Introduction: Arguments for a more grounded approach in management education

The article mentions the aims and objectives of the "Academy of Management Learning & Education" and introduces four essays in this issue. Milton R. Blood focuses on the role of business schools in generating actionable knowledge. Peter Navarro asserts that macroeconomics is necessary in MBA programs. Scott Julian and Joseph C. Ofori-Dankwa comment on business school accreditation and competition status. Michael Harmon offers an argument that competition status is negatively affecting research, teaching, and social objectives.

Learning difficulties in numeracy in Australia

In this article, we provide an understanding of the term numeracy as it is used in Australia and a description of numeracy education in this country. In particular, we discuss the role of outcomes-based curriculum frameworks and outline the dominant teaching approaches. The focus is on students with learning difficulties and how they are identified and supported in schools. We create two vignettes based on real students with difficulties in numeracy, which highlight two of the most common problems. We report on the prevalence of learning difficulties in numeracy in Australia and describe some of the initiatives related to the assessment and enhancement of learning in numeracy that are being undertaken in various states in Australia. Finally, we identify some of the future challenges facing the Australian education system in this area. These challenges relate to teacher knowledge, mandated assessment, and the role of parents. We conclude the article by calling for the evaluation of current assessment and intervention initiatives as well as the development of a national program to support the goal of numeracy for all students.

Learning mathematics in a classroom community of inquiry

This article considers the question of what specific actions a teacher might take to create a culture of inquiry in a secondary school mathematics classroom. Sociocultural theories of learning provide the framework for examining teaching and learning practices in a single classroom over a two-year period. The notion of the zone of proximal development (ZPD) is invoked as a fundamental framework for explaining learning as increasing participation in a community of practice characterized by mathematical inquiry. The analysis draws on classroom observation and interviews with students and the teacher to show how the teacher established norms and practices that emphasized mathematical sense-making and justification of ideas and arguments and to illustrate the learning practices that students developed in response to these expectations.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

The role of the learning community in the development of discipline knowledge and generic graduate outcomes

In this paper we describe a study of learning outcomes at a research-intensive Australian university. Three graduate outcome variables (discipline knowledge and skills, communication and problem solving, and ethical and social sensitivity) are analysed separately using OLS regression and comparisons are made of the patterns of unique contributions from four independent variables (the CEQ Good Teaching and Learning Communities Scales, and two new, independent, scales for measuring Teaching and Program Quality). Further comparisons of these patterns are made across the Schools of the university. Results support the view that teaching and program quality are not the only important determinants of students' learning outcomes. It is concluded that, whilst it continues to be appropriate for universities to be concerned with the quality of their teaching and programs, the interactive, social and collaborative aspects of students' learning experiences, captured in the notion of the Learning Community, are also very important determinants of graduate outcomes, and so should be included in the focus of attempts at enhancing the quality of student learning.

A machine learning approach to intraday trading on foreign exchange markets

Foreign Exchange trading has emerged in recent times as a significant activity in many countries. As with most forms of trading, the activity is influenced by many random parameters so that the creation of a system that effectively emulates the trading process will be very helpful. In this paper we try to create such a system using Machine learning approach to emulate trader behaviour on the Foreign Exchange market and to find the most profitable trading strategy.