An evaluation of the impact of using authentic design and build industry projects in project-based learning

BACKGROUNDChisholm’s ‘first year experience’ is a significant feature of the new industry focused Bachelor of Engineering Technology program delivered in association with the South East Melbourne Manufacturers’ Alliance (SEMMA). This conceive-design-implement-operate (CDIO Initiative) program commenced as a full time program in first semester 2012. Whereas it is common for CDIO Initiative programs to have a first year experience program containing a project typical of the type of industry project they would complete as a graduate engineer or engineering technologist, this goes further by using real industry projects provided by SEMMA members.This design-and-build industry project runs across both semesters supporting project-based learning in three first year subjects. A concern is that the industry involvement of the projects adds substantially to an already heavy student workload. This has been further increased by the addition of two additional first year initiatives: writing workshops, and training in, and substantial use of, student oral presentations. It is recognised that an excessive workload could lead students to adopt surface learning approaches in other subjects.PURPOSEThe goal of the project is to evaluate student perceptions of the value and work load impact of the industry project and the other new first year initiatives.DESIGN/METHODCentral to this project is a student survey-based evaluation of the industry project based learning that is the core of the ‘first year experience’. The participants were limited to the small group of students who, in a single year, completed all three subjects that comprise the ‘first year experience’. To avoid compromising the results the survey was administered by Chisholm Institute’s Department of Strategy and Planning with no engineering technology degree program staff present. The survey included questions to enable responses to be linked with specific student demographics without identifying any of the respondents.RESULTSThe study showed the industry project-based learning had worthwhile outcomes but placed considerable time pressures on most respondents. For some, this also impacted on their other subjects. A first year oral presentation program was also shown to have worthwhile outcomes. However no conclusions could be reliably drawn on the third initiative – writing workshops.CONCLUSIONSThe results confirm that the authentic industry project is considered a worthwhile initiative but contributes significantly to student overload. This applies also – to a lesser extent – to the first year oral presentation program. Both also require new approaches to delivery as student numbers increase. Strategies to address these issues are discussed.

Has oil price predicted stock returns for over a century?

This paper contributes to the debate on the role of oil prices in predicting stock returns. The novelty of the paper is that it considers monthly time-series historical data that span over 150. years (1859:10-2013:12) and applies a predictive regression model that accommodates three salient features of the data, namely, a persistent and endogenous oil price, and model heteroscedasticity. Three key findings are unraveled: first, oil price predicts US stock returns. Second, in-sample evidence is corroborated by out-sample evidence of predictability. Third, both positive and negative oil price changes are important predictors of US stock returns, with negative changes relatively more important. Our results are robust to the use of different estimators and choice of in-sample periods.

The systematic influences on student evaluation of teaching in engineering education

BACKGROUND Student evaluation of teaching (SET) has a long history, has grown in prevalence and importance over a period of decades, and is now common-place in many universities internationally. SET data are collected for a range of purposes, including: as diagnostic feedback to improve the quality of teaching and learning; as an input to staff performance management processes and personnel decisions such as promotion for staff; to provide information to prospective students in their selection of courses and programs; and as a source of data for research on teaching. Rovai et al. (2006) report that while SET research provides mixed results, there is evidence that, for course-related factors, smaller classes are rated more favourably than large classes, upper-year-level classes are rated more favourably than lower-year classes, and that there are rating differences between discipline areas. While additional course-related factors are also noted, other reviews of the literature on SET also identify these three factors as commonly reported systematic influences on SET ratings. The School of Engineering at Deakin University in Australia offers undergraduate and postgraduate engineering programs, and these programs are delivered in both on-campus and off-campus modes.PURPOSEThe paper presents a quantitative investigation of SET data for the School of Engineering at Deakin University to identify whether the commonly reported systematic influences on SET ratings of class size and year level are also observed here. The influence of online mode of offer is also explored.DESIGN/METHOD Deakin University’s Student Evaluation of Teaching and Units (SETU) questionnaire is administered to students enrolled in every unit of study every time that unit is offered, unless it is specially exempted. Following data collation, summary results are reported via a public website. The publicly available SETU data for all School of Engineering units of study were collected for a two year period. The collected data were subjected to analysis of variance (ANOVA) analysis to identify any significant systematic influences on mean student SETU ratings.RESULTS SETU data from 100 separate units of study over the two year period were collected, representing 3375 sets of SETU ratings, and covering unit enrolment sizes from 12 to 462 students. Although this was a modest sized investigation, significantly higher mean ratings for some SETU items were observed for units with small enrolments, for postgraduate level units compared to undergraduate level units, and for units offered in conventional mode compared to online mode of offer. The presence of the commonly observed systematic influences on SET ratings was confirmed.CONCLUSIONS While the use of SET data may have originally been primarily for formative purposes to improve teaching and learning, they are also increasingly used for summative judgements of teaching quality and teaching staff performance that have implications for personnel decision making. There may be an acceptance of the need for SET, however there remains no universal consensus as to what constitutes quality in university teaching and learning, and the increasing use of SET for high-stakes decision making puts pressure on institutions to ensure that their SET practices are sound, equitable and defensible.

A proposed definition of the engineering methodology

BACKGROUND An adequately concise and accurate definition of the profession of engineering that can simultaneously encompass a majority of the profession and be reasonably understood by a majority of society arguably remains as an elusive goal yet to be attained.While numerous definitions of the profession exist they tend to describe specific methods or approaches deployed in the practice of engineering rather than be suitably descriptive of the profession of engineering.The lack of an adequate, accurate and relevant definition of the profession of engineering has, and continues to, present disadvantages to the profession. While acknowledging this problem the profession continues to rely on existing inadequate, inaccurate, or irrelevant definitions of itself as it struggles to attain the degree of awareness, recognition, and appreciation of its significant benefits that directly impact society and the individual.Accordingly in many countries the choice of engineering as a career path often ranks below other profession choices such as medicine, law, and management - especially with adolescent girls. Also the relevance and role of professional engineering in socio-economic and socio-political contexts is often undervalued or neglected – especially in national and international policy discussions and development.PURPOSETo provide a clear, concise, and accurate definition of the profession of engineering that is acceptable for most, if not all, major stakeholders.METHOD A review of historical and contemporary definitions of professional engineering is provided. Using Koen’s definition of the engineering method in conjunction with Shulman’s set of characteristics common to professions a more generic definition is derived that seeks to simultaneously accommodate the homogenous multi-disciplinary attributes of professional engineering as well as accommodate the discipline specific attributes.RESULTS A proposed definition of the engineering methodology has been developed. A background introduction and justified derivation is provided for the proposed definition.CONCLUSIONS The limitations and inadequacies of historical and contemporary definitions of professional engineering have been considered. Using Koen’s definition as a basis a more generic multi-disciplinary and more contemporary definition is derived and presented. The goal of the proposed definition of the engineering methodology is to provide a more concise, more accurate, and most importantly a more comprehensible definition of the profession of engineering for the purpose of being applied to all major stakeholders of the profession.

Nanocapsules loaded with iron-saturated bovine lactoferrin have antimicrobial therapeutic potential and maintain calcium, zinc and iron metabolism

Aim: This study aimed to evaluate the potential antimicrobial efficacy of alginate gel-encapsulated ceramic nanocarriers loaded with iron-saturated bovine lactoferrin (Fe-bLf) nanocarriers/nanocapsules (AEC-CP-Fe-bLf NCs). Materials & methods: The antimicrobial activities of non-nanoformulated apo (iron free), Fe-bLf and native forms of Australian bLf against pathogenic Salmonella typhimurium (wild strain) were studied in vitro. The efficacy of AEC-CP-Fe-bLf NCs were checked in vivo using Balb/c mice model. Results: The study revealed that native bLf is more effective in combating infection than the conventional drug ciprofloxacin (0.4 mg/ml). The efficacy of the drug was also revealed in vivo when BALB/c mice that, after being challenged with S. typhimurium (200 μl of 10(8) CFU/ml suspension), were fed orally with a nanoformulated bLf diet and the infection was observed to be eliminated. However, chronic infection developed in the group of infected mice that did not receive any drug treatment, as well as the mice treated with ciprofloxacin. The immune response to bacterial infection and to various drug treatments thereafter was studied in the mice. Conclusion: The study concludes that bLf and nanoformulated Fe-bLf are more effective in the treatment of Salmonella-infected mice than ciprofloxacin.

Omega-3 fatty acid production from enzyme saccharified hemp hydrolysate using a novel marine thraustochytrid strain

In this work, a newly isolated marine thraustochytrid strain, Schizochytrium sp. DT3, was used for omega-3 fatty acid production by growing on lignocellulose biomass obtained from local hemp hurd (Cannabis sativa) biomass. Prior to enzymatic hydrolysis, hemp was pretreated with sodium hydroxide to open the biomass structure for the production of sugar hydrolysate. The thraustochytrid strain was able to grow on the sugar hydrolysate and accumulated polyunsaturated fatty acids (PUFAs). At the lowest carbon concentration of 2%, the PUFAs productivity was 71% in glucose and 59% in the sugars hydrolysate, as a percentage of total fatty acids. Saturated fatty acids (SFAs) levels were highest at about 49% of TFA using 6% glucose as the carbon source. SFAs of 41% were produced using 2% of SH. This study demonstrates that SH produced from lignocellulose biomass is a potentially useful carbon source for the production of omega-3 fatty acids in thraustochytrids, as demonstrated using the new strain, Schizochytrium sp. DT3.

The effect of chromate on the pitting susceptibility of AA7075-T651 studied using potentiostatic transients

The effect of chromate on metastable pitting of AA7075-T651 as determined via potentiostatic polarisation is reported. A systematic study of metastable pitting and its correlation with stable pits was conducted in various concentrations of sodium chromate (Na2CrO4), revealing the metastable pitting rate was able to provide a quantitative metric for pitting corrosion. The size and number of metastable pits decreased significantly in the presence of chromate. The present study is intended as a general baseline for the assessment of future chromate replacement technologies, as elaborated herein. © 2014 Elsevier Ltd.

Modelling multilevel data in multimedia : A hierarchical factor analysis approach

Multimedia content understanding research requires rigorous approach to deal with the complexity of the data. At the crux of this problem is the method to deal with multilevel data whose structure exists at multiple scales and across data sources. A common example is modeling tags jointly with images to improve retrieval, classification and tag recommendation. Associated contextual observation, such as metadata, is rich that can be exploited for content analysis. A major challenge is the need for a principal approach to systematically incorporate associated media with the primary data source of interest. Taking a factor modeling approach, we propose a framework that can discover low-dimensional structures for a primary data source together with other associated information. We cast this task as a subspace learning problem under the framework of Bayesian nonparametrics and thus the subspace dimensionality and the number of clusters are automatically learnt from data instead of setting these parameters a priori. Using Beta processes as the building block, we construct random measures in a hierarchical structure to generate multiple data sources and capture their shared statistical at the same time. The model parameters are inferred efficiently using a novel combination of Gibbs and slice sampling. We demonstrate the applicability of the proposed model in three applications: image retrieval, automatic tag recommendation and image classification. Experiments using two real-world datasets show that our approach outperforms various state-of-the-art related methods.

A bayesian nonparametric framework for activity recognition using accelerometer data

Monitoring daily physical activity of human plays an important role in preventing diseases as well as improving health. In this paper, we demonstrate a framework for monitoring the physical activity levels in daily life. We collect the data using accelerometer sensors in a realistic setting without any supervision. The ground truth of activities is provided by the participants themselves using an experience sampling application running on mobile phones. The original data is discretized by the hierarchical Dirichlet process (HDP) into different activity levels and the number of levels is inferred automatically. We validate the accuracy of the extracted patterns by using them for the multi-label classification of activities and demonstrate the high performances in various standard evaluation metrics. We further show that the extracted patterns are highly correlated to the daily routine of users.

Stable feature selection for clinical prediction: Exploiting ICD tree structure using Tree-Lasso

Modern healthcare is getting reshaped by growing Electronic Medical Records (EMR). Recently, these records have been shown of great value towards building clinical prediction models. In EMR data, patients' diseases and hospital interventions are captured through a set of diagnoses and procedures codes. These codes are usually represented in a tree form (e.g. ICD-10 tree) and the codes within a tree branch may be highly correlated. These codes can be used as features to build a prediction model and an appropriate feature selection can inform a clinician about important risk factors for a disease. Traditional feature selection methods (e.g. Information Gain, T-test, etc.) consider each variable independently and usually end up having a long feature list. Recently, Lasso and related l1-penalty based feature selection methods have become popular due to their joint feature selection property. However, Lasso is known to have problems of selecting one feature of many correlated features randomly. This hinders the clinicians to arrive at a stable feature set, which is crucial for clinical decision making process. In this paper, we solve this problem by using a recently proposed Tree-Lasso model. Since, the stability behavior of Tree-Lasso is not well understood, we study the stability behavior of Tree-Lasso and compare it with other feature selection methods. Using a synthetic and two real-world datasets (Cancer and Acute Myocardial Infarction), we show that Tree-Lasso based feature selection is significantly more stable than Lasso and comparable to other methods e.g. Information Gain, ReliefF and T-test. We further show that, using different types of classifiers such as logistic regression, naive Bayes, support vector machines, decision trees and Random Forest, the classification performance of Tree-Lasso is comparable to Lasso and better than other methods. Our result has implications in identifying stable risk factors for many healthcare problems and therefore can potentially assist clinical decision making for accurate medical prognosis.

Intervention-driven predictive framework for modeling healthcare data

Assessing prognostic risk is crucial to clinical care, and critically dependent on both diagnosis and medical interventions. Current methods use this augmented information to build a single prediction rule. But this may not be expressive enough to capture differential effects of interventions on prognosis. To this end, we propose a supervised, Bayesian nonparametric framework that simultaneously discovers the latent intervention groups and builds a separate prediction rule for each intervention group. The prediction rule is learnt using diagnosis data through a Bayesian logistic regression. For inference, we develop an efficient collapsed Gibbs sampler. We demonstrate that our method outperforms baselines in predicting 30-day hospital readmission using two patient cohorts - Acute Myocardial Infarction and Pneumonia. The significance of this model is that it can be applied widely across a broad range of medical prognosis tasks. © 2014 Springer International Publishing.