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

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

Developing a robust self evaluation framework for active learning:the first stage of an ERASMUS+ project (QAEMarketPlace4HEI)

In ensuring the quality of learning and teaching in Higher Education, self-evaluation is an important component of the process. An example would be the approach taken within the CDIO community whereby self-evaluation against the CDIO standards is part of the quality assurance process. Eight European universities (Reykjavik University, Iceland; Turku University of Applied Sciences, Finland; Aarhus University, Denmark; Helsinki Metropolia University of Applied Sciences, Finland; Ume? University, Sweden; Telecom Bretagne, France; Aston University, United Kingdom; Queens University Belfast, United Kingdom) are engaged in an EU funded Erasmus + project that is exploring the quality assurance process associated with active learning. The development of a new self-evaluation framework that feeds into a ?Marketplace? where participating institutions can be paired up and then engage in peer evaluations and sharing around each institutions approach to and implementation of active learning. All of the partner institutions are engaged in the application of CDIO within their engineering programmes and this has provided a common starting point for the partnership to form and the project to be developed. Although the initial focus will be CDIO, the longer term aim is that the approach could be of value beyond CDIO and within other disciplines. The focus of this paper is the process by which the self-evaluation framework is being developed and the form of the draft framework. In today?s Higher Education environment, the need to comply with Quality Assurance standards is an ever present feature of programme development and review. When engaging in a project that spans several countries, the wealth of applicable standards and guidelines is significant. In working towards the development of a robust Self Evaluation Framework for this project, the project team decided to take a wide view of the available resources to ensure a full consideration of different requirements and practices. The approach to developing the framework considered: a) institutional standards and processes b) national standards and processes e.g. QAA in the UK c) documents relating to regional / global accreditation schemes e.g. ABET d) requirements / guidelines relating to particular learning and teaching frameworks e.g. CDIO. The resulting draft self-evaluation framework is to be implemented within the project team to start with to support the initial ?Marketplace? pairing process. Following this initial work, changes will be considered before a final version is made available as part of the project outputs. Particular consideration has been paid to the extent of the framework, as a key objective of the project is to ensure that the approach to quality assurance has impact but is not overly demanding in terms of time or paperwork. In other words that it is focused on action and value added to staff, students and the programmes being considered.

The state of the nations:current and future developments in technology enhanced learning

Right across Europe technology is playing a vital part in enhancing learning for an increasingly diverse population of learners. Learning is increasingly flexible, social and mobile and supported by high quality multi-media resources. Institutional VLEs are seeing a shift towards open source products and these core systems are supplemented by a range of social and collaborative learning tools based on web 2.0 technologies. Learners undertaking field studies and those in the workplace are coming to expect that these off-campus experiences will also be technology-rich whether supported by institutional or user-owned devices. As well as keeping European businesses competitive, learning is seen as a means of increasing social mobility and supporting an agenda of social justice. For a number of years the EUNIS E-Learning Task Force (ELTF) has conducted snapshot surveys of e-learning across member institutions, collected case studies of good practice in e-learning see (Hayes, et al., 2009) in references, supported a group looking at the future of e-learning, and showcased the best of innovation in its e-learning Award. Now for the first time the ELTF membership has come together to undertake an analysis of developments in the member states and to assess what this might mean for the future. The group applied the techniques of World Café conversation and Scenario Thinking to develop its thoughts. The analysis is unashamedly qualitative and draws on expertise from leading universities across eight of the EUNIS member states. What emerges is interesting in terms of the common trends in developments in all of the nations and similarities in hopes and concerns about the future development of learning.

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.

Applications of a Collaborative Open Object Oriented Learning Environment to Electronics, Computing and Mathematics Education

A lightweight Java application suite has been developed and deployed allowing collaborative learning between students and tutors at remote locations. Students can engage in group activities online and also collaborate with tutors. A generic Java framework has been developed and applied to electronics, computing and mathematics education. The applications are respectively: (a) a digital circuit simulator, which allows students to collaborate in building simple or complex electronic circuits; (b) a Java programming environment where the paradigm is behavioural-based robotics, and (c) a differential equation solver useful in modelling of any complex and nonlinear dynamic system. Each student sees a common shared window on which may be added text or graphical objects and which can then be shared online. A built-in chat room supports collaborative dialogue. Students can work either in collaborative groups or else in teams as directed by the tutor. This paper summarises the technical architecture of the system as well as the pedagogical implications of the suite. A report of student evaluation is also presented distilled from use over a period of twelve months. We intend this suite to facilitate learning between groups at one or many institutions and to facilitate international collaboration. We also intend to use the suite as a tool to research the establishment and behaviour of collaborative learning groups. We shall make our software freely available to interested researchers.

Application of active and transfer learning to wound image segmentation

Wound management is a fundamental task in standard clinical practice. Automated solutions already exist for humans, but there is a lack of applications on wound management for pets. The importance of a precise and efficient wound assessment is helpful to improve diagnosis and to increase the effectiveness of treatment plans for the chronic wounds. The goal of the research was to propose an automated pipeline capable of segmenting natural light-reflected wound images of animals. Two datasets composed by light-reflected images were used in this work: Deepskin dataset, 1564 human wound images obtained during routine dermatological exams, with 145 manual annotated images; Petwound dataset, a set of 290 wound photos of dogs and cats with 0 annotated images. Two implementations of U-Net Convolutioal Neural Network model were proposed for the automated segmentation. Active Semi-Supervised Learning techniques were applied for human-wound images to perform segmentation from 10% of annotated images. Then the same models were trained, via Transfer Learning, adopting an Active Semi- upervised Learning to unlabelled animal-wound images. The combination of the two training strategies proved their effectiveness in generating large amounts of annotated samples (94% of Deepskin, 80% of PetWound) with the minimal human intervention. The correctness of automated segmentation were evaluated by clinical experts at each round of training thus we can assert that the results obtained in this thesis stands as a reliable solution to perform a correct wound image segmentation. The use of Transfer Learning and Active Semi-Supervied Learning allows to minimize labelling effort from clinicians, even requiring no starting manual annotation at all. Moreover the performances of the model with limited number of parameters suggest the implementation of smartphone-based application to this topic, helping the future standardization of light-reflected images as acknowledge medical images.

Flicker perimetry in healthy subjects: influence of age and gender, learning effect and short-term fluctuation

PURPOSE: To determine the mean critical fusion frequency and the short-term fluctuation, to analyze the influence of age, gender, and the learning effect in healthy subjects undergoing flicker perimetry. METHODS: Study 1 - 95 healthy subjects underwent flicker perimetry once in one eye. Mean critical fusion frequency values were compared between genders, and the influence of age was evaluated using linear regression analysis. Study 2 - 20 healthy subjects underwent flicker perimetry 5 times in one eye. The first 3 sessions were separated by an interval of 1 to 30 days, whereas the last 3 sessions were performed within the same day. The first 3 sessions were used to investigate the presence of a learning effect, whereas the last 3 tests were used to calculate short-term fluctuation. RESULTS: Study 1 - Linear regression analysis demonstrated that mean global, foveal, central, and critical fusion frequency per quadrant significantly decreased with age (p<0.05).There were no statistically significant differences in mean critical fusion frequency values between males and females (p>0.05), with the exception of the central area and inferonasal quadrant (p=0.049 and p=0.011, respectively), where the values were lower in females. Study 2 - Mean global (p=0.014), central (p=0.008), and peripheral (p=0.03) critical fusion frequency were significantly lower in the first session compared to the second and third sessions. The mean global short-term fluctuation was 5.06±1.13 Hz, the mean interindividual and intraindividual variabilities were 11.2±2.8% and 6.4±1.5%, respectively. CONCLUSION: This study suggests that, in healthy subjects, critical fusion frequency decreases with age, that flicker perimetry is associated with a learning effect, and that a moderately high short-term fluctuation is expected.

Learning Profile Identification Based on the Analysis of the User Context of Interaction

One of the e-learning environment goal is to attend the individual needs of students during the learning process. The adaptation of contents, activities and tools into different visualization or in a variety of content types is an important feature of this environment, bringing to the user the sensation that there are suitable workplaces to his profile in the same system. Nevertheless, it is important the investigation of student behaviour aspects, considering the context where the interaction happens, to achieve an efficient personalization process. The paper goal is to present an approach to identify the student learning profile analyzing the context of interaction. Besides this, the learning profile could be analyzed in different dimensions allows the system to deal with the different focus of the learning.

Accelerating autonomous learning by using heuristic selection of actions

This paper investigates how to make improved action selection for online policy learning in robotic scenarios using reinforcement learning (RL) algorithms. Since finding control policies using any RL algorithm can be very time consuming, we propose to combine RL algorithms with heuristic functions for selecting promising actions during the learning process. With this aim, we investigate the use of heuristics for increasing the rate of convergence of RL algorithms and contribute with a new learning algorithm, Heuristically Accelerated Q-learning (HAQL), which incorporates heuristics for action selection to the Q-Learning algorithm. Experimental results on robot navigation show that the use of even very simple heuristic functions results in significant performance enhancement of the learning rate.

Visualization of learning in multilayer perceptron networks using principal component analysis

This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as back-propagation and can also be used to provide insight into the learning process and the nature of the error surface.

Does affective learning exist in the absence of contingency awareness?

The proposal that affective learning, the learning of likes and dislikes, can exist in the absence of contingency awareness, whereas signal learning, the learning of stimulus relationships, cannot, was investigated in a differential conditioning paradigm that was embedded in a visual masking task. Startle magnitude modulation and changes in verbal ratings served as measures of affective learning, whereas skin conductance was taken to reflect signal learning. Awareness was assessed online with an expectancy dial and in a postexperimental questionnaire. Both between-subject comparisons of verbalizers and nonverbalizers and within-subject comparisons of verbalizers before and after verbalization failed to reveal any evidence for learning, whether affective or otherwise, in the absence of knowledge of the stimulus contingencies. (C) 2001 Academic Press.

Fate of urea nitrogen applied to a banana crop in the wet tropics of Queensland

This paper reports a study in the wet tropics of Queensland on the fate of urea applied to a dry or wet soil surface under banana plants. The transformations of urea were followed in cylindrical microplots (10.3 cm diameter x 23 cm long), a nitrogen (N) balance was conducted in macroplots (3.85 m x 2.0 m) with N-15 labelled urea, and ammonia volatilization was determined with a mass balance micrometeorological method. Most of the urea was hydrolysed within 4 days irrespective of whether the urea was applied onto dry or wet soil. The nitrification rate was slow at the beginning when the soil was dry, but increased greatly after small amounts of rain; in the 9 days after rain 20% of the N applied was converted to nitrate. In the 40 days between urea application and harvesting, the macroplots the banana plants absorbed only 15% of the applied N; at harvest the largest amounts were found in the leaves (3.4%), pseudostem (3.3%) and fruit (2.8%). Only 1% of the applied N was present in the roots. Sixty percent of the applied N was recovered in the soil and 25% was lost from the plant-soil system by either ammonia volatilization, leaching or denitrification. Direct measurements of ammonia volatilization showed that when urea was applied to dry soil, and only small amounts of rain were received, little ammonia was lost (3.2% of applied N). In contrast, when urea was applied onto wet soil, urea hydrolysis occurred immediately, ammonia was volatilized on day zero, and 17.2% of the applied N was lost by the ninth day after that application. In the latter study, although rain fell every day, the extensive canopy of banana plants reduced the rainfall reaching the fertilized area under the bananas to less than half. Thus even though 90 mm of rain fell during the volatilization study, the fertilized area did not receive sufficient water to wash the urea into the soil and prevent ammonia loss. Losses by leaching and denitrification combined amounted to 5% of the applied N.

Early intervention: Narratives of learning, discipline and enculturation

Current understandings about literacy have moved away from the belief that literacy is simply a process that individuals do in their heads. These understandings do not negate the importance of the individual aspects of literacy learning, but they emphasize understandings of literacy as a social practice. In many cases, responses to early literacy intervention seem to be grounded in theories that appear out of step with current literacy research and consequent evidence that literacy is socially and culturally constructed. One such response is the Reading Recovery programme based on Clay’s theory of literacy acquisition. Clay (1992) describes the programme as a second chance to learn. However, others have suggested that programmes like Reading Recovery may in fact work toward the marginalization of particular groups, thereby helping to maintain the status quo along class, gender and ethnic lines. This article allows two professionals to bring their insider’s knowledge of Reading Recovery to an analysis of the construction of the programme. The article interweaves this analysis with the personal narratives of the researchers as they negotiated the borders between different understandings and beliefs about literacy and literacy pedagogy.

Dynamic equilibrium through reinforcement learning

Reinforcement Learning is an area of Machine Learning that deals with how an agent should take actions in an environment such as to maximize the notion of accumulated reward. This type of learning is inspired by the way humans learn and has led to the creation of various algorithms for reinforcement learning. These algorithms focus on the way in which an agent’s behaviour can be improved, assuming independence as to their surroundings. The current work studies the application of reinforcement learning methods to solve the inverted pendulum problem. The importance of the variability of the environment (factors that are external to the agent) on the execution of reinforcement learning agents is studied by using a model that seeks to obtain equilibrium (stability) through dynamism – a Cart-Pole system or inverted pendulum. We sought to improve the behaviour of the autonomous agents by changing the information passed to them, while maintaining the agent’s internal parameters constant (learning rate, discount factors, decay rate, etc.), instead of the classical approach of tuning the agent’s internal parameters. The influence of changes on the state set and the action set on an agent’s capability to solve the Cart-pole problem was studied. We have studied typical behaviour of reinforcement learning agents applied to the classic BOXES model and a new form of characterizing the environment was proposed using the notion of convergence towards a reference value. We demonstrate the gain in performance of this new method applied to a Q-Learning agent.

Learning to manage in knowledge based organizations: the role of portfolios

Paper presented at the 8th European Conference on Knowledge Management, Barcelona, 6-7 Sep. 2008 URL: http://www.academic-conferences.org/eckm/eckm2007/eckm07-home.htm