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

organized in a didactic way that conflicted with inquiry-based learning. The results also showed that student
satisfaction with the tours was high but that the level of science learning was low. Vavoula et al. (2009) developed a
Myartspace service on mobile phones for inquiry learning in museums. The Myartspace service allowed students to
gather information and send the information to a website during museum field trips. In this manner, the students
were able to view, share, and present the information regardless of whether they were in a classroom or at home.
That is to say, a collaborative learning context was formed (Hwang, Huang, & Wu, 2011; Lin, Huang, Cheng, 2010).
Their results showed that the Myartspace service was effective in assisting students in gathering information in
museums and with providing resources for effective construction and reflection in the classroom.
Overall, museum learning has been extensively discussed. However, methods by which to assist visitors to ease IO
have rarely been considered. At present, the issue of IO in museums has only been mentioned in a study of museum
fatigue (Bitgood, 2009). To make up for this deficiency, the use of a PGR system to mitigate IO in museum contexts
is being considered in this paper.
Information overload and its solutions
IO is one of important issues in learning and teaching scenarios (Chen, 2009; Paulo, 1999). IO means that there is too
much information; it has negative connotations, and it is a widespread problem, especially in computer-mediated
communication (CMC) contexts (Paulo, 1999). Moreover, IO is different from cognitive overload (CO). IO usually
arises in an attention process in which users encounter disturbances due to excessive information, and it results in the
loss of information (Chen, 2009). CO usually arises in knowledge construction processes in which users encounter
difficulties in the storage and retrieval process, and it leads to failure in knowledge construction (Chen, 2009).
Therefore, CO studies are focused on assisting learners in constructing knowledge (Cierniak, Scheiter, & Gerjets,
2009; Huang, Huang, Liu, & Tsai, 2011) while IO studies are focused on assisting learners in avoiding excessive
information (Itmazi & Megias, 2008; Lee & Kwon, 2008; Yang & Chen, 2010). In this study, we focused on
museum contexts that are IO contexts (Bitgood, 2009), so the term “IO” is studied in this paper rather than “CO.”
Recommendation systems are one of several useful techniques designed to cope with IO (Itmazi & Megias, 2008;
Lee & Kwon, 2008; Yang & Chen, 2010) that have been used to assist users in identifying relevant information from
a vast amount of information (Ghauth & Abdullah, 2010). In this manner, users are not exposed the risk of IO.
Collaborative filtering (CF), content-based filtering (CBF), and hybrid filtering (HF) are the three most famous
methods among the proposed recommendation systems (Ghauth & Abdullah, 2010; Wang, Tsai, Lee, & Chiu, 2007).
CF refers to a system that use user attributes, such as browsing behavior, to predict items of interest for a user
(Khribi, Jemni, & Nasraoui, 2009; Manouselis, Vuorikari, & Van Assche, 2010; Rodríguez, Sicilia, Sánchez-Alonso,
Lezcano, García-Barriocanal, in press). That is to say, new items that have not been browsed by the user and new
items that are of interest to similar users will be recommended to the user (Ghauth & Abdullah, 2010). CBF refers to
a system that uses the attributes among items, such as categories of items, to predict items of interest for a user
(Ghauth & Abdullah, 2010; Huang, Huang, Wang, & Hwang, 2009; Khribi et al., 2009; Yang & Chen, 2010; Yang,
Huang, Tsai, Chung, & Wu, 2009). In other words, if the user was interested in an item in the past, it was assumed
that he/she would probably be interested in other similar items in the future (Wang et al., 2007). HF refers to a
system that combines CF and CBF to predict user likes or dislikes (Khribi et al., 2009; Wang et al., 2007). At
present, HF is usually believed to be a better method for recommendation systems (Wang et al., 2007). In this study,
the HF method is adopted in a PGR system that uses both collective and individual visiting behavior to recommend
personalized guides for visitors, the details of which are described in the next section.
Personalized guide recommendation (PGR) system
The PGR system developed in this study was composed of a back-end subsystem and a front-end subsystem, as
shown in Figure 1. The back-end subsystem was designed for staff to use, and it would be used on a desktop
computer. The front-end subsystem was designed for visitors to use, and it would be used on netbook computer
(Notebook, 2010). The functions of these subsystems are described in more detail below.
152