Bloom’s dichotomous key: a new tool for evaluating the cognitive difficulty of assessments

ONE OF THE MORE WIDELY USED TOOLS to both inform course design and measure expert-like skills is Bloom’s taxonomy of educational objectives for the cognitive domain (2, 13, 22). This tool divides assessment of cognitive skills into six different levels: knowledge/remember, comprehension/understand, application/apply, analysis/analyze, synthesis/create, and evaluation/evaluate (2, 6). The first two levels are generally considered to represent lower levels of mastery (lower-order cognitive skills) and the last three represent higher-order levels of mastery involving critical thinking (higher-order cognitive skills) with apply-level questions often bridging the gap between the two (e.g., Refs. 5, 8, 10, 11, 23, and 24). While Bloom’s taxonomy is widely used by science educators, learning and mastering the concepts of the cognitive domain to categorize educational materials into the six levels identified in Bloom’s taxonomy are not trivial tasks. As with any complex task, experts and novices differ in the key abilities needed to cue into and evaluate information (4, 7, 9). Across disciplines, novices are less adept at noticing salient features and meaningful patterns, recognizing the context of applicability of concepts, and using organized conceptual knowledge rather than superficial cues to guide their decisions. Newer users of Bloom’s taxonomy demonstrate similar difficulties as they work to gain expertise, leading to inconsistencies in Bloom’s ratings (1, 8, 15) (see BDK Development for examples)

A new method for extracting skin microbes allows metagenomic analysis of whole-deep skin

In the last decade, an extensive effort has been made to characterize the human microbiota, due to its clinical and economic interests. However, a metagenomic approach to the skin microbiota is hampered by the high proportion of host DNA that is recovered. In contrast with the burgeoning field of gut metagenomics, skin metagenomics has been hindered by the absence of an efficient method to avoid sequencing the host DNA. We present here a method for recovering microbial DNA from skin samples, based on a combination of molecular techniques. We have applied this method to mouse skin, and have validated it by standard, quantitative PCR and amplicon sequencing of 16S rRNA. The taxonomic diversity recovered was not altered by this new method, as proved by comparing the phylogenetic structure revealed by 16S rRNA sequencing in untreated vs. treated samples. As proof of concept, we also present the first two mouse skin metagenomes, which allowed discovering new taxa (not only prokaryotes but also viruses and eukaryots) not reachable by 16S rRNA sequencing, as well as to characterize the skin microbiome functional landscape. Our method paves the way for the development of skin metagenomics, which will allow a much deeper knowledge of the skin microbiome and its relationship with the host, both in a healthy state and in relation to disease

Design of an Algebraic Concept Operator for Adaptive Feedback in Physics

International audienceIn an adaptive learning environment, the feedback provided during problem-solving requires a means, target, goal, and strategy. One of the challenges of representing feedback to meet these criteria, is the representation of the effect of multiple concepts on a single concept. Currently, most of the methods (linguistic knowledge base, expert knowledge base, and ontology) used in representing knowledge in an adaptive learning environment only provide relationships between a pair of concept. However, a cognitive knowledge base which represents a concept as an object, attribute, and relations (OAR) model, provides a means to determine the effect of multiple concepts on a single concept. Using the OAR model, the relationships between multiple pedagogical, domain, and student attributes are represented for providing adaptive feedback. Most researchers have proposed adaptive feedback methods that are not fully grounded in pedagogical principles. In addition, the three knowledge components of the learning environment (pedagogical, domain and student models) are mostly treated in isolation. A reason for this could be the complex nature of representing multiple adaptive feedback characteristics across the main components of a learning environment. Thus, there is a need to design a concept operator that can relate the three facets of knowledge in an adaptive learning environment. Using the algebraic concept operator $$R_{i}^{in}$$, the effect of multiple attributes of the three knowledge components on the student’s performance is represented. The algebraic concept operator introduced in this article will allow teachers and pedagogy experts to understand and utilize a variety of effective feedback approaches