SCIE1000 covers mathematics, computer programming, communication, science and modelling. The main goal is to help students see that similar tools and approaches can be applied in a wide range of discipline areas and contexts. So my goal was mostly to develop students’ abilities and confidence in transferring knowledge from one context to another context that may initially appear to be quite distinct, but in reality the same general approaches apply. This is important in this course because we teach students from all areas of science. Rather than “freezing” when they encounter something unfamiliar, we want them to “know where to start” and to be able to apply the techniques that they know. I also want them to appreciate that understanding exactly why a particular approach or model works can help them understand a context in their own discipline area. This is a counterpoint to a common approach of saying “you need to contextualise the learning to engage students when learning generic techniques”… my counterpoint is that understanding at a deeper level why a standard technique works can help a student assimilate an unfamiliar context. For example, understanding truly what is meant by an area under a mathematical curve can help students understand contexts from finance, pharmacology, ecology and so on. During semester, we cover a number of context areas. The final exam is always based on a particular context that the students have never encountered. All questions are stated within this context, and students will often need to communicate an interpretation of their answers. The contexts are all genuine, using real data from real research or government publications. So we are not just examining students’ mathematics or modelling or programming abilities, but even more so, their ability to transfer skills to something unfamiliar. I have uploaded a few past exam papers. Course content did not change significantly over that time period

Details

CLASS SIZE
500+
CLASS LEVEL
First year
ASSESSMENT SECURITY
High security
TIME REQUIREMENTS
High time
CONDITIONS
Identity verified, Time limited
FEATURES
Authentic
TAGS
active learning, analysis, critical reflection
Photo of Professor Peter Adams

Emeritus Professor Peter Adams

p.adams@uq.edu.au

Research Interests: Bioinformatics: DNA Sequencing - Development of a novel technique for sequencing problematic genomic regions, using a combination of techniques from mathematics, computer science and molecular biology. We are developing methods to avoid problems that arise during sequencing and cloning. This work is the subject of three provisional patent applications, has attracted sizeable government funding and investor capital, and forms the basis of a biotechnology start-up company. The work is joint with the Australian Genome Research Facility; Bioinformatics: Drug Discovery - Research into methods for virtual screening of virtual libraries of compounds, thus creating focused libraries to be used in drug discovery. We use a combination of highperformance computing, combinatorial searches, graph theory and molecular biology to identify protein surface shapes that are common to many proteins. This work has also been patented, has attracted a large amount of investor capital and forms the basis of another start-up company. The research is joint with the Centre for Drug Design and Development; Graph Theory and Design Theory - My pure research is in combinatorics, with specific interest in graphs, recursive constructions, decomposition problems, properties of latin squares, and arrangements of objects; High-performance Computing - Developing computational methods and algorithms for rapidly finding specific combinatorial designs and graph decompositions. Methods we are developing include automated code generation and efficient parallel algorithms for CPU clusters. Find out more