# 2018/2019

## Prerequisite/Recommended prerequisite for participation in the module

Interaction Design (2nd semester), Mathematics for Multimedia Applications (2nd semester), Percep-tion (3rd semester)

## Content, progress and pedagogy of the module

A crucial aspect of designing medialogy systems, tools or applications is the need to evaluate the work experimentally. The knowledge of how to properly design experiments to collect and evaluate data is essential to answer many of the problems within medialogy. Examples are testing which of two tracking algorithms that is the most efficient; how users perform with different kinds of feedback; possible relationship between age and performance etc.

### Learning objectives

#### Knowledge

Students who complete the module will obtain the following qualifications:

• Must be able to understand the basic concepts of probability: sample space of all possible events; combinatorics; independent events; conditional probability; binomial distribution etc.
• Must display knowledge about basic statistic terminology and treatment of data: distribution functions; measures of central tendency and variability; histogram; central limit theorem etc.
• Must be able to understand advantages and disadvantages with different types of designs and studies (between-group and within-group design; correlational study; blind/double blind etc.)
• Must be able to relate frequency distribution to the concept of hypothesis testing (understanding)
• Must be able to understand possible ethical concerns for a study

#### Skills

Students who complete the module will obtain the following qualifications:

• Must be able to design an experiment to measure changes in a dependent variable, identifying and efficiently controlling all relevant independent variables (application)
• Must be able to properly inform and instruct persons participating in a study (application)
• Must be able to understand and select among the most common methods for statistical analysis and assessment of experimental data (e.g. t-test, chi-square tests, correlation and simple linear regression)
• Must be able to understand different measurement scales and discuss experiments in terms of reliability, bias and sensitivity
• Must be able to discuss own data in terms of assumptions for statistical testing (application)
• Must be able to use an existing statistical package to analyze and present experimental results
• Must be able to discuss and represent empirical data in different ways (describing text, numbers, formulas, graphs and figures) and shift between these according to the needs of the situation and context (application)
• Must be able to read, understand and implement experimental and empirical work as described in relevant literature (application)

### Type of instruction

Refer to the overview of instruction types listed in the start of chapter 3. The types of instruction for this course are decided in accordance with the current Framework Provisions and directions are decided and given by the Study Board for Media Technology.

## Exam

### Exams

 Name of exam Design and Analysis of Experiments Type of exam Active participation and/or written assignment Exam format: In accordance with the current Framework Provisions and directions on examination from the Study Board for Media Technology: Individual assessment based on active participation during the course. The assessment is performed with the Pass/Fail grade. Note that if parts of the assessment are to be based on written work/exercises, a deadline is stipulated for when the work must be handed in. If the student hands in paper(s)/exercise(s) after the deadline, the student has used an examination attempt. ECTS 5 Permitted aids With certain aids: See semester description Assessment Passed/Not Passed Type of grading Internal examination Criteria of assessment The criteria for the evaluation are specified in the Framework Provisions.