data Set ◽  
Effective Learning ◽  
Key Technologies ◽  
Model Improvement

The article is an essay on the development of technologies for natural language processing, which formed the basis of BERT (Bidirectional Encoder Representations from Transformers), a language model from Google, showing high results on the whole class of problems associated with the understanding of natural language. Two key ideas implemented in BERT are knowledge transfer and attention mechanism. The model is designed to solve two problems on a large unlabeled data set and can reuse the identified language patterns for effective learning for a specific text processing problem. Architecture Transformer is based on the attention mechanism, i.e. it involves evaluation of relationships between input data tokens. In addition, the article notes strengths and weaknesses of BERT and the directions for further model improvement.

Nature: A Tool Resulting from the Union of Artificial Intelligence and Natural Language Processing for Searching Research Projects in Colombia

2021 ◽  
Vol 12 (04) ◽  
pp. 01-21
Author(s):  
Felipe Cujar-Rosero ◽  
David Santiago Pinchao Ortiz ◽  
Silvio Ricardo Timarán Pereira ◽  
Jimmy Mateo Guerrero Restrepo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Virtual Environment ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Research Projects ◽  
Machine Learning Model ◽  
The University

This paper presents the final results of the research project that aimed for the construction of a tool which is aided by Artificial Intelligence through an Ontology with a model trained with Machine Learning, and is aided by Natural Language Processing to support the semantic search of research projects of the Research System of the University of Nariño. For the construction of NATURE, as this tool is called, a methodology was used that includes the following stages: appropriation of knowledge, installation and configuration of tools, libraries and technologies, collection, extraction and preparation of research projects, design and development of the tool. The main results of the work were three: a) the complete construction of the Ontology with classes, object properties (predicates), data properties (attributes) and individuals (instances) in Protegé, SPARQL queries with Apache Jena Fuseki and the respective coding with Owlready2 using Jupyter Notebook with Python within the virtual environment of anaconda; b) the successful training of the model for which Machine Learning algorithms were used and specifically Natural Language Processing algorithms such as: SpaCy, NLTK, Word2vec and Doc2vec, this was also performed in Jupyter Notebook with Python within the virtual environment of anaconda and with Elasticsearch; and c) the creation of NATURE by managing and unifying the queries for the Ontology and for the Machine Learning model. The tests showed that NATURE was successful in all the searches that were performed as its results were satisfactory.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence

WHO DO WE THINK WE ARE? COMPARING INTERSECTIONAL IDENTITY TRENDS IN ASEE AND CEEA-ACEG USING NATURAL LANGUAGE PROCESSING AND REVIEW OF PROCEEDINGS

2019 ◽  
Author(s):  
Pamela Rogalski ◽  
Eric Mikulin ◽  
Deborah Tihanyi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Activity Theory ◽  
Language Processing ◽  
Division Of Labour ◽  
Cultural Historical Activity Theory ◽  
Original Question ◽  
Micro Level ◽  
Historical Activity ◽  
Cultural Historical Activity

In 2018, we overheard many CEEA-AGEC members stating that they have "found their people"; this led us to wonder what makes this evolving community unique. Using cultural historical activity theory to view the proceedings of CEEA-ACEG 2004-2018 in comparison with the geographically and intellectually adjacent ASEE, we used both machine-driven (Natural Language Processing, NLP) and human-driven (literature review of the proceedings) methods. Here, we hoped to build on surveys—most recently by Nelson and Brennan (2018)—to understand, beyond what members say about themselves, what makes the CEEA-AGEC community distinct, where it has come from, and where it is going. Engaging in the two methods of data collection quickly diverted our focus from an analysis of the data themselves to the characteristics of the data in terms of cultural historical activity theory. Our preliminary findings point to some unique characteristics of machine- and human-driven results, with the former, as might be expected, focusing on the micro-level (words and language patterns) and the latter on the macro-level (ideas and concepts). NLP generated data within the realms of "community" and "division of labour" while the review of proceedings centred on "subject" and "object"; both found "instruments," although NLP with greater granularity. With this new understanding of the relative strengths of each method, we have a revised framework for addressing our original question.  

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