Research Methods in Computer Science

University of Oulu
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Course overview

 
The course is no longer in Study Programme. Content of this page is out of date.

ECTS Credits 5   cr
Language of instruction EN.
Timing

Autumn, periods 2-3.

Learning outcomes

Upon completing the course the student is able to explain the scientific method, create a research plan, design and conduct experimental studies for computer science, write in academic style, and give presentations.

Contents

Scientific method, research planning, statistics, research tools, research methods, studying humans, academic writing, presentation skills.

Mode of delivery

Face to face teaching.

Learning activities and teaching methods

Lectures, exercises, and practical work. The course is passed with an approved practical work. The implementation is fully English.

Target group

Computer Science and Engineering students and other Students of the University of Oulu.

Prerequisites and co-requisites

No prior courses are required.

Recommended optional programme components

The course is an independent entity and does not require additional studies carried out at the same time.

Recommended or required reading

All necessary material will be provided by the instructor.

Assessment Methods and criteria

The assessment is project-based. Students have to complete four individual activities throughout the semester: develop a research plan (20%), complete statistics tests (20%), generate graphs and figures (20%), conduct a mini experiment (40%). Passing criteria: all four elements (research plan, statistics tests, graphs and figures, mini experiment) must be completed, each receiving more than 50% of the available points.

Read more about assessment criteria at the University of Oulu webpage.

Grading

The course unit utilizes a numerical grading scale 1-5. In the numerical scale zero stands for a fail.

This course is designed to give students an overview of the scientific approach and research methods in the discipline of Computer Science. The course is aimed at doctoral and masters level students. Upon completing the course the student is able to explain the scientific method, create a research plan, design and conduct experimental studies in Computer Science, write in academic style, and give academic presentations. The course involves lectures, exercises, and practical work. The course is passed with an approved practical work. This course is taught only in English.

Instructors

  • Vassilis Kostakos
  • Jorge Goncalves (assistant)
  • Saad Akram (assistant)
  • Ekaterina Gilman (assistant)

Topics

  • Introduction: Overview. What do researchers do? What is research? Why do research?
  • Research planning: Choosing variables, procedures, hypotheses. Writing a research plan.
  • Statistics 1: Testing the obtained results: t-test, Anova, correlation, R2.
  • Statistics 2: model fitting, PCA, exploratory data analysis.
  • Research tools: Tools for analysis, writing, graphs, diagrams, figures.
  • Research Methods: Simulation, emulation, graphs, networks, DB, machine learning, deployment, mechanical turk.
  • Humans 1: Questionnaires, surveys, observations.
  • Humans 2: Conducting lab studies, ethics.
  • Writing: Literature review, presenting results, sections of a paper. Writing a grant proposal. Conference vs. Journal papers.  Reviewing papers.
  • Presenting your research: Hints, tips, mistakes to avoid.  

Assessment

There is no exam for this course. All assessment is based on exercises completed by the students, and regular attendance in theory and practice sessions is required. To pass, a student must complete all exercises, each one with more than half of the available points.  The four exercises are:

  1. Develop a research plan (literature review plus work) (20%)
  2. Run statistics tests (data is given) (20%)
  3. Create a set of graphs & figures (data given) (20%)
  4. Mini-experiment, collect data (humans & comp) report and present results. (40%)

The final grade is computed by taking into account the respective weigths of the exercises and is given on the numerical scale 1 to 5, with 0 standing for fail.

Material

All material for this course is available in Optima. This includes

  • Slides
  • Exercises
  • Datasets
  • Templates for exercises and presentations
  • Statistical tools

Some example answers for exercise 1:

  • Chaudhry, Saad (2012). Usability evaluation of single-stroke vs. multi-stroke gesture recognition for rapid text input on small screen mobile devices. [pdf]
  • Heinikoski, Atte (2012). Simple multi-touch gesture usability and performance in laptop computers. [pdf]
  • Li, Xiaobai (2012). Recognizing Affective States from Gait. [pdf]
  • Heinikoski, Atte (2012). Simple multi-touch gesture usability and performance in laptop computers. [pdf]
  • Maki, Saku-Matti (2012). Guiding the user for the discovery of gestures in mobile phone user interfaces. [pdf]
  • Ollila, Kimmo (2012). Are mouse gestures better way to interact with PC compared to shortcuts and direct manipulation? [pdf]
  • Ramalingam, Archana (2012). Single touch based interaction for people with symptoms of Parkinson’s disease. [pdf]
  • Ylimaki, Markus (2012). Whether to use a camera or an accelerometer for controlling mobile devices with head movements. [pdf]
  • Alapuranen, Sakari (2013). New generation of gaming consoles - will gestures finally break through? [pdf]
  • Hu, Subingqian (2013). Development of Repetitive Strain Injuries in Leap Motion Control Based System. [pdf]
  • Kemppainen, Ville (2013). Head gestures in laptop interface. [pdf]
  • Seppanen, Tuomas (2013). Unwanted side effects of immersive virtual reality using head-mounted displays. [pdf]
  • Xu, Yingyue (2013). Usability of facial expressions and gestures in recognizing learning states in the process of e-learning. [pdf]
  • Yang, Jilin (2013). Better input method fo rpublic large-screen displays: virtual keypad or handwriting. [pdf]
  • Zhu, Zeyun (2013). Correct the sport posture for people with Kinect. [pdf]

 


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