Inference of distributional parameters from compositional samples containing nondetects

Low concentrations of elements in geochemical analyses have the peculiarity of being compositional data and, for a given level of significance, are likely to be beyond the capabilities of laboratories to distinguish between minute concentrations and complete absence, thus preventing laboratories from reporting extremely low concentrations of the analyte. Instead, what is reported is the detection limit, which is the minimum concentration that conclusively differentiates between presence and absence of the element. A spatially distributed exhaustive sample is employed in this study to generate unbiased sub-samples, which are further censored to observe the effect that different detection limits and sample sizes have on the inference of population distributions starting from geochemical analyses having specimens below detection limit (nondetects). The isometric logratio transformation is used to convert the compositional data in the simplex to samples in real space, thus allowing the practitioner to properly borrow from the large source of statistical techniques valid only in real space. The bootstrap method is used to numerically investigate the reliability of inferring several distributional parameters employing different forms of imputation for the censored data. The case study illustrates that, in general, best results are obtained when imputations are made using the distribution best fitting the readings above detection limit and exposes the problems of other more widely used practices. When the sample is spatially correlated, it is necessary to combine the bootstrap with stochastic simulation

The Role of the online learner: onsite students becoming online learners

As universities are offering tuition through online learning environments, “onsite students” in higher education are increasingly becoming “online learners”. Since the medium for learning (and teaching) online is a digital environment, and at a distance, the role taken by students and teaching staff is different to the one these are used to in onsite, traditional settings. Therefore the Role of the Online Learner, presented in this paper, is key to onsite students who are to become online learners. This role consists of five competences: Operational, Cognitive, Collaborative, Self-directing, Course-specific. These five competences integrate the various skills, strategies, attitudes and awareness that make up the role of online learner, which learners use to perform efficiently online. They also make up the basis of a tutorial for would-be online learners, going over the Role of the Online Learner by means of concepts, examples and reflective activities. This tutorial, available to students in the author’s website, is also helpful to teaching and counselling staff in guiding their students to become online learners