Intelligent eLearning with XML

Presented by:
Kim Adolphe, President and CEO
Gemini Performance Solutions Inc.
Calgary, Alberta, Canada

Winston Churchill once said, "The empires of the future are the empires of the mind". Never before has that been more true than today. First came the Global Economy, then it was the Information Age and today we are in the Knowledge-based Economy. The challenge is to harness that knowledge. Those of us that do that most effectively will lead the rest.

The Internet will revolutionize education. It is no longer disputed that the Internet will revolutionize education and therefore not surprising that eLearning has emerged as one of the hottest trends in Information Technology, with IDC estimating the industry's growth to nearly double in size every year, reaching approximately $11.5 billion by the year 2003. By 2005, expenditures on eLearning are projected to reach $40 billion.

Given this new knowledge-based economy, it is also not surprising that XML has become the latest widely accepted markup language. It is a standard that is enabling structured data to be deployed easily and powerfully in a broad range of applications. XML promotes data interchange and encourages the modularization of data. In short, the sheer flexibility of XML, combined with the Internet, provides an ideal vehicle for intelligent eLearning strategies.

Addressing the exploding eLearning phenomenon and the construction of an intelligent, data-driven eLearning system, this presentation explores the pedagogically informed use of XML technology.

An intelligent, data-driven eLearning system requires both rich and complex data, and an easy way to structure that data in order to author and deliver effective eLearning courses. XML technology offers a way to fit both criteria. The robustness and flexibility of XML enables users to author courses containing rich data that can be repurposed in a myriad of way. XML based courses have the ability to be engineered to support a variety of multimedia and interactive elements.

However, the allure of a visually appealing course is not the ultimate in XML-powered eLearning solutions. In addition to using extra-textual elements to enrich the written content, eLearning products and courses must be based on pedagogically sound principles to be effective. This is especially true with one-on-one eLearning courses that are not reliant on an external instructor or virtual classroom environment.

Years of extensive research and development has been undertaken into applying Artificial Intelligence (AI) techniques to training. The results have proven that more effective training can be achieved through AI. However, the application of AI techniques to teaching and training has not become common in industrial and organizational settings, in part because their complexity has proven difficult to manage outside of the research lab.

SWIFT is the first commercial application of its kind to successfully bridge the gap between research and practical application. This has been accomplished by simplifying the research techniques while striving to maintain as much of the pedagogic intelligence as possible. SWIFT is a minimalist Intelligent Tutoring System (ITS) that uses XML technology, combined with AI techniques, in its adaptive testing facility, instructional planner, diagnosis, situation recognition, and guidance.

In general, our strategies have taken three paths: first, we have found ways to minimize ITS techniques utilizing XML without compromising too much of their power; second, we have found additional mechanisms to make our solutions more robust; and third, we have taken advantage of the abilities of learners and our knowledge of the eventual user population.

A defining feature of an ITS is a semantic representation of the instructional domain, where concepts are encoded in data structures that allow the system to reason about the course. A minimalist ITS must also employ semantic representation, for an understanding of the concepts in the domain is the basis of much of a system's intelligent behavior. Instructional design principles provide other criteria for the structure of courses, including a multi-level hierarchy, and learning objects that have specific attributes. However, the detail and sophistication of this representation can vary.

At the time we began development of SWIFT in 1993, XML had not yet been conceived. Therefore, our first DTD was in XML's predecessor, SGML, which provided the means to implement this pedagogically sound course structure and to provide rich course content.

We implemented a representation scheme that allows the system to reason about the domain. SWIFT courses are stored in a hierarchical structure that divides the instructional material into smaller and smaller pieces, much as a book does with chapters, sections, and subsections. A SWIFT course has three levels: the first contains a set of topics, which are divided at the second level into sets of modules, which are divided at the third level into concepts. A semantic representation of the course also allows the specification of dependencies between concepts.

Because of the hierarchical structure of courses built within the SWIFT technology, learning goals are built incrementally, with the most basic goal appearing first and working up to a more sophisticated level of knowledge attainment. In practice, the learner can direct his or her own learning path by choosing from the learning goals. Course authors can easily direct the course to bundle certain concepts and modules for the learner who chooses a particular path. For instance, the learner who is taking a course on UNIX and who has no prior knowledge of operating systems can opt for the novice track. The system will direct the learner to the basic level of instruction first, and then will lead him or her through increasingly more difficult levels of instruction. On the other hand, more advanced learners can select only the modules they need to fill in their knowledge gaps.

Another intelligent feature of SWIFT is learner diagnosis. Diagnosis attempts to understand problems and misconceptions in a learner's knowledge of the domain (e.g. [Johnson & Soloway, 1985], [McCalla & Greer, 1990]). Although any learner action may be considered, diagnosis is commonly applied to a learner's answers to test or exercise questions. Diagnosis entails drawing conclusions about the learner's knowledge based on features in their answers; good diagnosis allows systems to provide appropriate feedback and remediation as well as simple indication of whether an answer is right or wrong. Diagnosis can require significant inferencing power and domain knowledge, which are not strengths of a minimalist system, nor economically feasible to build.

An alternative to a fully knowledge-based approach is to detail a number of categories, or cases, of typical errors and misconceptions. Using a case-based approach transforms the inference problem to one of classifications, but effective classification can also be difficult to achieve. One problem occurs in specifying the answers that belong to a particular class. The obvious method is to encode every answer. However, this technique implies that any variation of an answer, even those that do not change its essential parts, must also be included. This can be a daunting task for any but the most trivial of exercises.

An AI technique called pattern recognition was applied to the short answer question type in SWIFT. This approach to the problem allows the author to concentrate on the qualitative differences in the possible answers to a question, rather than on syntactic variations. Our case-based diagnosis subsystem uses regular expression constructs that allow an author to specify a large number of possible variations with a single answer pattern. The system can examine and evaluate any short textual answers for which cases have been designed. The author specifies patterns for classes of correct and incorrect answers, and can annotate each class with appropriate feedback and remediation information. Feedback and remediation are proven learning techniques that both motivate and improve learner retention.

This strategy still requires that the author understands the kinds of difficulties that learners can have in a particular area, and how each problem can be manifested in answers to questions. However, we have provided a framework for structuring and using that pedagogic knowledge that is both powerful and efficient enough to be used in a minimalist system. Through the use of XML, we are capable of providing content authors the flexibility they need to fulfill the requirements of creating robust and meaningful cases for all situations that may occur while ensuring that rigid syntactic requirements are successfully met.

In addition to possessing varying levels of knowledge sets, learners also possess a variety of learning styles or intelligences (Delanghe; Gardner). Learning styles or preferences can be visual, auditory, kinesthetic, oral, or written. Thus, the most effective eLearning system supports multiple methods of content delivery: prose narrative, graphical illustration, summary, video representation or enactment, audio, hyperlinked association, and so on. Varying the method of content delivery appeals to a range of learning styles and meets the learner on his or her own turf, stimulating the learner to absorb and retain information more readily. XML supports the development of a wide array of instructional methods and allows for revision of courses to incorporate new Internet technologies as they become available and as new media types emerge by abstracting the presentation of the material away from its representation.

Some research indicates that over-use of interactive and visual elements clutters the learning space and hampers effective learning and training (Hartley). Thus, multimedia and interactivity in SWIFT supports the content, but does not replace content with technological glitz for the sake of using technology. Other research indicates the need for carefully considered use of visuals, such as screen captures, to support and enrich the learning experience (Gellevig). Recent research demonstrates the power of using narrative to provide a cohesive structure for an eLearning course (Weller). Narrative provides context, structure, and broad appeal to learners. Narrative also helps learners overcome a tendency to feel alienated from unfamiliar and newly accessed knowledge by performing an enculturation function and bridging the gap between old and new knowledge. Enculturation and bridging the gap are necessary both at a local level in the pedagogical deployment of knowledge in course design, and at a more global level within organizations and training departments.

In addition to appealing to a range of learning styles, an intelligent, data-driven eLearning system requires rich data surrounding course content in order to support its features. Data and course content represent valuable development resources. Therefore, it makes sense to get the most out of already developed data and content. Hypertext linking abilities of XML enable course authors to implement a multi-level narrative within the course.

The ability to view the same data in applications other than SWIFT and the ability to link to a wide range of external data and applications is a great advantage. Practically, this means organizations using an eLearning solution like SWIFT that utilizes XML will be able to leverage their existing content and resources to a greater degree by wrapping data within their SWIFT courses, or with other training or management tools.

The emergence and wide adoption of XML also means that course authors can use their favorite tools to create new content, avoiding the steep learning curve required with many authoring tools. Through the use of our XML DTD, resources are not consumed on document structure or format issues. This is an important consideration, as statistics show that at least 70% of development time is spent on structure and format alone. Course content can be revised without causing formatting havoc on other aspects of the course. Furthermore, the look and feel of courses will be consistent, even in collaborative content development environments such as the SWIFT Author. Most importantly, however, is that more investment can be dedicated to harnessing knowledge by creating rich and deep content and highly effective eLearning courses.

Because the use of XML in training and learning is new, there is a need to understand trainers' needs. In the face of the radical paradigm shift from instructor-led solutions to XML eLearning solutions, part of the intelligent use of XML learning is educating learners and trainers in the use of eLearning tools. Most organizations are not situated at one end or the other of the learning continuum, but somewhere in the middle between traditional instructor-led training and eLearning. A flexible eLearning solution enables corporations to make this paradigm shift at their own pace.

eLearning systems that will flourish and remain successful are those that can accommodate the changing nature of knowledge and content management. XML enabled eLearning systems can adjust to the ever-changing status of information and knowledge. As standards are implemented regarding course development, UI design and management, eLearning components that are XML enabled can be readily adapted to meet current and future standards. This ensures that investments in content will be valuable both now and far into the future.

More than just the avant-garde mode of learning, eLearning can be a powerful and pedagogically robust tool for the new and exciting Knowledge-based Economy.

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