Understanding Enterprise Architecture with Topic Modeling - Preliminary Research based on Journal Articles

Nardello, Marco; Møller, Charles; Gøtze, John

doi:10.5220/0006693106400648

Cited by 3 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Filtering for common words is also essential to screen out journal words that are invariant over time, such as “science,” ‘'student,” “education,” and “learn.” (It is no great discovery to find that the journal Science Education has focused on students learning science over the past 100 years.) This technique is also frequently used in LDA‐based research, where researchers often remove words appearing in anywhere from 25% to 99% of documents in their data set (Cvitanic et al, 2016; Denny & Spirling, 2018; Grimmer & Stewart, 2013; Hopkins & King, 2010; Jacobi et al, 2016; Larsen & Thorsrud, 2019; Nardello et al, 2018; Syed & Spruit, 2018). We note that this step must be handled carefully, as the words removed are likely to be quite central to certain topics of interest.…”

Section: Methodsmentioning

confidence: 99%

How has Science Education changed over the last 100 years? An analysis using natural language processing

2021

View full text Add to dashboard Cite

For well over a century, the journal Science Education has been publishing articles about the teaching and learning of science. These articles represent more than just a repository of past work: they have the potential to offer insights into both the history of science education as well as well as the dynamics of field‐specific change. It can be difficult, however, for educators, researchers, reformers, and policymakers to grasp the nuances of over 100 years of scholarship given the overwhelming amount of textual material. To address this problem, we have used latent Dirichlet allocation, an automated machine‐learning algorithm from the field of natural language processing, to perform an automated literature review and classification of the corpus of work in Science Education. Using this technique, we have classified research in the journal into 21 distinct topics, falling into three thematic groups: science content topics, teaching‐focused topics, and student‐focused topics. We have also quantified the rise and fall of these topics and groups over time, and used them to begin to extract insight into the development of the field, including the effects of national policy changes on topics of interest to the research community, the interrelationships between different research topics, and the effects of intellectual cross‐pollination. Based on this analysis, we argue that this technique shows great promise for even larger‐scale analyses of educational literature and other textual data.

show abstract

Section: Methodsmentioning

confidence: 99%

How has Science Education changed over the last 100 years? An analysis using natural language processing

2021

View full text Add to dashboard Cite

show abstract

“…These research questions aim to acquire a thorough understanding of BITA from the perspective of EA, to discover weak points in the status quo, and to identify future research directions. Nardello et al developed a topic model to help structure the EA research field and enable EA to evolve coherently [125]. In this study, the authors presented about 360 identified topics in EA literature and their evolution over time.…”

Section: Related Workmentioning

confidence: 99%

A Systematic Literature Review on Enterprise Architecture Visualization Methodologies

et al. 2020

View full text Add to dashboard Cite

EA (Enterprise Architecture) visualization methodologies have been explored by researchers and engineers to conduct EA modeling. The objectives of EA modeling are to clarify enterprise strategies, visualize business processes, and model information systems to manage resources, improve organization structure, adjust information strategy, and create new business value. Therefore, EA models can be broadly applied in various fields. For example, the applications include business modeling, information system architecture design, technology infrastructure configuration, software maintenance, and system security analysis. As the primary source of information, EA models are of paramount importance to researchers, architects, and developers. However, up to now, the purpose and means of these EA visualization methods have never been systematically analyzed and discussed, and a generalized EA visualization methodology with the ability to meet different demands is needed. The paper narrows this gap by conducting a systematic literature review on enterprise architecture visualization methodologies. In this study, 112 papers are retrieved by a manual search in 5 academic databases, a systematic literature review on EA visualization is explained to show a systematized category of visualization approaches, and then a general visualization approach is proposed by systematically reviewing the papers. Finally, the paper is concluded by discussing the contributions and limitations of the study.

show abstract

Satisfaction with Research-Based Learning and Academic Performance by Big Data Analysis

Torres-Delgado,

Ramos-Pulido,

Hernández-Gress

2024

Ieee Iceib 2024

View full text Add to dashboard Cite

Understanding Enterprise Architecture with Topic Modeling - Preliminary Research based on Journal Articles

Cited by 3 publications

References 26 publications

How has Science Education changed over the last 100 years? An analysis using natural language processing

How has Science Education changed over the last 100 years? An analysis using natural language processing

A Systematic Literature Review on Enterprise Architecture Visualization Methodologies

Satisfaction with Research-Based Learning and Academic Performance by Big Data Analysis

Contact Info

Product

Resources

About