Using collaborative and activity-based learning for engaging IT students

Active and collaborative learning are becoming essential strategies to attract, engage and retain students. These methods have been adopted within the Science and Engineering Faculty of Queensland University of Technology for use in its Science, Information Technology and Engineering degrees. This paper describes the adoption and application of these techniques in a specific first year unit in a new Bachelor of Information Technology degree which has majors in Computer Science and Information Systems. The paper reports on the design, development and implementation of this foundation subject and discusses how it uses active and collaborative learning to teach design thinking through a series of design challenges, and how it uses critiquing and reflection to ensure that students become more aware of design and team processes.

Green IT segment analysis : an academic literature review

Research on Green Information Technology (IT) is becoming a prevalent research theme in Green Information Systems (IS) research. This article provides a review of 98 papers published on Green IT between 2007−2013 to facilitate future research and to provide a retrospective analysis of existing knowledge and gaps thereof. While some researchers have discussed phenomena such as Green IT, motivation of Green IT and the Green IT adoption lifecycle, others have researched the importance of Green IT implementation within the organisational and individual level. Throughout the literature, scholars are trying to portray a constructive relationship between IT and the environment. Through our analysis, we can provide an assessment of the status of information systems literature on Green IT and, we provide taxonomy of segments of Green IT publications. Future research opportunities are identified based on the review.

A review selection method using product feature taxonomy

As of today, online reviews have become more and more important in decision making process. In recent years, the problem of identifying useful reviews for users has attracted significant attentions. For instance, in order to select reviews that focus on a particular feature, researchers proposed a method which extracts all associated words of this feature as the relevant information to evaluate and find appropriate reviews. However, the extraction of associated words is not that accurate due to the noise in free review text, and this affects the overall performance negatively. In this paper, we propose a method to select reviews according to a given feature by using a review model generated based upon a domain ontology called product feature taxonomy. The proposed review model provides relevant information about the hierarchical relationships of the features in the review which captures the review characteristics accurately. Our experiment results based on real world review dataset show that our approach is able to improve the review selection performance according to the given criteria effectively.

Enhancing interdisciplinary learning with a learning management system

Interdisciplinary learning is a form of knowledge production that is increasingly being embraced as an educational approach in higher education. A role of information and communication technologies (ICT) is to enhance interdisciplinary learning. Issues surrounding the mix of interdisciplinary pedagogic methodologies and emerging digital technologies are worthy of investigation. In this paper, the authors report the findings of a study that examined student perceptions of an interdisciplinary course on information technology (IT) and visual design that utilized a learning management system. Using questionnaire instrumentation, the authors sought the perceptions of first-year university students enrolled in a newly formed interdisciplinary IT course. Results indicate that ICT-based interdisciplinary learners prefer a self-directed and collaborative instructional modality, as well as teacher presence and interventions in the online environment. The types of student participation can significantly influence how students perceive ICT-based interdisciplinary learning design.

A business perspective of legacy systems

Abstract Legacy information systems evolved incrementally in response to changes in business strategy and information technology. Organizations are now being forced to change much more radically and quickly than previously and this change places new demands on information systems. Legacy information systems are usually considered from a technical perspective, addressing issues such as age, complexity, maintainability, design and technology. We wish to demonstrate that the business dimension to legacy information systems, represented by the organisation structure, business processes and procedures that are bound up in the design and operation of the existing IT systems, is also significant. This paper identifies the important role of legacy information systems in the formation of new strategies. We show that the move away from a stable to an unstable business environment accelerates the rate of change. Furthermore, the gap between what the legacy information systems can deliver and the strategic vision of the organization widens when the legacy information systems are unable to adapt to meet the new requirements. An analysis of fifteen case studies provides evidence that legacy information systems include business and technical dimensions and that the systems can present problems when there is a misalignment between the strategic vision of the business, the IT legacy and the old business model embodied in the legacy.

Constructing, deconstructing and negotiating the boundaries of digital cultures

The focus of this special issue is upon notions, and experiences of, the erosion and blurring of the boundaries constructed between work, play, the public and private as related to digital media. We seek to increase knowledge regarding the contemporary experiences and potential reshaping of the boundaries and structures of existing social organisation, and the altering of the ways in which people learn to experience life. We know that even as access to digital technologies continues to vary based on age, gender, nationality, residence, ethnicity, work, and other key aspects of society, it is clear the presence and uses of these digital technologies are increasingly important features of contemporary life...

Dataset validation within big data security architecture

In recent years, increasing focus has been made on making good business decisions utilizing the product of data analysis. With the advent of the Big Data phenomenon, this is even more apparent than ever before. But the question is how can organizations trust decisions made on the basis of results obtained from analysis of untrusted data? Assurances and trust that data and datasets that inform these decisions have not been tainted by outside agency. This study will propose enabling the authentication of datasets specifically by the extension of the RESTful architectural scheme to include authentication parameters while operating within a larger holistic security framework architecture or model compliant to legislation.

Twitter data: what do they represent?

Scholarly research into the uses of social media has become a major area of growth in recent years, as the adoption of social media for public communication itself has continued apace. While social media platforms provide ready avenues for data access through their Application Programming interfaces, it is increasingly important to think through exactly what these data represent, and what conclusions about the role of social media in society the research which is based on such data therefore enables. This article explores these issues especially for one of the currently leading social media platforms: Twitter.

Locality-sensitive hashing for protein classification

Determination of sequence similarity is a central issue in computational biology, a problem addressed primarily through BLAST, an alignment based heuristic which has underpinned much of the analysis and annotation of the genomic era. Despite their success, alignment-based approaches scale poorly with increasing data set size, and are not robust under structural sequence rearrangements. Successive waves of innovation in sequencing technologies – so-called Next Generation Sequencing (NGS) approaches – have led to an explosion in data availability, challenging existing methods and motivating novel approaches to sequence representation and similarity scoring, including adaptation of existing methods from other domains such as information retrieval. In this work, we investigate locality-sensitive hashing of sequences through binary document signatures, applying the method to a bacterial protein classification task. Here, the goal is to predict the gene family to which a given query protein belongs. Experiments carried out on a pair of small but biologically realistic datasets (the full protein repertoires of families of Chlamydia and Staphylococcus aureus genomes respectively) show that a measure of similarity obtained by locality sensitive hashing gives highly accurate results while offering a number of avenues which will lead to substantial performance improvements over BLAST..

Exploring the impact of shared domain knowledge on strategic alignment in the Australian public sector

In this age of ever-increasing information technology (IT) driven environments, governments/or public sector organisations (PSOs) are expected to demonstrate the business value of the investment in IT and take advantage of the opportunities offered by technological advancements. Strategic alignment (SA) emerged as a mechanism to bridge the gap between business and IT missions, objectives, and plans in order to ensure value optimisation from investment in IT and enhance organisational performance. However, achieving and sustaining SA remains a challenge requiring even more agility nowadays to keep up with turbulent organisational environments. The shared domain knowledge (SDK) between the IT department and other diverse organisational groups is considered as one of the factors influencing the successful implementation of SA. However, SDK in PSOs has received relatively little empirical attention. This paper presents findings from a study which investigated the influence of SDK on SA within organisations in the Australian public sector. The developed research model examined the relationship of SDK between business and IT domains with SA using a survey of 56 public sector professionals and executives. A key research contribution is the empirical demonstration that increasing levels of SDK between IT and business groups leads to increased SA.

Aspect-based opinion extraction from customer reviews

Text is the main method of communicating information in the digital age. Messages, blogs, news articles, reviews, and opinionated information abounds on the Internet. People commonly purchase products online and post their opinions about purchased items. This feedback is displayed publicly to assist others with their purchasing decisions, creating the need for a mechanism with which to extract and summarize useful information for enhancing the decision-making process. Our contribution is to improve the accuracy of extraction by combining different techniques from three major areas, named Data Mining, Natural Language Processing techniques and Ontologies. The proposed framework sequentially mines product’s aspects and users’ opinions, groups representative aspects by similarity, and generates an output summary. This paper focuses on the task of extracting product aspects and users’ opinions by extracting all possible aspects and opinions from reviews using natural language, ontology, and frequent “tag” sets. The proposed framework, when compared with an existing baseline model, yielded promising results.

Component modeling for SCADA network mapping

Supervisory Control and Data Acquisition systems (SCADA) are widely used to control critical infrastructure automatically. Capturing and analyzing packet-level traffic flowing through such a network is an essential requirement for problems such as legacy network mapping and fault detection. Within the framework of captured network traffic, we present a simple modeling technique, which supports the mapping of the SCADA network topology via traffic monitoring. By characterizing atomic network components in terms of their input-output topology and the relationship between their data traffic logs, we show that these modeling primitives have good compositional behaviour, which allows complex networks to be modeled. Finally, the predictions generated by our model are found to be in good agreement with experimentally obtained traffic.

Identifying product families using data mining techniques in manufacturing paradigm

Identifying product families has been considered as an effective way to accommodate the increasing product varieties across the diverse market niches. In this paper, we propose a novel framework to identifying product families by using a similarity measure for a common product design data BOM (Bill of Materials) based on data mining techniques such as frequent mining and clus-tering. For calculating the similarity between BOMs, a novel Extended Augmented Adjacency Matrix (EAAM) representation is introduced that consists of information not only of the content and topology but also of the fre-quent structural dependency among the various parts of a product design. These EAAM representations of BOMs are compared to calculate the similarity between products and used as a clustering input to group the product fami-lies. When applied on a real-life manufacturing data, the proposed framework outperforms a current baseline that uses orthogonal Procrustes for grouping product families.