Enhancing learning through self-explanation

Self-explanation is an effective teaching/learning strategy that has been used in several intelligent tutoring systems in the domains of Mathematics and Physics to facilitate deep learning. Since all these domains are well structured, the instructional material to self-explain can be clearly defined. We are interested in investigating whether self-explanation can be used in an open-ended domain. For this purpose, we enhanced KERMIT, an intelligent tutoring system that teaches conceptual database design. The resulting system, KERMIT-SE, supports self-explanation by engaging students in tutorial dialogues when their solutions are erroneous. We plan to conduct an evaluation in July 2002, to test the hypothesis that students will learn better with KERMIT-SE than without self-explanation

Examples and tutored problems: How can self-explanation make a difference to learning?

“The final publication is available at link.springer.com”.Learning from worked examples has been shown to be superior to unsupported problem solving in numerous studies. Examples reduce the cognitive load on the learner's working memory, thus helping the student to learn faster or deal with more complex questions. Only recently researchers started investigating the worked example effect in Intelligent Tutoring Systems (ITSs). We conducted a study to investigate the effect of using worked examples in combination with supported problem-solving in SQL-Tutor. We had three conditions: Examples Only (EO), Problems Only (PO), and Alternating Examples/Problems (AEP). After completing a problem, students received a self-explanation prompt that focused on the concepts used in the problem, to make sure that students acquire conceptual knowledge. On the other hand, examples were followed by self-explanation prompts that focused on procedural knowledge. The study showed that the AEP and PO conditions outperformed EO in learning gain, while AEP outperformed PO in conceptual knowledge acquisition. Therefore, interleaving examples with supported problems is an optimal choice compared to using examples or supported problems only in SQL-Tutor

Statistical analysis of emotions and opinions at Digg website

We performed statistical analysis on data from the Digg.com website, which enables its users to express their opinion on news stories by taking part in forum-like discussions as well as directly evaluate previous posts and stories by assigning so called "diggs". Owing to fact that the content of each post has been annotated with its emotional value, apart from the strictly structural properties, the study also includes an analysis of the average emotional response of the posts commenting the main story. While analysing correlations at the story level, an interesting relationship between the number of diggs and the number of comments received by a story was found. The correlation between the two quantities is high for data where small threads dominate and consistently decreases for longer threads. However, while the correlation of the number of diggs and the average emotional response tends to grow for longer threads, correlations between numbers of comments and the average emotional response are almost zero. We also show that the initial set of comments given to a story has a substantial impact on the further "life" of the discussion: high negative average emotions in the first 10 comments lead to longer threads while the opposite situation results in shorter discussions. We also suggest presence of two different mechanisms governing the evolution of the discussion and, consequently, its length.Comment: 26 pages, 16 figures, 6 table

Large-Scale Deployment of Three Intelligent Web-Based Database Tutors

We present our experiences with DatabasePlace, a Web portal aimed at university-level students enrolled in database courses. The portal was established by Addison-Wesley in January 2003. Besides presenting information about the textbooks, the portal also provides additional domain information, online quizzes and three Intelligent Tutoring Systems developed by the Intelligent Computer Tutoring Group (ICTG). We briefly present the three systems, and then discuss our experiences. We also compare the DatabasePlace students to our local students using the three ITSs

A Comparative Analysis of Cognitive Tutoring and Constraint-Based Modeling

Numerous approaches to student modeling have been proposed since the inception of the field more than three decades ago. hat the field is lacking completely is comparative analyses of different student modeling approaches. Such analyses are sorely needed, as they can identify the most promising approaches and provide guidelines for future research. In this paper we compare Cognitive Tutoring to Constraint-Based Modeling (CBM). We present our experiences in implementing a database design tutor using both methodologies and highlight their strengths and weaknesses. We compare their characteristics and argue the differences are often more apparent than real. For specific domains, one approach may be favoured over the other, making them viable complementary methods for supporting learning

Concept tagging in Moodle

The Moodle learning management system (LMS) is becoming increasingly widespread due to its adoption by education providers at all levels. The system however does not provide educators with the ability to create connections between online course material and domain concepts easily and visibly. We chose concept tagging as the method for allowing teachers to provide these links in a new concept tags plug-in for Moodle. Our concept tagging plug-in allows the creation, management and application of concept tags to course content. Students and teachers will mutually benefit from the use of such a mechanism, as it would aid information location in and retrieval from course content. An initial evaluation of the plug-in showed that teachers find the concept tagging of course modules to be a desirable and usable functionality that Moodle lacks. The work in this paper describes the first prototype of the plug-in, the aim of which is provide the first step in contributing to the addition of basic student modelling functionality to Moodle as part of a wider project. By continually keeping track of a student’s progress on each concept tag, Moodle will be able to suggest appropriate course modules for further study based on this progress

Data-driven misconception discovery in constraint-based intelligent tutoring systems

Students often have misconceptions in the domain they are studying. Misconception identification is a difficult task but allows teachers to create strategies to appropriately address misconceptions held by students. This project investigates a data-driven technique to discover students' misconceptions in interactions with constraint-based Intelligent Tutoring Systems(ITSs). This analysis has not previously been done. EER-Tutor is one such constraint-based ITS, which teaches conceptual database design using Enhanced Entity-Relationship (EER) data modelling. As with any ITS, a lot of data about each student's interaction within EER-Tutor are available: as individual student models, containing constraint histories, and logs, containing detailed information about each student action. This work can be extended to other ITSs and their relevant domains

Decision-making tutor: Providing on-the-job training for oil palm plantation managers

Over the years many Intelligent Tutoring Systems (ITSs) have been used successfully as teaching and training tools. Although many studies have proven the effectiveness of ITSs used in isolation, there have been very few attempts to embed ITSs with existing systems. This area of research has a lot of potential in providing life-long learning and work place training. We present DM-Tutor (Decision-Making Tutor), the first constraint-based tutor to be embedded within an existing system, the Management Information System (MIS) for oil palm plantation management. The goal of DM-Tutor is to provide scenario-based training using real-life operational data and actual plantation conditions. We present the system and the studies we have performed. The results show that DM-Tutor improved students’ knowledge significantly. The participants found DM-Tutor to be easy to understand and interesting to use