Research Methods 1

Recommended Prerequisites

English (B1 level)

Teaching Methods

Modules I and II are taught mainly as lectures, including the discussion of relevant case-studies. The assessment consists in preparing a critical review of an article where some kind of quantitative analysis is used, whether formal or experimental (35%). In module III, task-based group activities are preferred. There is a continuous assessment based on student performance in the lectures (17.5%) and a written test (17.5%). At the end, each student presents a state-of-the-art review of their research field in a joint workshop (30%). There is no exam.

Learning Outcomes

Development of the ability to conduct scientific research based on quantitative methods and of scientific communication skills, both orally and in writing. Competency acquisition in analysis and synthesis, organisation and planning, oral and written communication, knowledge of a foreign language, communication with non-experts,  working in an international context, decision making, research, critical reasoning, professional ambition, self-criticism and self-assessment.

Work Placement(s)

No

Syllabus

I.    Introduction
1.    Science, technology and research
2.    Critical thinking and scientific thinking
3.    Integrity and conduct
II.    Quantitative research methods in computing
1.    Modelling
2.    Formal methods
3.    Experimental methods
4.    Computer simulation
III.    Scientific communication
1.    Effective reading, textual organisation, summarising
2.    Grammar topics
3.    Analysing academic discourse
4.    Composing and delivering a short oral communication
5.    Argumentative writing

Head Lecturer(s)

Luís Filipe dos Santos Coelho Paquete

Assessment Methods

Assessment
Mini Tests: 17.5%
Other: 17.5%
Synthesis work: 30.0%
Research work: 35.0%

Bibliography

1.    G. Dodig-Crnkovic, Theory of Science, 2003.
2.    ESF and ALLEA, The European Code of Conduct for Research Integrity, Strasbourg: Ireg, 2011.
3.    A. V. Aho and J. D. Ullman, Foundations of Computer Science, W. H. Freeman, 1992.
4.    M. Kirby and G. Dangelmayr, Mathematical Modeling: A Comprehensive Introduction, in preparation (draft).
5.    R. Sheldon, Probability and Statistics for Engineers and Scientists, Academic Press, 2009.
6.    P. Cohen, Empirical Methods for Artificial Intelligence, MIT Press, 1995.
7.    T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss, Experimental Methods for the Analysis of Optimization Algorithms, Springer, 2010.
8.    K. Dooley (2002), “Simulation research methods,” in Companion to Organizations, Joel Baum (ed.), London: Blackwell, p. 829-848, 2002.
9.    J. P. Davis, K. M. Eisenhardt and C. B. Bingham, “Developing theory through simulation methods,” Academy of Management Review, 32(2):480-499, 2007.