Studies that rely on Web usage mining can be experimental or observational in nature. The focus of such studies is quite varied and may involve such topics as predicting online purchase intentions (Hooker & Finkelman, 2004; Moe, 2003; Montgomery, Li, Srinivsan, & Liechty, 2004), designing recommender systems for e-commerce products and sites (Cho & Kim, 2004; Kim & Cho, 2003), understanding navigation and search behavior (Chiang, Dholakia, & Westin, 2004; Gery & Haddad, 2003; Johnson, Moe, Fader, Bellman, & Lohse, 2004; Li & Zaiane, 2004), or a myriad of other subjects. Regardless of the issue being studied, data collection for Web usage mining studies often proves to be a vexing problem, and ideal research designs are frequently sacrificed in the interest of finding a reasonable data capture or collection mechanism. Despite the difficulties involved, the research community has recognized the value of Web-based experimental research (Saeed, Hwang, & Yi, 2003; Zinkhan, 2005), and has, in fact, called on investigators to exploit “non-intrusive means of collecting usage and exploration data” (Gao, 2003, p. 31) in future Web studies. In this article we discuss some of the methodological complexities that arise when conducting studies that involve Web usage mining. We then describe an innovative, software-based methodology that addresses many of these problems. The methods described here are most applicable to experimental studies, but they can be applied in ex-post observational research settings, as well.
Motivation for Learning - Adaptive Gamification for Web-based Learning Environments
