Experimental models for validating technology

Zelkowitz, Marvin V.; Wallace, Dolores R

doi:10.1109/2.675630

Cited by 359 publications

(170 citation statements)

References 5 publications

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“…This is the main reason that motivates an evaluation that combines both the educational and technological dimensions [Zel02], as already anticipated in the previous section. Similarly, the research methodology should also combine di erent methods to better take into account the multidisciplinary nature of the contributions.…”

Section: Methodsmentioning

confidence: 99%

“…This evaluation must assess whether the architecture meets the stakeholders' requirements, and overcomes the limitations of previous related works. Besides, since the objectives and contributions belong to both the CSCL and software integration elds, then both the educational and technological dimensions should be considered in this evaluation [Zel02]. Taking these considerations, the evaluation of this proposal comprises:…”

Section: Objectives and Contributionsmentioning

confidence: 99%

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GLUE!: An architecture for the integration of external tools in Virtual Learning Environments

Alario-Hoyos

Bote‐Lorenzo

Sánchez

et al. 2013

Computers & Education

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A mis padres. AcknowledgementsThe opening lines of this document serve to close this dissertation. In them, I want to sincerely thank all those who have made this work possible, and without whom this dissertation could not have been accomplished. First, I would like to thank my supervisors Miguel and Eduardo, with whom I share the credit of this work, and who have supported me throughout all these years in which we have worked together very closely. They believed in me from the very rst moment I ran into them, and they spent much time and e ort to make this dissertation succeed.Yannis is also one of the great minds that has most in uenced me over these years, and so I owe him much of this work. He insisted in showing me the right way, and he never hesitated to give me on-the-y advice or answers to my questions, despite his busy schedule. Of course, I have to specially thank Asen, Guillermo and Adolfo, who have very closely followed my dissertation, and with whom I had deep and intense discussions.Regarding the development tasks, I have been very lucky to be supported by David and Javier; without their work, this dissertation would have needed much more time, and the de- This fellowship gave me economic stability for almost three years and allowed me to make several short research stays abroad.Despite this ideal working environment, I could never have nished this dissertation without the support of my family and friends. Among them, special words of thanks are to Martín, iii Eduardo, Natalia, Miguel Ángel, Dani, Quini and many others who stood by my side to a greater or lesser extend throughout these years. Here, I would also like to remember all my college friends which led me to the front door of this PhD after a ve-year degree in which we all shared great times and memories.My parents, Julián and Tere, deserve my best words. They raised me in the values of sacri ce and knowledge. They always backed me up in every choice, and helped me overcome the di culties, and what is more important, they still keep doing it day by day. They are the ones that encourage me to get the best out of myself.Finally, my last words of gratitude must be for Bea, because she su ered this dissertation as I did. She was always by my side and never gave up on me. She gave me the strength to face this challenge, being even able to follow me to the end of the world. This dissertation proposes a middleware integration architecture called GLUE! (Group Learning Uniform Environment) that enables the lightweight integration of multiple existing external tools in multiple existing VLEs, overcoming these limitations. GLUE! fosters this integration by imposing few restrictions on VLE and tool providers, as well as by expecting an attainable e ort from developers. Besides, GLUE! facilitates the instantiation and enactment of collaborative learning situations within VLEs, leveraging the VLEs distinctive features for the management of users and groups. By means of GLUE!, practitioners may use external tools as if they were VLE built-in tools, and w...

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Section: Methodsmentioning

confidence: 99%

Section: Objectives and Contributionsmentioning

confidence: 99%

GLUE!: An architecture for the integration of external tools in Virtual Learning Environments

Alario-Hoyos

Bote‐Lorenzo

Sánchez

et al. 2013

Computers & Education

View full text Add to dashboard Cite

show abstract

“…As opposed to formal experiments, which often have a narrow focus and an emphasis on controlling context variables, case studies in software engineering test theories and collect data through observation of a project in an unmodified setting [25]. However, because the corporate, team, and project characteristics are unique to each case study, comparisons and generalizations of case study results are difficult and are subject to questions of external validity [14].…”

Section: Case Study Researchmentioning

confidence: 99%

“…Case studies are valuable because they involve factors that staged experiments generally do not exhibit, such as scale, complexity, unpredictability, and dynamism [18]. However, Zelkowitz and Wallace [25] reported that less than 10% of papers in the respected journals they examined involved a case study.…”

Section: Introductionmentioning

confidence: 99%

Motivations and measurements in an agile case study

Layman

Williams

Cunningham

2006

Journal of Systems Architecture

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With the recent emergence of agile software development technologies, the software community is awaiting sound, empirical investigation of the impacts of agile practices in a live setting. One means of conducting such research is through industrial case studies. However, there are a number of influencing factors that contribute to the success of such a case study. In this paper, we describe a case study performed at Sabre Airline Solutions evaluating the effects of adopting Extreme Programming (XP) practices with a team that had characteristically plan-driven risk factors. We compare the team's business-related results (productivity and quality) to two published sources of industry averages. Our case study found that the Sabre team yielded above-average post-release quality and average to above-average productivity.We discuss our experience in conducting this case study, including specifics of how data was collected, the rationale behind our process of data collection, and what obstacles were encountered during the case study. We also identify four factors that potentially impact the outcome of industrial case studies: availability of data, tool support, co-operative personnel and project status. We believe that recognizing and planning for these factors is essential to conducting industrial case studies, and that this information will be helpful to researchers and practitioners alike.

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“…Most notable in Software Engineering [8,9] and Computer-supported cooperative work [10,11]. Zelkowitz [8] identifies different forms of validation that can be found in the area of Software engineering and investigates the use of these different forms of validation in the Software Engineering literature in a quantitative study. In 2009 Zelkowitz repeated the study and reports the development over time [9].…”

Section: Empirical Studies Of Experimental Research In Computer Sciencementioning

confidence: 99%

On the Status of Experimental Research on the Semantic Web

Stuckenschmidt

Schuhmacher

Knopp

et al. 2013

Advanced Information Systems Engineering

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Abstract. Experimentation is an important way to validate results of Semantic Web and Computer Science research in general. In this paper, we investigate the development and the current status of experimental work on the Semantic Web. Based on a corpus of 500 papers collected from the International Semantic Web Conferences (ISWC) over the past decade, we analyse the importance and the quality of experimental research conducted and compare it to general Computer Science. We observe that the amount and quality of experiments are steadily increasing over time. Unlike hypothesised, we cannot confirm a statistically significant correlation between a paper's citations and the amount of experimental work reported. Our analysis, however, shows that papers comparing themselves to other systems are more often cited than other papers.

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Experimental models for validating technology

Cited by 359 publications

References 5 publications

GLUE!: An architecture for the integration of external tools in Virtual Learning Environments

GLUE!: An architecture for the integration of external tools in Virtual Learning Environments

Motivations and measurements in an agile case study

On the Status of Experimental Research on the Semantic Web

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