Empirical Research of Computational Solutions

In today's world, where an increasing number of computational solutions like recommender systems, social robots, and health monitoring and wearable devices are being introduced, empirical research plays a crucial role in driving innovation, uncovering insights, and making informed decisions. This course equips students with fundamental skills in conducting empirical research to evaluate these computational solutions and their underlying theories.

The course begins by providing students with a solid understanding of empirical research methods. They learn to set up a research study, collect and analyse data, and draw scientifically valid conclusions. Emphasis is placed on reproducible research practices, including techniques like pre-registration and creating a data plan.

To make sense of data samples, students study how to make statistical inferences about the population. They explore frequentist and Bayesian data analysis approaches, gaining the ability to make meaningful statistical inferences based on collected data.

Throughout the course, students work with computational tools that aid in conducting statistical analyses of real-world data. Using these tools, students gain practical experience analysing and interpreting data to draw meaningful conclusions.

By the end of this course, students will have a strong foundation in empirical research methods and the ability to evaluate computational solutions. They will be equipped with practical skills in data analysis, statistical inference, and working with real-world datasets. The course aims to empower students to become proficient researchers who can contribute to advancements in computational solutions and their underlying theories.

The course's main topics:

• Conceptualizing research questions and experimental design, and data planning

• Frequentist and Bayesian data analysis

• Generalized linear models for statistical analysis

• Multilevel modelling for hierarchical and longitudinal data analysis

• Measuring and sampling, validity and reliability

• Principles of statistical testing

The final grade of the course consists of the following components:

• Group Presentation: presentation of pre-registration of empirical study (including a data plan) and the results of data analysis assignment based on the Bayesian approach (weighting 35%)

• Individual Presentation: individual Q and A session about the pre-registration of empirical study (including a data plan) and the results of data analysis assignment based on the Bayesian approach (weighting 15%)

• Group Report: report on the results of data analysis assignment based on a frequentist approach (weighting 50%)

Final grade calculation = 0.35 * Group Presentation + 0.15 * Individual Presentation + 0.5 * Group Report

A passing final grade for the course can only be earned when for all components at least a 5.0 is earned, and the weighted final grade is at least a 5.8.

In case of an insufficient final result, repair options may exist in accordance with Article 17A Times and number of examinations, sub 1, of the Teaching and Examination Regulations, for:

• Group Presentation: Resit opportunity

• Individual Presentation: Resit opportunity

• Group Report: Repair opportunity

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 9, sub 5).