A comparison of different study load classifications and their association to student performance: an exploratory study

Purpose – The purpose of this paper is to investigate the alternative study load measures (dichotomous full-time/part-time classification and the number of units enrolled) and their association to student performance by using student data from a final year accounting unit in a large Australian university.

Design/methodology/approach – Using regression analysis, the authors compare the two measures to ascertain the explanatory power of the two approaches in explaining student performance.

Findings – A positive association is found between study loads and student performance when using the “number of units enrolled” measure. This relationship was not found when the dichotomous measure (full-time vs part-time) was used. The results suggest that a scaled measure of study loads is a better measure compared to a binary (dichotomous) measure.

Research limitations/implications – The study will assist future researchers to better control for study loads, and also to gain a better understanding of the association between study loads and student performance. This may possibly assist educational institutions and academics to use a more appropriate pedagogical design in the structure of courses when determining study load allocations across the different cohorts.

Practical implications – This study will help in methodology of future researchers controlling for study loads and student performance.
Originality/value – The study adds to existing literature by providing an alternate study load measure in methodology for controlling for student performance.

Predicting physiological capacity of human load carriage - a review

This review article aims to evaluate a proposed maximum acceptable work duration model for load carriage tasks. It is contended that this concept has particular relevance to physically demanding occupations such as military and firefighting. Personnel in these occupations are often required to perform very physically demanding tasks, over varying time periods, often involving load carriage. Previous research has investigated concepts related to physiological workload limits in occupational settings (e.g. industrial). Evidence suggests however, that existing (unloaded) workload guidelines are not appropriate for load carriage tasks. The utility of this model warrants further work to enable prediction of load carriage durations across a range of functional workloads for physically demanding occupations. If the maximum duration for which personnel can physiologically sustain a load carriage task could be accurately predicted, commanders and supervisors could better plan for and manage tasks to ensure operational imperatives were met whilst minimising health risks for their workers.

A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting

This paper presents a novel design of interval type-2 fuzzy logic systems (IT2FLS) by utilizing the theory of extreme learning machine (ELM) for electricity load demand forecasting. ELM has become a popular learning algorithm for single hidden layer feed-forward neural networks (SLFN). From the functional equivalence between the SLFN and fuzzy inference system, a hybrid of fuzzy-ELM has gained attention of the researchers. This paper extends the concept of fuzzy-ELM to an IT2FLS based on ELM (IT2FELM). In the proposed design the antecedent membership function parameters of the IT2FLS are generated randomly, whereas the consequent part parameters are determined analytically by the Moore-Penrose pseudo inverse. The ELM strategy ensures fast learning of the IT2FLS as well as optimality of the parameters. Effectiveness of the proposed design of IT2FLS is demonstrated with the application of forecasting nonlinear and chaotic data sets. Nonlinear data of electricity load from the Australian National Electricity Market for the Victoria region and from the Ontario Electricity Market are considered here. The proposed model is also applied to forecast Mackey-glass chaotic time series data. Comparative analysis of the proposed model is conducted with some traditional models such as neural networks (NN) and adaptive neuro fuzzy inference system (ANFIS). In order to verify the structure of the proposed design of IT2FLS an alternate design of IT2FLS based on Kalman filter (KF) is also utilized for the comparison purposes.