scholarly journals Arvutiteaduse kaasamine humanitaarharidusse / Integrating Computation into Humanities Education

2020 ◽  
Vol 21 (26) ◽  
Author(s):  
Mikko Tolonen ◽  
Eetu Mäkelä ◽  
Jani Marjanen ◽  
Tuuli Tahko

Digihumanitaaria-alane haridus peaks keskenduma selgelt määratletud allvaldkondadele, mis on mõttekad kohalikus kontekstis. Otsustasime Helsinki ülikoolis pöörata peatähelepanu interdistsiplinaarse digihumanitaaria valdkonnale. Käesolevas artiklis näitame, et digihumanitaaria-alaste uuringute edukaks läbiviimiseks on oluline interdistsiplinaarsus, ning väidame, et seda on digihumanitaarharidusse kõige parem liita humanitaarteaduslikel ühisuuringutel põhineva projektipõhise õppe kaudu.   Digital Humanities can be regarded as a complex landscape of partially overlapping and variously connected domains, including e.g. computational humanities, multimodal cultural heritage and digital cultural studies and cultural analytics (Svensson 2010). Yet, as a precondition for setting up an educational programme within an academic institution, one needs to be able to delineate the discipline being taught (Sinclair and Gouglas 2002, 168) in terms of a coherent academic identity, interrelations between courses, and skills that graduates will attain. Therefore any locally situated educational enterprise needs to focus on those areas of DH that can be reasonably tied to research conducted at the hosting institution. At the University of Helsinki, we have put particular effort into defining our educational profile in interdisciplinary computational humanities, taught both as a minor studies module (30 ECTS) and an MA track (120 ECTS). Because of the complexities of humanities data and the lack of standard protocols for dealing with it, it is very difficult for a humanities scholar to apply computational and statistical methods in a trustworthy manner without specialist help. At the same time, neither can computer scientists, statisticians or physicists answer humanities questions on their own, even if they understand the algorithms. Our solution to this problem is to argue that computational humanities research, and as a consequence also digital humanities education, should be fundamentally interdisciplinary endeavours, where statisticians, computer scientists and scholars in the humanities work together to develop, test and apply the methodology to solve humanities questions. Our version of computational humanities thus exists precisely and solely at the intersection of humanities and computer science rather than as separate from either of them. Consequently, people participating in this field should primarily anchor their academic profile to one of the parent disciplines instead of trying to find an identity purely in the middle. This is reflected in our educational approach. We provide students in the humanities with instruction on how to use ready-made tools, workflows or applied programming, granting them a general digital competency and agency, but our focus is on developing a broader literacy regarding data and computational methods. By learning to contextualize their skills within the field of computational humanities as a whole, the humanities students also learn to assess where their personal boundaries lie, and where an interdisciplinary collaboration is required instead. In this context, their computational literacy also helps them converse with the methodological experts coming to the field from computer science. In this interdisciplinary setting, we take a project-based approach to learning, tying teaching to actual research projects being conducted at the faculty. This approach both harnesses the varying competencies of our students and provides an excellent basis for learning interdisciplinary collaboration (Bell 2010). The culmination of our project courses is the Digital Humanities Hackathon, a multidisciplinary collaboration between the University of Helsinki digital humanities programme and the data science programmes at the Department of Computer Science and Aalto University. For researchers and students from computer and data sciences, the Hackathon is an opportunity to test their abstract knowledge against complex real-life problems; for people from the humanities and social sciences, it shows what is possible to achieve with such collaboration. For both, the Hackathon gives the experience of working with people from different backgrounds as part of an interdisciplinary team and simulates group work in such professional settings as the students may find themselves in after graduation, acculturating them to work outside academia (cf. Rockwell and Sinclair 2012). Our conception of computational humanities as intrinsically collaborative and interdisciplinary is based on the realisation that the traditional, single-author research culture of the humanities is a hindrance to successfully integrating computational approaches into humanities research. We feel that our formulation of the field has the power to contribute to the renewal of research culture and education within the humanities in general, adding value to traditional disciplinary curricula, as well as equipping students with skills relevant in the workplace.

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Cornelius König ◽  
Andrew Demetriou ◽  
Philipp Glock ◽  
Annemarie Hiemstra ◽  
Dragos Iliescu ◽  
...  

This article is based on conversations from the project “Big Data in Psychological Assessment” (BDPA) funded by the European Union, which was initiated because of the advances in data science and artificial intelligence that offer tremendous opportunities for personnel assessment practice in handling and interpreting this kind of data. We argue that psychologists and computer scientists can benefit from interdisciplinary collaboration. This article aims to inform psychologists who are interested in working with computer scientists about the potentials of interdisciplinary collaboration, as well as the challenges such as differing terminologies, foci of interest, data quality standards, approaches to data analyses, and diverging publication practices. Finally, we provide recommendations preparing psychologists who want to engage in collaborations with computer scientists. We argue that psychologists should proactively approach computer scientists, learn computer scientific fundamentals, appreciate that research interests are likely to converge, and prepare novice psychologists for a data-oriented scientific future.


Author(s):  
Subrata Dasgupta

In 1962, purdue University in West Lafayette, Indiana, in the United States opened a department of computer science with the mandate to offer master’s and doctoral degrees in computer science. Two years later, the University of Manchester in England and the University of Toronto in Canada also established departments of computer science. These were the first universities in America, Britain, and Canada, respectively, to recognize a new academic reality formally—that there was a distinct discipline with a domain that was the computer and the phenomenon of automatic computation. There after, by the late 1960s—much as universities had sprung up all over Europe during the 12th and 13th centuries after the founding of the University of Bologna (circa 1150) and the University of Paris (circa 1200)—independent departments of computer science sprouted across the academic maps on North America, Britain, and Europe. Not all the departments used computer science in their names; some preferred computing, some computing science, some computation. In Europe non-English terms such as informatique and informatik were used. But what was recognized was that the time had come to wean the phenomenon of computing away from mathematics and electrical engineering, the two most common academic “parents” of the field; and also from computer centers, which were in the business of offering computing services to university communities. A scientific identity of its very own was thus established. Practitioners of the field could call themselves computer scientists. This identity was shaped around a paradigm. As we have seen, the epicenter of this paradigm was the concept of the stored-program computer as theorized originally in von Neumann’s EDVAC report of 1945 and realized physically in 1949 by the EDSAC and the Manchester Mark I machines (see Chapter 8 ). We have also seen the directions in which this paradigm radiated out in the next decade. Most prominent among the refinements were the emergence of the historically and utterly original, Janus-faced, liminal artifacts called computer programs, and the languages—themselves abstract artifacts—invented to describe and communicate programs to both computers and other human beings.


i-com ◽  
2012 ◽  
Vol 11 (1) ◽  
pp. 3-6 ◽  
Author(s):  
Reinhard Keil ◽  
Martin Wessner

Summary Research in e-learning requires collaboration within the various fields of computer science as well as between computer scientists and a variety of other disciplines. The article lists important prerequisites and challenges of interdisciplinary collaboration.


Author(s):  
Andrew McDavid ◽  
Anthony M. Corbett ◽  
Jennifer L. Dutra ◽  
Andrew G. Straw ◽  
David J. Topham ◽  
...  

Abstract Introduction: In clinical and translational research, data science is often and fortuitously integrated with data collection. This contrasts to the typical position of data scientists in other settings, where they are isolated from data collectors. Because of this, effective use of data science techniques to resolve translational questions requires innovation in the organization and management of these data. Methods: We propose an operational framework that respects this important difference in how research teams are organized. To maximize the accuracy and speed of the clinical and translational data science enterprise under this framework, we define a set of eight best practices for data management. Results: In our own work at the University of Rochester, we have strived to utilize these practices in a customized version of the open source LabKey platform for integrated data management and collaboration. We have applied this platform to cohorts that longitudinally track multidomain data from over 3000 subjects. Conclusions: We argue that this has made analytical datasets more readily available and lowered the bar to interdisciplinary collaboration, enabling a team-based data science that is unique to the clinical and translational setting.


First Monday ◽  
2011 ◽  
Author(s):  
Michael Simeone ◽  
Jennifer Guiliano ◽  
Rob Kooper ◽  
Peter Bajcsy

This paper explores infrastructure supporting humanities–computer science research in large–scale image data by asking: Why is collaboration a requirement for work within digital humanities projects? What is required for fruitful interdisciplinary collaboration? What are the technical and intellectual approaches to constructing such an infrastructure? What are the challenges associated with digital humanities collaborative work? We reveal that digital humanities collaboration requires the creation and deployment of tools for sharing that function to improve collaboration involving large–scale data repository analysis among multiple sites, academic disciplines, and participants through data sharing, software sharing, and knowledge sharing practices.


2020 ◽  
Author(s):  
Ulrich Konrad

Currently, the qualitative spectrum of methods in the philological sciences is being substantially expanded, with far-reaching implications, through the integration of the empirical, quantitative, and evaluative possibilities of the Digital Humanities. The example of the planning and establishment of „Kallimachos,“ the Center for Philology and Digitality (ZPD) at the University of Würzburg, demonstrates how a research center in the field of interplay between the humanities and cultural studies, digital humanities, and computer science can bring about a surge of change by providing in-depth insights into each other‘s subjects and ways of thinking. It not only brings with it a new view of the epistemological interests of philology, its questions, its canon, and its key concepts, but also makes computer science aware of the ‚recalcitrance‘ of humanities subjects and thus confronts it with new tasks. The ZPD is the result of a systematic reflection on the digital transformation of philology, with its traditional focus on editing and analyzing, in order to advance this development both in terms of content and methodology. For example, the formation of linguistic conventions in speaking and writing about music in 19th-century composers‘ texts and in music journals would be an ideal subject for the application of digital methods of analysis and the development of new research questions based on them. Research networks that jointly develop and rethink methods on the level of data structures across disciplines are likely to be a proven means of preserving our own discipline in the future, even if this may occasionally be a relationship borne more by reason than by love.


2011 ◽  
Vol 11 (1-2) ◽  
pp. 93-108 ◽  
Author(s):  
Eric Chuk ◽  
Rama Hoetzlein ◽  
David Kim ◽  
Julia Panko

We report on the experience of creating a socially networked system, the Research-oriented Social Environment (RoSE), for representing knowledge in the form of relationships between people, documents, and groups. Developed as an intercampus, interdisciplinary project of the University of California, this work reflects on a collaboration between scholars in the humanities, software engineering, and information studies by providing an opportunity not only to synthesize different disciplinary perspectives, but also to interrogate and challenge the assumptions each brings to team-based design projects in the digital humanities. This work examines socially networked knowledge as both content and methodology for collaboration, calling for further critique and future investigation of epistemological questions in models of social networks.


10.28945/3155 ◽  
2007 ◽  
Author(s):  
Matti Tedre

The diversity and interdisciplinarity of computer science and the multiplicity of its uses in other sciences make it hard to define computer science and to prescribe how computer science should be carried out. The diversity of computer science also causes friction between computer scientists from different branches. Computer science curricula, as they stand, have been criticized for being unable to offer computer scientists proper methodological training or a deep understanding of different research traditions. At the Department of Computer Science and Statistics at the University of Joensuu we decided to include in our curriculum a course that offers our students an awareness of epistemological and methodological issues in computer science, and we wanted to design the course to be meaningful for practicing computer scientists. In this article the needs and aims of our course on the philosophy of computer science are discussed, and the structure and arrangements—the whys, whats, and hows—of that course are explained. The course, which is given entirely on-line, was designed for advanced graduate or postgraduate computer science students from two Finnish universities: the University of Joensuu and the University of Kuopio. The course has four relatively broad themes, and all those themes are tied to the students’ everyday work or their own research topics. I have prepared course readings about each of those four themes. The course readings describe, in a compact and simple form, the cruces of the topics that are discussed in the course. The electronic version of the course readings includes hyperlinks to a large number of articles that are available on-line. The course readings are publicly available on the course home page, and they are licensed under the creative commons license.


2021 ◽  
Vol 11 (5) ◽  
pp. 37
Author(s):  
Chia Hung Kao

Traditional mathematics curriculum faces several issues nowadays. The gap between course materials and students’ real-life mathematical experiences, the scattering of knowledge in different courses, and the lack of mathematics applications to other subjects all hinder the learning of students. The emerg-ing trends in data science, machine learning, and artificial intelligence also impel higher education to enrich and refine mathematics education. In order to better incubate students for future, the experience of enriching undergrad-uate mathematics curriculum with computer science courses is introduced in this study. The curriculum is designed and implemented for students who major in applied mathematics to better stimulate the learning, participation, exercise, and innovation. It provides students with comprehensive theoretical and practical knowledge for the challenges and industrial requirements now-adays. Evaluations, major findings, and lessons learned from three refined courses are discussed for more insight into the following deployment and re-finement of the curriculum.


Mousaion ◽  
2019 ◽  
Vol 37 (1) ◽  
Author(s):  
Olefhile Mosweu

Most curriculum components of archival graduate programmes consist of contextual knowledge, archival knowledge, complementary knowledge, practicum, and scholarly research. The practicum, now commonly known as experiential learning in the global hub, is now widely accepted in library and information studies (LIS) education as necessary and important. It is through experiential learning that, over and above the theoretical aspects of a profession, students are provided with the opportunity to learn by doing in a workplace environment. The University of Botswana’s Master’s in Archives and Records Management (MARM) programme has a six weeks experiential learning programme whose purpose is to expose prospective archivists and/or records managers to the real archival world in terms of practice as informed by archival theory. The main objective of the study was to determine the extent to which the University of Botswana’s experiential learning component exposes students to real-life archival work to put into practice theoretical aspects learnt in the classroom as intended by the university guidelines. This study adopted a qualitative research design and collected data through interviews from participants selected through purposive and snowball sampling strategies. Documentary review supplemented the interviews. The data collected were analysed thematically in line with research objectives. The study determined that experiential learning does indeed expose students to the real world of work. It thus helps to bridge the gap between archival theory and practice for students without archives and records management work experience. For those with prior archival experience, experiential learning does not add value. This study recommends that students with prior archives and records management experience should rather, as an alternative to experiential learning, undertake supervised research, and write a research essay in a chosen thematic area in archives and records management.


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