New research directions in learning and cognition

Research in learning and cognition continues to extend the boundaries of possibilities for theoretical frameworks, research designs, and questions interrogated. This overview captures a snapshot of recent research for the purpose of drawing attention to new directions. It includes: types of theoretical frameworks employed to study student learning, the reciprocity of teaching and learning, and identifying underpinning conceptual understanding that can contribute to curriculum development, including a discussion paper on possible roles of algorithms. Future directions for research are then discussed. The papers in this special issue are briefly introduced in relevant sections. This paper draws attention to the increased use of multi-theoretical perspectives and what they have enabled us to learn about the complexities of teaching and learning in classrooms. It also draws attention to some of the innovative research designs and analysis techniques that have been employed to enable the answering of various research questions.

Enhancing critical analysis and problem-solving skills in undergraduate psychology: An evaluation of a collaborative learning and problem-based learning approach

Critical analysis and problem-solving skills are two graduate attributes that are important in ensuring that graduates are well equipped in working across research and practice settings within the discipline of psychology. Despite the importance of these skills, few psychology undergraduate programmes have undertaken any systematic development, implementation, and evaluation of curriculum activities to foster these graduate skills. The current study reports on the development and implementation of a tutorial programme designed to enhance the critical analysis and problem-solving skills of undergraduate psychology students. Underpinned by collaborative learning and problem-based learning, the tutorial programme was administered to 273 third year undergraduate students in psychology. Latent Growth Curve Modelling revealed that students demonstrated a significant linear increase in self-reported critical analysis and problem-solving skills across the tutorial programme. The findings suggest that the development of inquiry-based curriculum offers important opportunities for psychology undergraduates to develop critical analysis and problem-solving skills. © 2013 The Australian Psychological Society.

LC-REHAB : randomised trial assessing the effect of a new patient education method - learning and coping strategies - in cardiac rehabilitation

Due to improved treatments and ageing population, many countries now report increasing prevalence in rates of ischemic heart disease and heart failure. Cardiac rehabilitation has potential to reduce morbidity and mortality, but not all patients complete. In light of favourable effects of cardiac rehabilitation it is important to develop patient education methods which can enhance adherence to this effective program. The LC-REHAB study aims to compare the effect of a new patient education strategy in cardiac rehabilitation called 'learning and coping' to that of standard care. Further, this paper aims to describe the theoretical basis and details of this intervention.

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.

Learning and concurrent engineering in the development of a high technology product/service system

This paper explores project management techniques that can support the development of novel product-service systems. Some observations from the development of an airborne earth properties measurement system are provided. The intellectual property and the data this system could potentially deliver was more important than the potential commercial value of the product itself. What was sought was a complete business service solution. A concurrent engineering approach was implemented linking both product development and survey data/analysis services. The blend of product and service was integrated using a function modeling technique. It was observed that the implementation of some functions required radical innovation whilst others could be implemented through incremental improvements to current practice. It is suggested in the paper that adapting production learning curve concepts that reflect the relative degrees of uncertainty involved in individual subsystems can enhance project management forecasting practice © 2013 The Authors and IOS Press.

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.

Child learning and development: concerns and perceptions of Chinese urban mothers

Child development is deservedly dominant in the discourses on education. In populated communities such as China’s, awareness of the prevalent ideas about child development within families is particularly important. Drawing on a series of conversations between parents and teachers through synchronous online text chat, this paper investigated the perceptions and concerns of Chinese urban parents on child development. The participants were mothers of three to six year old children from Changchun, China. Results were presented in terms of the nature of the questions the mothers raised and what they talked about when discussing their questions. Analyses of the mothers’ texts revealed their concerns on those elements of child development which challenged their roles in parenting, such as inappropriate social behaviours or regulated emotions. Data from the study provided insights into key characteristics of contemporary Chinese preschool children’s learning and development within families that might identify issues and trends of early childhood education on a larger contextual scope.

Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression

BACKGROUND: Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study.

METHODS: The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators.

RESULTS: After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001).

CONCLUSION: The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.

Blended learning and the flipped classroom: The affordances of cloud based, located, and virtual world environments to support student learning

In this paper the author reports on the introduction of the flipped classroom integrating located, online and virtual world learning environments to support the collaborative lived experiences of a group of students and the educator participating in a higher education undergraduate art unit, Navigating the Visual World. A qualitative narrative methodology, A/r/tography, incorporating both image making and textual recording is used to explore and identify interwoven aspects of the artist/ researcher/ educator relationship in the creative artistic process of exploring concepts of identity within inquiry based art practice. Selected student examples, including a collaborative group assessment project demonstrate effective student engagement with experiential blended learning within the flipped classroom.

Blended learning and art education: student participation in a collaborative, authentic assessment project

This paper reports on higher education student engagement with blended learning experiences incorporating located (on campus), cloud based (online e-learning ) and graphically built, socially networked 3D multi user virtual environments (MUVES). Immersion in this environment enabled collaboration between two groups of students enrolled in separate undergraduate art education and public relations units, to identify, develop and participate in an integrated, authentic assessment project. It is contended that immersive blended learning experiences support creative problem solving and encourages synchronous and asynchronous student participation in authentic problem solving and collaborative practice. Interacting with co-learners, students gain knowledge and skills through situated learning, defined as the application of knowledge, learned in one setting and transferred to another and where immersion in a virtual learning experience leads to higher level engagement on the transfer task in a real world setting. In this project, collaborative blended learning involved the creation of a collection of digital artworks by art education students using computer software located in a real world environment. These artworks were curated and exhibited by the students in a virtual gallery they designed and built on Deakin Arts Education island in Second Life. For public relations students, the virtual art exhibition was the focus of a virtual campaign, designed, researched and developed by them to promote the Deakin Virtual Art Gallery on Deakin island in Second Life. The final promotion for the Virtual Gallery was presented by the students at a symposium in both real world and virtual world environments.