A visual interactive modelling system as a learning tool

This paper presents a case study of the use of a visual interactive modelling system to investigate issues involved in the management of a hospital ward. Visual Interactive Modelling systems are seen to offer the learner the opportunity to explore operational management issues from a varied perspective and to provide an interactive system in which the learner receives feedback on the consequences of their actions. However to maximise the potential learning experience for a student requires the recognition that they require task structure which helps them to understand the concepts involved. These factors can be incorporated into the visual interactive model by providing an interface customised to guide the student through the experimentation. Recent developments of VIM systems in terms of their connectivity with the programming language Visual Basic facilitates this customisation.

The strengths, weaknesses, opportunities and threats of using social software in higher and further education teaching and learning

Social software is increasingly being used in higher and further education to support teaching and learning processes. These applications provide students with social and cognitive stimulation and also add to the interaction between students and educators. However, in addition to the benefits the introduction of social software into a course environment can also have adverse implications on students, educators and the education institution as a whole, a phenomenon which has received much less attention in the literature. In this study we explore the various implications of introducing social software into a course environment in order to identify the associated benefits, but also the potential drawbacks. We draw on data from 20 social software initiatives in UK based higher and further education institutions to identify the diverse experiences and concerns of students and educators. The findings are presented in form of a SWOT analysis, which allows us to better understand the otherwise ambiguous implications of social software in terms of its strengths, weaknesses, opportunities and threats. From the analysis we have derived concrete recommendations for the use of social software as a teaching and learning tool.

Induction by a Hilbert hypercube representation

This thesis describes a novel connectionist machine utilizing induction by a Hilbert hypercube representation. This representation offers a number of distinct advantages which are described. We construct a theoretical and practical learning machine which lies in an area of overlap between three disciplines - neural nets, machine learning and knowledge acquisition - hence it is refered to as a "coalesced" machine. To this unifying aspect is added the various advantages of its orthogonal lattice structure as against less structured nets. We discuss the case for such a fundamental and low level empirical learning tool and the assumptions behind the machine are clearly outlined. Our theory of an orthogonal lattice structure the Hilbert hypercube of an n-dimensional space using a complemented distributed lattice as a basis for supervised learning is derived from first principles on clearly laid out scientific principles. The resulting "subhypercube theory" was implemented in a development machine which was then used to test the theoretical predictions again under strict scientific guidelines. The scope, advantages and limitations of this machine were tested in a series of experiments. Novel and seminal properties of the machine include: the "metrical", deterministic and global nature of its search; complete convergence invariably producing minimum polynomial solutions for both disjuncts and conjuncts even with moderate levels of noise present; a learning engine which is mathematically analysable in depth based upon the "complexity range" of the function concerned; a strong bias towards the simplest possible globally (rather than locally) derived "balanced" explanation of the data; the ability to cope with variables in the network; and new ways of reducing the exponential explosion. Performance issues were addressed and comparative studies with other learning machines indicates that our novel approach has definite value and should be further researched.

Reflective journals:can they be used to identify struggling students?

The ability to identify early failure in knowledge accquisition amongst students is important because it enables tutors to put in place suitable interventions to help struggling students. We hypothesised that if a reflective learning journal is a useful learning tool, there ought to be relationship between the type of journal entries and the depth of knowledge acquisition. Our research question is: can reflectiuve journals be used to identify struggling students? Previous work with reflective journals has not related the level of reflection with module outcomes obtained by the student. In our study, we have classified journal entries written by first year students in a foundationalprogramming module based on the SOLO taxonomy and compared this against the outcomes of two module assessments. Our results suggest that there is potential for using reflective journals to identify struggling stuidents in first year programming.

Semi-supervised learning of hierarchical latent trait models for data visualisation

An interactive hierarchical Generative Topographic Mapping (HGTM) ¸iteH_GTM has been developed to visualise complex data sets. In this paper, we build a more general visualisation system by extending the HGTM visualisation system in 3 directions: bf (1) We generalize HGTM to noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM) developed in ¸iteKaban_pami. bf (2) We give the user a choice of initializing the child plots of the current plot in either em interactive, or em automatic mode. In the interactive mode the user interactively selects ``regions of interest'' as in ¸iteH_GTM, whereas in the automatic mode an unsupervised minimum message length (MML)-driven construction of a mixture of LTMs is employed. bf (3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualisation plots, since they can highlight the boundaries between data clusters. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. We illustrate our approach on a toy example and apply our system to three more complex real data sets.

Embedding e-portfolios for effective lifelong learning:a case study

Lifelong learning is a ‘keystone’ of educational policies (Faure, 1972) where the emphasis on learning shifts from teacher to learner. Higher Education (HE) institutions should be committed to developing lifelong learning, that is promoting learning that is flexible, diverse and relevant at different times, and in different places, and is pursued throughout life. Therefore the HE sector needs to develop effective strategies to encourage engagement in meaningful learning for diverse student populations. The use of e-portfolios, as a ‘purposeful aggregation of digital items’ (Sutherland & Powell, 2007), can meet the needs of the student community by encouraging reflection, the recording of experiences and achievements, and personal development planning (PDP). The use of e-portfolios also promotes inclusivity in learning as it provides students with the opportunity to articulate their aspirations and take the first steps along the pathway of lifelong learning. However, ensuring the uptake of opportunities within their learning is more complex than the students simply having access to the software. Therefore it is argued here that crucial to the effective uptake and engagement of the e-portfolio is embedding it purposefully within the curriculum. In order to investigate effective implementation of e-portfolios an explanatory case study on their use was carried out, initially focusing on 3 groups of students engaged in work-based learning and professional practice. The 3 groups had e-Portfolios embedded and assessed at different levels. Group 1 did not have the e-Portfolio embedded into their curriculum nor was the e-Portfolio assessed. Group 2 had the e-Portfolio embedded into the curriculum and formatively assessed. Group 3 also had the e-Portfolio embedded into the curriculum and were summatively assessed. Results suggest that the use of e-Portfolios needs to be integral to curriculum design in modules rather than used as an additional tool. In addition to this more user engagement was found in group 2 where the e-Portfolio was formatively assessed only. The implications of this case study are further discussed in terms of curriculum development.

Improving the use of visual interactive simulation as a knowledge elicitation tool

Knowledge elicitation is a well-known bottleneck in the production of knowledge-based systems (KBS). Past research has shown that visual interactive simulation (VIS) could effectively be used to elicit episodic knowledge that is appropriate for machine learning purposes, with a view to building a KBS. Nonetheless, the VIS-based elicitation process still has much room for improvement. Based in the Ford Dagenham Engine Assembly Plant, a research project is being undertaken to investigate the individual/joint effects of visual display level and mode of problem case generation on the elicitation process. This paper looks at the methodology employed and some issues that have been encountered to date. Copyright © 2007 Inderscience Enterprises Ltd.

Technological and organisational change in small- to medium-sized manufacturing companies:a learning organisation perspective

This paper examines the extent to which a learning organisation perspective is attainable in small- to medium-sized manufacturing companies. An audit tool is developed from the literature on organisational learning and recognised processes that lead towards becoming a learning organisation. The paper focuses on the application of the audit tool in three UK automotive component suppliers which are all experiencing pressures for change imposed by the major vehicle manufacturers. The main changes are concerned with tiering of the supply chain and substantial delegation of responsibilities to component suppliers including an increasing emphasis on innovation and continuous improvement. The companies presented in the paper are taken from a research project into the impact of changes in supply chain relationships on the operation of small- and medium-sized manufacturing firms in the West Midlands region of the UK. The ways in which the companies are responding to change are presented together with the results of a self-assessment using the developed audit tool. These results suggest that companies of this type tend to focus on change in those areas that involve least challenge to the established power and authority of management.

Does reviewing lead to better learning and decision making? Answers from a randomized stock market experiment

Background - The literature is not univocal about the effects of Peer Review (PR) within the context of constructivist learning. Due to the predominant focus on using PR as an assessment tool, rather than a constructivist learning activity, and because most studies implicitly assume that the benefits of PR are limited to the reviewee, little is known about the effects upon students who are required to review their peers. Much of the theoretical debate in the literature is focused on explaining how and why constructivist learning is beneficial. At the same time these discussions are marked by an underlying presupposition of a causal relationship between reviewing and deep learning. Objectives - The purpose of the study is to investigate whether the writing of PR feedback causes students to benefit in terms of: perceived utility about statistics, actual use of statistics, better understanding of statistical concepts and associated methods, changed attitudes towards market risks, and outcomes of decisions that were made. Methods - We conducted a randomized experiment, assigning students randomly to receive PR or non–PR treatments and used two cohorts with a different time span. The paper discusses the experimental design and all the software components that we used to support the learning process: Reproducible Computing technology which allows students to reproduce or re–use statistical results from peers, Collaborative PR, and an AI–enhanced Stock Market Engine. Results - The results establish that the writing of PR feedback messages causes students to experience benefits in terms of Behavior, Non–Rote Learning, and Attitudes, provided the sequence of PR activities are maintained for a period that is sufficiently long.

An investigation into inductive parameter learning in complex hierarchical knowledge structures representing clinical expertise

This dissertation investigates the very important and current problem of modelling human expertise. This is an apparent issue in any computer system emulating human decision making. It is prominent in Clinical Decision Support Systems (CDSS) due to the complexity of the induction process and the vast number of parameters in most cases. Other issues such as human error and missing or incomplete data present further challenges. In this thesis, the Galatean Risk Screening Tool (GRiST) is used as an example of modelling clinical expertise and parameter elicitation. The tool is a mental health clinical record management system with a top layer of decision support capabilities. It is currently being deployed by several NHS mental health trusts across the UK. The aim of the research is to investigate the problem of parameter elicitation by inducing them from real clinical data rather than from the human experts who provided the decision model. The induced parameters provide an insight into both the data relationships and how experts make decisions themselves. The outcomes help further understand human decision making and, in particular, help GRiST provide more accurate emulations of risk judgements. Although the algorithms and methods presented in this dissertation are applied to GRiST, they can be adopted for other human knowledge engineering domains.

Writing for success:mentoring as a pedagogical tool – a cross- institutional study

The prominent position given to academic writing across contemporary academia is reflected in the substantive literature and debate devoted to the subject over the past 30 years. However, the massification of higher education, manifested by a shift from elite to mass education, has brought the issue into the public arena, with much debate focusing on the need for ‘modern-day' students to be taught how to write academically (Bjork et al., 2003; Ganobcsik-Williams, 2006). Indeed, Russell (2003) argued that academic writing has become a global ‘problem' in Higher Education because it sits between two contradictory pressures (p.V). On one end of the university ‘experience' increasing numbers of students, many from non-traditional backgrounds, enter higher education bringing with them a range of communication abilities. At the other end, many graduates leave university to work in specialised industries where employers expect them to have high level writing skills (Ashton, 2007; Russell, 2003; Torrence et al., 1999). By drawing attention to the issues around peer mentoring within an academic writing setting in three different higher education Institutions, this paper makes an important contribution to current debates. Based upon a critical analysis of the emergent findings of an empirical study into the role of peer writing mentors in promoting student transition to higher education, the paper adopts an academic literacies approach to discuss the role of writing mentoring in promoting transition and retention by developing students' academic writing. Attention is drawn to the manner in which student expectations of writing mentoring actually align with mentoring practices - particularly in terms of the writing process and critical thinking. Other issues such as the approachability of writing mentors, the practicalities of accessing writing mentoring and the wider learning environment are also discussed.

Learning to reflect through technology, to enhance employability

"Reflection, if managed in an ordered way, can provide great opportunities for learning, understanding and clarifying thought, both in one's personal life and in learning and professional development." Moon, J (1999). Aston Business School's (ABS) 12cmonth professional placement programme has a number of espoused learning objectives, including helping students: 1. To benefit from the integration of university study and work experience in ways which facilitate critical reflection on each ofthese aspects. 2. To build a personal awareness of their own interests, competencies, values and potential. These objectives focus students' development to self-reflect critically, to make sense of their experiences/learning whilst undertaking their placements. Students complete a placement year Reflective Learning Journal, supported by the workplace supervisor, through regular meetings where objectives are agreed and reviewed. As well as reflection providing opportunities for students to make sense of their learning, it is also challenging! ABS is undertaking a pilot in 2008/9 to encourage students to engage with reflective practice, but employer feedback indicates an ability to think and analyse is often missing from the skill sets of graduates. Business students are using the PebblePad e-portfolio system as a tool to record their learning, reflect on their experiences in the workplace and to create their journals.

Engaging employers as partners in work-based learning assessment:proposal for a quality enhancement framework

This paper seeks to advance research and practice related to the role of employers in all stages of the assessment process of work-based learning (WBL) within a tripartite relationship of higher education institution (HEI), student and employer. It proposes a research-informed quality enhancement framework to develop good practice in engaging employers as partners in assessment. The Enhancement Framework comprises three dimensions, each of which includes elements and questions generated by the experiences of WBL students, HEI staff and employers. The three dimensions of the Enhancement Framework are: 1. ‘premises of assessment’ encompassing issues of learning, inclusion, standards and value; 2. ‘practice’, encompassing stages of assessment made up of course design, assessment task, responsibilities, support, grading and feedback; 3. ‘communication of assessment’ with the emphasis on role clarity, language and pathways. With its prompt questions, the Enhancement Framework may be used as a capacity-building tool for promoting, sustaining, benchmarking and evaluating productive dialogue and critical reflection about assessment between WBL partners. The paper concludes by emphasising the need for professional development as well as policy and research development, so that assessment in WBL can more closely correspond to the potentially transformative nature of the learning experience.

Semisupervised learning of hierarchical latent trait models for data visualization

Recently, we have developed the hierarchical Generative Topographic Mapping (HGTM), an interactive method for visualization of large high-dimensional real-valued data sets. In this paper, we propose a more general visualization system by extending HGTM in three ways, which allows the user to visualize a wider range of data sets and better support the model development process. 1) We integrate HGTM with noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM). This enables us to visualize data of inherently discrete nature, e.g., collections of documents, in a hierarchical manner. 2) We give the user a choice of initializing the child plots of the current plot in either interactive, or automatic mode. In the interactive mode, the user selects "regions of interest," whereas in the automatic mode, an unsupervised minimum message length (MML)-inspired construction of a mixture of LTMs is employed. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. 3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualization plots, since they can highlight the boundaries between data clusters. We illustrate our approach on a toy example and evaluate it on three more complex real data sets. © 2005 IEEE.