Project-oriented design-based learning: aligning students’ views with industry needs

This paper focuses on the alignment of students' views on project-oriented design-based learning (PODBL) with today's industrial needs. A Collaborative relationship between academic institutions and industrial expectations is a significant process towards analytical thinking (linking the theory and practice). Improving students' knowledge as well as the students' transition into industry, requires efficient joint ventures by both learning institutions and industry partners. Project-based learning (PBL) is well developed and implemented in most engineering schools and departments around the world. What requires closer attention is the focus on design within this project-based learning framework. Today design projects have been used to motivate and teach science in elementary, middle, and high school classrooms. They are also used to assist students with possible science and engineering careers. For these reasons, design-based learning (DBL) is intended to be an effective approach to learning that is centered on a design problem-solving structure adopted for a problem-oriented project-based education. Based on an industry design forum, which the authors conducted in Melbourne, Australia in 2012, a research study was performed to investigate the industry and academic requirements for students focusing on achieving design skills. To transform the present situation in the academic teaching and learning environment and to fulfill industry needs, this research study also investigated the students' views on design skills.

Equivalence and relational thinking: opportunities for professional learning

In this article an argument for the use of collaborative professional learning teams to improve teaching and children's achievement is presented together with an explanation of how this can be done. The case provided in this article concerns children's understanding of equivalence and the way in which teachers together can explore children's conceptions and misconceptions held by children in their classroom. An effective teaching strategy using a number talk about a true/false number sentence is also described.

Exploring distributed leadership for the quality management of online learning environments

Online learning environments (OLEs) are complex information technology (IT) systems that intersect with many areas of university organisation. Distributed models of leadership have been proposed as appropriate for the good governance of OLEs. Based on theoretical and empirical research, a group of Australian universities proposed a framework for the quality management of OLEs, and sought to validate the model via a survey of Australasian university representatives with OLE leadership responsibility. For the framework elements: Planning and Resourcing were rated most important; Organisational structure was rated least important; Technologies were rated low in importance and high in satisfaction; Resourcing and Evaluation were rated low in satisfaction; and Resourcing had the highest rating of importance coupled with low satisfaction. Considering distributed leadership in their institution, respondents reported that the organisational alignments represented by 'official' reporting and peer relationships were significantly more important and more effective than the organisational alignments linking the formal and informal leaders. From a range of desirable characteristics of distributed leadership, 'continuity and sustainability' received the highest rating of importance and a low rating of 'in evidence' - there are concerns about the sustainability of distributed leadership for the governance of OLEs in universities.

Extensions to knowledge acquisition and effect of multimodal representation in unsupervised learning

The phenomenal behaviour and composition of human cognition is yet to be defined comprehensibly. Developing the same, artificially, is a foremost research area in artificial intelligence and related fields. In this chapter we look at advances made in the unsupervised learning paradigm (self organising methods) and its potential in realising artificial cognitive machines. The first section delineates intricacies of the process of learning in humans with an articulate discussion of the function of thought and the function of memory. The self organising method and the biological rationalisations that led to its development are explored in the second section. The next focus is the effect of structure restrictions on unsupervised learning and the enhancements resulting from a structure adapting learning algorithm. Generation of a hierarchy of knowledge using this algorithm will also be discussed. Section four looks at new means of knowledge acquisition through this adaptive unsupervised learning algorithm while the fifth examines the contribution of multimodal representation of inputs to unsupervised learning. The chapter concludes with a summary of the extensions outlined.

Pre-service visual art teachers’ perceptions of assessment in online learning

This paper reports on a study conducted into how one cohort of Master of Teaching pre-service visual art teachers perceived their learning in a fully online learning environment. Located in an Australian urban university, this qualitative study provided insights into a number of areas associated with higher education online learning, including that of assessment, the focus of this paper. Authentic assessment tasks were designed within the University’s learning and teaching framework of constructive alignment and were sequenced across the three semesters of the visual art program. Analysis of data collected through a questionnaire and semi-structured interviews revealed that participants largely held very positive attitudes about the suite of online assessment tasks, particularly in light of (a) the collaborative learning that took place, (b) the nature, structure and sequence of the tasks, and (c) the ways in which the tasks contributed to their workplace readiness.

Learning latent activities from social signals with hierarchical dirichlet processes

Understanding human activities is an important research topic, most noticeably in assisted-living and healthcare monitoring environments. Beyond simple forms of activity (e.g., an RFID event of entering a building), learning latent activities that are more semantically interpretable, such as sitting at a desk, meeting with people, or gathering with friends, remains a challenging problem. Supervised learning has been the typical modeling choice in the past. However, this requires labeled training data, is unable to predict never-seen-before activity, and fails to adapt to the continuing growth of data over time. In this chapter, we explore the use of a Bayesian nonparametric method, in particular the hierarchical Dirichlet process, to infer latent activities from sensor data acquired in a pervasive setting. Our framework is unsupervised, requires no labeled data, and is able to discover new activities as data grows. We present experiments on extracting movement and interaction activities from sociometric badge signals and show how to use them for detecting of subcommunities. Using the popular Reality Mining dataset, we further demonstrate the extraction of colocation activities and use them to automatically infer the structure of social subgroups. © 2014 Elsevier Inc. All rights reserved.

Least squares learning and the US Treasury bill rate

Understanding how agents formulate their expectations about Fed behavior is important for market participants because they can potentially use this information to make more accurate estimates of stock and bond prices. Although it is commonly assumed that agents learn over time, there is scant empirical evidence in support of this assumption. Thus, in this paper we test if the forecast of the three month T-bill rate in the Survey of Professional Forecasters (SPF) is consistent with least squares learning when there are discrete shifts in monetary policy. We first derive the mean, variance and autocovariances of the forecast errors from a recursive least squares learning algorithm when there are breaks in the structure of the model. We then apply the Bai and Perron (1998) test for structural change to a forecasting model for the three month T-bill rate in order to identify changes in monetary policy. Having identified the policy regimes, we then estimate the implied biases in the interest rate forecasts within each regime. We find that when the forecast errors from the SPF are corrected for the biases due to shifts in policy, the forecasts are consistent with least squares learning. © 2014 Elsevier B.V.

Confronting globalisation: learning from intercontinental collaboration

Higher education institutions are responding to globalisation in various ways. This study describes and analyses challenges encountered in a recent case of global collaboration between four universities on different continents in developing a web‐based master’s program. The key issue was how to develop programs in a way that is fair for the different countries involved. The focus of the paper is on tensions between local and national contexts, rules and resources and the creation of a common global program. ‘Agency’, ‘structure’ and ‘frame factor’ are used as analytical concepts to help understand the dynamics of the collaboration and the character of the program.

Corrigendum to “Least squares learning and the US treasury bill rate” [Econ. Syst. 38 (2014) 194–204]

Understanding how agents formulate their expectations about Fed behavior is important for market participants because they can potentially use this information to make more accurate estimates of stock and bond prices. Although it is commonly assumed that agents learn over time, there is scant empirical evidence in support of this assumption. Thus, in this paper we test if the forecast of the three month T-bill rate in the Survey of Professional Forecasters (SPF) is consistent with least squares learning when there are discrete shifts in monetary policy. We first derive the mean, variance and autocovariances of the forecast errors from a recursive least squares learning algorithm when there are breaks in the structure of the model. We then apply the Bai and Perron (1998) test for structural change to a forecasting model for the three month T-bill rate in order to identify changes in monetary policy. Having identified the policy regimes, we then estimate the implied biases in the interest rate forecasts within each regime. We find that when the forecast errors from the SPF are corrected for the biases due to shifts in policy, the forecasts are consistent with least squares learning.

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.

KittyCat: a cognitive model of structure-form discovery

Cognition is a core subject to understand how humans think and behave. In that sense, it is clear that Cognition is a great ally to Management, as the later deals with people and is very interested in how they behave, think, and make decisions. However, even though Cognition shows great promise as a field, there are still many topics to be explored and learned in this fairly new area. Kemp & Tenembaum (2008) tried to a model graph-structure problem in which, given a dataset, the best underlying structure and form would emerge from said dataset by using bayesian probabilistic inferences. This work is very interesting because it addresses a key cognition problem: learning. According to the authors, analogous insights and discoveries, understanding the relationships of elements and how they are organized, play a very important part in cognitive development. That is, this are very basic phenomena that allow learning. Human beings minds do not function as computer that uses bayesian probabilistic inferences. People seem to think differently. Thus, we present a cognitively inspired method, KittyCat, based on FARG computer models (like Copycat and Numbo), to solve the proposed problem of discovery the underlying structural-form of a dataset.

Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition

Structural health monitoring (SHM) is related to the ability of monitoring the state and deciding the level of damage or deterioration within aerospace, civil and mechanical systems. In this sense, this paper deals with the application of a two-step auto-regressive and auto-regressive with exogenous inputs (AR-ARX) model for linear prediction of damage diagnosis in structural systems. This damage detection algorithm is based on the. monitoring of residual error as damage-sensitive indexes, obtained through vibration response measurements. In complex structures there are. many positions under observation and a large amount of data to be handed, making difficult the visualization of the signals. This paper also investigates data compression by using principal component analysis. In order to establish a threshold value, a fuzzy c-means clustering is taken to quantify the damage-sensitive index in an unsupervised learning mode. Tests are made in a benchmark problem, as proposed by IASC-ASCE with different damage patterns. The diagnosis that was obtained showed high correlation with the actual integrity state of the structure. Copyright © 2007 by ABCM.

Teaching and learning data structures supported by computers: An experiment using CADILAG tool

This paper aims to present the use of a learning object (CADILAG), developed to facilitate understanding data structure operations by using visual presentations and animations. The CADILAG allows visualizing the behavior of algorithms usually discussed during Computer Science and Information System courses. For each data structure it is possible visualizing its content and its operation dynamically. Its use was evaluated an the results are presented. © 2012 AISTI.