Exploring the Success of Student Experiential Learning through Entrepreneurial Opportunities

Academic literature has increasingly recognized the value of non-traditional higher education learning environments that emphasize action-orientated experiential learning for the study of entrepreneurship (Gibb, 2002; Jones & English, 2004). Many entrepreneurship educators have accordingly adopted approaches based on Kolb’s (1984) experiential learning cycle to develop a dynamic, holistic model of an experience-based learning process. Jones and Iredale (2010) suggested that entrepreneurship education requires experiential learning styles and creative problem solving to effectively engage students. Support has also been expressed for learning-by-doing activities in group or network contexts (Rasmussen and Sorheim, 2006), and for student-led approaches (Fiet, 2001). This study will build on previous works by exploring the use of experiential learning in an applied setting to develop entrepreneurial attitudes and traits in students. Based on the above literature, a British higher education institution (HEI) implemented a new, entrepreneurially-focused curriculum during the 2013/14 academic year designed to support and develop students’ entrepreneurial attitudes and intentions. The approach actively involved students in small scale entrepreneurship activities by providing scaffolded opportunities for students to design and enact their own entrepreneurial concepts. Students were provided with the necessary resources and training to run small entrepreneurial ventures in three different working environments. During the course of the year, three applied entrepreneurial opportunities were provided for students, increasing in complexity, length, and profitability as the year progressed. For the first undertaking, the class was divided into small groups, and each group was given a time slot and venue to run a pop-up shop in a busy commercial shopping centre. Each group of students was supported by lectures and dedicated class time for group work, while receiving a set of objectives and recommended resources. For the second venture, groups of students were given the opportunity to utilize an on-campus bar/club for an evening and were asked to organize and run a profitable event, acting as an outside promoter. Students were supported with lectures and seminars, and groups were given a £250 budget to develop, plan, and market their unique event. The final event was optional and required initiative on the part of the students. Students were given the opportunity to develop and put forward business plans to be judged by the HEI and the supporting organizations, which selected the winning plan. The authors of the winning business plan received a £2000 budget and a six-week lease to a commercial retail unit within a shopping centre to run their business. Students received additional academic support upon request from the instructor, and one of the supporting organizations provided a training course offering advice on creating a budget and a business plan. Data from students taking part in each of the events was collected, in order to ascertain the learning benefits of the experiential learning, along with the successes and difficulties they faced. These responses have been collected and analyzed and will be presented at the conference along with the instructor’s conclusions and recommendations for the use of such programs in higher educations.

Machine learning and data decompositions for complex networked dynamical systems

Thesis (Ph.D.)--University of Washington, 2016-08

Letting the managers manage: analyzing capacity to conserve biodiversity in a cross-border protected area network

Biodiversity loss is one of the most significant drivers of ecosystem change and is projected to continue at a rapid rate. While protected areas, such as national parks, are seen as important refuges for biodiversity, their effectiveness in stemming biodiversity decline has been questioned. Public agencies have a critical role in the governance of many such areas, but there are tensions between the need for these agencies to be more “adaptive” and their current operating environment. Our aim is to analyze how institutions enable or constrain capacity to conserve biodiversity in a globally significant cross-border network of protected areas, the Australian Alps. Using a novel conceptual framework for diagnosing biodiversity institutions, our research examined institutional adaptive capacity and more general capacity for conserving biodiversity. Several intertwined issues limit public agencies’ capacity to fulfill their conservation responsibilities. Narrowly defined accountability measures constrain adaptive capacity and divert attention away from addressing key biodiversity outcomes. Implications for learning were also evident, with protected area agencies demonstrating successful learning for on-ground issues but less success in applying this learning to deeper policy change. Poor capacity to buffer political and community influences in managing significant cross-border drivers of biodiversity decline signals poor fit with the institutional context and has implications for functional fit. While cooperative federalism provides potential benefits for buffering through diversity, it also means protected area agencies have restricted authority to address cross-border threats. Restrictions on staff authority and discretion, as public servants, have further implications for deploying capacity. This analysis, particularly the possibility of fostering “ambidexterity”—creatively responding to political pressures in a way that also achieves a desirable outcome for biodiversity conservation—is one promising way of building capacity to buffer both political influences and ecological pressures. The findings and the supporting analysis provide insight into how institutional capacity to conserve biodiversity can be enhanced in protected areas in Australia and elsewhere, especially those governed by public agencies and/or multiple organizations and across jurisdictions.

Children with Learning Disabilities and Challenging Behaviour: Relationship between Parents and Professionals

This multi-perspectival Interpretive Phenomenological Analysis (IPA) study explored how people in the ‘networks of concern’ talked about how they tried to make sense of the challenging behaviours of four children with severe learning disabilities. The study also aimed to explore what affected relationships between people. The study focussed on 4 children through interviewing their mothers, their teachers and the Camhs Learning Disability team members who were working with them. Two fathers also joined part of the interviews. All interviews were conducted separately using a semi-structured approach. IPA allowed both a consideration of the participant’s lived experiences and ‘objects of concern’ and a deconstruction of the multiple contexts of people’s lives, with a particular focus on disability. The analysis rendered five themes: the importance of love and affection, the difficulties, and the differences of living with a challenging child, the importance of being able to make sense of the challenges and the value of good relationships between people. Findings were interpreted through the lens of CMM (Coordinated Management of Meaning), which facilitated a systemic deconstruction and reconstruction of the findings. The research found that making sense of the challenges was a key concern for parents. Sharing meanings were important for people’s relationships with each other, including employing diagnostic and behavioural narratives. The importance of context is also highlighted including a consideration of how societal views of disability have an influence on people in the ‘network of concern’ around the child. A range of systemic approaches, methods and techniques are suggested as one way of improving services to these children and their families. It is suggested that adopting a ‘both/and’ position is important in such work - both applying evidence based approaches and being alert to and exploring the different ways people try and make sense of the children’s challenges. Implications for practice included helping professionals be alert to their constructions and professional narratives, slowing the pace with families, staying close to the concerns of families and addressing network issues.

Music Learning Networks: Supporting the Music Learning of Adolescents

Thesis (Ph.D.)--University of Washington, 2016-08

Convex Optimization Algorithms and Statistical Bounds for Learning Structured Models

Thesis (Ph.D.)--University of Washington, 2016-08

Automated Discovery of Social Networks in Online Learning Communities

As a way to gain greater insights into the operation of online communities, this dissertation applies automated text mining techniques to text-based communication to identify, describe and evaluate underlying social networks among online community members. The main thrust of the study is to automate the discovery of social ties that form between community members, using only the digital footprints left behind in their online forum postings. Currently, one of the most common but time consuming methods for discovering social ties between people is to ask questions about their perceived social ties. However, such a survey is difficult to collect due to the high investment in time associated with data collection and the sensitive nature of the types of questions that may be asked. To overcome these limitations, the dissertation presents a new, content-based method for automated discovery of social networks from threaded discussions, referred to as ‘name network’. As a case study, the proposed automated method is evaluated in the context of online learning communities. The results suggest that the proposed ‘name network’ method for collecting social network data is a viable alternative to costly and time-consuming collection of users’ data using surveys. The study also demonstrates how social networks produced by the ‘name network’ method can be used to study online classes and to look for evidence of collaborative learning in online learning communities. For example, educators can use name networks as a real time diagnostic tool to identify students who might need additional help or students who may provide such help to others. Future research will evaluate the usefulness of the ‘name network’ method in other types of online communities.

'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.

Network Training Services Ltd case study

Case study on developing a consistent and supportive approach to blended learning

Gibt es den „elektronischen Nürnberger Trichter? Das Konzept des Blended Learning, dargestellt am Lernnetz WiBA-Net

WiBA-Net ist ein Lernnetzwerk zum Thema „Werkstoffe im Bauwesen“ und wird in der universitären Ausbildung von Bauingenieuren und Architekten verwendet. Dieser Aufsatz soll das Konzept von WiBA-Net vorstellen und zeigen, wie neue Technologien und didaktische Konzepte überzeugend angewendet werden können, um „Blended Learning“ umzusetzen und damit den Hochschulalltag sowohl von Studierenden als auch von Professoren zu erleichtern.(DIPF/Orig.)

Aufbau von Blended Learning mit der open source E-Lernplattform ILIAS an einer Campus-Universität. Was motiviert zum Einsatz im Lehr- und Lernalltag?

In diesem Beitrag geht es um das Projekt E-Lernen auf der ILIAS-Plattform an der Universität der Bundeswehr Hamburg (E-L I-P UniBwH). Ziel des Projekts ist es, die Präsenzlehre mit dem Einsatz elektronischer Medien zu unterstützen. Im Beitrag werden Ansatzpunkte dargestellt, die die Lehrenden zum Gebrauch der E-Lernplattform motivieren. Es werden Bedarfsmöglichkeiten aufgezeigt, für die eine E-Lernplattform eine mögliche Lösung sein kann, sowie die Rahmenbedingungen benannt, unter denen sie eingesetzt wird. Ziel ist es, die Nachhaltigkeit des E-Lernens an der UniBwH zu fördern.(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.

MACVIA-ARIA Sentinel NetworK for allergic rhinitis (MASK-rhinitis): the new generation guideline implementation

Several unmet needs have been identified in allergic rhinitis: identification of the time of onset of the pollen season, optimal control of rhinitis and comorbidities, patient stratification, multidisciplinary team for integrated care pathways, innovation in clinical trials and, above all, patient empowerment. MASK-rhinitis (MACVIA-ARIA Sentinel NetworK for allergic rhinitis) is a simple system centred around the patient which was devised to fill many of these gaps using Information and Communications Technology (ICT) tools and a clinical decision support system (CDSS) based on the most widely used guideline in allergic rhinitis and its asthma comorbidity (ARIA 2015 revision). It is one of the implementation systems of Action Plan B3 of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA). Three tools are used for the electronic monitoring of allergic diseases: a cell phone-based daily visual analogue scale (VAS) assessment of disease control, CARAT (Control of Allergic Rhinitis and Asthma Test) and e-Allergy screening (premedical system of early diagnosis of allergy and asthma based on online tools). These tools are combined with a clinical decision support system (CDSS) and are available in many languages. An e-CRF and an e-learning tool complete MASK. MASK is flexible and other tools can be added. It appears to be an advanced, global and integrated ICT answer for many unmet needs in allergic diseases which will improve policies and standards.

A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function

Spiking neural networks - networks that encode information in the timing of spikes - are arising as a new approach in the artificial neural networks paradigm, emergent from cognitive science. One of these new models is the pulsed neural network with radial basis function, a network able to store information in the axonal propagation delay of neurons. Learning algorithms have been proposed to this model looking for mapping input pulses into output pulses. Recently, a new method was proposed to encode constant data into a temporal sequence of spikes, stimulating deeper studies in order to establish abilities and frontiers of this new approach. However, a well known problem of this kind of network is the high number of free parameters - more that 15 - to be properly configured or tuned in order to allow network convergence. This work presents for the first time a new learning function for this network training that allow the automatic configuration of one of the key network parameters: the synaptic weight decreasing factor.

Synchronization of Heteroclinic Circuits Through Learning in Chains of Neural Motifs

The synchronization of oscillatory activity in networks of neural networks is usually implemented through coupling the state variables describing neuronal dynamics. In this study we discuss another but complementary mechanism based on a learning process with memory. A driver network motif, acting as a teacher, exhibits winner-less competition (WLC) dynamics, while a driven motif, a learner, tunes its internal couplings according to the oscillations observed in the teacher. We show that under appropriate training the learner motif can dynamically copy the coupling pattern of the teacher and thus synchronize oscillations with the teacher. Then, we demonstrate that the replication of the WLC dynamics occurs for intermediate memory lengths only. In a unidirectional chain of N motifs coupled through teacher-learner paradigm the time interval required for pattern replication grows linearly with the chain size, hence the learning process does not blow up and at the end we observe phase synchronized oscillations along the chain. We also show that in a learning chain closed into a ring the network motifs come to a consensus, i.e. to a state with the same connectivity pattern corresponding to the mean initial pattern averaged over all network motifs.