Study design of information search

2.4.1. Participants

Study participants were 355 fifth grade students attending an elementary school in

central Taiwan. Each student’s thinking style level was determined using a questionnaire to be described in a later section. Of the 350 students who completed the questionnaire, 311 were instructed to use Google to search for information on pollution and to fill out a worksheet. All of the participants had two years’ worth of training in computer usage, meaning that they had basic skills with Windows, Microsoft Word, a Web browser, and Web information search techniques.

2.4.2. Search task

Bilal (2000, 2001) categorizes search tasks as fact-finding or research-based.

Fact-finding tasks involve searches for specific answers to simple questions and

research-based tasks involve searches for less clear-cut answers to more complex questions.

He also notes that different search task types influence children’s cognitive and physical search behaviors. My aim was not to address the impact of various search task types, but to analyze the impact of various strengths of thinking style level on search target settings and search behaviors. Achieving this required the use of a research-based search task to encourage students to perform more extensive searches for the purpose of attaining comprehensive understandings of their personal preferences.

The topic chosen for the participating students was “pollution”—something that Taiwanese students are well aware of in their daily lives. They had to establish initial search targets in order to attain desired results. After browsing ordered lists of search results, the students made decisions on refining their targets to move closer to their preferred results.

They were asked to write down their “search targets” (i.e., Google search keywords) on their worksheets and to regularly revise their sheets according to their current search target interests.

Participants were given 80 minutes to complete the task.

2.4.3. Procedure

Students were given training on basic search skills using the Google search engine.

Specifically, they were asked to type in the keyword “energy resources” as practice to ensure that they knew how to use a computer mouse and keypad to browse for information. Next, the 355 students in the original sample were asked to complete the “level dimension” of the thinking styles questionnaire described in the following section. Of the 350 students who completed the questionnaire, 311 performed searches on the topic of pollution and completed their worksheets. Searches were recorded using the Camtasia Recorder 3.0 screen capture program for further analysis.

2.4.4. Data collection and pre-analysis

1. Investigation of thinking style level

The questionnaire used in this research was adapted from the Sternberg–Wagner Thinking Styles Inventory (Sternberg & Wagner, 1999). A modified version (Huang, 2004) suitable for Taiwanese elementary school students was created to measure the strength of the participants’ style preferences when dealing with relatively large and abstract issues (global) compared to detailed and concrete issues (local). The test consists of 10 items with answers measured along a scale of 1 to 5. According to the test results (N = 311), 72 students

constituting the highest 27% of the global group were classified as high global, 66 students constituting the lowest 27% were classified as low global, and the remaining 173 students were classified as medium global. Using the same percentages, the respective numbers of students in the high local, medium local, and low local groups were 65, 184, and 62.

Representative data were used due to the complexity of analyzing the search strategies and processes of 311 students. I created four conditions: a) 26 students who were concurrently

in the highest 27% of the global group and lowest 27% of the local group, designated as the high global style (HG) group; b) 32 students who were concurrently in the highest 27% of the local group and lowest 27% of the global group, designated as the high local style (HL) group;

c) 6 students who were concurrently in the highest 27% of the global and local groups, designated as the bi-high style (Bi-H) group; and d) 6 students who were concurrently in the lowest 27% of the global and local groups, designated as the bi-low style (Bi-L) group. The remaining 241 students were excluded from the search behavior analysis.

2. Investigation of student prior knowledge

To determine if the students’ prior knowledge of natural science affected the search target setting and search behavior variables, I collected, averaged, and used their grades for introductory natural and social science courses to represent their prior knowledge of the pollution topic. The 87 students in the highest 27% grade group were classified as having high prior knowledge, 81 students in the lowest 27% grade group were classified as having low prior knowledge, and the remaining 143 students were classified as having medium prior knowledge.

3. Investigation of search target settings with worksheets

Students were asked to write down their Google search engine target terms on their personal worksheets and to revise the terms as their search intentions changed. The data were quantified and recorded as number of search targets (T), coverage of search targets (C), and maximum extension of search targets (E). As shown in Figure 3, the six search targets could be divided into the concept categories of “air pollution” and “noise pollution,” resulting in a coverage value of 2. Four of the six search targets focused on air pollution and the other two on noise pollution, so the maximum extension value was 4. To apply the search targets to subsequent analyses, I divided them into three types: focused (C<=2 AND E>2), dispersed (C>2 AND E<=2), and mixed.

Figure 3. Search target quantification (three indicators).

4. Investigation of search behavior

Files containing data on keyboard and mouse operations were reformatted into navigation flow maps (Lin & Tsai, 2005)—graphic displays of relationships among search keywords, visited Web pages, and task questions. The maps and search target settings

recorded on the students’ worksheets were used to analyze their information search behaviors according to six factors adapted from Lin and Tsai: a) number of keywords (variation in searched information); b) visited pages (variation in task information sources); c) maximum depth of exploration; d) average depth of Web page adoption (average exploration depth for task completion); e) revisited pages (degree of search navigation recursion); and f) Web pages for refining answers (frequency of refining or improving answer quality).