Digital Humanities

The described learning objectives for knowledge, skills and competences will be supported by the specific forms of instruction and work methods as described below. At the same time the forms of instruction and work methods are organised in accordance with the form of examination as described under Examination requirements, which is considered to be the most appropriate frame for testing the student's fulfilment  of the learning objectives of the subject.

On completion of the course students should have gained knowledge on:

• digital humanities as a different approach to handle humanities data (e.g. cultural heritage)
• workflows and models for processing humanities data (e.g. cultural heritage)
• related state-of-the-art tools from digital humanities
• computational thinking, including algorithmic thinking and data modeling
• basics of programming (e.g. in Python)
• basics of data visualization

Career Management Skills:

• understand the correlation between the overall competence profile and the academic labour market

On completion of the course students should have acquired the skills to:

• generate data models for humanities information (e.g. cultural heritage)
• compose digital humanities data sets
• apply existing state-of-the-art digital humanities tools
• utilize computational thinking in problem analysis and problem solutions
• write basic programs for data processing (e.g. in Python)
• generate visualizations reflecting patterns in humanities data

Career Management Skills:

• communicate their own skills/competences in writing and verbally
• identify and work on their own network from a career perspective

• assess what type of research questions can be supported by digital means and how
• discuss strategies to modeling humanities data
• adapt discussed humanities data processing scenarios to related problems
• argue on the basis of quantitatively gained results (statistics and visual output)
  

Career Management Skills:

• make personal career choices and set career goals

The following main topics are contained in the course:

• overview of the digital humanities research domain including a discussion of related research projects
• computational thinking
• strategies for modeling humanities data and data set creation (e.g., a text corpus)
• introduction to data visualization
• quantitative humanities data analysis (e.g. social media)
• close reading in digital environments
• distant reading of text corpora with state-of-the-art tools from the digital humanities
• operating with geospatial-temporal data sets
• operating with image data sets
• text alignment and visualization to support research questions in textual criticism
• network structures in digital humanities and visual analysis thereof
• basics of programming for data processing including: (1) data structures, (2) control flow element, (3) application of related existing libraries (e.g. in Python)

Through the sessions in Career Management Skills the student will be trained to exploit their own career resources in the project-oriented company. Themes in the sessions includes clarification of competences, written communication in a job seeking context i.e. applications and CV, understanding and using networking in a career perspective, understanding the dynamics in a job interview situation, as well as development of a personal career strategy.   

The following forms of instruction and work may be applied in the course: Lectures, class teaching, group work, presentations, discussions, exercises, peer and teacher feedback, problem-based learning.

The 4 mandatory sessions in Career Management Skills will be workshop based. 

Teaching is organised in such a way as to support the humanities model for active learning and activating teaching, cf. section Didactic, pedagogical basis and contact to research environment in the curriculum. The teacher will inform students about how study activities are organised on commencement of teaching. 

The activities in the course are primarily aimed at achieving learning objectives and preparing students for the form of examination being a group-based project presentation accompanied with a subsequent short individual 10 minute oral examination but may also consist of written (programming, applying tools, etc.) activities.

The teaching of the course activates the student in the following teaching spaces/learning spaces:

• Teaching space where the university teacher has the responsibility for planning and is present
• Study space where the university teacher has the responsibility for planning but is not present
• Teaching space where the university teacher is present but the students have the responsibility for planning specific sub-activities
• Study space where the students have the responsibility for planning, and the university teacher is not present

Time of classes - fall 2021
Tuesday kl. 14-18

Time of CMS classes - fall 2021

Changes may occur.
For more information see MyTimetable or link above to MitSkema/MyTimetable.