Exploring key sustainable development themes through learning activities

The Masters programme in Engineering for Sustainable Development at Cambridge University explores a number of key themes, including dealing with: complexity, uncertainty, change, other disciplines, people, environmental limits, whole life costs, and trade-offs. This paper examines how these concepts are introduced and analyses the range of exercises and assignments which are designed to encourage students to test their own assumptions and abilities to develop competencies in these areas. Student performance against these tasks is discussed and student feedback is also presented, with a focus on how their awareness of the themes are met through a range of activities.

Exploring key sustainable development themes through learning activities

Purpose: The paper examines how a number of key themes are introduced in the Masters programme in Engineering for Sustainable Development at Cambridge University through student centred activities. These themes include dealing with complexity, uncertainty, change, other disciplines, people, environmental limits, whole life costs, and trade-offs. Design/methodology/approach: The range of exercises and assignments designed to encourage students to test their own assumptions and abilities to develop competencies in these areas are analysed by mapping the key themes onto the formal activities which all students undertake throughout the core MPhil programme. The paper reviews the range of these activities that are designed to help support the formal delivery of the taught programme. These include residential field courses, role plays, change challenges, games, systems thinking, multi criteria decision making, awareness of literature from other disciplines and consultancy projects. An axial coding approach to the analysis of routine feedback questionnaires drawn from recent years has been used to identify how student’s own awareness develops. Also results of two surveys are presented which tests the students’ perceptions about whether or not the course is providing learning environments to develop awareness and skills in these areas. Findings: Students generally perform well against these tasks with a significant feature being the mutual support they give to each other in their learning. The paper concludes that for students from an engineering background it is an holistic approach to delivering a new way of thinking through a combination of lectures, class activities, assignments, interactions between class members, and access to material elsewhere in the University that enables participants to develop their skills in each of the key themes. Originality /value: The paper provides a reflection on different pedagogical approaches to exploring key sustainable themes and reports students own perceptions of the value of these kinds of activities. Experiences are shared of running a range of diverse learning activities within a professional practice Masters programme.

Measurement of stressful postures during daily activities: An observational study with older people.

This study measured the postures of older people during cooking and laundry. A sample of men and women aged 75+ years (n=27) was recruited and observed in a home-like environment. Postures were recorded with a measurement system in an objective and detailed manner. The participants were videotaped to be able to see where 'critical' postures occurred, as defined by a trunk inclination of ≥60°. Analysis of data was facilitated by specially developed software. Critical postures accounted for 3% of cooking and 10% of laundry, occurring primarily during retrieving from and putting in lower cabinets, the refrigerator, laundry basket or washing machine as well as disposing into the waste bin. These tasks involve a great variation in postural changes and pose a particular risk to older people. The results suggest that the use of stressful postures may decrease efficiency and increase fatigue, eventually leading to difficulties with daily activities. The specific tasks identified during which critical postures occurred should be targeted by designers in order to improve the activities. A few examples are given of how better design can reduce or eliminate some of the postural constraints.

Motor task variation induces structural learning.

When we have learned a motor skill, such as cycling or ice-skating, we can rapidly generalize to novel tasks, such as motorcycling or rollerblading [1-8]. Such facilitation of learning could arise through two distinct mechanisms by which the motor system might adjust its control parameters. First, fast learning could simply be a consequence of the proximity of the original and final settings of the control parameters. Second, by structural learning [9-14], the motor system could constrain the parameter adjustments to conform to the control parameters' covariance structure. Thus, facilitation of learning would rely on the novel task parameters' lying on the structure of a lower-dimensional subspace that can be explored more efficiently. To test between these two hypotheses, we exposed subjects to randomly varying visuomotor tasks of fixed structure. Although such randomly varying tasks are thought to prevent learning, we show that when subsequently presented with novel tasks, subjects exhibit three key features of structural learning: facilitated learning of tasks with the same structure, strong reduction in interference normally observed when switching between tasks that require opposite control strategies, and preferential exploration along the learned structure. These results suggest that skill generalization relies on task variation and structural learning.

Perceptual learning, roving and the unsupervised bias

Perceptual learning improves perception through training. Perceptual learning improves with most stimulus types but fails when . certain stimulus types are mixed during training (roving). This result is surprising because classical supervised and unsupervised neural network models can cope easily with roving conditions. What makes humans so inferior compared to these models? As experimental and conceptual work has shown, human perceptual learning is neither supervised nor unsupervised but reward-based learning. Reward-based learning suffers from the so-called unsupervised bias, i.e., to prevent synaptic " drift" , the . average reward has to be exactly estimated. However, this is impossible when two or more stimulus types with different rewards are presented during training (and the reward is estimated by a running average). For this reason, we propose no learning occurs in roving conditions. However, roving hinders perceptual learning only for combinations of similar stimulus types but not for dissimilar ones. In this latter case, we propose that a critic can estimate the reward for each stimulus type separately. One implication of our analysis is that the critic cannot be located in the visual system. © 2011 Elsevier Ltd.

SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases

The Internet has enabled the creation of a growing number of large-scale knowledge bases in a variety of domains containing complementary information. Tools for automatically aligning these knowledge bases would make it possible to unify many sources of structured knowledge and answer complex queries. However, the efficient alignment of large-scale knowledge bases still poses a considerable challenge. Here, we present Simple Greedy Matching (SiGMa), a simple algorithm for aligning knowledge bases with millions of entities and facts. SiGMa is an iterative propagation algorithm which leverages both the structural information from the relationship graph as well as flexible similarity measures between entity properties in a greedy local search, thus making it scalable. Despite its greedy nature, our experiments indicate that SiGMa can efficiently match some of the world's largest knowledge bases with high precision. We provide additional experiments on benchmark datasets which demonstrate that SiGMa can outperform state-of-the-art approaches both in accuracy and efficiency.

Modelling and control of nonlinear systems using Gaussian processes with partial model information

Gaussian processes are gaining increasing popularity among the control community, in particular for the modelling of discrete time state space systems. However, it has not been clear how to incorporate model information, in the form of known state relationships, when using a Gaussian process as a predictive model. An obvious example of known prior information is position and velocity related states. Incorporation of such information would be beneficial both computationally and for faster dynamics learning. This paper introduces a method of achieving this, yielding faster dynamics learning and a reduction in computational effort from O(Dn2) to O((D - F)n2) in the prediction stage for a system with D states, F known state relationships and n observations. The effectiveness of the method is demonstrated through its inclusion in the PILCO learning algorithm with application to the swing-up and balance of a torque-limited pendulum and the balancing of a robotic unicycle in simulation. © 2012 IEEE.