Academic journal article Journal of Interactive Learning Research

Mining Student Data Captured from a Web-Based Tutoring Tool: Initial Exploration and Results

Academic journal article Journal of Interactive Learning Research

Mining Student Data Captured from a Web-Based Tutoring Tool: Initial Exploration and Results

Article excerpt

In this article we describe the initial investigations that we have conducted on student data collected from a web-based tutoring tool. We have used some data mining techniques such as association rule and symbolic data analysis, as well as traditional SQL queries to gain further insight on the students' learning and deduce information to improve teaching. In our work, applying data mining facilities serves two purposes: (a) understand better both how students grasp the tool and assimilate the knowledge they need to learn and (b) get pedagogically relevant information that may influence or help improve teaching.

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With the emergence of e-learning, flexible education, and the increasing number of students in some fields, online teaching tools are becoming more and more important. Online teaching tools provide a more or less personalised environment where learners can learn at their own pace, have access to tutorial lessons, practice exercises, be given explanations and feedback on their performance, and so on. These benefits to the learners are extremely valuable and we assist to "a quiet revolution taking place in the classrooms" (Forster, 2002). However, less attention has been given to the reflection and monitoring that can be made to improve the teaching. Since online teaching tools are computer-based, they allow storing complete student answers, including mistakes made while solving exercises. The fact that they are online tools means that all this information, for all students using the tool, can be stored on a common server rather than stored locally. Having electronic access to complete student answers makes it possible to extract pedagogically relevant information and provide feedback to the teacher about how a class, a group of students or an individual student is going. It also makes it possible to get more insight on how students get along with the tool and the content.

The Logic-ITA is a web-based Intelligent Teaching Assistant system that is currently used within the School of Information Technologies, University of Sydney, for an undergraduate course on formal languages and logic. Its aim is to facilitate the whole teaching and learning process by helping the teacher as well as the learner. It allows students to practice formal proofs in propositional logic while receiving feedback and also keeps the lecturer informed about the progress the class is making and problems encountered. The system embeds the Logic Tutor, a web-based intelligent tutoring system destined to the students that stores their complete work including mistakes, along with tools dedicated to the teacher for managing teaching configuration settings and material, as well as collecting and analysing data. A multimedia article on the Logic Tutor can be found in Abraham, Crawford, Lesta, Merceron, and Yacef (2001) and a description of the Logic-ITA in Lesta and Yacef (2002). We are now extending the capabilities of the system to provide more information and more intelligent help to the teacher. First results in this direction can be found in Merceron and Yacef (2003).

In this article we investigate the impact that the analysis of the data collected in such a tool can have on the whole process of teaching and learning. We use data mining techniques on the data stored by the Logic-ITA to better investigate the impact on learning and to improve teaching. More precisely, symbolic data analysis allows us to gain further insight into the students' learning and associations, rules of items that we apply to mistakes opens new perspectives to improve teaching. With e-learning, complete student answers will be more and more available in electronic format. This work shows some possibilities of what can be done with them.

There is an increasing interest in providing assistance to the human teacher and to integrate him or her formally into the loop (Jean, 2000; Kinshuk, Patel, Oppermann, & Russell, 2001; Kinshuk, Hong, & Patel, 2001; Leroux, Vivet, & Brezillon, 1996; Virvou & Moundridou, 2001; Vivet, 1992; Yacef, 2002) and this is supported by the combining of computational intelligence with web-based education (Calvo & Grandbastien, 2003; Vasilakos, Devedzic, Kinshuk, & Pedrycz, 2004; Yacef, 2003). …

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