DS815: The quality of data from social encounters

Study Board of Science

Teaching language: Danish
EKA: N340066102
Assessment: Second examiner: None
Grading: Pass/Fail
Offered in: Odense
Offered in: Spring
Level: Master

STADS ID (UVA): N340066101
ECTS value: 5

Date of Approval: 01-11-2022


Duration: 1 semester

Version: Approved - active

Comment

Will not be offered in Spring 2024

Entry requirements

A Bachelor's degree in humanities or social sciences, or a relevant Bachelor of profession.
The course cannot be taken by students enrolled in the master programme in Computer Science.

Academic preconditions

The course is introductory.

Course introduction

The course aims to enable the student to assess field validity and the quality of data collected on human behavior. This is important in assessing challenges both for writing algorithms and for generalizations that can be ascertained on the basis of big data.

The course provides a professional basis for applying knowledge, competences and skills acquired in the study program to social data.
In relation to the education's competency profile, the course explicitly focuses on:
  • Provide competence to assess the possibility and relevance of algorithms for systems that interact with human users.
  • Provide skills in analyzing human interaction.
  • Provide knowledge of methods for analyzing social interaction.

Expected learning outcome

In order to achieve the purpose of the course, the learning objective of the course is that the student demonstrates the ability to:

  • analyze empirical cases of social interactions to describe the underlying practices;
  • evaluate the quality of big data collected from such interactions in order to formulate requirements for algorithms for systems that interact with users.

Content

The following main topics are contained in the course:
  •  Research on social interactions
  • Video-analysis and video-ethnography

Literature

See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)

Timing

Spring

Tests

Project

EKA

N340066102

Assessment

Second examiner: None

Grading

Pass/Fail

Identification

Full name and SDU username

Language

Normally, the same as teaching language

Examination aids

To be announced during the course.

ECTS value

5

Indicative number of lessons

26 hours per semester

Teaching Method

The teaching method is based on three phase model.
  • Intro phase: 13 hours
  • Skills training phase: 13 hours, hereof tutorials: 0 hours and laboratory exercises: 13 hours
Activities during the study phase:
  • Video data collection
  • Annotation of video data

Teacher responsible

Name E-mail Department
Christina Melanie Cooper cooper@sdu.dk Institut for Medier, Design, Læring og Erkendelse

Timetable

Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Educational Law & Registration

NAT

Offered in

Odense

Recommended course of study

Profile Education Semester Offer period

Transition rules

Transitional arrangements describe how a course replaces another course when changes are made to the course of study. 
If a transitional arrangement has been made for a course, it will be stated in the list. 
See transitional arrangements for all courses at the Faculty of Science.