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Using sequential pattern mining for links recommendation in adaptive hypermedia educational systems C. Romero Morales *, A. R. Porras Pérez, S. Ventura Soto, C. Hervás Martínez and A. Zafra Gómez Universidad de Córdoba, Campus de Rabanales, Edificio C-2, 14071 Córdoba, Spain In this paper we are going to describe a data mining tool, whose aim is to help authors or teachers to discovery interesting information from students’ usage information. The teacher can use this information to improve and personalize adaptive hypermedia courses to theirs students. We propose to use sequential pattern mining algorithms to discover the most used path by the students and from this information can recommend links to the new students automatically meanwhile they browse in the course. We have developed a specific author tool in order to help the teacher to apply all the data mining process. Keywords sequential pattern mining; recommendation systems; adaptive hypermedia educational systems
1. Introduction Adaptive and intelligent web-based educational systems (AIWBES) provide an alternative to the traditional just-put-it-on-the-web approach in the development of web-based educational courseware [3]. AIWBES attempt to be more adaptive by building a model of the goals, preferences and knowledge of each individual student and using this model throughout the interaction with the student in order to adapt to the needs of that student. AIWES are the result of a joint evolution of intelligent tutoring systems (ITS) and adaptive hypermedia systems (AHS). Some examples of ITS are SQL-Tutor, German Tutor, ActiveMath, VC-Prolog-Tutor, and some examples of AHS are AHA!, InterBook, KBS-Hyperbook, WebCOBALT [3]. Currently, there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community [11]. The objective of data mining is to discover new interesting and useful knowledge using classification, association, prediction, clustering, etc. The problem of sequential pattern mining is one of the several that has deserved particular attention on the general area of data mining. Sequential pattern mining [1] attempts to find inter-session patterns such as the presence of a set of items followed by another item in a time-ordered set of sessions or episodes. There are some works about the application of these techniques in web-based educational systems. [5] performs web page traversal path analysis for customized education, and web page associations for virtual knowledge structures, which can be formed by learners themselves as they navigate web pages. [8] uses association fuzzy rules in a personalized e-learning material recommender system. This report uses fuzzy matching rules to discover associations between student’s requirements and a list of learning materials. [9] analyzes some student's individual sessions. First, they define the learning period (of time) of each student and then split web server log files into individual sessions, calculate session statistics, and search for session patterns and time series. [7] also uses recommender agents for e-learning systems which use association rule mining to discover associations between user actions and URLs. The agent recommends online learning activities or shortcuts in a course web site. In this paper we are going to describe a sequential pattern mining tool for recommending links to the students. We have integrated in AHA! [4] because is a well-known adaptive hypermedia architecture. Next, we describe the use and the interface of this tool and then we describe the conclusions and future work.
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Corresponding author: e-mail:
[email protected], Phone: +34 957212172
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2. Sequential mining tool for links recommendation We have developed a mining tool in order to help authors to discovery interesting information from students’ usage information that can be used to improve the courses. Currently, we have developed a “links recommendation” facility based on sequential pattern mining. The recommendation of links (to content pages, activities, etc) is very important in e-learning systems in order to personalize (or adapt) the learning for each student and to guide them to the best learning path. One way to automate this process is the application of data mining techniques into the students’ usage information. In most e-learning systems, all the pages accessed by students are saved in log files (either one log file for each student or just one big log file for everyone) which contain all the information about the interaction of the students with the system. Therefore, after pre-processing this information, it is possible to discover sequential patterns from these log files by using some data mining algorithms. Sequential pattern mining can be defined as the process of discovering all sub-sequences that appear frequently on a given sequence database and have minimum support threshold. Our objective is to use the discovered sequential patterns to create interesting recommendation links to show to the students while they use the e-learning system. To do that, all the sequential patterns are split in sequences of only two components. These obtained sequences can be considered as a rule with only one antecedent and one consequent, the antecedent represents the page in which the recommendation is shown and the consequent is the link recommended to the student. We have developed a mining tool in order to help the teacher to carry out all this process. This application has been integrated into the well known AHA! [4] (Adaptive Hypermedia Architecture) system. This application is a Java Applet, just like other AHA! authoring tools. In order to use it, the author has to identify himself. Once the user has logged, it is showed the main window of the tool, as we can see in Fig. 1. This window is divided in main areas. At the top, we can see the information panel that it will show all the information about the program execution and the current state of the application. At the bottom, we can see the sequential pattern panel, where the discovered sequential patterns will be shown.
Fig. 1 Main window of Sequential Mining Tool.
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First, the author has to create or select a data file with the sequences of links previously visited by the students. To do that, he has to select “Create Data file” in the “File menu”. Next, the application shows a dialog (see Fig.2 at left) with a list of the courses in order to he selects what are the courses he want to use to extract the information. Next, a new dialog (see Fig.2 at right) shows a list with all the students registered in the selected courses. The author can select all the students or a group of them. At last, the application asks to the author about setting a limit time. This time can useful if he wants to ignore all data before a fixed time. Then, the tool creates a data file with all these previosly selected information and save it in the local hard disk of the author. This file is saved in KEEL [2] format, that is very similar and compatible with Weka format [13] that it is a well-known data mining tool.
Fig. 2 Interface for creating a data file: Course selection and Student selection.
Once a data file has been selected, the author can execute one of the pattern discovering algorithms by selecting it from the “algorithm” menu. The availables algorithms are AprioriAll [1], GSP [12] and PrefixSpan [10] that they are the most popular pattern discovering algorithms. Furthermore, the author can change the default values for some parameters that each algorithm needs. After the previous step, the algorithm is runned until it finishes. Then, the application shows the results in the sequential pattern panel in the main window. These sequences can be saved in a text file and they also can be seen better in the sequence view window (see Fig. 3) in a graphic mode so that they can be analyzed better.
Fig. 3 Sequence view window.
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Analyzing these sequences the teacher can have an idea about what is students’ general behavior during their learning process. Instead of analyzing the obtained sequences, the user can be interested in recommending links from the discovered sequences. In order to do it, the author can select “Recommend links” from the “options” menu in the main window. Then, all the sequence patterns are splited in 2length sub-sequences and a new dialog is shown with all the possible recommendations (see Fig 4). The recommendation is composed of 2-length sub-sequences considered as rules with only one antecedent and one consequent, so that the antecedent represents the page in which the recommendation will be shown and the consequent is the link recommended to the student. Furthermore, the user can decide what recommendations can be more appropriate than other ones, because each recommendation is shown with its support. The author can select all the recommendations or some of them in order to add them to the course interface. After selecting the recommendation, the author has to introduce a text for the link of each recommendation that it will
appears in the course interface.
Fig. 4 Recommendation Dialog.
Finally, the recommendations are saved in the AHA! system. In the Figure 5 we can see the interface of an AHA! course with Recommended Links; in this case, the student is recommended to go to the “graph author” concept if he is currently reading the “pages” concept.
Fig. 5 AHA! Tutorial with Recommended Links.
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4. Conclusions In this paper we have described a tool for discovering sequential patterns from student’s usage information of an adaptive hypermedia course. We have integrated it in AHA! but with a few modifications it can be used in other web-based systems. We have shown how the teacher can discover the sequences and can recommend links directly into the course interface. In the future, we want to experiment with huge student’s usage information of different AHA! courses and other sequence mining algorithms [6] as SPADE, FreeSpan, CloSpan, PSP, etc. in order to compare them and to can see what are the most appropriated algorithms for our data and our problem. Acknowledgements The authors gratefully acknowledge the financial support provided by the Spanish department of Research under TIN2005-08386-C05-02 Project.
References [1] R. Agrawal and R. Srikant, Mining Sequential Patterns, Proceedings of the Eleventh International Conference on Data Engineering, 1995, pp. 3-14. [2] J. Alcalá, M.J. del Jesús, J.M. Garrell, F. Herrera, C. Hervás, L. Sánchez, Proyecto KEEL: Desarrollo de una Herramienta para el Análisis e Implementación de Algoritmos de Extracción de Conocimiento Evolutivos. Tendencias de la Minería de Datos en España, Eds. J. Giradles, J.C. Riquelme, J.S. Aguilar, 2004, pp. 413-423. [3] P. Brusilovsky, C. Peylo, Adaptive and Intelligent Web-based Educational Systems, International Journal of Artificial Intelligence in Education, 13 (2003), pp. 156–169. [4] P. De Bra, L. Calvi, AHA! An open Adaptive Hypermedia Architecture, The New Review of Hypermedia and Multimedia, 4 (1998), pp. 115-139. [5] S. Ha, S. Bae and S. Park,Web Mining for Distance Education, Third IEEE International Conference on Management of Innovation and Technology, 2000, pp. 715–719. [6] J. Han, J. Pei and X. Yan, Sequential Pattern Mining by Pattern-Growth: Principles and Extensions, StudFuzz 180 (2005), pp. 183–220. [7] J. Li and O. Zaïane, Combining Usage, Content, and Structure Data to Improve Web Site Recommendation, International Conference on e-commerce and Web Technologies, 2004, pp. 305–315. [8] J. Lu, Personalized E-learning Material Recommender System, International Conference on Information Technology for Application, 2004, pp. 374–379. [9] C. Pahl and C. Donnellan, Data Mining Technology for the Evaluation of Web-based Teaching and Learning Systems, Proceedings of the Congress e-learning, Montreal, Canada. 2003. [10] J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q.Chen, U.Dayal and M. Hsu, PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth, Proceedings of the Seventeenth International Conference on Data Engineering (ICDE 01), 2001. [11] C. Romero, S. Ventura, Educational Data Mining: a Survey from 1995 to 2005, Expert Systems with Applications, Elsevier, 1, 33 (2006), pp. 1-12. [12] R. Srikant and R. Agrawal, Mining Sequential Patterns: Generalizations and Performance Improvements, Proceedings International Conference on Extending Database Technology, 1996, pp.3-17. [13] I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann 2005.
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