A cognitive assistant for learning java featuring social dialogue

Coronado, Miguel López; Iglesias, Carlos Á.; Carrera, Álvaro; Mardomingo, Alberto

doi:10.1016/j.ijhcs.2018.02.004

Cited by 33 publications

(45 citation statements)

References 25 publications

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“…Some research works claim that chatbot technology is so disruptive that it will eliminate the need for websites and apps [11]. Chatbots have been used in different educational settings, such as language learning [12], health-related coaching agents [13], chatbots designed to provide feedback to students [14], programming language learning [15], administrative support [16], or increase students' motivation [17]. These are examples if we do not consider opendomain solutions such as Amazon or Google's [18], aiming to answer any question, instead of a specific area of knowledge.…”

Section: Related Workmentioning

confidence: 99%

JAICOB: A Data Science Chatbot

et al. 2020

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The application of natural language to improve students' interaction with information systems is demonstrated to be beneficial. In particular, advances in cognitive computing enable a new way of interaction that accelerates insight from existing information sources, thereby contributing to the process of learning. This work aims at researching the application of cognitive computing in blended learning environments. We propose a modular cognitive agent architecture for pedagogical question answering, featuring social dialogue (small talk), improved for a specific knowledge domain. This system has been implemented as a personal agent to assist students in learning Data Science and Machine Learning techniques. Its implementation includes the training of machine learning models and natural language understanding algorithms in a human-like interface. The effectiveness of the system has been validated through an experiment. INDEX TERMS Cognitive informatics, Educational technology, Human-computer interaction, Machine learning, Natural language processing.

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Section: Related Workmentioning

confidence: 99%

JAICOB: A Data Science Chatbot

et al. 2020

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“…Chatbot became important in many life areas; one of the primary uses of chatbots is in education as a question answering system for a specific knowledge domain. In [8], the authors proposed a system that has been implemented as a personal agent to assist students in learning Java programming language. The developed prototype has been evaluated to analyze how users perceive the interaction with the system.…”

Section: A Chatbots Applications and Usesmentioning

confidence: 99%

Generating and Analyzing Chatbot Responses using Natural Language Processing

Aleedy¹,

Shaiba²,

Bezbradica³

2019

IJACSA

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Customer support has become one of the most important communication tools used by companies to provide before and after-sale services to customers. This includes communicating through websites, phones, and social media platforms such as Twitter. The connection becomes much faster and easier with the support of today's technologies. In the field of customer service, companies use virtual agents (Chatbot) to provide customer assistance through desktop interfaces. In this research, the main focus will be on the automatic generation of conversation "Chat" between a computer and a human by developing an interactive artificial intelligent agent through the use of natural language processing and deep learning techniques such as Long Short-Term Memory, Gated Recurrent Units and Convolution Neural Network to predict a suitable and automatic response to customers' queries. Based on the nature of this project, we need to apply sequence-to-sequence learning, which means mapping a sequence of words representing the query to another sequence of words representing the response. Moreover, computational techniques for learning, understanding, and producing human language content are needed. In order to achieve this goal, this paper discusses efforts towards data preparation. Then, explain the model design, generate responses, and apply evaluation metrics such as Bilingual Evaluation Understudy and cosine similarity. The experimental results on the three models are very promising, especially with Long Short-Term Memory and Gated Recurrent Units. They are useful in responses to emotional queries and can provide general, meaningful responses suitable for customer query. LSTM has been chosen to be the final model because it gets the best results in all evaluation metrics.

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“…Cognitive computing refers to smart systems that learn at scale, reason with purpose, and interact with humans and other smart systems naturally [9]. Cognitive systems are able to learn from incoming data and from their interactions with humans, opening new possibilities to produce better products taking advantage of the combination of computers' analytic capability and encyclopedic knowledge and humans' creativity and expertise [10].…”

Section: A Cognitive Computingmentioning

confidence: 99%

“…The available values for this attribute are also limited to the detection of mentions about bugs or feature requests. The agent has also enabled the Small Talk bundle, that includes predefined phrases to the most popular requests, what makes the bot to look more like a human [10].…”

Section: A Servermentioning

confidence: 99%

A Cognitive Agent for Mining Bugs Reports, Feature Suggestions and Sentiment in a Mobile Application Store

Muñoz

Araque

Llamas

et al. 2018

2018 4th International Conference on Big Data Innovations and Applications (Innovate-Data)

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Over the last years, mobile applications and their corresponding distribution platforms have gained momentum. Applications stores allow users to write reviews and ratings about the apps, giving feedback to developers. User ratings and reviews may help to improve software quality, solve bugs and develop new features. However, this data is hard to be handled by an individual due to the ever growing amount of textual reviews. This paper proposes the use of cognitive computing technologies for addressing this challenge, by developing a smart agent able to mine bugs reports, feature suggestions and sentiment expressed in mobile app reviews. The main contributions of this paper are: the design of a cognitive agent for assisting developers in managing their interaction with their users, the application of machine learning algorithms for bug and feature request detection, and the agent implementation in a real scenario.

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A cognitive assistant for learning java featuring social dialogue

Cited by 33 publications

References 25 publications

JAICOB: A Data Science Chatbot

JAICOB: A Data Science Chatbot

Generating and Analyzing Chatbot Responses using Natural Language Processing

A Cognitive Agent for Mining Bugs Reports, Feature Suggestions and Sentiment in a Mobile Application Store

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