An intelligent teaching system for database modeling.

Database (DB) modelling, like other analysis and design tasks, can only be learnt through extensive practice. Conventionally, DB modelling is taught in a classroom environment where the instructor demonstrates the task using typical examples and students practise modelling in labs or tutorials. Although one-to-one human tutoring is the most effective mode of teaching, there will never be sufficient resources to provide individualised attention to each and every student. However, Intelligent Teaching Systems (ITS) offer bright prospects to fulfilling the goal of providing individualised pedagogical sessions to all students. Studies have shown that ITSs with problem-solving environments are ideal tools for enhancing learning in domains where extensive practice is essential. This thesis describes the design, implementation and evaluation of an ITS named KERMIT, developed for the popular database modelling technique, Entity Relationship (ER) modelling. KERMIT, the Knowledge-based Entity Relationship Modelling Intelligent Tutor, is developed as a problem-solving environment in which students can practice their ER modelling skills with the individualised assistance of the system. KERMIT presents a description of a scenario for which the student models a database using ER modelling constructs. The student can ask for guidance from the system during any stage of the problem solving process, and KERMIT evaluates the solution and presents feedback on its errors. The system adapts to each individual student by providing individualised hint messages and selecting new problems that best suit the student. The effectiveness of KERMIT was tested by three evaluations. The first was a think-aloud study to gain first-hand experience of the student's perception of the system. The second study, conducted as a classroom experiment, yielded some positive results, considering the time limitations and the instabilities of the system. The third evaluation, a similar classroom experiment, clearly demonstrated the effectiveness of KERMIT as a teaching system. Students were divided into an experimental group that interacted with KERMIT and a control group that used a conventional drawing tool to practice ER modelling. Both group's learning was monitored by pre- and post-tests, and a questionnaire recorded their perception of the system. The results of the study showed that students using KERMIT showed a significantly higher gain in their post-test. Their responses to the questionnaire reaffirmed their positive perception of KERMIT. The usefulness of feedback from the system and the amount learnt from the system was also on a significantly higher scale. Their free-form comments were also very positive

Widening the Knowledge Acquisition Bottleneck for Intelligent Tutoring Systems

Empirical studies have shown that Intelligent Tutoring Systems (ITS) are effective tools for education. However, developing an ITS is a labour-intensive and time-consuming process. A major share of the development effort is devoted to acquiring the domain knowledge that accounts for the intelligence of the system. The goal of this research is to reduce the knowledge acquisition bottleneck and enable domain experts to build the domain model required for an ITS. In pursuit of this goal an authoring system capable of producing a domain model with the assistance of a domain expert was developed. Unlike previous authoring systems, this system (named CAS) has the ability to acquire knowledge for non-procedural as well as procedural tasks. CAS was developed to generate the knowledge required for constraint-based tutoring systems, reducing the effort as well as the amount of expertise in knowledge engineering and programming required. Constraint-based modelling is a student modelling technique that assists in somewhat easing the knowledge acquisition bottleneck due to the abstract representation. CAS expects the domain expert to provide an ontology of the domain, example problems and their solutions. It uses machine learning techniques to reason with the information provided by the domain expert for generating a domain model. A series of evaluation studies of this research produced promising results. The initial evaluation revealed that the task of composing an ontology of the domain assisted with the manual composition of a domain model. The second study showed that CAS was effective in generating constraints for the three vastly different domains of database modelling, data normalisation and fraction addition. The final study demonstrated that CAS was also effective in generating constraints when assisted by novice ITS authors, producing constraint sets that were over 90% complete

Towards an Intelligent Tutor for Mathematical Proofs

Computer-supported learning is an increasingly important form of study since it allows for independent learning and individualized instruction. In this paper, we discuss a novel approach to developing an intelligent tutoring system for teaching textbook-style mathematical proofs. We characterize the particularities of the domain and discuss common ITS design models. Our approach is motivated by phenomena found in a corpus of tutorial dialogs that were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor for textbook-style mathematical proofs can be built on top of an adapted assertion-level proof assistant by reusing representations and proof search strategies originally developed for automated and interactive theorem proving. The resulting prototype was successfully evaluated on a corpus of tutorial dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453

An Animated Pedagogical Agent for SQL-Tutor

Animated pedagogical agents are animated characters that inhabit interactive learning environments. In addition to providing problem-solving advice in response to a student’s actions, they are also able to play a powerful motivational role. This project develops an animated pedagogical agent for the computer based teaching system, SQL-Tutor. The introduction of a pedagogical agent to SQL-Tutor enables it to provide higher motivational support to the students and enhances their quality of learning. An evaluation of the impact of the agent on the student’s learning experience was carried out with second year Computer Science students from the University of Canterbury. The study revealed that the presence of an animated character with an interesting personality has a strong positive effect on student’s perception of the learning experience. The study also demonstrated that students were more motivated to interact with the system equipped with the agent compared with the SQL-Tutor with no agent

Automatic Acquisition of Knowledge for Constraint-Based Tutors

straint based modelling (CBM) [7] is a student modelling technique that somewhat eases the knowledge acquisition bottleneck by using a more abstract representation of the domain compared to other popular domain modelling techniques [8]. However, building a constraint base still remains a major challenge. Mitrovic reported that, she took just over an hour to produce a constraint for SQL-Tutor, which currently contains more than 650 constraints [9]. Our research is focussed on automating the process of acquiring knowledge for constraintbased tutors. The authoring system will be an extension to the web-based tutoring shell, named WETAS [10], that facilitates building constraint-based tutors. WETAS provides all the domain-independent components for a text-based ITS, including the user interface, pedagogical module and student modeller. The main limitation of WETAS is its lack of support for authoring the domain model. We propose a four-stage process to infer constraints automatically. D

Designing an Intelligent Tutoring System for Database Modelling

Database (DB) modelling is the cornerstone of an efficient database. Students require extensive practice to excel in modelling databases due to the analysis and design nature of the task. Conventionally, DB modelling is taught in a classroom environment where the instructor demonstrates the task using typical cases and students practice modelling in labs or tutorials. However, there will never be enough resources to provide one-to-one human tutoring to every student. Intelligent Teaching Systems (ITS) are bright prospects that can be used to provide individualised pedagogical sessions to each and every student. Studies have shown that ITSs with problem-solving environments are ideal tools for enhancing learning in domains where extensive practice is essential. However, there have been very few research attempts at developing ITSs for students learning DB modelling

Evaluating an Animated Pedagogical Agent

The paper presents SmartEgg, an animated pedagogical agent developed for SQLT-Web, an intelligent SQL tutor on the Web. It has been shown in previous studies that pedagogical agents have a significant motivational impact on students. Our hypothesis was that even a very simple and constrained agent, like SmartEgg, would enhance learning. We report on an evaluation study that confirmed our hypothesis