7 research outputs found
Natural Language Interface to Relational Database (NLI-RDB) Through Object Relational Mapping (ORM)
The book is a timely report on advanced methods and applications of computational intelligence systems
SEEKER: A Conversational Agent as a Natural Language Interface to a relational Database
Managers of companies are typically not SQL (Structured Query Language) experts but require information 24/7. Therefore, a growing need for Natural Language Interfaces to Databases (NLIDs) has been identified, with a vast amount of research being undertaken in the area. The existing approaches to NLIDs present many weaknesses including the inability to deal with grammatical mistakes in user input, the inability to communicate with the user to correct mistakes and the inability to allow refinement of query results. This paper proposes a system, SEEKER, which uses a Conversational Agent (CA) as the Natural Language Interface (NLI) in a NLID. The CA is used to capture key words in the user's utterance. Once these key words have been identified, the most appropriate SQL template is selected by the expert system using rule based reasoning. The identified variables are mapped to the SQL template in order to create an SQL query. SEEKER allows for refinement of query results. SEEKER was evaluated in terms of user satisfaction and task completion. The results of the evaluation were promising
A conversational intelligent tutoring system to automatically predict learning styles
This paper proposes a generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a student’s learning style. Oscar aims to mimic a human tutor by implicitly modelling the learning style during tutoring, and personalising the tutorial to boost confidence and improve the effectiveness of the learning experience. Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic. The Oscar CITS methodology and architecture are independent of the learning styles model and tutoring subject domain. Oscar CITS was implemented using the Index of Learning Styles (ILS) model (Felder & Silverman, 1988) to deliver an SQL tutorial. Empirical studies involving real students have validated the prediction of learning styles in a real-world teaching/learning environment. The results showed that all learning styles in the ILS model were successfully predicted from a natural language tutoring conversation, with an accuracy of 61–100%. Participants also found Oscar’s tutoring helpful and achieved an average learning gain of 13%
Personalising Learning with Dynamic Prediction and Adaptation to Learning Styles in a Conversational Intelligent Tutoring System
This thesis presents research that combines the benefits of intelligent tutoring
systems (ITS), conversational agents (CA) and learning styles theory by constructing
a novel conversational intelligent tutoring system (CITS) called Oscar. Oscar CITS
aims to imitate a human tutor by implicitly predicting individuals’ learning style
preferences and adapting its tutoring style to suit them during a tutoring
conversation.
ITS are computerised learning systems that intelligently personalise tutoring
based on learner characteristics such as existing knowledge and learning style. ITS
are traditionally student-led, hyperlink-based learning systems that adapt the
presentation of learning resources by reordering or hiding links. Research suggests
that students learn more effectively when instruction matches their learning style,
which is typically modelled explicitly using questionnaires or implicitly based on
behaviour. Learning is a social process and natural language interfaces to ITS, such
as CAs, allow students to construct knowledge through discussion. Existing CITS
adapt tutoring according to student knowledge, emotions and mood, however no
CITS adapts to learning styles.
Oscar CITS models a human tutor by directing a tutoring conversation and
automatically detecting and adapting to an individual’s learning styles. Original
methodologies and architectures were developed for constructing an Oscar Predictive
CITS and an Oscar Adaptive CITS. Oscar Predictive CITS uses knowledge captured
from a learning styles model to dynamically predict learning styles from an
individual’s tutoring dialogue. Oscar Adaptive CITS applies a novel adaptation
algorithm to select the best tutoring style for each tutorial question. The Oscar CITS
methodologies and architectures are independent of the learning styles model and
subject domain. Empirical studies involving real students have validated the
prediction and adaptation of learning styles in a real-world teaching/learning
environment. The results show that learning styles can be successfully predicted
from a natural language tutoring dialogue, and that adapting the tutoring style
significantly improves learning performance