Kolegij
The aim of the course is to introduce students to the innovative methodology of using Big Data approaches in the social sciences and humanities (DH) and to master the basic methods of DH.
Course content:
The course covers the history, theory and practice of digital humanities and social sciences, paying special attention to the ways in which digital humanities and social sciences transform contemporary research.
Students will become familiar with the current state of development in the field of digital humanities and social sciences.
Students will become familiar with a series of discussions in the field of DH.
Students will develop a digital project proposal.
Students will become familiar with the resources available to support work in the field of Digital Humanities and master the basic methods of DH.
1. Master the basic concepts of DH 2. Use an interdisciplinary approach in work; understand the scientific achievements of Digital Humanities and apply them in an academic environment; 3. Acquire the ability to independently conduct research using the DH method, collect and select information, independently assess the importance of each source, and have a critical attitude towards data sources. 4. Write a clear and structured written work. 5. Give a clear and structured oral presentation. 6. Participate actively and with arguments in the discussion. 7. Acquire the necessary skills in working with DH methods (Text-Mining; Google Trends; Google Ngram, Social
Network analysis).
Gold, Matthew K, and Lauren F. Klein. Debates in the Digital
Humanities: 2016, Minneapolis: University of Minnesota Press, 2016
— available for free online at http://dhdebates.gc.cuny.edu.; Anne
Burdick, Johanna Drucker, Peter Lunenfeld, Todd Presner, and Jeffrey
Schnapp. Digital_Humanities (MIT Press, 2012)
Tado Jurić, Big (Crisis) Data in Social Sciences and Humanities: Predicting Crises, Verlag Dr. Kovač, Hamburg.
Debates in the Digital Humanities: 2016, edited by
Lauren F. Klein and Matthew K. Gold, University of Minnesota Press,
2016, p. 54.; Noble, Safiya. Algorithms of Oppression: How Search Engines
Reinforce Racism. New York University Press, 2018.; Seaver, Nick. 2013.
“Knowing Algorithms.” In Media in Transition 8. Cambridge,
MA. http://nickseaver.net/s/seaverMiT8.pdf.; Kirschenbaum,
Matthew G. Mechanisms: New Media and the Forensic Imagination. The
MIT Press, 2012.; Kirschenbaum, Matthew. “Books After The Death Of
The Book.” Public Books, 31 Mar. 2017.; Jackson, Steven. “Rethinking
Repair.” Media Technologies: Essays on Communication, Materiality and
Society, edited by Tarleton Gillespie and Pablo Boczkowski, The MIT
Press, 2014.; Cohen, Daniel J., and Roy Rosenzweig.
[“Introduction.”] Digital History: A Guide To Gathering, Preserving, And
Presenting The Past On The Web.; Robertson, Stephen. The Differences
between Digital History and Digital Humanities. 23 May 2014.;
Kirschenbaum, Matthew. “Digital Humanities As/Is a Tactical
Term”.; Fitzpatrick, Kathleen. “The Humanities, Done Digitally”.;
Spiro, Lisa. “This Is Why We Fight’: Defining the Values of the Digital
Humanities”.; Alvarado, Rafael. “The Digital Humanities Situation”.;
Burke, Timothy. “The Humane Digital”.; Ramsay, Stephen. “Humane
Computation”.; Hockey, Susan. “The History of Humanities
Computing” A Companion to Digital Humanities, edited by Susan
Schreibman et al., Blackwell, 2004.; Jurić, Tado (2022). Forecasting
migration and integration trends by using digital demography and big
data – the case study of Austria and Germany, Comparative Southeast
European Studies, https://doi.org/10.1515/soeu-2021-0090.; Jurić,
Tado (2022). Big (Crisis) Data in Refugee and Migration Studies – Case
Study of Ukrainian Refugees, Comparative Southeast European
Studies 2022; 70(3): 540–553, https://doi.org/10.1515/soeu-2022-
0048.; Jurić, T. (2022). Predicting refugee flows from Ukraine with an
approach to Big (Crisis) Data: a new opportunity for refugee and
humanitarian studies. Athens journal of technology & engineering,
https://doi.org/10.1101/2022.03.15.22272428, doi: 10.30958/ajte.9-3-1
1. Attending classes according to the study program and teaching plan;
2. Properly completed seminar obligations – prepared and presented seminar presentation and submitted written seminar paper;
3. Obtaining a minimum success of 35% of the total grade during classes within the assigned teaching activities – cumulatively achieved in seminar paper and continuous monitoring.
1) Teaching activities – seminar paper; continuous monitoring (class activity, preparation for the lesson, reflective review of teaching content)
2) Final exam (written exam)
Continuous evaluation of student work results in an overall grade based on 100 points:
sufficient (2) – 50-64%
good (3) – 65-79%
very good (4) – 80-89%
excellent (5) – 90% and above
Method of gaining points:
1. a) Teaching activities – 70%
1) seminar paper – max. 35%
2) continuous monitoring (activity in class, preparation for the lesson, reflective review of teaching content) – max. 35%
1. b) Final exam – 30% %
1) final exam – max. 30% (to pass, it is necessary to solve 50% of the final exam)
ECTS %
Seminarski rad 1.33 35
Kontinuirani rad 1.33 35
Ukupno tijekom nastave 3.86 70
Završni ispit 1.14 30
UKUPNO BODOVA
(nastava+zav.ispit) 5 100
Ishodi učenja
1.Ovladati temeljnim pojmovima DH 2. Koristiti interdisciplinarni pristup u radu; razumjeti znanstvena dostignuća Digital Humanities i primijeniti ih u akademskom okruženju; 3. Usvojiti sposobnost
samostalnog istraživanja metodom DH, skupljanja i odabira informacija, samostalne procjene važnosti pojedinog izvora, kritičkog odnosa spram izvora podataka. 4. Napisati jasan i strukturiran pisani
rad. 5. Održati jasno i strukturirano usmeno izlaganje. 6. Aktivno i argumentirano sudjelovati u raspravi. 7. Steći potrebne vještine u radu s metodama DH (Text-Mining; Google Trends; Google Ngram, Social Network analysis).
Ishodi učenja
1.Ovladati temeljnim pojmovima DH 2. Koristiti interdisciplinarni
pristup u radu; razumjeti znanstvena dostignuća Digital Humanities i
primijeniti ih u akademskom okruženju; 3. Usvojiti sposobnost
samostalnog istraživanja metodom DH, skupljanja i odabira
informacija, samostalne procjene važnosti pojedinog izvora, kritičkog
odnosa spram izvora podataka. 4. Napisati jasan i strukturiran pisani
rad. 5. Održati jasno i strukturirano usmeno izlaganje. 6. Aktivno i
argumentirano sudjelovati u raspravi. 7. Steći potrebne vještine u radu
s metodama DH (Text-Mining; Google Trends; Google Ngram, Social
Network analysis).
Ishodi učenja
1.Ovladati temeljnim pojmovima DH 2. Koristiti interdisciplinarni pristup u radu; razumjeti znanstvena dostignuća Digital Humanities i primijeniti ih u akademskom okruženju; 3. Usvojiti sposobnost
samostalnog istraživanja metodom DH, skupljanja i odabira informacija, samostalne procjene važnosti pojedinog izvora, kritičkog odnosa spram izvora podataka. 4. Napisati jasan i strukturiran pisani
rad. 5. Održati jasno i strukturirano usmeno izlaganje. 6. Aktivno i argumentirano sudjelovati u raspravi. 7. Steći potrebne vještine u radu s metodama DH (Text-Mining; Google Trends; Google Ngram, Social Network analysis).
Ishodi učenja
1.Ovladati temeljnim pojmovima DH 2. Koristiti interdisciplinarni
pristup u radu; razumjeti znanstvena dostignuća Digital Humanities i
primijeniti ih u akademskom okruženju; 3. Usvojiti sposobnost
samostalnog istraživanja metodom DH, skupljanja i odabira
informacija, samostalne procjene važnosti pojedinog izvora, kritičkog
odnosa spram izvora podataka. 4. Napisati jasan i strukturiran pisani
rad. 5. Održati jasno i strukturirano usmeno izlaganje. 6. Aktivno i
argumentirano sudjelovati u raspravi. 7. Steći potrebne vještine u radu
s metodama DH (Text-Mining; Google Trends; Google Ngram, Social
Network analysis).