Addressing challenges to teach traditional and agile project management in academia

In order to prepare students for a professional IT career, most universities attempt to provide a current educational curriculum in the Project Management (PM) area to their students. This is usually based on the most promising methodologies used by the software industry. As instructors, we need to balance traditional methodologies focused on proven project planning and control processes leveraging widely accepted methods and tools along with the newer agile methodologies. Such new frameworks emphasize that software delivery should be done in a flexible and iterative manner and with significant collaboration with product owners and customers. In our experience agile methodologies have witnessed an exponential growth in many diverse software organizations, and the various agile PM tools and techniques will continue to see an increase in adoption in the software development sector. Reflecting on these changes, there is a critical need to accommodate best practices and current methodologies in our courses that deliver Project Management content. In this paper we analyse two of the most widely used methodologies for traditional and agile software development – the widely used ISO/PMBOK standard provided by the Project Management Institute and the well-accepted Scrum framework. We discuss how to overcome curriculum challenges and deliver a quality undergraduate PM course for a Computer Science and Information systems curricula. Based on our teaching experience in Europe and North America, we present a comprehensive comparison of the two approaches. Our research covers the main concepts, processes, and roles associated with the two PM frameworks and recommended learning outcomes. The paper should be of value to instructors who are keen to see their computing students graduate with a sound understanding of current PM methodologies and who can deliver real-world software products.Accepted manuscrip

Connected Learning Journeys in Music Production Education

The field of music production education is a challenging one, exploring multiple creative, technical and entrepreneurial disciplines, including music composition, performance electronics, acoustics, musicology, project management and psychology. As a result, students take multiple ‘learning journeys’ on their pathway towards becoming autonomous learners. This paper uniquely evaluates the journey of climbing Bloom’s cognitive domain in the field of music production and gives specific examples that validate teaching music production in higher education through multiple, connected ascents of the framework. Owing to the practical nature of music production, Kolb’s Experiential Learning Model is also considered as a recurring function that is necessary for climbing Bloom’s domain, in order to ensure that learners are equipped for employability and entrepreneurship on graduation. The authors’ own experiences of higher education course delivery, design and development are also reflected upon with reference to Music Production pathways at both the University of Westminster (London, UK) and York St John University (York, UK)

Teaching programming with computational and informational thinking

Computers are the dominant technology of the early 21st century: pretty well all aspects of economic, social and personal life are now unthinkable without them. In turn, computer hardware is controlled by software, that is, codes written in programming languages. Programming, the construction of software, is thus a fundamental activity, in which millions of people are engaged worldwide, and the teaching of programming is long established in international secondary and higher education. Yet, going on 70 years after the first computers were built, there is no well-established pedagogy for teaching programming. There has certainly been no shortage of approaches. However, these have often been driven by fashion, an enthusiastic amateurism or a wish to follow best industrial practice, which, while appropriate for mature professionals, is poorly suited to novice programmers. Much of the difficulty lies in the very close relationship between problem solving and programming. Once a problem is well characterised it is relatively straightforward to realise a solution in software. However, teaching problem solving is, if anything, less well understood than teaching programming. Problem solving seems to be a creative, holistic, dialectical, multi-dimensional, iterative process. While there are well established techniques for analysing problems, arbitrary problems cannot be solved by rote, by mechanically applying techniques in some prescribed linear order. Furthermore, historically, approaches to teaching programming have failed to account for this complexity in problem solving, focusing strongly on programming itself and, if at all, only partially and superficially exploring problem solving. Recently, an integrated approach to problem solving and programming called Computational Thinking (CT) (Wing, 2006) has gained considerable currency. CT has the enormous advantage over prior approaches of strongly emphasising problem solving and of making explicit core techniques. Nonetheless, there is still a tendency to view CT as prescriptive rather than creative, engendering scholastic arguments about the nature and status of CT techniques. Programming at heart is concerned with processing information but many accounts of CT emphasise processing over information rather than seeing then as intimately related. In this paper, while acknowledging and building on the strengths of CT, I argue that understanding the form and structure of information should be primary in any pedagogy of programming

An investigation as to how a computerised multimedia intervention could be of use for practitioners supporting learners with Autism Spectrum Disorder (ASD)

This practice-based action research investigation seeks to make a valuable, original and academic contribution to knowledge in the computing, language, communication and educational fields. The aim was to establish the therapeutic (language and communication skills) and educational (literacy and numeracy skills) use of individual tailored computer games for practitioners supporting learners (end-users) with Autism Spectrum Disorder (ASD). This was achieved through a continuous collaboration of cohorts of computing undergraduate students and academics (the development team) carrying out an assignment for a module designed and successfully led by this PhD student (the researcher). The researcher continually collaborated with practitioners (users – teaching staff and speech and language therapists in schools) of learners with ASD over many years. The researcher developed a Computerised Multimedia Therapeutic/Educational Intervention (CMT/EI) process, which used an iterative holistic Design-For-One approach for developing individual computer games. An action research methodology was adopted using methodological triangulation ‘quantitative’ and ‘qualitative’ data collection methods. This was to ascertain as to how tailor-made computerised multimedia games developed, could be evaluated by the users as being of therapeutic/educational use for their learners (end-users) with ASD. The researcher originated profiles to establish the diversity of each learner’s spectrum of therapeutic/educational autistic needs, preferences, capabilities, likes, dislikes and interests. The researcher orchestrated, collaborated and supervised the whole process from individual profiles completed by the practitioners, through to the profiles used as a baseline, by the development team, and to the designing, developing and evaluating iterative customised personalised computer games. Four hundred and sixty-four learners with ASD (end-users) and forty-nine practitioners (users) from nine educational establishments across the UK participated in this investigation. Two stages were carried out in an initial application procedure (with one school) and prototype procedure (with a further six schools and 2 educational establishments). Stage I - Planning, collection, organisation, Design-For-One approach and development. Stage II - Testing, Evaluation, Monitoring, Reflection and Maintenance. Optimistic ‘quantitative’ and ‘qualitative’ evidence emerged (using content analysis) from the implementation of games in the classroom and the practitioner’s therapeutic and educational evaluation of storyboards and games. The documented positive findings led to a conclusion that personalised games which had been developed over a ten-year period, showed to be of therapeutic/educational use to practitioners and their learners with ASD

Data Mining Applications in Higher Education and Academic Intelligence Management

Higher education institutions are nucleus of research and future development acting in a competitive environment, with the prerequisite mission to generate, accumulate and share knowledge. The chain of generating knowledge inside and among external organizations (such as companies, other universities, partners, community) is considered essential to reduce the limitations of internal resources and could be plainly improved with the use of data mining technologies. Data mining has proven to be in the recent years a pioneering field of research and investigation that faces a large variety of techniques applied in a multitude of areas, both in business and higher education, relating interdisciplinary studies and development and covering a large variety of practice. Universities require an important amount of significant knowledge mined from its past and current data sets using special methods and processes. The ways in which information and knowledge are represented and delivered to the university managers are in a continuous transformation due to the involvement of the information and communication technologies in all the academic processes. Higher education institutions have long been interested in predicting the paths of students and alumni (Luan, 2004), thus identifying which students will join particular course programs (Kalathur, 2006), and which students will require assistance in order to graduate. Another important preoccupation is the academic failure among students which has long fuelled a large number of debates. Researchers (Vandamme et al., 2007) attempted to classify students into different clusters with dissimilar risks in exam failure, but also to detect with realistic accuracy what and how much the students know, in order to deduce specific learning gaps (Piementel & Omar, 2005). The distance and on-line education, together with the intelligent tutoring systems and their capability to register its exchanges with students (Mostow et al., 2005) present various feasible information sources for the data mining processes. Studies based on collecting and interpreting the information from several courses could possibly assist teachers and students in the web-based learning setting (Myller et al., 2002). Scientists (Anjewierden et al., 2007) derived models for classifying chat messages using data mining techniques, in order to offer learners real-time adaptive feedback which could result in the improvement of learning environments. In scientific literature there are some studies which seek to classify students in order to predict their final grade based on features extracted from logged data ineducational web-based systems (Minaei-Bidgoli & Punch, 2003). A combination of multiple classifiers led to a significant improvement in classification performance through weighting the feature vectors. The author’s research directions through the data mining practices consist in finding feasible ways to offer the higher education institutions’ managers ample knowledge to prepare new hypothesis, in a short period of time, which was formerly rigid or unachievable, in view of large datasets and earlier methods. Therefore, the aim is to put forward a way to understand the students’ opinions, satisfactions and discontentment in the each element of the educational process, and to predict their preference in certain fields of study, the choice in continuing education, academic failure, and to offer accurate correlations between their knowledge and the requirements in the labor market. Some of the most interesting data mining processes in the educational field are illustrated in the present chapter, in which the author adds own ideas and applications in educational issues using specific data mining techniques. The organization of this chapter is as follows. Section 2 offers an insight of how data mining processes are being applied in the large spectrum of education, presenting recent applications and studies published in the scientific literature, significant to the development of this emerging science. In Section 3 the author introduces his work through a number of new proposed directions and applications conducted over data collected from the students of the Babes-Bolyai University, using specific data mining classification learning and clustering methods. Section 4 presents the integration of data mining processes and their particular role in higher education issues and management, for the conception of an Academic Intelligence Management. Interrelated future research and plans are discussed as a conclusion in Section 5.data mining,data clustering, higher education, decision trees, C4.5 algorithm, k-means, decision support, academic intelligence management