Weblogs in Higher Education - Why Do Students (Not) Blog?

Positive impacts on learning through blogging, such as active knowledge construction and reflective writing, have been reported. However, not many students use weblogs in informal contexts, even when appropriate facilities are offered by their universities. While motivations for blogging have been subject to empirical studies, little research has addressed the issue of why students choose not to blog. This paper presents an empirical study undertaken to gain insights into the decision making process of students when deciding whether to keep a blog or not. A better understanding of students' motivations for (not) blogging may help decision makers at universities in the process of selecting, introducing, and maintaining similar services. As informal learning gains increased recognition, results of this study can help to advance appropriate designs of informal learning contexts in Higher Education. The method of ethnographic decision tree modelling was applied in an empirical study conducted at the Vienna University of Technology, Austria. Since 2004, the university has been offering free weblog accounts for all students and staff members upon entering school, not bound to any course or exam. Qualitative, open interviews were held with 3 active bloggers, 3 former bloggers, and 3 non‑ bloggers to elicit their decision criteria. Decision tree models were developed out of the interviews. It turned out that the modelling worked best when splitting the decision process into two parts: one model representing decisions on whether to start a weblog at all, and a second model representing criteria on whether to continue with a weblog once it was set up. The models were tested for their validity through questionnaires developed out of the decision tree models. 30 questionnaires have been distributed to bloggers, former bloggers and non‑ bloggers. Results show that the main reasons for students not to keep a weblog include a preference for direct (online) communication, and concerns about the loss of privacy through blogging. Furthermore, the results indicate that intrinsic motivation factors keep students blogging, whereas stopping a weblog is mostly attributable to external factors

Predictive hypothesis identification

While statistics focusses on hypothesis testing and on estimating (properties of) the true sampling distribution, in machine learning the performance of learning algorithms on future data is the primary issue. In this paper we bridge the gap with a general principle (PHI) that identifies hypotheses with best predictive performance. This includes predictive point and interval estimation, simple and composite hypothesis testing, (mixture) model selection, and others as special cases. For concrete instantiations we will recover well-known methods, variations thereof, and new ones. PHI nicely justifies, reconciles, and blends (a reparametrization invariant variation of) MAP, ML, MDL, and moment estimation. One particular feature of PHI is that it can genuinely deal with nested hypotheses