Reflections and Feedback

This session provides an overview of student feedback on the Methods in Cognitive Neuroscience course. We also explore suggestions for improvements in teaching and course structure.

Key Learning Points from Feedback

Students noted strengths and areas for improvement, which are summarized below:

Positive:

  • Task clarity and organization were appreciated.
  • Hands-on practice with E-Prime, OpenSesame, as well as Python
  • Understanding the “why” behind the task design choices.

Challenges:

  • Difficulty in balancing task coding with conceptual learning.
  • Transition between OpenSesame and E-Prime was challenging due to time constraints.

Student Suggestions for Course Improvement

Based on collected feedback, here are suggestions for enhancing the learning experience:

  1. Focusing on a Single Software:
    • Many students suggested concentrating on OpenSesame or another single platform (e.g., PsychoPy) to streamline the learning curve.
    • This would allow more in-depth exploration of coding and experimental design.
  2. More Emphasis on Coding Basics:
    • Introducing coding with structured, step-by-step tutorials.
    • Allocating dedicated sessions solely for coding practice would enhance proficiency.
  3. Improving Task Clarity with Additional Resources:
    • Video Tutorials: Recording sessions or providing video walkthroughs of tasks could help students revisit complex topics.
    • Supplementary Reading: Curated lists of online tutorials or articles on experimental design would aid independent study.
  4. Increasing Emphasis on Experimental Design:
    • While coding is essential, more context around why certain designs are chosen and how they relate to research questions would benefit overall understanding.

Reflection and Plans for Next Year

In light of this feedback, the following changes are being considered for course 2025/26:

  1. Dedicated Coding Sessions:
    • Allocating specific days for coding practice will provide more time for skill development.
  2. Software Focus:
    • Based on student preference, OpenSesame might be the primary tool, with reduced emphasis on E-Prime.
  3. Enhanced Task Explanations:
    • Adding detailed rationales for task design choices to improve understanding of the research context behind each experiment.