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Article Excerpt
E-learning is becoming more and more popular with the widespread use of computers and the Internet in educational institutions. Current e-learning courses are nearly always developed using course management systems (CMS), such as WebCT or Blackboard. Although CMS tools provide support for some administrative tasks and enable instructors to provide online instructional material, they offer no deep support for learning: students have access to online material, simple multi-choice quizzes, and chat tools, but there is no ability to track student's progress and adapt the learning material and instructional session to the individual student. In this article we present our experiences with three web-based intelligent tutoring systems in the area of databases. SQL-Tutor teaches the SQL query language, NORMIT is a data normalization tutor, and KERMIT teaches conceptual database modelling using the Entity-Relationship data model. All three tutors in DB-suite have been used and evaluated in the context of genuine teaching activities. We present the most important features of these systems, as well as evaluation results. The DB-suite tutors have proved to be very effective in supporting deep learning, and are well liked by students.
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Intelligent Tutoring Systems (ITS) have been proven to be very effective in domains that require extensive practice (Corbett, Trask, Scarpinatto, & Hadley, 1998; Koedinger, Anderson, Hadley, & Mark, 1997; Mitrovic & Ohlsson, 1999). In this article, we present DB-suite, consisting of three web-enabled ITSs that teach various database skills to university students. Data-bases are ubiquitous in today's information systems. Our tutors are web-enabled, and thus are classroom and platform independent (Vasilakos, Devedzic, Kinshuk, & Pedrycz, 2004). The most mature of the three systems is SQL-Tutor (Mitrovic, 1998a; 1998b; Mitrovic & Ohlsson, 1999; Mitrovic, Mayo, Suraweera, & Martin, 2001), an ITS that teaches the SQL query language. KERMIT (Knowledge-based Entity Relationship Modelling Intelligent Tutor) (Suraweera & Mitrovic, 2001) teaches conceptual database modelling, while NORMIT (NORMalization Intelligent Tutor) teaches database normalization (Mitrovic, 2003). All three tutors comprising DB-suite are problem-solving environments, where the system presents problems to solve and offers adaptive problem-solving support and feedback.
The DB-suite tutors are based on Constraint-Based Modeling (CBM) (Ohlsson, 1994). The Intelligent Computer Tutoring Group (ICTG) has also developed other constraint-based tutors: for example, CAPIT (Mayo, Mitrovic, & McKenzie, 2000; Mayo & Mitrovic, 2001) is a MS Windows-based, standalone tutor that teaches punctuation and capitalization rules in English, and LBITS (Martin & Mitrovic, 2002b) teaches vocabulary skills to elementary school children. Based on our experiences developing these tutors, we have also implemented WETAS (Martin & Mitrovic, 2002a; 2003), an authoring shell for developing constraint-based tutors. WETAS is now being used for developing new tutors, including a web-enabled version of KERMIT.
We start by briefly describing CBM and our database tutors. The following three sections are devoted to SQL-Tutor, KERMIT and NORMIT respectively. The effectiveness and the students' perception of DB-suite tutors were evaluated in several empirical evaluation studies. We present these studies, which demonstrate the effectiveness of the systems for student's learning. Finally, we present the conclusions and directions for future work.
CONSTRAINT-BASED TUTORS
Intelligent tutoring systems are developed with the goal of automating one-to-one human tutoring, which is the most effective mode of teaching (Bloom, 1984). ITSs offer greater flexibility in contrast to nonintelligent software tutors since they can adapt to each individual student. Although ITSs have been proven to be effective in a number of domains, the number of ITSs used in real courses is still extremely small (Mitrovic, Martin & Mayo, 2002). Our goal when developing DB-suite was twofold: to provide our students with a flexible learning environment that will adapt to their needs, and to develop a powerful methodology for developing constraint-based tutors. Our methodology is based on Ohlsson's (1996) theory of learning from performance errors.
The typical architecture of constraint-based tutors is given in Figure 1. The tutors are developed in AllegroServe web server, an extensible server provided with Allegro Common Lisp. All student models are kept on the server. At the beginning of interaction, a student is required to enter his/her name, which is necessary to establish a session. The session manager requires the student modeller to retrieve the model for the student, if there is one, or to create a new model for a new student. DB-suite tutors identify the students by their login name, which is embedded in a hidden tag of HTML forms. All student actions are sent to the session manager, to be linked to the appropriate session and stored in the student's log. The action is then sent to the pedagogical module (PM). If the submitted action is a solution to the current step, the PM sends it to the student modeller, which diagnoses the solution, updates the student model, and sends the result of the diagnosis back to the PM, which generates feedback.
[FIGURE 1 OMITTED]
SQL-Tutor and NORMIT are web-enabled tutors with a centralized architecture, with all tutoring functions performed on the server side. In these two domains, solutions produced by students are textual, and the amount of information to be sent to the server is small, so that the centralized architecture is suitable. In KERMIT, students draw diagrams, and some tutoring functions related to drawing are performed on the client side. The tutoring functions are therefore distributed between the server and the Java applet, as described later.
Domain knowledge consists of a set of constraints. Constraint-Based Modeling (CBM) (Ohlsson, 1994; Mitrovic & Ohlsson, 1999) is a student modeling approach that is not interested in the exact sequence of states in the problem space the student has traversed, but in what state he/she is in currently. As long as the student never reaches a state that is known to be wrong, they are free to perform whatever actions they please. The domain model is a collection of state descriptions of the form: If is true, then had better also be true, otherwise something has gone wrong.
The knowledge base consists of constraints used for testing the student's solution for syntax errors and comparing it against the system's ideal solution to find semantic errors. The knowledge base enables the tutor to identify student solutions that are identical to the system's ideal solution. More importantly, this knowledge also enables the system to identify valid alternative solutions, that is, solutions that are correct but not identical to the system's solution. Each constraint specifies a fundamental property of a domain that must be satisfied by all solutions. Constraints are problem-independent and modular, and therefore easy to evaluate. They are written in Lisp, and can contain built-in functions as well as domain-specific ones. For examples of constraints, please see (Mitrovic, 1998a, 2002; 2003; Suraweera & Mitrovic, 2001; 2002; Martin & Mitrovic, 2003; Mitrovic, Koedinger, & Martin, 2003). If the satisfaction condition of a relevant constraint is met by the student solution, the solution is correct. In the opposite case, the student will be given feedback on errors.
One of the advantages of CBM over other student modeling approaches (Mitrovic, Koedinger, & Martin, 2003) is its independence from the problem-solving strategy employed by the student. CBM models students' evaluative, rather than generative knowledge and therefore does not attempt to induce the student's problem-solving strategy. CBM does not require an executable domain model, and is applicable in situations in which such a model would be difficult to construct (such as database design or SQL query generation). Furthermore, CBM eliminates the need for bug libraries, that is, collections of typical errors made by students. On the contrary, CBM focuses on correct knowledge only. If a student performs an incorrect action, that action will violate some constraints. Therefore, a CBM-based tutor can react to misconceptions although it does not represent them explicitly. A violated constraint means that student's knowledge is incomplete/incorrect, and the system can respond by generating an appropriate feedback message....
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