Printer Friendly

Search engine technology impetus for the knowledge revolution in business education.

Two equally powerful forces are helping shape the future of business education. First is the growing requirement for competent business managers on a worldwide basis. Second are the changing demands on our academic libraries as a result of the ongoing digital revolution. These dynamics call for new and innovative education systems such as asynchronous learning networks. Internet based learning nets are designed to optimize content delivery in a user-friendly environment. Search engine technology is both a basic ingredient of asynchronous learning as well as the centerpiece of the modern academic library. A primary role for such knowledge amplifiers is to identify content that specifically supports the lesson plan. The purpose of this article is to highlight the growing relationship between advances in search engine technology and the delivery of robust business educational through the Internet.


The number of working adults who are returning to the classroom is growing rapidly (King & Bannon, 2002). This trend, particularly with respect to business education, is being fueled by growing competitive pressures. Working adults need both flexibility and off-line support in undertaking a business degree program. Typically, the working adult is interested in a practical curriculum that focuses on convenience and results. Search engine technology (SET) represents a key strategic initiative for helping meet these requirements (Edwards & Bruce, 2002). Specifically, SET can provide a more direct and effective delivery link among the growing body of content material, the lesson plan, and students. Furthermore, SET is emerging as the mainstay of the "new" academic library (Novak, 2002). As a result, the traditional brick and mortar library is giving way to the digital revolution where content is provided through the Internet at a time and place of the user's choosing (Beagle, 2000). This revolution in the way knowledge and content is delivered, like all other revolutions, is not without its challenges and concerns (Harden, 2002). Financial pressures are a major challenge facing many libraries as a result of the current crisis in state and local funding. For example, the library system for the state of Virginia experienced an 11% budget cut (nearly $2 million) for fiscal year 2002-2003 (Albanese, 2002). These reductions are not limited to state funded universities. Private university endowments have also suffered as a result of the declining stock market and an overall weak economy (Levine, 2003). The net effect is a general decline in library collections, support staff, and operating hours that, if left unchecked, could impact the delivery of content. One approach to help mitigate these problems is through the use of a library portal.

A library portal is an interactive web site that provides a broad array of resources and services. Many portals offer customized and personalized functionality as well as a variety of research tools. For example, a common feature of current portals is "One-stop shopping" access to multiple databases. The advent of the Internet library portal, of which SET is a major component, represents a major step to improve "high quality" content delivery in a distance-learning environment. The design characteristics of the portal are tailored to help address the library budget crisis (Jackson, 2002). Specifically, digital material through the portal can be more effectively monitored, managed, and distributed than traditional methods (Moore, 2003).

Some current developments in library portals include Online Computer Library Center (OCLC), 2002):

* navigation by subject through all resources (federated searching);

* full text search;

* customized links to individual journal titles;

* results grouping;

* updates on new resources by customized subject;

* discovery and retrieval of materials from disparate collections;

* download citation lists; and

* increased administrative functionality (including performance metrics).

For example, portal-based federated searching is a process that provides a common user interface for searching and retrieving information across heterogeneous datasets. This is an important new development since many libraries now have 30 or more individual databases. Federated searching can be configured to access and search these multiple web databases. Typically, a set of configuration files are used to link with the full-text search engine of the web database. This process interacts with web servers by simulating user input into the search process. Currently, two major technical challenges are (a) how to address the lack of uniform control for various metadata fields in terms of building a rich unified search interface; and (b) how to easily incorporate new collections and freshly harvested data in existing repositories into the federation search process (Liu et al., 2002).

The portal developments previously listed are being driven by the need for more effective search and results reporting systems (Pelz, 2002). Most of the current academic based portal systems require the user to select from a variety of databases and to collate the results from each database. These complications (database and results selections) make the "google" type search still the first choice among many students (Peek, 2002). In short, students do not like having to chose from a variety of databases or sift through a large number of subject "hits". This is where the new generation of SET can make a difference. Enhancing the search process by requiring fewer user inputs and more results compliation means students can concentrate on how the selected material supports the lesson plan rather than on the searching process. One way in which SET can be better linked with the lesson plan is in conjunction with another emerging technology called learning nets.


Educating working adults in business management requires an integrated, as well as, time efficient pedagogy. Integrated learning is an instructional approach that brings all aspects of a course into a common focus (Wang, Hinn, & Kanfer, 2001). Core content components of an integrated business learning experience should include an e-text, lecture notes, interactive testing, virtual tours, simulation, and content search. Asynchronous learning networks (ALN) are designed to deliver an integrated course experience. More specifically, an ALN is an Internet-based learning system that provides integrated and customized instructional content and material in a distance-learning environment which is independent of time, location and learning pace (Coppola & Hiltz, 2002). ALN are receiving increased attention throughout higher education and are destined to become a primary vehicle for delivering business content particulary for working adults (Herberger, 2001).

ALN provide a proactive approach to student learning through the use of a highly interactive process. A proactive learning net is one where a student is both challenged and tasked to take the initiative in the learning process. This proactive practice can be facilitated through the use of virtual tours, online simulations, and real time testing. The interactive and integrated ALN design outlined in this article is based on the Instructional Management System (IMS) cooperative initiative (Graves, 1999). The intent of the IMS is to promote systematic thinking regarding the use of the Internet in higher education, to improve learning outcomes and to increase return on instructional investments. Figure 1 illustrates the integrated nature of the ALN design. The ALN serves as both a two-way content "distributor" and an integrator of the course lesson plan. ALN provide a 24-7 learning environment that is helpful for working adults. Furthermore, ALN foster real time feedback that is essential for optimizing knowledge acquisition and retention in a distance-learning context. Online testing is one vehicle for providing such feedback. Furthermore online testing can be used in conjunction with SET.

Specifically, SET can provide a conduit to both customized and supportive materials as a result of test performance. This capability is designed to offer a tailored learning experience for each student.


Beyond merely providing access to the general body of knowledge (Intranet and Internet) there are a variety of specific ways in which search engine technology can enhance content delivery and support the objectives of the lesson plan. Figure 2 illustrates how SET can be used in acquiring and integrating both supportive and customized content into the lesson plan. Supportive materials are those content items that offer further insights into a specific theme or topic. On the supportive side, instructors can develop and post a specific list of materials that can be accessed from a simple drop-down menu, for example, organizational testimonials on continuous improvement. This type of "presearching" by the instructor using SET reduces the time students would have to spend on searching and identifies material that has already been prescreened by the instructor that directly supports the lesson plan. The supportive materials list can be regularly updated based on student usage patterns and the availability of new and more effective content.


The development of customized content through SET can be based on both testing and student characteristics such as industry group, management level, and functional work area, for example, finance. Customized materials can be designed for a specific student who is having difficulty in mastering a particular concept or is interested in more details, for example, how benchmarking is used to evaluate an organization's forecasting system. This capability of providing customized content based on specific factors is particularly useful for working adults whose job assignments often mirror the specifics found in the identified content. Used in this way a student can directly apply the lesson plan material to the workplace.


Table 1 shows more specifically how SET could be used to support a lesson plan involving a course/lecture on business forecasting. The first column lists some basic learning plan objectives. The second column identifies the primary resources used by a student in connection with each session objective. The third column shows additional material identified by the instructor that is designed to support each lesson objective. For example, after developing a forecast using a virtual applet a student may wish to better understand the mechanics behind how a forecast is developed. Here an interactive simulation provides students with an indepth view of the forecasting process. This type of capability is sufficiently general to evoke a wide range of interest among students. The fourth column highlights some examples of customized content discovered by SET based on both student test performance and characteristics. For example, after reviewing and discussing a case on improving the forecasting process through benchmarking, a student can choose to view performance testimonials specific to his/her industry. This level of detail and specificity helps reinforce the basic ideas introduced in the forecasting process case. Again the specific testimonials are "captured" by SET in real time. This type of customized learning is analogous to the introduction in the 1990s of interactive Internet marketing based on customer characteristics (Wind & Rangaswatty, 2001). Interestingly, many working adults who are returning to the classroom have extensive experience in Internet marketing customization.

Recent empirical evidence indicates that SET's role in supporting the lesson plan is increasing (Jones, 2002). More specifically:

* SET is an efficient and discipline way of accessing the growing body of business content.

* SET provides the learner with a purposeful entry to the Internet and online resources.

* SET offers a gateway to a new era of learning technologies.

* SET connects learners and instructors in a virtual environment that maximizes flexibility and convenience.

* SET cultivates new patterns of relationships between education, business and content.

* SET can be used with artificial agents to further enhance the delivery of customized content.


One promising technology to support the continued development of SET is artificial intelligence (AI). The use of AI to assist in the search and compilation process is receiving increased attention (Schwartz, 2000). AI holds much promise for providing machine-initiated content searching and analysis that could significantly impact the future role of the academic library (Carlson, 2003). Synthetic agents, a branch of artificial intelligence, are defined as purposeful autonomous entities capable of adapting to changing requirements and opportunities such as found in content searching (Goldsborough, 2000). These systems allow the active reconfiguration of the search strategy according to current requirements and the availability of information sources of varying quality (Detlor & Arsenault, 2002). Typically, synthetic agents should possess the following four basic characteristics. They should be (a) autonomous, (b) proactive, (c) flexible, and (d) user-friendly (Allen, 2000). A well-designed synthetic agent should be able to access the user's past search history in designing an optimum search plan. The "social" interface between the agent and the user should be highly visual with limited user required inputs. It is within this design context that the specific search objectives such as supporting the lesson plan can be best achieved. Figure 3 illustrates the basic agent design structure. The agent interface would record the user's previous search history as well as specific user characteristics such as management level. These data would be used to help optimize the search process particularly as it relates to customized content searching. User diagnostics such as those derived from ongoing testing can be used by the agent to improve content delivery.

One specific way synthetic agents can be built into SET is in the form of weighted rankings. The nexus of this approach involves a user profile built into the portal. This profile would contain few user inputs beyond a login and password (Chen, Mengn, Fowler, & Zhu, 2001). The profile would contain useful information taken from the repeated use of the system by both a particular user as well as other users. The following steps illustrate the process for including a weighting system for optimizing the search process.

1. Link the search terms to the corresponding generic area (e.g. "Time Series" would fall under the heading "Forecasting").

2. Identify which results were selected for more inquiry, for example, full text.

3. Log the database where the item was located in a portion of the profile. Create a file where tallies would be recorded on the number of articles selected from each database.

4. Weight the subdatabases according to the greatest number of relevant links.

5. Repeat the process to form a weighted set of preferences for content searching.


The net result of this procedure would be to filter out possibly irrelevant or low content articles/knowledge. The functional impact of this algorithm, in regard to many of the portal products on the market, is that it would bypass the screen that shows the multiple databases menu and multiple output files. By applying rankings to the different databases the most relevant articles would ideally "float" to the top. The final search results would be displayed in an aggregated output list. The general form of this output ranking algorithm is:

Aggregate Ranking = Database Weighting * Relevance Ranking within Database

Again, as more searches and weighting modification occur the aggregate rankings would change. Table 2 demonstrates the overall aggregation process. Factor weights are based on user behavior including access frequency and full text viewing. In this example, database 1 is considered 5% more useful than database 2.

This illustration is simply one of many possible uses of synthetic agents to support SET (Bauer & Leake, 2002). Another major resource to support the development of automated content searching comes from so-called enterprise solutions (Yuan, 2003). An increasing number of electronic commercial sites provide "shopping" agents to assist consumers. The reasoning is the same as in the case of content searching, namely consumers find it difficult to determine and specify their exact needs. A typical enterprise system consists of a user behavior extractor, a user profile manager, an online learning personalized ranking module and a content information collector. Currently most electronic-based commercial agents provide integrated product/service rank lists based on price. The current generation of support agents also includes a behavioral component that provides for product/service differentiation based on desired characteristics. These enterprise system technologies provide an effective platform for supporting content searching. A major advantage of applying enterprise-based agents to lesson plan content searching is that most of the system development has been completed. This approach should result in a lower cost solution to the task of providing more effective content searching at a time when libraries are under both increased budgetary pressures and user demands.

Speech recognition represents another specific AI development for enhancing the student-SET interface. Specifically, an AI-based speech interface system allows a student to initiate search requests by way of speech commands. The latest generation in speech recognition technology offers significant productivity gains over traditional keyboard interfacing (Rebman, Aiken, & Cegielski, 2003). Speech technology can also be used for indexing, archiving, and retrieving audio and video footage in support of the lesson plan. Nevertheless, word error rates and user acceptance remain as ongoing challengers to the full acceptance of this promising technology.


Internet based distance-learning in business education is on the rise. This trend is due, in part, to the growing number of working adults returning to the classroom. Working adults need both flexibility and offline support in undertaking a business degree program. Asynchronous learning nets (ALN) are designed to optimize content delivery in a user-friendly environment and are specifically structured for meeting the learning challenges associated with working adults. Search engine technology (SET) is a basic ingredient of asynchronous learning as well as the centerpiece of the modern academic library. The purpose of this article is to highlight the growing relationship between advances in SET and the delivery of robust business educational content through the Internet. SET are "knowledge amplifiers" that provide a key link between the lesson plan and digitally-based content. Specifically, they can be used to support the lesson plan by providing both supportive and customized content via the Internet. In the latter case, content selection would be based on user history, characteristics, and performance. SET can be configured to access both the Internet and Intranet (library portal) thus significantly expanding access to the growing body of business knowledge and know-how.

The next generation of SET will require more user-friendly input and results processing. Typically, most students do not like to choose from among various databases or assess the content from a long list of hits. The availability of federated searching and results ranking will allow a student to concentrate more on content application rather than on content searching. For working adults with limited study time this is an important development. Artificial intelligence based synthetic agents represent a significant development in the evolution of SET. Specifically, synthetic agents allow the active reconfiguration of the search strategy according to current requirements, user characteristics, and the availability of information sources of varying quality. Speech recognition is another AI-based technology that holds out much promise for improving the search process and thus enhancing the learning experience. Enterprise solutions are yet another major existing resource to support the evolution of SET and to enhance its role in delivering "high value" content material. These same technologies can be applied to lesson plan content searching. The development of "smart" SET systems not only can help meet the growing demand for cost-effective distance-learning education but can also radically improve the functionality of academic libraries.
Table 1 Example Application of SET Support

Session Learning Objectives Primary Resource

Appreciate the role of forecasting Introductory lecture notes
in business operations
Understand the difference between Forecasting e-chapter
qualitative and quantitative forecasting
Accessing large-scale databases Streaming video
How to develop a forecast using Forecasting applet
computer models
How to improve the forecasting process Case on benchmarking and best

Session Learning Objectives SET Support Resources

Appreciate the role of forecasting Slide show on how forecasting
in business operations is being used
Understand the difference between Articles that illustrate the
qualitative and quantitative forecasting various types of forecasting
Accessing large-scale databases Bureau of labor statistics
How to develop a forecast using Interactive simulation
computer models
How to improve the forecasting process Articles on implementation

Session Learning Objectives SET Customized Resources

Appreciate the role of forecasting Industry specific forecasting
in business operations applications
Understand the difference between Role planning simulation based
qualitative and quantitative forecasting on management background
Accessing large-scale databases Data marts associated with a
 student's industry
How to develop a forecast using Slide show based on industry
computer models and management level
How to improve the forecasting process Industry specific performance

Table 2 Content Aggregation Process

Non-Weighted Output Component Weighting Weighted Output
Database Content Relevance Factor Relevance Rank

DB1 Article 1 98% 1.05 103% 1
DB1 Article 2 95% 1.05 100% 2
DB1 Article 3 92% 1.05 97% 5
DB2 Article 4 99% 1.00 99% 3
DB2 Article 5 98% 1.00 98% 4
DB2 Article 6 93% 1.00 93% 6


Allen, M. (2000). The myth of intelligent agents. Online, 24(6), 45.

Albanese, A. (2002). Library budgets squeezed by lower revenues, Library Journal, 127(16), 19.

Bauer, T., & Leake, D. (2002). Using document access sequences to recommend customized information. IEEE Intelligent Systems, 17(6), 27.

Beagle, D. (2000). Web-based learning environments: Do libraries matter? College & Research Libraries, 61(4), 367.

Carlson, S. (2003, June 6). Essays imagine the future of libraries--and librarians take the predictions seriously, The Chronicle of Higher Education, A35.

Chen, Z., Meng, X., Fowler, R.H., Zhu, B. (2001). Real-time adaptive feature and document learning for web search. Journal of the American Society for Information Science and Technology, 52(8), 655.

Coppola, N. W., & Hiltz, S.R. (2002). Becoming a virtual professor: Pedagogical roles and asynchronous learning networks. Journal of Management Information Systems, 18(4), 169.

Detlor, B., & Arsenault, C. (2002). Web information seeking and retrieval in library contexts: Towards an intelligent agent solution. Online Information Review, 26(6), 404.

Edwards, S., & Bruce, C. (2002), Reflective internet searching: An action research model. The Learning Organization, 9(3/4), 180.

Goldsborough, R. (2000). In search of a higher intelligence. Office Solutions, 17(1), 52.

Graves, W. (1999). The instructional management system cooperative: converting random acts of progress into global progress. Educom Review, 34(6), 32.

Harden, R. (2002). E-learning and all that jazz. Medical Teacher, 24(5), 572.

Herberger, R. (2001). The future of E-learning. Selections, 1(1), p28.

Jackson, M. (2002). The advent of portals. Library Journal, 127(15), 36.

Jones, S. (2002). The internet goes to college. Pew internet & American life project, Washington, DC.

King, T. & Bannon, E. (2002). At what cost? Higher Education Project, Washington, DC.

Levine, D. (2002, December 16). Lengthy stock slump gives university endowments a failing grade. San Francisco Business Times.

Liu, X., Maly, K., Zubair, M., Hong, Q., Nelson, M.L., Knudson, F., & Holtkamp, I. (2002). Federated search interface techniques for heterogeneous OAI repositories. Journal of Digital Information, 2, 14.

Moore, A. (2003). The digital content market: Rushing toward fate. E-Content Leadership Series, 1, 2-3.

Novak, D. (2002). The evolution of internet research: Shift allegiances. Online, 27(1), 18.

Online Computer Library Center (OCLC), (2002, June). OCLC White Paper on the Information Habits of College Students. How academic librarians can influence students' web-based information choices. Dublin, OH: OCLC.

Peek, R. (2002). The internet's role on campus. Information Today, 19(11), 36.

Pelz, A. (2002). The next generation of search. KM World, 11(1), 14.

Rebman, C., Aiken, M., Cegielski, C. (2003). Speech recognition in the human-computer interface. Information & Management, 40(6), 509.

Schwartz, E. (2000). Information management gets smart. Online, 24(6), 45.

Wang, X.C., Hinn, M., & Kanfer, A.G. (2001). Potential of computer-supported collaborative learning with different learning styles. Journal of Research on Technology in Education, 34(1), 75.

Wind, J., & Rangaswatty, A. (2001). Customerization: The next revolution in mass customization. Journal of Interactive Marketing, 15(1), 13.

Yuan, S.T. (2003). A personalized and integrative comparison-hopping engine and its applications. Decision Support Systems, 34(2), 139.


Pepperdine University, USA
COPYRIGHT 2004 Association for the Advancement of Computing in Education (AACE)
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2004, Gale Group. All rights reserved. Gale Group is a Thomson Corporation Company.

Article Details
Printer friendly Cite/link Email Feedback
Author:Hall, Owen P., Jr.
Publication:Journal of Interactive Learning Research
Geographic Code:1USA
Date:Jun 22, 2004
Previous Article:Patterns for E-learning content development.
Next Article:The theoretical framework for designing desktop virtual reality-based learning environments.

Related Articles
Reappraisals of the Scientific Revolution.
The cyber-revolution in its historical context.
Google intelligence! Sure, search engines deliver, but what about the off-line world?
Going beyond Google: there's a treasure trove of valuable research online--if you know how to find it.
Odvard Egil Dyrli on visual search tools: Grokker and related tools are transforming online research.
Retrieval from a case-based reasoning database.

Terms of use | Privacy policy | Copyright © 2021 Farlex, Inc. | Feedback | For webmasters