Using MediaWiki as an efficient data repository and ubiquitous learning tool : an Australian example

The functionality of MediaWiki ensures it is a valuable learning repository for sharing and storing information. Constructivist learning can be promoted alongside a wiki repository and various wireless u-learning tools such as mobile phones and digital cameras, to encourage students to gather and share a range of primary and secondary information in a variety of subject areas. This paper outlines one initiative adopted at an Australian University specialising in distance education, which uses a MediaWiki as the primary method for content delivery. Over a period of three-years, the Drugs, Crime and Society wiki has evolved into an organic information repository for storing and accessing current research, press and drug agency material that supplements core themes examined in each topic of the curriculum. A constructivist approach has been employed to encourage students to engage in a range of assessable and non-assessable information sharing activities. The paper also demonstrates how the Drugs, Crime and Society wiki can be accessed through various wireless u-learning technologies, which enables students undertaking field placements to add and share primary information with other students and practitioners working in the drugs field.

Knowledge development, social capital and alliance learning

Purpose – The purpose of this paper is to elucidate information on what creates the different types of knowledge.

Design/methodology/approach – In the conceptual model it is argued that the concept of social capital provides an interesting view on the creation of market-specific and firm-specific knowledge.

Findings – The major finding from the paper is that knowledge is an important by-product of an alliance forming process, a process commonly termed as alliance learning.

Research limitations/implications – Both market-specific and firm-specific knowledge have implications on two main types of alliance learning, that of mutual and non-mutual learning.

Practical implications – Alliance managers need to be aware that knowledge is a key driver as well as a beneficial outcome in the formation of alliances.

Originality/value – This paper examines how the different types of knowledge evolve and how these different types of knowledge impact upon alliance learning.

Proficiency and collaborative learning

This study reports on the effect of different levels of proficiency on the students' achievements in collaborative learning instruction among 30 Persian-speaking EFL college students. Having been divided into dyads with different levels of proficiency, these subjects participated in nine sessions of collaborative instruction based on the form focused collaborative text-editing task. The results of this study are obtained through the analyses of the proficiency gains in formal aspects of language in text-editing pre and post-tests and text-reconstruction post-test. The study highlights the significant effect of this type of instruction on more proficient students. Reasons provided in this study are the language analytic ability of the students, learning styles and strategies, and motivation. The findings of this study are helpful to teachers of the classes with heterogeneous language proficiency.

Special issue : Computational intelligence models for image processing and information reasoning

Computational Intelligence (CI) models comprise robust computing methodologies with a high level of machine learning quotient. CI models, in general, are useful for designing computerized intelligent systems/machines that possess useful characteristics mimicking human behaviors and capabilities in solving complex tasks, e.g., learning, adaptation, and evolution. Examples of some popular CI models include fuzzy systems, artificial neural networks, evolutionary algorithms, multi-agent systems, decision trees, rough set theory, knowledge-based systems, and hybrid of these models. This special issue highlights how different computational intelligence models, coupled with other complementary techniques, can be used to handle problems encountered in image processing and information reasoning.

Supporting athlete selection and strategic planning in track cycling omnium: A statistical and machine learning approach

This article describes the implementation of machine learning techniques that assist cycling experts in the crucial decision-making processes for athlete selection and strategic planning in the track cycling omnium. The omnium is a multi-event competition that was included in the Olympic Games for the first time in 2012. Presently, selectors and cycling coaches make decisions based on experience and opinion. They rarely have access to knowledge that helps predict athletic performances. The omnium presents a unique and complex decision-making challenge as it is not clear what type of athlete is best suited to the omnium (e.g., sprint or endurance specialist) and tactical decisions made by the coach and athlete during the event will have significant effects on the overall performance of the athlete. In the present work, a variety of machine learning techniques were used to analyze omnium competition data from the World Championships since 2007. The analysis indicates that sprint events have slightly more influence in determining the medalists, than endurance-based events. Using a probabilistic analysis, we created a model of performance prediction that provides an unprecedented level of supporting information that assists coaches with strategic and tactical decisions during the omnium.

Enhancing and assessing group and team learning in architecture and related design contexts

EXECUTIVE SUMMARYTeamwork skills are essential in the design industry where practitioners negotiate often-conflicting design options in multi-disciplinary teams. Indeed, many of the bodies that accredit design courses explicitly list teamwork skills as essential attributes of design graduates e.g., the Australian Institute of Architects (AIA), Royal Institute of British Architects (RIBA), the National Council of Architectural Registration Boards (NCARB) of the United States and the Institution of Engineers, Australia (IEAust). In addition to the need to meet the demands of the accrediting bodies, there are many reasons for the ubiquitous use of teamwork assignments in design schools. For instance, teamwork learning is seen as being representative of work in practice where design is nearly always a collaborative activity. Learning and teaching in teamwork contexts in design education are not without particular challenges. In particular, two broad issues have been identified: first, many students leave academia without having been taught the knowledge and skills of how to design in teams; second, teaching, assessment and assignment design need to be better informed by a clear understanding of what leads to effective teamwork and the learning of teamwork skills. In recognition of the lack of a structured approach to integrating teamwork learning into the curricula of design programs, this project set out to answer three primary research questions: • How do we teach teamwork skills in the context of design? • How do we assess teamwork skills?• How do design students best learn teamwork skills?In addition, four more specific questions were investigated:1. Is there a common range of learning objectives for group-and-team-work in architecture and related design disciplines that will enable the teaching of consistent and measurable outcomes?2. Do group and team formation methods, learning styles and team-role preferences impact students’ academic and course satisfaction outcomes?3. What combinations of group-and-team formation methods, teaching and assessment models significantly improve learning outcomes?4. For design students across different disciplines with different learning styles and cultural origins, are there significant differences in performance, student satisfaction (as measured through questionnaires and unit evaluations), group-and-team working abilities and student participation?To elucidate these questions, a design-based research methodology was followed comprising an iterative series of enquiries: (a) A literature review was completed to investigate: what constitutes effective teamwork, what contributes to effectiveness in teams, what leads to positive design outcomes for teams, and what leads to effective learning in teams. The review encompassed a range of contexts: from work-teams in corporate settings, to professional design teams, to education outside of and within the design disciplines. The review informed a theoretical framework for understanding what factors impact the effectiveness of student design teams. (b) The validity of this multi-factorial Framework of Effectiveness in Student Design Teams was tested via surveys of educators’ teaching practices and attitudes, and of students’ learning experiences. 638 students and 68 teachers completed surveys: two pilot surveys for participants at the four partner institutions, which then informed two national surveys completed by participants from the majority of design schools across Australia. (c) The data collected provided evidence for 22 teamwork factors impacting team effectiveness in student design teams. Pedagogic responses and strategies to these 22 teamwork factors were devised, tested and refined via case studies, focus groups and workshops. (d) In addition, 35 educators from a wide range of design schools and disciplines across Australia attended two National Teaching Symposiums. The first symposium investigated the wider conceptualisation of teamwork within the design disciplines, and the second focused on curriculum level approaches to structuring the teaching of teamwork skills identified in the Framework.The Framework of Effectiveness in Student Design Teams identifies 22 factors impacting effective teamwork, along with teaching responses and strategies that design educators might use to better support student learning. The teamwork factors and teaching strategies are categorised according to three groups of input (Task Characteristics, Individual Level Factors and Team Level Factors), two groups of processes (Teaching Practice & Support Structures and Team Processes), and three categories of output (Task Performance, Teamwork Skills, and Attitudinal Outcomes). Eight of the 22 teamwork factors directly relate to the skills that need to be developed in students, one factor relates to design outputs, and the other thirteen factors inform pedagogies that can be designed for better learning outcomes. In Table 10 of Section 4, we outline which of the 22 teamwork factors pertain to each of five stakeholder groups (curriculum leaders, teachers, students, employers and the professional bodies); thus establishing who will make best use the information and recommendations we make. In the body of this report we summarise the 22 teamwork factors and teaching strategies informed by the Framework of Effectiveness in Student Design Teams, and give succinct recommendations arising from them. This material is covered in depth by the project outputs. For instance, the teaching and assessment strategies will be expanded upon in a projected book on Teaching Teamwork in Design. The strategies are also elucidated by examples of good practice presented in our case studies, and by Manuals on Teamwork for Teachers and Students. Moreover, the project website ( visited by representatives of stakeholder groups in Australia and Canada), is seeding a burgeoning community of practice that promises dissemination, critical evaluation and the subsequent refinement of our materials, tools, strategies and recommendations. The following three primary outputs have been produced by the project in answer to the primary research questions:1. A theoretical Framework of Effectiveness in Student Design Teams;2. Manuals on Teamwork for Teachers and Students (available from the website);3. Case studies of good/innovative practices in teaching and assessing teamwork in design;In addition, five secondary outputs/outcomes have been produced that provide more nuanced responses:4. Detailed recommendations for the professional accrediting bodies and curriculum leaders;5. Online survey data (from over 700 participants), plus Team Effectiveness Scale to determine the factors influencing effective learning and successful outputs for student design teams;6. A community of practice in policy, programs, practice and dialogue;7. A detailed book proposal (with sample chapter), submitted to prospective publishers, on Teaching Teamwork in Design; 8. An annotated bibliography (accessed via the project website) on learning, teaching and assessing teamwork.The project has already had an international impact. As well as papers presented in Canada and New Zealand, the surveys were participated in by six Canadian schools of architecture, whose teaching leaders also provided early feedback on the project aims and objectives during visits made to them by the project leader. In addition, design schools in Vancouver, Canada, and San Diego in the USA have already utilised the Teacher’s Manual, and in February 2014 the project findings were discussed at Tel Aviv University in a forum focusing on the challenges for sustainability in architectural education.

The assessment of the quality of sugar using electronic tongue and machine learning algorithms

The correct classification of sugar according to its physico-chemical characteristics directly influences the value of the product and its acceptance by the market. This study shows that using an electronic tongue system along with established techniques of supervised learning leads to the correct classification of sugar samples according to their qualities. In this paper, we offer two new real, public and non-encoded sugar datasets whose attributes were automatically collected using an electronic tongue, with and without pH controlling. Moreover, we compare the performance achieved by several established machine learning methods. Our experiments were diligently designed to ensure statistically sound results and they indicate that k-nearest neighbors method outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison. © 2012 IEEE.

Can neuroimaging be used as a support to diagnosis of borderline personality disorder? An approach based on computational neuroanatomy and machine learning

Several recent studies in literature have identified brain morphological alterations associated to Borderline Personality Disorder (BPD) patients. These findings are reported by studies based on voxel-based-morphometry analysis of structural MRI data, comparing mean gray-matter concentration between groups of BPD patients and healthy controls. On the other hand, mean differences between groups are not informative about the discriminative value of neuroimaging data to predict the group of individual subjects. In this paper, we go beyond mean differences analyses, and explore to what extent individual BPD patients can be differentiated from controls (25 subjects in each group), using a combination of automated-morphometric tools for regional cortical thickness/volumetric estimation and Support Vector Machine classifier. The approach included a feature selection step in order to identify the regions containing most discriminative information. The accuracy of this classifier was evaluated using the leave-one-subject-out procedure. The brain regions indicated as containing relevant information to discriminate groups were the orbitofrontal, rostral anterior cingulate, posterior cingulate, middle temporal cortices, among others. These areas, which are distinctively involved in emotional and affect regulation of BPD patients, were the most informative regions to achieve both sensitivity and specificity values of 80% in SVM classification. The findings suggest that this new methodology can add clinical and potential diagnostic value to neuroimaging of psychiatric disorders. (C) 2012 Elsevier Ltd. All rights reserved.

From the Vice President for Library and Information Technology: Spring 2012

Param Bedi discusses technology adoption by students and its impact on teaching and learning.

Combining Wireless Sensor Networks and Machine Learning for Flash Flood Nowcasting

This paper addresses an investigation with machine learning (ML) classification techniques to assist in the problem of flash flood now casting. We have been attempting to build a Wireless Sensor Network (WSN) to collect measurements from a river located in an urban area. The machine learning classification methods were investigated with the aim of allowing flash flood now casting, which in turn allows the WSN to give alerts to the local population. We have evaluated several types of ML taking account of the different now casting stages (i.e. Number of future time steps to forecast). We have also evaluated different data representation to be used as input of the ML techniques. The results show that different data representation can lead to results significantly better for different stages of now casting.

Undergraduate palliative care teaching in Swiss medical faculties: a nationwide survey and improved learning objectives

BACKGROUND In 2007, a first survey on undergraduate palliative care teaching in Switzerland has revealed major heterogeneity of palliative care content, allocation of hours and distribution throughout the 6 year curriculum in Swiss medical faculties. This second survey in 2012/13 has been initiated as part of the current Swiss national strategy in palliative care (2010 - 2015) to serve as a longitudinal monitoring instrument and as a basis for redefinition of palliative care learning objectives and curriculum planning in our country. METHODS As in 2007, a questionnaire was sent to the deans of all five medical faculties in Switzerland in 2012. It consisted of eight sections: basic background information, current content and hours in dedicated palliative care blocks, current palliative care content in other courses, topics related to palliative care presented in other courses, recent attempts at improving palliative care content, palliative care content in examinations, challenges, and overall summary. Content analysis was performed and the results matched with recommendations from the EAPC for undergraduate training in palliative medicine as well as with recommendations from overseas countries. RESULTS There is a considerable increase in palliative care content, academic teaching staff and hours in all medical faculties compared to 2007. No Swiss medical faculty reaches the range of 40 h dedicated specifically to palliative care as recommended by the EAPC. Topics, teaching methods, distribution throughout different years and compulsory attendance still differ widely. Based on these results, the official Swiss Catalogue of Learning Objectives (SCLO) was complemented with 12 new learning objectives for palliative and end of life care (2013), and a national basic script for palliative care was published (2015). CONCLUSION Performing periodic surveys of palliative care teaching at national medical faculties has proven to be a useful tool to adapt the national teaching framework and to improve the recognition of palliative medicine as an integral part of medical training.

Optimization and Machine Learning Frameworks for Complex Network Analysis

Thesis (Ph.D.)--University of Washington, 2016-06