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General Elective (GE) Modules

Enjoy the convenience of reading GE modules within PGP House!

PGP House offers a one-year living and learning programme where all freshmen enjoy the convenience of taking 2 academic modules (GER Quantitative Reasoning and GEQ Asking Questions) with their residential mates on residence.

GEQ1000: Asking Questions

There are many ways to ask questions, and many kinds of questions that different disciplines investigate. For a start, this module introduces six dominant modes of questioning from the perspective of computational thinking, design thinking, engineering, philosophy, science, and social sciences. These six perspectives serve as a starting point to introduce all undergraduate students to different modes of questioning across these disciplines, and provide an initial exposure to how scholars from these disciplines pursue specific lines of questioning of everyday issues. We emphasize that while there is only limited time and space within one module to devote to specific disciplinary lines of investigations, we encourage all students to actively think about other lines of questioning, other questions that need to be asked, particularly in disciplines not represented in this introductory platform as we move through this journey together. We expect that in future subsequent offerings, other disciplinary modes of investigations may also be introduced.

GER1000: Quantitative Reasoning

The module will help students develop a framework for quantitative reasoning, as follows:

  1. Frame the question(s) to ask (in a manner amenable to quantitative reasoning).
  2. Specify what & how to measure (in a quantitative manner).
  3. Collect relevant data.
  4. Analyse data, possibly using a mathematical or statistical model, in order to answer the question(s).
  5. Communicate findings in clear non-technical language.

In the application of this framework, attention will be drawn to potential pitfalls such as:

  • Questions that are not amenable to quantitative reasoning or too challenging to answer;
  • Measurement procedures that skew the original intention of the measured quantity;
  • Biases in data collection;
  • Questionable assumptions in analysis;
  • Reading too much into conclusions, such as confusing association with causation.