M OTIVATION

Digital data and information have become necessities of modern life.

However, the increased volume of information incorporated in webpages has skyrocketed beyond our imagination. For example, in 2012, the number of non-duplicate webpages indexed in Google search engines was approximately 30 trillion (Sullivan, 2012; Wikipedia, retrieved July 25, 2012). We are

inundated with unprecedented volumes of information. Moreover, given that the Internet is a dynamic hypermedia system for acquiring information, users easily become lost when looking for information (Navarro-Prieto, Scaife, &

Rogers, 1999). Therefore, each individual should be aware of effective ways to search for the information that they need in this era of explosive information.

As software and hardware constantly evolve to further advancement and the technology of webpage design and the bandwidth of the Internet are drastically enhanced, the modern webpage has transformed from text

information into multimedia platforms offering auditory, pictorial and animated information. The search indexes of Yahoo search engines included

approximately 19.2 billion web documents, 1.6 billion images, and over 50 million audio and video files in August of 2005 (Yahoo, 2005; Boutell.Com, 2007). Thus, all types of search engines have been flourishing. Examples include Google, Yahoo, Google Image, Google Maps, Google Earth and YouTube. Additional new search functions providing extra help for users emerge every single day. For example, there is a search engine providing

users with selections of words to be added before or after original keywords or with other related keywords. By means of search engines, users are free to locate the information they want in the massive sea of digital resources.

The invention of the search engine has changed the ways in which everyone in the world pursues knowledge and studies. Today, people

(including students) immediately turn to the Internet to meet a large majority of their information needs. This transformation has brought about changes to education because transmission and instilment of knowledge are no longer the first and foremost goals of education. Students are now trained to actively seek and evaluate information and to construct knowledge from online searches performed on a daily basis (Bilal & Kirby, 2002). They are required to integrate the acquired information with their prior knowledge in order to cope with other tasks and problems (Brand-Gruwel, Wopereis, & Vermetten, 2005). The

“ability of information search” has been elevated to the status of a new problem-solving skill (Laxman, 2010; Park & Black, 2007; Walraven,

Brand-Gruwel, & Boshuizen, 2008). The collection, analysis, assessment and integration of online information impacts everyone’s learning effectiveness and quality of life.

The information search process is the user’s constructive activity of finding meaning from information in order to extend or change his or her state of knowledge on a particular problem or topic. Thus, information search is a form of learning (Kuhlthau, 1991; Marchionini, 1995). Before embarking upon any meaningful search activities, the searcher must understand the questions that they have, or the nature of the search tasks, by means of their existing knowledge. They must decide upon the approximate whereabouts of their

target in the cyber world (for example, the user must decide between searching general webpages, news articles, blogs, pictures or maps) and choose a search engine. Then, they can formulate keywords for the search. In the course of the search, the searcher must review each search result and move on to subsequent searches if necessary after the assessment, during which they may be required to modify their techniques, adjust their keywords or rephrase their questions in order to achieve their purposes. The entire process comprises planning, monitoring, evaluating, and revising activities;

these are also metacognitive learning strategies (Brown, 1987). It is also a self-regulated learning process. In addition, because many search results are now displayed in some form of multimedia, learners have more opportunities to repeatedly use sounds, pictures and text to construct knowledge, thus making knowledge acquisition is a concrete representation of cognitive elaboration (Reigeluth & Stein, 1983). This study regards search as an active process of cognition and learning when learning tasks occur until the learner completes their tasks. This study aims at investigating how different learners “learn” to look for the information that they need on the Internet and how they locate useful results.

People possess individual differences in learning. Individuals tend to use distinctly different behaviors and strategies to perform identical search tasks, such as reading multiple pages of search results in detail versus skimming one page of results before trying a new keyword, following multiple links versus stopping after the first webpage, and utilizing one versus multiple search engines. Accordingly, individuals achieve different search outcomes and learning effects. Regarding differences in text search behaviors and

performance, researchers have looked at individual difference factors, such as personal experiences (e.g., background, Internet experience, and experience with Boolean searching), personal cognition (e.g., domain knowledge, reading skill, problem-solving ability, and understanding of the search task), personal approaches (e.g., study approaches, perceptions of and preferred approaches to web-based information seeking, and cognitive style), environmental factors (e.g., search engines), and task types (e.g., locating web sites versus locating information, close-ended versus open-ended) (Bystrom & Jarvelin, 1995; Allen, 1998, 2000; Hsieh-Yee, 2001; Bilal & Kirby, 2002; Kim & Allen, 2002; Rouet, 2003; Ford, Miller, & Moss, 2005; Park & Black, 2007). While it seems obvious that differences in individual characteristics and cognitive development may influence text search behaviors and performance (Ford, Wood, & Walsh, 1994;

Kim & Allen, 2002), very few researchers have made the effort to test these ideas or to identify specific factors that influence other types of searches.

However, there is a drastic difference between search for texts and search for other types of information (such as images, landmarks, music, and videos).

According to the cognitive theory of multimedia learning (Mayer, 2001), humans have two information processing systems; one is for verbal material, and the other is for visual material. Some people are more advanced in dealing with words, while others show better performance with images (Mayer &

Massa, 2003). In presenting an instructional message to learners, designers have two main formats available: words and pictures. Words include speech and printed text, while pictures include static graphics (such as illustrations and photos) and dynamic graphics (such as animations and videos). Because different types of search engines possess different search and cognitive

processes, it seems reasonable to consider search engines within four types of formats: (1) text search, where the searched information is mainly printed text (such as Google, Yahoo, and Bing); (2) image search, where the searched information is mainly static photos (such as Google Image and Yahoo! Image Search); (3) video search, where the searched information mainly consists of dynamic graphics (such as Google Video, YouTube, and Yahoo! Video Search);

and (4) landmark search, where the searched information mainly includes static illustrations (such as Google Maps, Google Earth, and Yahoo! Maps).

Text searches require the comprehension of connotations for a given topic and the use of related ideas to formulate keywords. Because it is easy for students to focus on and search for information via definitive keywords, it only takes them a few searches to retrieve the correct documents or answers.

In contrast, picture or image searches require theme formulation and the ability to envision potential results. Given that many current image retrieval systems are keyword-based, users must translate their visions into literal descriptions, and pictures stored in databases must have descriptive words or metadata that match selected keywords (Fukumoto, 2006; Hou & Ramani, 2004). Search systems transmit some pictures for users to compare, assess, and decide whether or not they need to continue a search. Accordingly, image searches can be analyzed as mixed acts of image-text cross-referencing, observation, judgment, decision-making, and correction. Note that the

existence of semantic gaps and the lack of precise characteristics make image searches more abstract and complex than text searches (Choi, 2010;

Cunningham & Masoodian, 2006). In image searches, descriptive and thematic queries are more commonly used than unique term queries. Most

users perform a large amount of query modification but they seem unable to find the images they desire in an effective way (Jörgensen & Jörgensen, 2005).

Approximately one-fifth of all image search queries result in zero hits (Pu, 2008). Yet little is known about what factors improve the odds for successful image searches.

In terms of video searches, video search engines provide a tool that is less frequently observed in the other types of search engines: the video recommendation system. Such a tool “recommends” other videos that are relevant to the viewed video (regardless of the keyword usage) for the users’

reference by analyzing the search processes of individual users and the knowledge structure within the system. This function is very common on video and music sharing websites for business purposes. Such a design aims to bring convenience to users rather than to help them learn better. Thus, it may be an additional significant factor in the video search process. The retrieved videos may come from the suggestion of the users based on the relevance between the pictures and the texts, and they may also be contributed from the recommendations made by the system. In terms of learning, we must be careful in distinguishing the attributions to the search results – from the students or from the guidance of the system? Will the issue related to recommendation by the system be aggravated if the students use improper keywords or view irrelevant videos? The “video recommendation system” can bring about both benefits and risks for the users. This function allows users to quickly browse videos related to the topics that they care about, but it could also lead the users to watch a series of videos irrelevant to their original search targets, which may induce them to believe that such search results are useful.

Thus, when evaluating learning effectiveness in the course of a video search, we must take this function into consideration.

When we use geographic information systems to search for landmarks, the basic steps of collecting spatial information from Internet are described below. To successfully complete search of the given famous landmarks when only the names of the landmarks are provided, one must have rich knowledge of global landmarks -- knowing at which place(s) the landmark may be. Some landmarks may be learned through the textbooks and geography instructions;

while others may be gained from real life experiences by viewing the landmarks or walking in/surrounding by them. Personal environmental experiences are processed and stored through the function named as large scale environmental cognition (Evans, 1980; Hegarty, Montello, Richardson, Ishikawa & Lovelace, 2006). Landmark searches involve internal

representations of correct spatial information, a cognitive function referred to as spatial ability by Linn and Petersen (1985). If a landmark does not appear at a predicted location, an individual must use a combination of reasoning,

guessing, exploring, using partial correct geographical knowledge and

excluding incorrect hypotheses. This type of abstract reasoning is considered a central characteristic of general intelligence. In sum, landmark searches require complex cognitive processing.

In sum, the similarities and differences of four types of search engines are compared and presented in Table 1. These show that various types of

multimedia search engines possess different search and cognitive processes.

Therefore, different types of search engines may lead to diverse factors to influence search behaviors, strategies and performance due to different search

and cognitive processes. This study aims at analyzing, as well as defining, which cognitive abilities or individual characteristics affect students’ abilities to effectively search using a variety of multimedia search engines.

Table 1. The similarities and differences of four types of search engines.

Text search Image search Video search Landmark search

Input Keywords Keywords Keywords Keywords or

navigation

Output Texts Images Videos Landmarks

Information

Search result Open answers Open answers Open answers Single answer Distinctive

Not specified Not specified Video

recommendation

Visual literacy Absorbed in the information of

Google, Yahoo Google Image, Yahoo! Image