Content Creation and Data (5cr)
Code: 2M00DP99-3002
General information
- Enrolment period
- 24.11.2021 - 10.01.2022
- Registration for the implementation has ended.
- Timing
- 10.01.2022 - 13.05.2022
- Implementation has ended.
- Credits
- 5 cr
- Virtual portion
- 4 cr
- RDI portion
- 1 cr
- Mode of delivery
- Blended learning
- Unit
- MD in Emerging Media
- Campus
- TAMK Mediapolis
- Teaching languages
- English
- Degree programmes
- Master's Degree Programme in Emerging Media
- Teachers
- Media-ala Virtuaalihenkilö
- Leena Mäkelä
- Jukka Holm
- Person in charge
- Leena Mäkelä
- Groups
-
21MEMEMaster´s Degree in Emerging Media, fall 2021
- Course
- 2M00DP99
Objectives (course unit)
After the course, students can
- critically evaluate the role, opportunities, and pitfalls of automated data collection and analytics in content creation and audience research
- understand how recommendation systems work and how they are applied
- compare and point out prominent data technologies in content creation
- develop content concepts that utilize data technologies
Content (course unit)
During the course, the participants study the basic concepts and processes of data analysis and information visualization. They critically analyze and experiment with the role, opportunities, and pitfalls of data technologies in content creation and audience research. They examine how automated data collection and user analytics impact on content creation. They explore how emerging data technologies, such as machine learning (ML) and artificial intelligence (AI), are used in content creation. As a synthesis of the exploration, students create and demonstrate an art and media concept that utilizes or reflects data technologies.
Topics
- Data analytics and information visualization
- Ethics of data analytics
- Audience metrics and content creation
- Machine learning and artificial intelligence
- Recommendation systems
- Personalization of services
- Emerging data technologies and content creation
Assessment criteria, satisfactory (1-2) (course unit)
The student defines the basic concepts related to data analysis and discusses the ethics of data analysis. S/he describes different ways data analysis influences content creation in her/his working field. S/he knows the common means of data analysis related to content creation and applies at least one of them in a practical case. S/he demonstrates ideas for applications that integrate content and data analysis. The student takes responsibility for her/his work.
Assessment criteria, good (3-4) (course unit)
The student knows the basic concepts of data. S/he critically analyzes and provides various examples of how data analysis influences content creation in her/his field – now and in the future. S/he critically discusses the ethics of data analysis. S/he demonstrates creative ideas for applications that integrate content and data analysis. The student develops committedly her/his knowledge and skills in emerging media.
Assessment criteria, excellent (5) (course unit)
The student critically analyzes and provides alternative scenarios of how data analysis influences content creation in her/his field – now and in the future. S/he compares the situation across different industrial sectors and points out inter-dependencies and connections between them. S/he demonstrates creative and attractive ideas for applications that integrate content and data analysis.The student demonstrates excellent and open-minded attitude to her/his work, as well as towards fellow students’ knowledge and skills.
Assessment criteria, pass/fail (course unit)
The student critically analyzes and provides alternative scenarios of how data analysis influences content creation in her/his field – now and in the future. S/he critically discusses the ethics of data analysis. S/he compares the situation across different industrial sectors and points out inter-dependencies and connections between them. S/he demonstrates creative and attractive ideas for applications that integrate content and data analysis. The student demonstrates an excellent and open-minded attitude to her/his work, as well as towards fellow students’ knowledge and skills.
Location and time
The study time of the online course is 10.1.-13.5.2022. There are three remote sessions in Teams:
1) Fri 21.1. 2022 13-16 pm, orientation + information visualization, visiting lecturer Dr. Harri Siirtola
2) Fri 11.2. 2022 13-16 pm visiting lecturers Ritva Leino, Professor of Practice at Tampere University: "Why content creation can’t be just data driven? Tools to focus on user needs" and Tapio Haaja, Head of Strategy and Development at Videolle (https://www.videolle.fi/en /): "How to create better video marketing based on data? What video metrics creatives should focus on?".
3) Fri 22.4.2022 13-16 pm lecturer(s) TBA
Assessment methods and criteria
During the course, the students are required to complete the tasks of six different units. As part of this course, the students will do some tasks of the Elements of AI open course by Helsinki University. Those tasks are assessed pass/fail. The tasks provided by the TAMK's course instructors are evaluated numerically (scale1-5).
Assessment scale
0-5
Teaching methods
The course is carried out mainly as an asynchronous online Moodle course including course tasks and materials. There will be three three-hour remote sessions in Teams.
Learning materials
The online course consists of six units which materials are listed in Moodle.
Student workload
1 cr consists of 27 hours of a student's work.
Content scheduling
The course consists of six units with respective online tasks. In general, there is 2-3 weeks time to complete the task(s) of each unit. The units are: 1) Data analysis and information visualization, 2) Ethics and data, 3) Audience research and content creation, 4) Machine learning and artificial intelligence, 5) Recommendation systems and personalization and 6) Implementation: intelligent assistants.
Completion alternatives
TAMK principles of recognition of elsewhere acquired competence (whole course or some parts of the course) apply to this course.
Practical training and working life cooperation
TUNI multidisciplinary project collaboration.
International connections
International course materials.
Further information
The study time of the online course in Moodle is 10.1.-13.5.2022. There are three remote sessions in Teams: 1) Fri 21.1. 2022 13-16 pm, 2) Fri 11.2. 2022 13-16 pm and 3) 22.4.2022 13-16 pm (Finnish time). The course consists of six units with respective online tasks. In general, there is 2-3 weeks time to complete the task(s) of each unit. The units are: 1) Data analysis and information visualization, 2) Ethics and data, 3) Audience research and content creation, 4) Machine learning and artificial intelligence, 5) Recommendation systems and personalization and 6) Implementation: intelligent assistants.
Assessment criteria - fail (0) (Not in use, Look at the Assessment criteria above)
The student does not show evidence on the defined learning outcomes, e.g. there are missing or incomplete tasks.
Assessment criteria - satisfactory (1-2) (Not in use, Look at the Assessment criteria above)
The student defines the basic concepts related to data analysis and discusses the ethics of data analysis. S/he describes different ways data analysis influences content creation in her/his working field. S/he knows the common means of data analysis related to content creation and applies at least one of them in a practical case. S/he demonstrates ideas for applications that integrate content and data analysis. The student takes responsibility for her/his work.
Assessment criteria - good (3-4) (Not in use, Look at the Assessment criteria above)
The student knows the basic concepts of data. S/he critically analyzes and provides various examples of how data analysis influences content creation in her/his field – now and in the future. S/he critically discusses the ethics of data analysis. S/he demonstrates creative ideas for applications that integrate content and data analysis. The student develops committedly her/his knowledge and skills in emerging media.
Assessment criteria - excellent (5) (Not in use, Look at the Assessment criteria above)
The student critically analyzes and provides alternative scenarios of how data analysis influences content creation in her/his field – now and in the future. S/he compares the situation across different industrial sectors and points out inter-dependencies and connections between them. S/he demonstrates creative and attractive ideas for applications that integrate content and data analysis.The student demonstrates excellent and open-minded attitude to her/his work, as well as towards fellow students’ knowledge and skills.