SMUA4300 Advanced Research Methods Course description

Twenty per cent of the examination papers will be assessed by one external and one internal examiner. The remaining papers will then be graded by two internal examiners. The assessments from the first part are summarised to serve as guidelines for the assessments of the two internal examiners.

None

10 credits overlap with VERN1210/VERNL1210/VERN1210

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge:

The student has advanced knowledge of:

• research methods, especially in engineering research
• statistical and analytical techniques, including knowledge of the most common libraries and tools used in statistical analysis and visualisation of the results
• designing experiments, preparing data and interpreting analysis results
• how relevant statistical and computational techniques relate to each other and where they are used

Skills:

The student has:

• required skills in setting up sound experiments, hypotheses and research questions, and in finding and preparing relevant data
• required skills in identifying which statistical and analytical techniques are to be used and how and where they should be used
• hands-on experience with some of the most common computational techniques and libraries as well as related tools for statistical analysis
• hands-on experience with relevant tools for use in analyses

General competence:

The student:

• has broad overview of the computational tools and techniques used in analysis and engineering research, including statistical techniques and techniques related to data science and machine learning
• has an overview of the terminology related to statistical analysis and data science.
• is able to design experiments for successful engineering research, analyses and critical interpretation of results
• can extend his/her knowledge and skills in programming/scripting, analysing, managing and visualizing data

The course is delivered through seminars and hands-on computer workshops.

Teaching will be organized in topic-blocks with practical and theoretical parts in each block.

Lab assignments must be handed in on Canvas

Students who fail to meet the coursework requirements can be given up to one re-submission opportunity.

Portfolio assessment:

-Perform an individual GIS- analysis from a chosen urban area

-Individual or group in-depth report, approx. 10-15 pages for single students, for groups the page count depends on composition and task. Group size is dependent on complexity of the task, but a median group size of two with up to five participants are anticipated. Students may write the report alone as well.

-Oral presentation

Each student's work will be assessed together as a portfolio with one individual grade at the end of the semester, but all three parts that make up the portfolio must be assessed as 'pass' in order for the student to pass the course. The overall assessment can be appealed.

All aids allowed.

Graded scale A-F.

Two internal examiners. External examiners are used regularly.

John Östh; Email: john.osth@oslomet.no