Dynamic Sequencing of Learning Objects - CiteSeerX

Dynamic Sequencing of Learning Objects Rafael Morales and Ana Silvia Agüera Instituto de Investigaciones Eléctricas, Mexico E-mail: {rmorales,asaguera}@iie.org.mx Abstract

towards this end it is considered necessary to create standards for organizing, classifying, coding and distributing educational content on the Internet. Among the most important works in this direction are the standards produced by the IMS Global Learning Consortium, the Advanced Distributed Initiative (ADL) and the IEEE. A central element in all this standards is the proposal to organize educational content in learning objects, conceived as relatively small chunks of educational material which can be shared, reused in different contexts and recombined in different ways for building larger blocks of educational content [1]. Our research group’s interest on Web-based intelligent training systems led us to define a model of reference consisting of four layers [2]: Educational Includes the different teaching-learning models implemented by a system [3]. User interaction Includes the facilities used by a system to adapt itself dynamically to the information and communication needs of each particular user [4, 5] Architectural Defines the abstract, generic or conceptual components of a system and their interrelations [6, 7]. Implementation Includes Internet-related technology, learning objects and related standards [8], as well as AI techniques applied in the development of intelligent tutoring systems. This paper presents our work at the implementation level, which includes the Web as computational and communications platform and the organization of educational contents as collections of learning objects according to SCORM—ADL’s standard reference model for educational systems based on learning objects [8]. This first stage of our research project focused on developing an extension to the Sample Run-Time Environment distributed by ADL, which is capable of delivering learning objects for presentation in two different sequences, which we call traditional and socratic.

This paper describes a prototype of a learning management system we have developed following the Sharable Content Object Reference Model (SCORM) by the Advanced Distributed Learning Initiative. The system is able to deliver the learning objects composing a course either by following the organization defined in the course’s manifesto, or by dynamically choosing the sequence in which the learning objects that compose a lesson should be delivered. The latter sequencing is done on the basis of the learner responses to tests. Some problems in the way of transforming dynamic sequencing into intelligent sequencing of learning objects are discussed.

1. Introduction In the context of e-learning there are two lines of research and development which have contributed to conform the technological basis for a new generation of educational systems. One approach, which corresponds to what is known as computer-based instruction, or CAI, has produced systems of increasing sophistication in the use of information and telecommunication technologies. However, these systems are relatively rigid and static in the generation of learning experiences. The other approach has lead to intelligent tutoring systems (ITS), developed under the hypothesis that computers are able to model human learning and to select the best teaching strategy in each particular case. Although the idea of an ITS is appealing, and some studies have indicated ITS’s can be more effective and efficient than traditional classroom education and CAI systems, few ITS’s have reached the status of a robust application, suitable for general use. The advent of the Internet and the Web, with their potential benefits for education—consequence of their distributed nature and their facility to store and deliver information—have fueled and increasing interest in combining the technologies of both CAI and ITS systems to produce a new generation of educational systems of widespread use. In order to get a significative advance

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2. SCORM ADL’s proposal for making creation, distribution and delivering of electronic educational contents more

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Tomcat both as Web server and application (servlets) server, and MySQL as database management system. Our motivation for choosing this platform was to make our system less expensive and more portable, in comparison with expensive commercial LMS.

effective and profitable is to create repositories storing a great variety of learning objects that can be delivered on the Web. The SCORM (Share Content Objects Reference Model) specification defines how to tag and package educational content as learning objects, a common way of delivering learning objects on the Web, a common mechanism for communicating learning objects and learning management systems (LMS), and a common vocabulary (data model) to perform this communication. A SCORM learning object is, by definition, the smallest piece of educational content a learning management system tracks. It represents a collection of one or more assets— files of different sorts: text, image, sound, video, etcetera— tagged with metadata describing the object (a standardized XML file, called manifesto, containing information such as the object’s contents, classification, difficulty level, suitable contexts for use, etcetera) and packaged in a standard way. As expected, SCORM also defines how to aggregate learning objects to compose bigger chunks of educational content. In SCORM, the term LMS implies a system on the server side in which resides all the intelligence needed to deliver learning objects to learners. This means SCORM’s learning objects do not decide their own or other objects’ delivery; this decision corresponds to the LMS. It is expected that, in this way, the development of sharable and reusable learning contents would be easier. Although one of the main goals of developing SCORM is to favour e-learning adaptable to a great variety of learners—after the experimental success of intelligent tutoring systems—the reference model does not specify facilities nor mechanism to accomplish this goal, and little research has been done in this direction (see [9] for an example).

4. Course design Figure 1 shows the organization a course must have to be integrated to our system: a hierarchical structure with four levels. The root node correspond to the whole course, which is composed of modules, which in turn are composed of lessons; finally, the lessons are composed of learning objects at the lowest level in the hierarchy.

Figure 1. Course structure. Our system distinguishes four different classes of learning objects: explanations, examples, exercises, tests and feedback. Learning objects should be organized in this order inside each lesson, mimicking the traditional sequencing of material in a typical textbook. Each learning object’s manifesto should contain the following minimal information: identifier, name, location, theme and class. The first three pieces of information are required in the manifesto by the standard, while the last two pieces are included in the manifesto as part of the metadata component of it. This might sound a bit confusing, since the manifesto as a whole can be regarded as metadata. Yet it can contain a component called metadata, containing further information about the object. In order to test the system we developed a minicourse on electricity generation, with two modules (nuclear energy and thermoelectric power plants), eight lessons (four per module) and over fifty learning objects containing assets in HTML, JavaScript, GIF, Flash and VRML.

3. The system ADL distributes a Sample Run-Time Environment (SRTE) with the aim of demonstrating the basic components and run-time working of a system conforming the SCORM specification. Although the release 1.1 of their SRTE had many limitations, we decided to use it as the starting point of our development and extended it in a few directions: 1. to provide flexible access to several courses described in a database; 2. to import courses following SCORM guidelines for tagging and packaging (by parsing the courses’ XML manifest, extracting metadata information and using it to fill the database); 3. to dynamically decide the sequencing of learning objects in a course. The system currently runs on the Linux and Solaris operating systems. It is mostly written in Java, using

5. Dynamic sequencing of learning objects As it was said in Section 2, sequencing of learning objects is the responsibility of a SCORM complaint

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system. The learning objects presupose the existence of an environment with the capacity to decide which object is to be presented next. To accomplish adaptation of the educational content to the particular needs of every learner it is necessary, however, for content to be described appropriately and in enough detail for a system (for example, an intelligent tutoring agent) to be able to automatically and dynamically establish the most appropriate sequencing of the learning objects for each learner. It is necessary to include instructional design information in the learning objects’ metadata, to support the decision making process (and for the system to know about it and access it). Our extension to the SRTE incorporates facilities for delivering and presenting the learning objects of a course following two different organizations, we called traditional and socratic.

Otherwise, if the performance of the learner in the test was unsatisfactory, a feedback object on the same theme is delivered to the learner, followed by and exercise and, afterwards by the test again. If the learner fails at the test again, this time an example is provided instead of the exercise, followed again by the application of the test. An explanation object, with all the information required to answer the test, is delivered to the learner as a last resource.

5.1. Traditional sequencing

Figure 3. Socrating sequencing of learning objects.

This is the simplest sequencing, since the system simple delivers the learning objects following the organization defined by the course author and stored in the course manifesto. Figure 2 shows the order of delivering learning objects for traditional sequencing.

6. Towards intelligent sequencing Currently, our system can take the same learning objects conforming a lesson and deliver them in two different ways: a fixed sequence, predefined by the course author, and a sequence dynamically determined by the system on the basis of learner responses to tests. Although this behavior is interesting, adds flexibility to the presentation of educational content, and might be more effective in promoting learning—yet we have not compared the effectiveness of both sequencing methods— it still has some important limitations: 1. the sequence of presentation for modules and lessons is fixed; 2. learning objects can be recombined only within a lesson; 3. learning objects cannot be reused across lessons, modules nor courses; 4. the method of sequencing is chose at the beginning by the learner, not decided by the system; 5. the socratic sequencing is still very rigid, compared to what has been achieved by some intelligent tutoring systems [10, 11, 12]. We plan to extend our prototype into a full fledged learning management system and overcome all these limitations in the near future. We envisioned our system populated with a few intelligent agents implementing different teaching strategies, recombining and adapting learning objects from a common repository to meet the specific needs of each individual learners. This is a vision shared with many others [8], yet there are a number of difficult problems in the way to make it a reality. − Knowledge representation. Intelligent tutoring

Figure 2. Traditional sequencing of learning objects. It is important to mention that the learning objects conforming the explanation section in a lesson may be related to more than one theme (for example, nuclear fision and fusion), but they are all delivered before any object in the examples section. In the same way, all example objects are delivered before any exercise ones, and so on.

5.2. Socrating sequencing For the socratic sequencing the system delivers the learning objects ordered by theme. The idea is to scaffold the learner by providing him with no more help nor information than necessary to answer correctly a test on the theme. Figure 3 describes graphically the socratic sequencing of learning objects. First, a test object is presented to the learner. If the objects reports the learner successfully answered the test, then another test (on the same theme, if available) is presented to the learner.

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experimental results obtained with intelligent tutoring systems, the merge of ITS and learning objects technologies seems not only an attractive direction for development but a necessary step to make Web-based elearning the revolutionary move so much touted but still waited for.

systems usually rely on a good representation of the domain knowledge to be taught [13], which goes far beyond tagging individual pieces of multimedia resources. For example, the Web implementation of DCG [14] uses and AND/OR graph for representing the conceptual structure of the domain, which is kept separately from the teaching materials. Content planning for a learning session is done using this conceptual structure. An interesting question here is whether learning objects are good places for representing and storing domain knowledge, or whether they should be restricted to the role of contents to be delivered to the learner. The first approach would allow exchanging of domain knowledge representations between LMS, very much in the same way they should be able to exchange educational contents. Common language. Most intelligent tutoring systems have achieved their flexibility and adaptability thanks to a careful design of their components, specially tailored for each ITS to achieve its goals (yet there is some interesting work on shells and servers). This applies, in particular, to the domain knowledge component of ITS’s. Even if domain knowledge is not stored in learning objects, it will be necessary to develop a common language to describe it and tag the objects, so at least those can be shared among several systems. Current work on ontologies for Web-based education goes in this direction. Compatibility of learning objects. The notion of what a learning object is, or should be, is still under much debate [1]. Important issues discussed are, among others, the granularity (scope) of learning objects and their instructional theory neutrality. Flexibility and adaptability seem to demand very small and neutral objects, whereas less sophisticated systems benefit from bigger objects, implementing well established teaching strategies.

8. References [1] David A. Wiley, “Connecting learning objects to instructional design theory: A definition, a metaphor, and a taxonomy,” in The Instructional Use of Learning Objects: Online Version, David A. Wiley, Ed. 2000. [2] Rafael Morales and Ana Silvia Agüera, “Capacitación basada en objetos reusables de aprendizaje,” Boletín IIE, to be published, in Spanish. [3] Bruce Joyce, Emily Calhoun, and David Hopkins, Model of Learning—Tools for Teaching, Open University Press, Buckingham, 1997. [4] Peter Brusilovsky, “Adaptive and intelligent technologies for web-based education,” Künstliche Intelligenz, Special Issue on Intelligent Systems and Teleteaching, vol. 4, pp. 19–25, 1999. [5] Paul De Bra, “Design issues in adaptive web-site development,” in Second Workshop on Adaptive Systems and User Modeling on the World Wide Web, Peter Brusilovsky and Paul de Bra, Eds., 1999. [6] Hugh L. Burns and Charles G. Capps, “Foundations of intellgent tutoring systems: An introduction,” In Polson and Richardson [15], chapter 1, pp. 1–19. [7] Learning Technology Standards Committee of the IEEE Computer Society, IEEE P1484.1/D8, 2001- 04-06 Draft Standard for Learning Technology — Learning Technology Systems Architecture (LTSA), 2001. [8] Advanced Distributed Learning, Sharable Content Object Reference Model Version 1.2, 2001.

7. Conclusions

[9] Nicola Capuano, Marco Marsella, and Severio Salerno, “ABITS: An agent based intelligent tutoring system for distance learning,” in Proceedings of the International Workshop on Adaptive and Intellgent WebBased Educational Systems, Christop Peylo, Ed., 2000, pp. 17–28.

In this paper we have described a prototype of a learning management system developed following SCORM guidelines, which is able to dynamically choose the sequence in which the learning objects of a lesson should be delivered, given the user responses to tests. This has been a preliminary step in the way to our goal of developing intelligent agents capable of performing a more tailored sequencing of learning objects, designed according to different methods of teaching and planned to meet the specific needs of each individual learner. Given the tendency of learning objects to become the technology of choice for delivering educational content on the Web, the share number and diversity of Web users and the good

[10] John R. Anderson, C. Franklin Boyle, Albert T. Corbett, and Matthew W. Lewis, “Cognitive modeling and intelligent tutoring,” Artificial Intelligence, vol. 42, pp. 7–49, 1990. [11] Fiona Spensley, Mark Elsom-Cook, Paul Byerley, Massimo Federici, and Claudia Scaroni, “Using multiple teaching strategies in an ITS,” in Intelligent Tutoring 505

Systems: At the CrossRoad of Artificial Intelligence and Education, Claude Frasson and Gilles Gauthier, Eds., pp. 188–205. Ablex, Norwood, N.J., 1990.

and Richardson [15], chapter 2, pp. 21–53. [14] Julita Vassileva and Ralph Deters, “Dynamic courseware generation on the WWW,” British Journal of Educational Technologies, 1998.

[12] Julita Vassileva, “DCG+GTE: Dynamic courseware generation with teaching expertise,” Instructional Science, vol. 26, no. 3/4, pp. 317–332, 1998.

[15] Martha C. Polson and J. Jeffrey Richardson, Eds., Foundations of Intelligent Tutoring Systems, Lawrence Erlbaum Associates, New Jersey, 1988.

[13] John R. Anderson, “The expert module,” In Polson

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