October 2012 Volume 15 Number 4 - Educational Technology ...

Huang, Y.-M., Liu, C.-H., Lee, C.-Y., & Huang, Y.-M. (2012). Designing a Personalized Guide Recommendation System to
Mitigate information Overload in Museum Learning. Educational Technology & Society, 15 (4), 150–166.
Designing a Personalized Guide Recommendation System to Mitigate
Information Overload in Museum Learning
Yong-Ming Huang 1 , Chien-Hung Liu 2 , Chun-Yi Lee 1 and Yueh-Min Huang 1,3*
1 Department of Engineering Science, National Cheng Kung University, Taiwan // 2 Department of Network
Multimedia Design, Hsing Kuo University of Management, Taiwan // 3 Department of Applied Geoinformatics, Chia
Nan University of Pharmacy and Science, Taiwan // ym.huang.tw@gmail.com // chliu@mail.hku.edu.tw //
chunyilee@yahoo.com // huang@mail.ncku.edu.tw
* Corresponding author
(Submitted January 08, 2011; Revised July 25, 2011; Accepted July, 28, 2011)
ABSTRACT
Museum learning has received a lot of attention in recent years. Museum learning refers to people’s use of
museums to acquire knowledge. However, a problem with information overload has caused in engaging in such
learning. Information overload signifies that users encounter a mass of information and need to determine
whether certain information needs to be retained. In this paper, we proposed a personalized guide
recommendation (PGR) system to mitigate this problem. The system used association rule mining to discover
guide recommendation rules both from collective visiting behavior and individual visiting behavior, and then the
rules were personalized. Using this system, visitors can obtain a PGR and avoid exposure to excessive exhibit
information. To investigate user satisfaction with the PGR system, a user satisfaction questionnaire was
developed to analyze the user satisfaction in a sample consisting of individuals of different genders and ages.
The results showed that both men and women consistently accepted the PGR system and revealed that there
were significant differences with regard to attitudes toward the system’s service quality among different user
ages. It was inferred that one possible reason for this was an effect related to users’ prior experience with
computers. A summary of the findings suggested that the PGR system generally obtained positive feedback.
Keywords
Museum learning, Information overload, Personalized guide recommendation, Association rule mining
Introduction
In recent years, it has been shown that museums are one of the most important institutions serving as sources for
informal learning (Sung, Chang, Hou, & Chen, 2010; Sung, Chang, Lee, & Yu, 2008; Tan, Liu, & Chang, 2007;
Vavoula, Sharples, Rudman, Meek, & Lonsdale, 2009). Over time, museums have gradually developed into public
learning centers, and they have been seen as serving a role in public education (Semper, 1990). This implies that
museums have been viewed as one type of informal educational context and as an important asset by which to
acquire knowledge. Consequently, museums play a significant role in providing people with in depth knowledge
beyond formal educational contexts (Ramey-Gassert, Walberg III, & Walberg, 1994; Semper, 1990).
Although museums are accepted as a means to pursue knowledge, the problem of information overload (IO) still
remains in existing museum contexts (Bitgood, 2009). IO implies that users encounter a mass of information and
need to make a decision as to whether to retain information about a certain topic (Toffler, 1970). In museums,
visitors often have to confront a vast number of exhibits and, due to time pressure, must make a decision about
whether to view more details about a particular exhibit or to move on (Bitgood, 2009). However, such a situation
may lead to a generation of IO because the large number of exhibits leads to confusion. More specifically, visitors
may have an inability to process input because too many exhibits are presented at once or because the information is
presented too quickly over time (Bitgood, 2009). Consequently, visitors may acquire only a superficial understanding
through a quick and casual viewing of any given exhibit.
In this paper, a personalized guide recommendation (PGR) system is proposed to mitigate IO in museum contexts.
Previous studies have indicated that recommendation systems can help reduce IO (Itmazi & Megias, 2008; Lee &
Kwon, 2008; Yang & Chen, 2010). A recommendation system refers to a system that actively provides relevant
information to users according to their interests so that they are no longer required to handle too much information.
This means that the recommendation system can be used to ease visitor IO. In order to develop an appropriate
recommendation system for museum contexts, collective and individual visiting behavior was analyzed in order to
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