Admission requirements
Statistics for Computer Scientists or a similar statistical course
Description
In this course, students learn about the research cycle, and how to formulate research questions. We also discuss how to design studies, simulate data (using R) and perform analyses on the resulting data to answer those research questions. Next, we will introduce the concept of inappropriate or questionable research practices (what they entail, what their impact is, and how to avoid them). In addition, students will learn how to evaluate scientific papers, and how to build on them by formulating their own research proposal, which they will ultimately also present to each other.
Course objectives
By the end of the course, students can:
Simulate a hypothetical study in R
Recognize and appraise research practices as appropriate or inappropriate, and explain the motivations behind using them
Evaluate the effects of questionable research practices on research outcomes
Identify the different steps of the academic research cycle and the publication process
Distinguish the concepts of reproducibility, replicability, and robustness
Formulate a testable research question based on a description of available variables
Summarize and review an academic paper
Formulate a research idea in a written research proposal
Evaluate the quality of a given research proposal
Present a research proposal to a broad audience
Timetable
Mode of instruction
Each week will have a two-hour lecture and a two-hour workgroup session. The final week has a three-hour presentation and Q&A session.
In the first assignment, students will come up with a research question, simulate data in R, and examine the impact of questionable research practices on the outcome of a study. In the second assignment, students will evaluate an existing study in the field of AI, and subsequently use it as inspiration for their own research proposal, which includes a description of the proposed methodology and expected outcomes. Both assignments are to be submitted as a written report, accompanied by R code for the first assignment.
In addition, students will also present their research proposals to their peers, and provide feedback on each other’s proposals as part of a Q&A session.
In preparation for the two assignments, students need to reach weekly milestones, which focus on a particular aspect of the assignment.
Assessment method
The course grade is the weighted average of:
A closed-book multiple choice exam covering the theoretical knowledge discussed in the lectures and work group sessions (40%)
An assignment grade (60%), as a weighted average of three components: - Group assignment 1 (20%) - Group assignment 2 (25%) - Group presentation including Q&A (15%)
Assessment requirements:
Reaching the weekly milestones is a requirement in order to receive a grade for the assignment, and students will receive feedback on what they handed in during the following workgroup. This will in turn allow students to improve on the assignment they eventually hand in.
Not meeting the milestones for a given assignment or failing to hand in an assignment in a timely fashion, will result in a 0 for the respective assignment.
Failing to give a presentation will likewise result in a 0 for that component (for exceptions students ought to contact the study advisor).
Resit opportunities:
Students have the opportunity to retake the exam. If, after the resit, the theory grade is (still) below 5.5, the student needs to retake the theory part of the course (i.e., the lectures).
There is a resit opportunity for both assignments, but not for the presentation. The maximum grade for a resit assignment is 6. If the overall assignment grade is below 5.5 (after a potential resit), students need to retake the practical part of the course (i.e., assignments + presentation).
Policy on generative AI:
The general university policy on the use of generative AI applies. Specifically, the use of generative AI such as ChatGPT, to, for example, generate code or text for the assignments as well as the presentation, is not allowed.
We reserve the right to organize short oral discussions regarding assignments to check for the use of generative AI, and discuss the contributions of each group member, among other things. The outcome of such discussions can impact the assignment grade.
Reading list
Course material includes slides, exercises, and articles that will be made available via the online course platform.
Registration
From the academic year 2022-2023 on every student has to register for courses with the new enrollment tool MyStudymap. There are two registration periods per year: registration for the fall semester opens in July and registration for the spring semester opens in December. Please see this page for more information.
Please note that it is compulsory to both preregister and confirm your participation for every exam and retake. Not being registered for a course means that you are not allowed to participate in the final exam of the course. Confirming your exam participation is possible until ten days before the exam.
Extensive FAQ on MyStudymap can be found here.
Contact
Education coordinator LIACS bachelors
Remarks
Not applicable.