Applying Enterprise: Active Learning Environments for Business Higher National Diploma Students

This article details an approach to teaching entrepreneurship to Higher National Diploma (HND) students that combines lecture-based and experiential learning processes to increase student learning, comprehension, and entrepreneurial skills. A UK university redesigned an entrepreneurship course to have students design and implement business plans for a pop-up shop and an event in the local community, while working closely with instructors and outside stakeholders. The lectures used in the lessons were designed to complement the enterprise activities and be immediately applied in group work settings. Data was collected from student reflections and analyzed against instructor reflections to highlight both the success and challenges of this approach, as well as any areas of dissonance between student and instructor observations. While literature on the benefits of active and experiential learning processes are highlighted in the literature, this article examines these teaching methods specifically in a HND context, an area in which research on the benefits of these teaching methods for developing entrepreneurial students and for developing students prepared for undergraduate education has been limited.

Integration of learning and practice as part of the educational system development in Eritrea. "School Management" - a distance learning program by technical university of Kaiserslautern

The distance learning program "School Management" supports decision makers at the school and ministerial levels in the shaping of formal and informal learning processes at different levels in schools and curricula in Eritrea. This paper examines how the distance learning program is interconnected to educational system development. (DIPF/Orig.)

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

The influence of initial teacher training in future teachers' perceptions about mathematics teaching and learning

ICEMST 2014 INTERNATIONAL CONFERENCE ON EDUCATION IN MATHEMATICS, SCIENCE & TECHNOLOGY PROCEEDING BOOK (pp.865-869). Disponível em http://www.2014.icemst.com/

'Explicit Learning: an Effort towards Human Scheduling Algorithms'

Abstract Scheduling problems are generally NP-hard combinatorial problems, and a lot of research has been done to solve these problems heuristically. However, most of the previous approaches are problem-specific and research into the development of a general scheduling algorithm is still in its infancy. Mimicking the natural evolutionary process of the survival of the fittest, Genetic Algorithms (GAs) have attracted much attention in solving difficult scheduling problems in recent years. Some obstacles exist when using GAs: there is no canonical mechanism to deal with constraints, which are commonly met in most real-world scheduling problems, and small changes to a solution are difficult. To overcome both difficulties, indirect approaches have been presented (in [1] and [2]) for nurse scheduling and driver scheduling, where GAs are used by mapping the solution space, and separate decoding routines then build solutions to the original problem. In our previous indirect GAs, learning is implicit and is restricted to the efficient adjustment of weights for a set of rules that are used to construct schedules. The major limitation of those approaches is that they learn in a non-human way: like most existing construction algorithms, once the best weight combination is found, the rules used in the construction process are fixed at each iteration. However, normally a long sequence of moves is needed to construct a schedule and using fixed rules at each move is thus unreasonable and not coherent with human learning processes. When a human scheduler is working, he normally builds a schedule step by step following a set of rules. After much practice, the scheduler gradually masters the knowledge of which solution parts go well with others. He can identify good parts and is aware of the solution quality even if the scheduling process is not completed yet, thus having the ability to finish a schedule by using flexible, rather than fixed, rules. In this research we intend to design more human-like scheduling algorithms, by using ideas derived from Bayesian Optimization Algorithms (BOA) and Learning Classifier Systems (LCS) to implement explicit learning from past solutions. BOA can be applied to learn to identify good partial solutions and to complete them by building a Bayesian network of the joint distribution of solutions [3]. A Bayesian network is a directed acyclic graph with each node corresponding to one variable, and each variable corresponding to individual rule by which a schedule will be constructed step by step. The conditional probabilities are computed according to an initial set of promising solutions. Subsequently, each new instance for each node is generated by using the corresponding conditional probabilities, until values for all nodes have been generated. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the Bayesian network is updated again using the current set of good rule strings. The algorithm thereby tries to explicitly identify and mix promising building blocks. It should be noted that for most scheduling problems the structure of the network model is known and all the variables are fully observed. In this case, the goal of learning is to find the rule values that maximize the likelihood of the training data. Thus learning can amount to 'counting' in the case of multinomial distributions. In the LCS approach, each rule has its strength showing its current usefulness in the system, and this strength is constantly assessed [4]. To implement sophisticated learning based on previous solutions, an improved LCS-based algorithm is designed, which consists of the following three steps. The initialization step is to assign each rule at each stage a constant initial strength. Then rules are selected by using the Roulette Wheel strategy. The next step is to reinforce the strengths of the rules used in the previous solution, keeping the strength of unused rules unchanged. The selection step is to select fitter rules for the next generation. It is envisaged that the LCS part of the algorithm will be used as a hill climber to the BOA algorithm. This is exciting and ambitious research, which might provide the stepping-stone for a new class of scheduling algorithms. Data sets from nurse scheduling and mall problems will be used as test-beds. It is envisaged that once the concept has been proven successful, it will be implemented into general scheduling algorithms. It is also hoped that this research will give some preliminary answers about how to include human-like learning into scheduling algorithms and may therefore be of interest to researchers and practitioners in areas of scheduling and evolutionary computation. References 1. Aickelin, U. and Dowsland, K. (2003) 'Indirect Genetic Algorithm for a Nurse Scheduling Problem', Computer & Operational Research (in print). 2. Li, J. and Kwan, R.S.K. (2003), 'Fuzzy Genetic Algorithm for Driver Scheduling', European Journal of Operational Research 147(2): 334-344. 3. Pelikan, M., Goldberg, D. and Cantu-Paz, E. (1999) 'BOA: The Bayesian Optimization Algorithm', IlliGAL Report No 99003, University of Illinois. 4. Wilson, S. (1994) 'ZCS: A Zeroth-level Classifier System', Evolutionary Computation 2(1), pp 1-18.

Intercultural musical learning in the era of technology

Foreign students abroad need to feel integrated in the new community, which includes complex learning processes in multicultural environments. The fact that we have experienced these processes ourselves was certainly a motivation for this research, especially knowing that we could contribute to help our fellow Portuguese brass players undergoing the same experience. From the singularity of music performance in the style of playing and communication emerge many cultural aspects, which have been developed through centuries of orchestral practice. As the new students are confronted with the aesthetic musical concepts and both professional and social practices of the country they arrive in, they strive to understand these concepts and adapt themselves to the values promoted by the new music practice. The aim of this on-going research is the study of the integration of brass music students in German universities and in the German society. Notably, through the understanding of intercultural processes experienced by the students, professors can become more aware of the challenges that concern music education. In this research all ten Portuguese brass students enrolled in any German music university in the last five years were interviewed in order to deeply understand this process. With a growing importance of the technological facilities, students are able to gather more information, to prepare themselves for the new concepts they try to embrace and to better deal with a different culture.

Learning to construct our identities over the life course: a study with lesbian, gay, bisexual and transgender adults in Scotland

To date, adult educational research has had a limited focus on lesbian, gay, bisexual and transgendered (LGBT) adults and the learning processes in which they engage across the life course. Adopting a biographical and life history methodology, this study aimed to critically explore the potentially distinctive nature and impact of how, when and where LGBT adults learn to construct their identities over their lives. In-depth, semi-structured interviews, dialogue and discussion with LGBT individuals and groups provided rich narratives that reflect shifting, diverse and multiple ways of identifying and living as LGBT. Participants engage in learning in unique ways that play a significant role in the construction and expression of such identities, that in turn influence how, when and where learning happens. Framed largely by complex heteronormative forces, learning can have a negative, distortive impact that deeply troubles any balanced, positive sense of being LGBT, leading to self- censoring, alienation and in some cases, hopelessness. However, learning is also more positively experiential, critically reflective, inventive and queer in nature. This can transform how participants understand their sexual identities and the lifewide spaces in which they learn, engendering agency and resilience. Intersectional perspectives reveal learning that participants struggle with, but can reconcile the disjuncture between evolving LGBT and other myriad identities as parents, Christians, teachers, nurses, academics, activists and retirees. The study’s main contributions lie in three areas. A focus on LGBT experience can contribute to the creation of new opportunities to develop intergenerational learning processes. The study also extends the possibilities for greater criticality in older adult education theory, research and practice, based on the continued, rich learning in which participants engage post-work and in later life. Combined with this, there is scope to further explore the nature of ‘life-deep learning’ for other societal groups, brought by combined religious, moral, ideological and social learning that guides action, beliefs, values, and expression of identity. The LGBT adults in this study demonstrate engagement in distinct forms of life-deep learning to navigate social and moral opprobrium. From this they gain hope, self-respect, empathy with others, and deeper self-knowledge.

Rural Participation in the Territorial Development Processes

This paper discusses the urgency of creating a bridge between social participation and civic capacity building.  The permanent dialogue between expert and local knowledge should sustain significant, relevant learning processes from/to the rural areas of the Central American region.  Consistency and persistence of these processes will enhance human welfare based on the changes experienced in the rural areas.  Numerous Central American initiatives require effective social and institutional participation to be implemented.  Education, in its different forms and through its different resources, has the crucial responsibility of helping citizens to take advantage of those initiatives.

Student success : the identification and support of first year university students at risk of attrition

The engagement behaviour of 1,524 student-enrolments (“students”) in five first year units was monitored and 608 (39.9%) were classified as “at risk” using the criterion of not submitting or failing their first assignment. Of these, 327 (53.8%) were successfully contacted (i.e., spoken to by phone) and provided with advice and/or referral to learning and personal support services while the remaining 281 (46.2%) could not be contacted. Nine hundred and sixteen students (60.1%) were classified as “not at risk.” Overall, the at risk group who were contacted achieved significantly higher end-of-semester final grades than, and persisted (completed the unit) at more than twice the rate of, the at risk group who were not contacted. There were variations among the units which were explained by the timing of the first assignment, specific teaching-learning processes and the structure of the curriculum. Implications for curriculum design and supporting first year students within a personal, social and academic framework are discussed.

Nonlinear pedagogy : implications for teaching games for understanding (TGfU)

Nonlinear Dynamics, provides a framework for understanding how teaching and learning processes function in Teaching Games for Understanding (TGfU). In Nonlinear Pedagogy, emergent movement behaviors in learners arise as a consequence of intrinsic self-adjusted processes shaped by interacting constraints in the learning environment. In a TGfU setting, representative, conditioned games provide ideal opportunities for pedagogists to manipulate key constraints so that self-adjusted processes by players lead to emergent behaviors as they explore functional movement solutions. The implication is that, during skill learning, functional movement variability is necessary as players explore different motor patterns for effective skill execution in the context of the game. Learning progressions in TGfU take into account learners’ development through learning stages and have important implications for organisation of practices, instructions and feedback. A practical application of Nonlinear Pedagogy in a national sports institute is shared to exemplify its relevance for TGfU practitioners.

Mainstreaming sustainability into pre-service teacher edcuation in Australia

There is a growing interest in and support for education for sustainability in Australian schools. Australian Government schemes such as the Australian Sustainable Schools Initiative (AuSSI), along with strategies such as Educating for a Sustainable Future: A National Environmental Education Statement for Australian Schools(NEES(Australian Government and Curriculum Corporation (2005) and Living Sustainably: The Australian Government’s National Action Plan for Education for Sustainability (Australian Government 2009), recognise the need and offer support for education for sustainability in Australian schools. The number of schools that have engaged with AuSSI indicates that this interest also exists within Australian schools. Despite this, recent research indicates that pre-service teacher education institutions and programs are not doing all they can to prepare teachers for teaching education for sustainability or for working within sustainable schools. The education of school teachers plays a vital role in achieving changes in teaching and learning in schools. Indeed, the professional development of teachers in education for sustainability has been identified as ‘the priority of priorities’. Much has been written about the need to ‘reorient teacher education towards sustainability’. Teacher education is seen as a key strategy that is yet to be effectively utilised to embed education for sustainability in schools. Mainstreaming sustainability in Australian schools will not be achieved without the preparation of teachers for this task. The Mainstreaming Sustainability model piloted in this study seeks to engage a range of stakeholder organisations and key agents of change within a system to all work simultaneously to bring about a change, such as the mainstreaming of sustainability. The model is premised on the understanding that sustainability will be mainstreamed within teacher education if there is engagement with key agents of change across the wider teacher education system and if the key agents of change are ‘deeply’ involved in making the change. The model thus seeks to marry broad engagement across a system with the active participation of stakeholders within that system. Such a systemic approach is a way of bringing together diverse viewpoints to make sense of an issue and harness that shared interpretation to define boundaries, roles and relationships leading to a better defined problem that can be acted upon more effectively. Like action research, the systemic approach is also concerned with modelling change and seeking plausible solutions through collaboration between stakeholders. This is important in ensuring that outcomes are useful to the researchers/stakeholders and the system being researched as it creates partnerships and commitments to the outcomes by stakeholder participants. The study reported on here examines whether the ‘Mainstreaming Sustainability’ model might be effective as a means to mainstream sustainability in pre-service teacher education. This model, developed in an earlier study, was piloted in the Queensland teacher education system in order to examine its effectiveness in creating organisational and systemic change. The pilot project in Queensland achieved a number of outcomes. The project: • provided useful insights into the effectiveness of the Mainstreaming Sustainability model in bringing about change while also building research capacity within the system • developed capacities within the teacher education community: o developing competencies in education for sustainability o establishing more effective interactions between decision-makers and other stakeholders o establishing a community of inquiry • changed teaching and learning approaches used in participating teacher education institutions through: o curriculum and resource development o the adoption of education for sustainability teaching and learning processes o the development of institutional policies • improved networks within the teacher education system through: o identifying key agents of change within the system o developing new, and building on existing, partnerships between schools, teacher education institutions and government agencies • engaged relevant stakeholders such as government agencies and non-government organisations to understand and support the change Our findings indicate that the Mainstreaming Sustainability model is able to facilitate organisational and systemic change – over time – if: • the individuals involved have the conceptual and personal capacities needed to facilitate change, that is, to be a key agent of change • stakeholders are engaged as participants in the process of change, not simply as ‘interested parties’ • there is a good understanding of systemic change and the opportunities for leveraging change within systems. In particular, in seeking to mainstream sustainability in pre-service teacher education in Queensland it has become clear that one needs to build capacity for change within participants such as knowledge of education for sustainability, conceptual skills in systemic thinking, action research and organisational change, and leadership skills. It is also of vital importance that key agents of change – those individuals who are ‘hubs’ within a system and can leverage for change across a wide range of the system – are identified and engaged with as early as possible. Key agents of change can only be correctly identified, however, if the project leaders and known participants have clearly identified the boundary to their system as this enables the system, sub-system and environment of the system to be understood. Through mapping the system a range of key organisations and stakeholders will be identified, including government and nongovernment organisations, teacher education students, teacher education academics, and so on. On this basis, key agents of change within the system and sub-system can be identified and invited to assist in working for change. A final insight is that it is important to have time – and if necessary the funding to ‘buy time’ – in seeking to bring about system-wide change. Seeking to bring about system-wide change is an ambitious project, one that requires a great deal of effort and time. These insights provide some considerations for those seeking to utilise the Mainstreaming Sustainability model to bring about change within and across a pre-service teacher education system.

The development and evaluation of an innovative nursing practice model to improve undergraduate nursing students' competence in paediatric physical assessment

Introduction The purpose of this study was to develop, implement and evaluate the impact of an educational intervention, comprising an innovative model of clinical decisionmaking and educational delivery strategy for facilitating nursing students‘ learning and development of competence in paediatric physical assessment practices. Background of the study Nursing students have an undergraduate education that aims to produce graduates of a generalist nature who demonstrate entry level competence for providing nursing care in a variety of health settings. Consistent with population morbidity and health care roles, paediatric nursing concepts typically form a comparatively small part of undergraduate curricula and students‘ exposure to paediatric physical assessment concepts and principles are brief. However, the nursing shortage has changed traditional nursing employment patterns and new graduates form the majority of the recruitment pool for paediatric nursing speciality staff. Paediatric nursing is a popular career choice for graduates and anecdotal evidence suggests that nursing students who select a clinical placement in their final year intend to seek employment in paediatrics upon graduation. Although concepts of paediatric nursing are included within undergraduate curriculum, students‘ ability to develop the required habits of mind to practice in what is still regarded as a speciality area of practice is somewhat limited. One of the areas of practice where this particularly impacts is in paediatric nursing physical assessment. Physical assessment is a fundamental component of nursing practice and competence in this area of practice is central to nursing students‘ development of clinical capability for practice as a registered nurse. Timely recognition of physiologic deterioration of patients is a key outcome of nurses‘ competent use of physical assessment strategies, regardless of the practice context. In paediatric nursing contexts children‘s physical assessment practices must specifically accommodate the child‘s different physiological composition, function and pattern of clinical deterioration (Hockenberry & Barrera, 2007). Thus, to effectively manage physical assessment of patients within the paediatric practice setting nursing students need to integrate paediatric nursing theory into their practice. This requires significant information processing and it is in this process where students are frequently challenged. The provision of rules or models can guide practice and assist novice-level nurses to develop their capabilities (Benner, 1984; Benner, Hooper-Kyriakidis & Stannard, 1999). Nursing practice models are cognitive tools that represent simplified patterns of expert analysis employing concepts that suit the limited reasoning of the inexperienced, and can represent the =rules‘ referred to by Benner (1984). Without a practice model of physical assessment students are likely to be uncertain about how to proceed with data collection, the interpretation of paediatric clinical findings and the appraisal of findings. These circumstances can result in ad hoc and unreliable nursing physical assessment that forms a poor basis for nursing decisions. The educational intervention developed as part of this study sought to resolve this problem and support nursing students‘ development of competence in paediatric physical assessment. Methods This study utilised the Context Input Process Product (CIPP) Model by Stufflebeam (2004) as the theoretical framework that underpinned the research design and evaluation methodology. Each of the four elements in the CIPP model were utilised to guide discrete stages of this study. The Context element informed design of the clinical decision-making process, the Paediatric Nursing Physical Assessment model. The Input element was utilised in appraising relevant literature, identifying an appropriate instructional methodology to facilitate learning and educational intervention delivery to undergraduate nursing students, and development of program content (the CD-ROM kit). Study One employed the Process element and used expert panel approaches to review and refine instructional methods, identifying potential barriers to obtaining an effective evaluation outcome. The Product element guided design and implementation of Study Two, which was conducted in two phases. Phase One employed a quasiexperimental between-subjects methodology to evaluate the impact of the educational intervention on nursing students‘ clinical performance and selfappraisal of practices in paediatric physical assessment. Phase Two employed a thematic analysis and explored the experiences and perspectives of a sample subgroup of nursing students who used the PNPA CD-ROM kit as preparation for paediatric clinical placement. Results Results from the Process review in Study One indicated that the prototype CDROM kit containing the PNPA model met the predetermined benchmarks for face validity and the impact evaluation instrumentation had adequate content validity in comparison with predetermined benchmarks. In the first phase of Study Two the educational intervention did not result in statistically significant differences in measures of student performance or self-appraisal of practice. However, in Phase Two qualitative commentary from students, and from the expert panel who reviewed the prototype CD-ROM kit (Study One, Phase One), strongly endorsed the quality of the intervention and its potential for supporting learning. This raises questions regarding transfer of learning and it is likely that, within this study, several factors have influenced students‘ transfer of learning from the educational intervention to the clinical practice environment, where outcomes were measured. Conclusion In summary, the educational intervention employed in this study provides insights into the potential e-learning approaches offer for delivering authentic learning experiences to undergraduate nursing students. Findings in this study raise important questions regarding possible pedagogical influences on learning outcomes, issues within the transfer of theory to practice and factors that may have influenced findings within the context of this study. This study makes a unique contribution to nursing education, specifically with respect to progressing an understanding of the challenges faced in employing instructive methods to impact upon nursing students‘ development of competence. The important contribution transfer of learning processes make to students‘ transition into the professional practice context and to their development of competence within the context of speciality practice is also highlighted. This study contributes to a greater awareness of the complexity of translating theoretical learning at undergraduate level into clinical practice, particularly within speciality contexts.

Survey of novice motorcycle riders and their riding experience before licensing

Graduated licensing schemes have been found to reduce the crash risk of young novice drivers, but there is less evidence of their success with novice motorcycle riders. This study examined the riding experience of a sample of Australian learner-riders to establish the extent and variety of their riding practice during the learner stage. Riders completed an anonymous questionnaire at a compulsory rider-training course for the licensing test. The majority of participants were male (81%) with an average age of 33 years. They worked full time (81%), held an unrestricted driver's license (81%), and owned the motorcycle that they rode (79%). These riders had held their learner's license for an average of 6 months. On average, they rode 6.4 h/week. By the time they attempted the rider-licensing test, they had ridden a total of 101 h. Their total hours of on-road practice were comparable to those of learner-drivers at the same stage of licensing, but they had less experience in adverse or challenging road conditions. A substantial proportion had little or no experience of riding in the rain (57%), at night (36%), in heavy traffic (22%), on winding rural roads (52%), or on high-speed roads (51%). These findings highlight the differences in the learning processes between unsupervised novice motorcycle riders and supervised novice drivers. Further research is necessary to clarify whether specifying the conditions under which riders should practice during the graduated licensing process would likely reduce or increase their crash risk.

Re-generating the praxis of academic staff development

Effective academic workforce staff development remains a challenge in higher education. This thesis-by-publication examined the importance of alternative paradigms for academic staff development, focusing specifically on arts-based learning as a non-traditional approach to transformative learning for management and self-development within the business of higher education. The research question asked was whether or not the facilitation of staff development through the practice of arts-based transformational learning supported academic aims in higher education, based on data obtained with the participants of the academic staff development program at one Australian university over a three year period. Over that three year period, eighty academics participated in one large metropolitan Australian university’s arts-based academic development program. The research approach required analysis of the transcribed one-on-one hermeneutic-based conversations with fifteen self-selected academics, five from each year, and with a focus group of twenty other self-selected academics from all three years. The study’s findings provided evidence that supported the need for academic staff development that prepared academics to be engaged and creative and therefore more likely to be responsive to emerging issues and to be innovative in the presence of constraints, including organisational constraints. The qualitative participative conversation transcription data found that arts-based lifelong learning processes provided participant perception of enhanced capabilities for self-creation and clarity of transformational action in academic career management. The study presented a new and innovative Artful Learning Wave Trajectory learning model to engender academic professional artistry. The findings provided developers with support for using a non-traditional strategy of transformational learning.

A constraints-led approach to talent development in cricket

The aim of this chapter is to increase understanding of how a sound theoretical model of the learner and learning processes informs the organisation of learning environments and effective and efficient use of practice time. Drawing on an in-depth interview with Greg Chappell, the head coach at the Centre of Excellence—the Brisbane-based centre for training and development in cricket of the Australian Institute of Sport (AIS) and Cricket Australia—it describes and explains many of the key features of non-linear pedagogy. Specifically, after backgrounding the constraints-led approach, it deals with environmental constraints; the focus of the individual and the implications of self-organisation for coaching strategies; implications for the coach–athlete relationship; manipulating constraints; representative practice; developing decision-makers and learning design including discovery and implicit learning. It then moves on to a discussion of more global issues such as the reactions of coaches and players when a constraints-led approach is introduced, before finally considering the widely held belief among coaches that approaches such as Teaching Games for Understanding (TGfU) ‘take longer’ than traditional coaching methods.