In order to fully realise the value of open data researchers must first address the quality of the datasets

There has been a phenomenal increase in the availability of data over the last decade. Open data is provided as a means of empowering users with information and in the hope of sparking innovation and increased efficiency in governments and businesses. However, in spite of the many success stories based on the open data paradigm, concerns remain over the quality of such datasets. Marta Indulska and Shazia Sadiq argue that in order to facilitate more effective and efficient realisation of value from open data, research must reach a shared consensus on the definition of data quality dimensions, provide methods and guidelines for assessing the potential usefulness of open datasets using exploratory tools and techniques, and develop rigorous theoretical underpinnings on effective use of open data

On Managing Process Variants as an Information Resource

Many business solutions provide best practice process templates, both generic as well as for specific industry sectors. However, it is often the variance from template solutions that provide organizations with intellectual capital and competitive differentiation. Although variance must comply with various contractual, regulatory and operational constraints, it is still an important information resource, representing preferred work practices. In this paper, we present a modeling framework that is conducive to constrained variance, by supporting user driven process adaptations. The focus of the paper is on providing a means of utilizing the adaptations effectively for process improvement through effective management of the process variants repository (PVR). In particular, we will provide deliberations towards a facility to provide query functionality for PVR that is specifically targeted for effective search and retrieval of process variants

Effectiveness of Modular Approach in Teaching at University Level

The key purpose of this research was to find out the effectiveness of modular approach in teaching in order to assess the student learning, performance and achievement and to determine whether the modular teaching is more effective than traditional methods. The study was experimental type. Equivalent group study design was used. Population was university students of Master in educational planning and management. Sample size was consisted on 30 students. The data were collected from both groups(controlled and experimental) analyzed and interpreted by using mean, standard deviation and t-test through the use of statistical package SPSS. The result’ scores were in the favor of usage of modular teaching approach. So it is recommended that the modular approach should be widely used at various levels of education. Key words: Modular Approach, Self learning, Individualized Instruction

Impact of personalized recommendation and social comparison on learning behaviours and outcomes

ELearning suffers from the lack of face-to-face interaction and can deprive learners from the benefits of social interaction and comparison. In this paper we present the results of a study conducted for the impact of social comparison. The study was conducted by collecting students&rsquo; engagement with an eLearning tool, the attendance, and grades scored by students at specific milestones and presented these metrics to students as feedback using Kiviat charts. The charts were complemented with appropriate recommendations to allow them to adapt their study strategy and behaviour. The study spanned over 4 semesters (2 with and 2 without the Kiviats) and the results were analysed using paired T tests to test the pre and post results on topics covered by the eLearning tool. Survey questionnaires were also administered at the end for qualitative analysis. The results indicated that the Kiviat feedback with recommendation had positive impact on learning outcomes and attitudes.<br /

Towards Understanding Learner Experiences In Elearning Tools

An understanding of how learners interact with eLearning tools and the relationship of different forms of interaction on subsequent learning outcomes is fundamental to improved learning outcomes as well as the effectiveness of eLearning tools. In this paper our main objective is to present methods to extract and analyse some crucial experiences and patterns, from an eLearning tool, that have significant effect on students learning. The proposed methods are presented in the context of a study conducted with undergraduates and postgraduates taking a course inan information system discipline. We demonstrate how the extracted experiences and patterns can be used as feedback to learners to improve learning. Academicians and lecturers can also use the analysis as a gauging instrument to measure the effectiveness of the eLearning tool thereby allowing the tool and learning practices to be improved

CROSS-DISCIPLINARY COLLABORATIONS IN DATA QUALITY RESEARCH

Data Quality has been the target of research and development for over four decades, and due to its cross-disciplinary nature has been approached by business analysts, solution architects, database experts and statisticians to name a few. As data quality increases in importance and complexity, there is a need to motivate the exploitation of synergies across diverse research communities in order to form holistic solutions that span across its organizational, architectural and computational aspects. As a first step towards bridging gaps between the various research communities, we undertook a comprehensive literature study of data quality research published in the last two decades. In this study we considered a broad range of Information System (IS) and Computer Science (CS) publication outlets. The main aims of the study were to understand the current landscape of data quality research, create better awareness of (lack of) synergies between various research communities, and, subsequently, direct attention towards holistic solutions. In this paper, we present a summary of the findings from the study that outline the overlaps and distinctions between the two communities from various points of view, including publication outlets, topics and themes of research, highly cited or influential contributors and strength and nature of co-authorship networks

Discovering Data Quality Problems

Existing methodologies for identifying dataquality problems are typically user-centric, where dataquality requirements are first determined in a top-downmanner following well-established design guidelines, orga-nizational structures and data governance frameworks. In thecurrent data landscape, however, users are often confrontedwith new, unexplored datasets that they may not have anyownership of, but that are perceived to have relevance andpotential to create value for them. Such repurposed datasetscan be found in government open data portals, data marketsand several publicly available data repositories. In suchscenarios, applying top-down data quality checkingapproaches is not feasible, as the consumers of the data haveno control over its creation and governance. Hence, dataconsumers – data scientists and analysts – need to beempowered with data exploration capabilities that allowthem to investigate and understand the quality of suchdatasets to facilitate well-informed decisions on their use.This research aims to develop such an approach fordiscovering data quality problems using generic exploratorymethods that can be effectively applied in settings where datacreation and use is separated. The approach, named LANG,is developed through a Design Science approach on the basisof semiotics theory and data quality dimensions. LANG isempirically validated in terms of soundness of the approach,its repeatability and generalizability