Evidence-informed development of engineering practical work: Effecting change through staff and student engagement

This paper discusses innovations in curriculum development in the Department of Engineering at the University of Cambridge as a participant in the Teaching for Learning Network (TFLN), a teaching and learning development initiative funded by the Cambridge-MIT Institute a pedagogic collaboration and brokerage network. A year-long research and development project investigated the practical experiences through which students traditionally explore engineering disciplines, apply and extend the knowledge gained in lectures and other settings, and begin to develop their professional expertise. The research project evaluated current practice in these sessions and developed an evidence-base to identify requirements for new activities, student support and staff development. The evidence collected included a novel student 'practice-value' survey highlighting effective practice and areas of concern, classroom observation of practicals, semi-structured interviews with staff, a student focus group and informal discussions with staff. Analysis of the data identified three potentially 'high-leverage' strategies for improvement: development of a more integrated teaching framework, within which practical work could be contextualised in relation to other learning; a more transparent and integrated conceptual framework where theory and practice were more closely linked; development of practical work more reflective of the complex problems facing professional engineers. This paper sets out key elements of the evidence collected and the changes that have been informed by this evidence and analysis, leading to the creation of a suite of integrated practical sessions carefully linked to other course elements and reinforcing central concepts in engineering, accompanied by a training and support programme for teaching staff.

Award Winner in the Young Investigator Category, 2014 Society for Biomaterials Annual Meeting and Exposition, Denver, Colorado, April 16-19, 2014: Periodically perforated core-shell collagen biomaterials balance cell infiltration, bioactivity, and mechanical properties.

Orthopedic tissue engineering requires biomaterials with robust mechanics as well as adequate porosity and permeability to support cell motility, proliferation, and new extracellular matrix (ECM) synthesis. While collagen-glycosaminoglycan (CG) scaffolds have been developed for a range of tissue engineering applications, they exhibit poor mechanical properties. Building on previous work in our lab that described composite CG biomaterials containing a porous scaffold core and nonporous CG membrane shell inspired by mechanically efficient core-shell composites in nature, this study explores an approach to improve cellular infiltration and metabolic health within these core-shell composites. We use indentation analyses to demonstrate that CG membranes, while less permeable than porous CG scaffolds, show similar permeability to dense materials such as small intestine submucosa (SIS). We also describe a simple method to fabricate CG membranes with organized arrays of microscale perforations. We demonstrate that perforated membranes support improved tenocyte migration into CG scaffolds, and that migration is enhanced by platelet-derived growth factor BB-mediated chemotaxis. CG core-shell composites fabricated with perforated membranes display scaffold-membrane integration with significantly improved tensile properties compared to scaffolds without membrane shells. Finally, we show that perforated membrane-scaffold composites support sustained tenocyte metabolic activity as well as improved cell infiltration and reduced expression of hypoxia-inducible factor 1α compared to composites with nonperforated membranes. These results will guide the design of improved biomaterials for tendon repair that are mechanically competent while also supporting infiltration of exogenous cells and other extrinsic mediators of wound healing.

Student-t processes as alternatives to Gaussian processes

We investigate the Student-t process as an alternative to the Gaussian process as a non-parametric prior over functions. We derive closed form expressions for the marginal likelihood and predictive distribution of a Student-t process, by integrating away an inverse Wishart process prior over the co-variance kernel of a Gaussian process model. We show surprising equivalences between different hierarchical Gaussian process models leading to Student-t processes, and derive a new sampling scheme for the inverse Wishart process, which helps elucidate these equivalences. Overall, we show that a Student-t process can retain the attractive properties of a Gaussian process - a nonparamet-ric representation, analytic marginal and predictive distributions, and easy model selection through covariance kernels - but has enhanced flexibility, and predictive covariances that, unlike a Gaussian process, explicitly depend on the values of training observations. We verify empirically that a Student-t process is especially useful in situations where there are changes in covariance structure, or in applications such as Bayesian optimization, where accurate predictive covariances are critical for good performance. These advantages come at no additional computational cost over Gaussian processes.