Emancipating and developing learning activity: Systemic intervention and re-instrumentation in higher education.

The central theme of this thesis is the emancipation and further development of learning activity in higher education in the context of the ongoing digital transformation of our societies. It was developed in response to the highly problematic mainstream approach to digital re-instrumentation of teaching and studying practises in contemporary higher education. The mainstream approach is largely based on centralisation, standardisation, commoditisation, and commercialisation, while re-producing the general patterns of control, responsibility, and dependence that are characteristic for activity systems of schooling. Whereas much of educational research and development focuses on the optimisation and fine-tuning of schooling, the overall inquiry that is underlying this thesis has been carried out from an explicitly critical position and within a framework of action science. It thus conceptualises learning activity in higher education not only as an object of inquiry but also as an object to engage with and to intervene into from a perspective of intentional change. The knowledge-constituting interest of this type of inquiry can be tentatively described as a combination of heuristic-instrumental (guidelines for contextualised action and intervention), practical-phronetic (deliberation of value-rational aspects of means and ends), and developmental-emancipatory (deliberation of issues of power, self-determination, and growth) aspects. Its goal is the production of orientation knowledge for educational practise. The thesis provides an analysis, argumentation, and normative claim on why the development of learning activity should be turned into an object of individual|collective inquiry and intentional change in higher education, and why the current state of affairs in higher education actually impedes such a development. It argues for a decisive shift of attention to the intentional emancipation and further development of learning activity as an important cultural instrument for human (self-)production within the digital transformation. The thesis also attempts an in-depth exploration of what type of methodological rationale can actually be applied to an object of inquiry (developing learning activity) that is at the same time conceptualised as an object of intentional change within the ongoing digital transformation. The result of this retrospective reflection is the formulation of “optimally incomplete” guidelines for educational R&D practise that shares the practicalphronetic (value related) and developmental-emancipatory (power related) orientations that had been driving the overall inquiry. In addition, the thesis formulates the instrumental-heuristic knowledge claim that the conceptual instruments that were adapted and validated in the context of a series of intervention studies provide means to effectively intervene into existing practise in higher education to support the necessary development of (increasingly emancipated) networked learning activity. It suggests that digital networked instruments (tools and services) generally should be considered and treated as transient elements within critical systemic intervention research in higher education. It further argues for the predominant use of loosely-coupled, digital networked instruments that allow for individual|collective ownership, control, (co-)production, and re-use in other contexts and for other purposes. Since the range of digital instrumentation options is continuously expanding and currently shows no signs of an imminent slow-down or consolidation, individual and collective exploration and experimentation of this realm needs to be systematically incorporated into higher education practise.

Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein-protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence “Protein A causes protein B to bind protein C” can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing. Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.

Perceived learning needs in staff development of some care providers in five long-term care settings in Southern Ontario /

This exploratory descriptive study described what 20 care providers in 5 long-term care facilities perceived to aid or hinder their learning in a work-sponsored learning experience. A Critical Incident Technique (Woolsey, 1986) was the catalyst for the interviews with the culturally and professionally diverse participants. Through data analysis, as described by Moustakas (1994), I found that (a) humour, (b) the learning environment, (c) specific characteristics of the presenter such as moderate pacing, speaking slowly and with simple words, (d) decision-making authority, (e) relevance to practice, and (f) practical applications best met the study participants' learning needs. Conversely, other factors could hinder learning based on the participants' perceptions. These were: (a) other presenter characteristics such as a program that was delivered quickly or spoken at a level above the participants' comprehension, (b) no perceived relevance to practice, (c), other environmental situations, and (d) the timing of the learning session. One of my intentions was to identify the emic view among cultural groups and professional/vocational affiliations. A surprising finding of this study was that neither impacted noticeably on the perceived learning needs of the participants. Further research with a revised research design to facilitate inclusion of more diverse participants will aid in determining if the lack of a difference was unique to this sample or more generalizable on a case-to-case transfer basis to the study population.

An exploratory study of nurses' perceptions of their action learning experience /

Each year, the College of Nurses of Ontario (CNO) requires all registered nurses and registered practical nurses in Ontario to complete a Reflective Practice learning activity. In doing so, nurses are expected to perform a self- assessment, identify a practice problem or issue, create and implement a personal learning plan, and evaluate the learning and outcomes accomplished. The process and components of CNO's Reflective Practice program are very similar to an Action Learning activity. The purpose of this qualitative research was to explore the perceptions of 1 1 nurses who completed at least 1 Action Learning activity. Data analysis of their comments provided insight into their perceptions of the Action Learning experience, perceptions of the negative and positive characteristics of various activities within the Action Learning process, and perceptions of barriers or challenges within this experience. The author concluded that participants perceived their Action Learning activities to be a positive experience because the process focused on practice problems and issues, enhanced thinking about practice problems, and achieved practice-relevant outcomes. However, the results indicated that self-directed learning and journal writing were difficult activities for some participants, and some experienced negative emotional responses during reflection. The research concluded that barriers to implementation of Action Learning include a lack of understanding of the process and a perceived lack of support from employers.

The practical examination and how it correlates with the triple jump, tutorial, and written examination

This study explored the relationship between the practical examination and other course evaluation methods~ specifically, the triple jump, tutorial, and written examination. Studies correlating academic and clinical grades tended to indicate that they may not be highly correlated because each evaluation process contributes different kinds of information regarding student knowledge, skills, and attitudes. Six hypotheses were generated stating a positive relationship between the four evaluation methods. A correlation matrix was produced of the Pearson Product Moment correlation co-efficients on the four evaluation methods in the second and third year Occupational Therapy Technique and Clinical Problem Solving courses of the 1988 and 1989 graduates (n~45). The results showed that the highest correlations existed between the triple jump and the tutorial grades and the lowest correlations existed between the practical examination and written examination grades. Not all of the correlations~ however~ reached levels of significance. The correlations overall. though, were only low to moderate at best which indicates that the evaluation methods may be measuring different aspects of student learning. This conclusion supports the studies researched. The implications and significance of this study is that it will assist the faculty in defining what the various evaluation methods measure which will in turn promote more critical input into curriculum development for the remaining years of the program.

A case study investigation of the learning needs of the Niagara grape and wine community

The Niagara Grape and Wine Community (NGWC) is an industry that has undergone rapid change and expansion as a result of changes in governmental regulations and consumer preferences. As a result of these changes, the demands of the wine industry workforce have changed to reflect the need to implement new strategies and practices to remain viable and competitive. The influx of people into the community with little or no prior practical experience in grape growing (viticulture) or winemaking (oenology) has created a need for additional training and learning opportunities to meet workforce needs. This case study investigated the learning needs of the members of this community and how these needs are currently being met. The barriers to, and the opportunities for, members acquiring new knowledge and developing skills were also explored. Participants were those involved in all levels of the industry and sectors (viticulture, processing, and retail), and their views on needs and suggestions for programs of study were collected. Through cross analyses of sectors, areas of common and unique interest were identified as well as formats for delivery. A common fundamental component was identified by all sectors - any program must have a significant applied component or demonstration of proficiency and should utilize members as peer instructors, mentors, and collaborators to generate a larger shared collective of knowledge. Through the review of learning organizations, learning communities, communities of practices, and learning networks, the principles for the development of a Grape and Wine Learning Network to meet the learning needs of the NGWC outside of formal institutional or academic programs were developed. The roles and actions of members to make such a network successful are suggested.

Exploring Reciprocity in International Service Learning Programs

Using aspects of grounded theory methodology, this study explored the perceptions and practical implementation of reciprocity in International Service Learning (ISL) Programs. Data were collected through interviews with nine ISL practitioners representing a variety of organizations offering international service learning programs. Findings suggest that multiple conceptualizations of ISL programs exist. ISL programs are interdisciplinary in nature and that using reciprocity as a guiding framework is problematic. Further attention is needed in relation to shifting the guiding framework of ISL programs from reciprocity to interdependence.

The Effective Use of 21st-Century Learning iPad Applications in the Primary/Junior Literacy Classroom

The purpose of this major research project was to develop a practical tool in the form of a handbook that could facilitate educators’ effective use of technology in primary and junior classrooms. The main goal was to explore the use of iPad devices and applications in the literacy classroom. The study audited available free applications against set criteria and selected only those that promoted 21st-century learning. The researcher used such applications to develop literacy lessons that aligned with curriculum expectations and promoted 21st-century skills and traditional skills alike. The study also created assessment models to evaluate the use of iPads in student work and explored the benefits and limitations of technology usage in student learning.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Reinforcement Learning Solution for Unit Commitment Problem Considering Minimum Start Up and Shut Down Times

Unit Commitment Problem (UCP) in power system refers to the problem of determining the on/ off status of generating units that minimize the operating cost during a given time horizon. Since various system and generation constraints are to be satisfied while finding the optimum schedule, UCP turns to be a constrained optimization problem in power system scheduling. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision making task and an efficient Reinforcement Learning solution is formulated considering minimum up time /down time constraints. The correctness and efficiency of the developed solutions are verified for standard test systems

Reinforcement Learning solution for Unit Commitment Problem through pursuit method

Unit commitment is an optimization task in electric power generation control sector. It involves scheduling the ON/OFF status of the generating units to meet the load demand with minimum generation cost satisfying the different constraints existing in the system. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision task and Reinforcement Learning solution is formulated through one efficient exploration strategy: Pursuit method. The correctness and efficiency of the developed solutions are verified for standard test systems

Teaching an Old Robot New Tricks: Learning Novel Tasks via Interaction with People and Things

As AI has begun to reach out beyond its symbolic, objectivist roots into the embodied, experientialist realm, many projects are exploring different aspects of creating machines which interact with and respond to the world as humans do. Techniques for visual processing, object recognition, emotional response, gesture production and recognition, etc., are necessary components of a complete humanoid robot. However, most projects invariably concentrate on developing a few of these individual components, neglecting the issue of how all of these pieces would eventually fit together. The focus of the work in this dissertation is on creating a framework into which such specific competencies can be embedded, in a way that they can interact with each other and build layers of new functionality. To be of any practical value, such a framework must satisfy the real-world constraints of functioning in real-time with noisy sensors and actuators. The humanoid robot Cog provides an unapologetically adequate platform from which to take on such a challenge. This work makes three contributions to embodied AI. First, it offers a general-purpose architecture for developing behavior-based systems distributed over networks of PC's. Second, it provides a motor-control system that simulates several biological features which impact the development of motor behavior. Third, it develops a framework for a system which enables a robot to learn new behaviors via interacting with itself and the outside world. A few basic functional modules are built into this framework, enough to demonstrate the robot learning some very simple behaviors taught by a human trainer. A primary motivation for this project is the notion that it is practically impossible to build an "intelligent" machine unless it is designed partly to build itself. This work is a proof-of-concept of such an approach to integrating multiple perceptual and motor systems into a complete learning agent.

A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks

We propose a nonparametric method for estimating derivative financial asset pricing formulae using learning networks. To demonstrate feasibility, we first simulate Black-Scholes option prices and show that learning networks can recover the Black-Scholes formula from a two-year training set of daily options prices, and that the resulting network formula can be used successfully to both price and delta-hedge options out-of-sample. For comparison, we estimate models using four popular methods: ordinary least squares, radial basis functions, multilayer perceptrons, and projection pursuit. To illustrate practical relevance, we also apply our approach to S&P 500 futures options data from 1987 to 1991.

The UK Professional Standards Framework for teaching and supporting learning in higher education

This is a part of a collection of materials developed by the HEAcademy Subject Centre for Languages, linguistics and area studies. The materials provide reflective activities designed to engage teachers with some of the key issues in working with international students and practical ideas for ways in which these can be addressed. They will be of particular interest to new staff or anyone new to working with international students.

Technology Enhanced Learning Guide

Technology is changing how students learn and how we research. Perhaps you want to use technology to enhance communication or improve student support. You may want create a distance learning activity, a flexibly delivered module or indeed a whole course. You may simply want to find out where to find authoritative information, or to see what support exists for this type of work. The University is committed to delivering high quality learning and teaching, using technology where appropriate, in order to offer a distinctive Southampton educational experience. Technology Enhanced Learning (TEL), also known as e‑learning, is becoming increasingly important to students, teaching staff and the institution. This guide highlights some of the most important matters to consider. It is intended to help you to tackle the key issues that determine the success of TEL projects and to work on those projects in a considered way. Written with the input of colleagues from around the University, it prompts you to ask important questions and points you to sources of up-to-date knowledge and advice. Technology changes rapidly. This guide is about managing the work in a practical way. The University supports the use of a variety of TEL approaches for teaching and learning and colleagues are ready to offer their experience and advice. Each person has distinctive skills and specific experiences. No single person will have all the answers you are looking for. Be ready to investigate alternative approaches that suit you and your students’ needs in different ways. - Madeline Paterson, University of Southampton