The purpose of this chapter was to identify the characteristics of attractive quest-based learning activities as evidenced by learner selection and completion. Guiding questions for this study included: (1) What characteristics are common in those quests most selected by students in a quest-based learning environment? (2) What characteristics are evident in those quests that are completed?
The following methods provided the strategy for answering this and the related research questions and provided rationale for the procedures that were used. These methods also identify the participants used in the study and their characteristics, demographics, and sample orientation. The measures and instruments used are also clearly outlined and detailed.
Case Design
This case study utilized a quantitative research design to identify the characteristics of attractive quest-based learning. This was done by employing data-mining techniques and tools SAS Enterprise Miner version 6.2 using data captured from the 3-D GameLab learning management system. Fayyad, Piatetsky-Shapiro, & Smyth (1996a) offer data mining as a process of Knowledge Discovery in Databases (KDD) through 1) data selection, 2) data cleaning, 3) data transformation, 4) data mining, and 5) results evaluation and interpretation. This process was used to find quantitative evidence.
Characteristics of this quantitative research design included descriptive statistics. These descriptive statistics guided the process of data mining. This was done to identify patterns in the data that might not be otherwise observable. Analysis was focused on a large volume of LMS interactions collected from 98 students.
The survey instrument was validated using the SPSS. Martin (2010) submits that the use of un-validated instruments or techniques in the classroom is problematic. He suggests that evidence-based pedagogy and practice are critical. This is necessary to avoid what Yates (2005) describes as “illusory correlations and fundamental computational bias.” In inferential statistics, many suggest that research producing strong reliable evidence should be conducted such that a high degree of importance is placed on effect size, statistical power, confidence intervals, reliability and validity coefficients, and a randomization where possible (Horn et al., 2009 ; Shelby & Vaske, 2008, Smith, Levine, & Lachlan, 2002; Zientek, Capraro, & Capraro, 2008). However, the whole data set was collected and analyzed, an inferential measure of reducing the error were not necessary (Fayyad, Piatetsky-Shapiro, & Smyth, 1996b).
Participants and Sample
The research was conducted using four face-to-face sections of an introductory educational technology course for pre-service teachers enrolled at a university in the northwest United States. The course focused on the use of productivity and Internet tools for teachers in a classroom setting. It provided practical skills and methodological/pedagogical strategies for the implementation of word processing, presentation, spreadsheet, and Internet technologies for teaching and learning. The course was offered as one of two pre-requisites for admission to upper-division education courses. For this reason, students often take it in their second year of undergraduate studies.