Dealing with abstraction: Case study generalisation as a method for eliciting design patterns

Developing a pattern language is a non-trivial problem. A critical requirement is a method to support pattern writers with abstraction, so as they can produce generalised patterns. In this paper, we address this issue by developing a structured process of generalisation. It is important that this process is initiated through engaging participants in identifying initial patterns, i.e. directly dealing with the 'cold-start' problem. We have found that short case study descriptions provide a productive 'way into' the process for participants. We reflect on a 1-year interdisciplinary pan-European research project involving the development of almost 30 cases and over 150 patterns. We provide example cases, detailing the process by which their associated patterns emerged. This was based on a foundation for generalisation from cases with common attributes. We discuss the merits of this approach and its implications for pattern development

Machine Learning and the Cognitive Basis of Natural Language

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An evolving approach to learning in problem solving and program development : the distributed learning model

Technological advances are paving the way for improvements in many sectors of society. The US education system needs to undergo a transformation of existing pedagogical methods to maximize utilization of new technologies. Traditional education has primarily been teacher driven, lectured-based in one location. Advances in technology are challenging existing paradigms by developing tools and educational environments that reach diverse learning styles and surpass the boundaries of current teaching methods. Distributed learning is an emerging paradigm today that has promise to contribute significantly to learning and improve overall academic success. This research first explores various systems that provide different modes of learning. The problem domain of this research is the difficulty novice programmers\u27 face when learning to program. This paper proposes how distributed learning can be used in a teaching environment to enrich learning and the impacts for the given problem domain

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System

What makes a great MOOC? An interdisciplinary analysis of student retention in online courses

Massive Open Online Courses (MOOCs) have experienced rapid expansion and gained significant popularity among students and educators. Although the broad acceptance of MOOCs, there is still a long way to go in terms of satisfaction of students\u27 needs, as witnessed in the extremely high drop-out rates. Working toward improving MOOCs, we employ the Grounded Theory Method (GTM) in a quantitative study and explore this new phenomenon. In particular, we present a novel analysis using a real-world data set with user-generated online reviews, where we both identify the student, course, platform, and university characteristics that affect student retention and estimate their relative effect. In the conducted analysis, we integrate econometric, text mining, opinion mining, and machine learning techniques, building both explanatory and predictive models, toward a more complete analysis. This study also provides actionable insights for MOOCs and education, in general, and contributes to the related literature discovering new findings

Hacks, Cracks, and Crime: An Examination of the Subculture and Social Organization of Computer Hackers

This dissertation examines both the subculture and social organization practices of computer hackers. The concept of normative orders (Herbert, 1998: 347) is used to explore hacker subculture in different contexts. To assess hacker social organization, I use Best and Luckenbill¿s (1994) framework of organizational sophistication as well as measures from Decker et al. (1998). The relationships between subculture, social organization, and behavior are explored as well. I collected three qualitative data sets to explore these issues, including posts from six on-line hacker discussion forums, in-depth interviews with active hackers, and field observations at the Defcon 12 hacker convention. These data were triangulated and used to investigate the research questions. The findings suggest the social world of hackers is shaped by five normative orders: technology, knowledge, commitment, categorization, and law. These orders are interrelated, and overwhelmingly influenced by technology. Furthermore, hackers tend to perform hacks alone, but have relatively loose social networks that are used to share information and introduce subcultural norms to new hackers. These networks are couched in a larger hacker community that provided access to a variety of resources and materials. Finally, this analysis demonstrates the dynamic relationships between subculture, social organization, and behavior. I found that subculture and social organization structure the nature of deviant relationships, norms, and behavior. At the same time, the nature of deviant acts appears to influence social organization and subculture. I also discuss the implications for research on computer hackers and crime generally

'Scraping' Reddit posts for academic research? Addressing some blurred lines of consent in growing internet-based research trend during the time of COVID-19.

The global scale of COVID-19 has constrained academics from conducting much person-facing research. Reactively, trend is increasing for digital-based methodologies capturing already existing online data. Scholars often "scrape" user-postings from internet forums using coding algorithms and text capture tools, before analysing data, drawing conclusions and publishing findings. The online social news aggregation and discussion website Reddit is a particularly rich source of data for researchers. The public nature of Reddit materials may suggest rationale for user-data to be replicated, analysed and archived; indefinitely and in multiple locations, for scholarly research. However, this position overlooks several key ethical considerations. This paper presents an overview and explanation of Reddit, followed by an exploration of studies that use Reddit-acquired data. Arising ethical issues are discussed, and solutions to salient dilemmas presented. This is to enhance awareness of potential problems and improve protections for those whose data is unknowingly used for research