Search for: All records

Threshold concepts are transformative elements of domain knowledge that enable those who attain them to engage domain tasks in a more sophisticated way. Existing research tends to focus on the identification of threshold concepts within undergraduate curricula as challenging concepts that prevent attainment of subsequent content until mastered. Recently, threshold concepts have likewise become a research focus at the level of doctoral studies. However, such research faces several limitations. First, the generalizability of findings in past research has been limited due to the relatively small numbers of participants in available studies. Second, it is not clear which specific skills are contingent upon mastery of identified threshold concepts, making it difficult to identify appropriate times for possible intervention. Third, threshold concepts observed across disciplines may or may not mask important nuances that apply within specific disciplinary contexts. The current study therefore employs a novel Bayesian knowledge tracing (BKT) approach to identify possible threshold concepts using a large data set from the biological sciences. Using rubric-scored samples of doctoral students’ sole-authored scholarly writing, we apply BKT as a strategy to identify potential threshold concepts by examining the ability of performance scores for specific research skills to predict score gains on other research skills. Findings demonstrate the effectiveness of this strategy, as well as convergence between results of the current study and more conventional, qualitative results identifying threshold concepts at the doctoral level. 

Zwitterionic materials are an important class of antifouling biomaterials for various applications. Despite such desirable antifouling properties, molecular-level understanding of the structure–property relationship associated with surface chemistry/topology/hydration and antifouling performance still remains to be elucidated. In this work, we computationally studied the packing structure, surface hydration, and antifouling property of three zwitterionic polymer brushes of poly(carboxybetaine methacrylate) (pCBMA), poly(sulfobetaine methacrylate) (pSBMA), and poly((2-(methacryloyloxy)ethyl)phosporylcoline) (pMPC) brushes and a hydrophilic PEG brush using a combination of molecular mechanics (MM), Monte Carlo (MC), molecular dynamics (MD), and steered MD (SMD) simulations. We for the first time determined the optimal packing structures of all polymer brushes from a wide variety of unit cells and chain orientations in a complex energy landscape. Under the optimal packing structures, MD simulations were further conducted to study the structure, dynamics, and orientation of water molecules and protein adsorption on the four polymer brushes, while SMD simulations to study the surface resistance of the polymer brushes to a protein. The collective results consistently revealed that the three zwitterionic brushes exhibited stronger interactions with water molecules and higher surface resistance to a protein than the PEG brush. It was concluded that both the carbon space length between zwitterionic groups and the nature of the anionic groups have a distinct effect on the antifouling performance, leading to the following antifouling ranking of pCBMA > pMPC > pSBMA. This work hopefully provides some structural insights into the design of new antifouling materials beyond traditional PEG-based antifouling materials.