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.

Statistics anxiety, state anxiety during an examination, and academic achievement.

BACKGROUND: A large proportion of students identify statistics courses as the most anxiety-inducing courses in their curriculum. Many students feel impaired by feelings of state anxiety in the examination and therefore probably show lower achievements. AIMS: The study investigates how statistics anxiety, attitudes (e.g., interest, mathematical self-concept) and trait anxiety, as a general disposition to anxiety, influence experiences of anxiety as well as achievement in an examination. SAMPLE: Participants were 284 undergraduate psychology students, 225 females and 59 males. METHODS: Two weeks prior to the examination, participants completed a demographic questionnaire and measures of the STARS, the STAI, self-concept in mathematics, and interest in statistics. At the beginning of the statistics examination, students assessed their present state anxiety by the KUSTA scale. After 25 min, all examination participants gave another assessment of their anxiety at that moment. Students' examination scores were recorded. Structural equation modelling techniques were used to test relationships between the variables in a multivariate context. RESULTS: Statistics anxiety was the only variable related to state anxiety in the examination. Via state anxiety experienced before and during the examination, statistics anxiety had a negative influence on achievement. However, statistics anxiety also had a direct positive influence on achievement. This result may be explained by students' motivational goals in the specific educational setting. CONCLUSIONS: The results provide insight into the relationship between students' attitudes, dispositions, experiences of anxiety in the examination, and academic achievement, and give recommendations to instructors on how to support students prior to and in the examination.

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.