So I’ve been doing alot of self-reflection lately, about alot of different things, and I thought I would write some of them down. There’s been alot going on, but in this post I just want to write about two of them: The Meyer-Briggs Personality Typing or Type Indicator (Jungian stuff), and my Research Interests.

MBTI

So awhile ago, one of my education lecturers Robert, introduced us to the concept of MBTI in the STiC course about which I made a few posts. I’d seen it a few times before, and even tried reading about it and taking some tests but always struggled to understand the details. Robert was using it as a tool to raise awareness amongst the pre-service teachers about how different people can be very different essentially, and view and experience and interact with the world in dramatically different ways — not everyone is like you — and was using this message to impart the importance of differentiation in the classroom. A valid and important message I think, but perhaps MBTI was an overly confusing tool for getting it accross I still think it worked to some degree or another. Since I’ve been kinda fascinated by the underlying thought process, both Roberts, and that behind the MBTI itself. So recently I sat myself down and decided I would read about the MBTI properly and at least understand the concept — primarily for the purposes of understanding what other people mean when they advertise their MBTI. Since it’s a widely known construct, it comes up reasonably often. I finally feel like I actually managed to understand it more or less, and so I wanted to write that understanding down so I could refer to it later if I needed too, so here it is:

There are four binary dichotomies:

  • Attitudes: Extraversion (E) or Intraversion (I),
  • Function: Perceiving (or “irrational”) Functions: Sensing (S) or Intuition (N),
  • Function: Judging (or “rational”) Functions: Thinking (T) or Feeling (F), and
  • Dominant Extraversion Function: Either Perceiving (P) or Judging (J).

Attitudes is probably the most continuous and least binary in practice maybe, but also the most different from the rest. The essential concept is everyone has four key functions: Sensing (S), Intuition (N), Thinking (T) and Feeling (F), grouped into two subsets called perceiving functions and judging functions. Within each of these subgroups a person has a preference for one of the functions in that category. The thought is that people also have a preference for using either perceiving functions or judging functions with interacting with ‘the outside world’ i.e. extraversion, and the other when they are introspecting i.e. introversion. The fourth dichotomy is supposed to capture this, so for an extravert the dominant extraversion function would be their primary way of being, while for an introvert it would be their auxillary way of being.

My Research Interests

So recently I’ve been wrapping up one of the research projects I’ve been working on for awhile, and one of the other projects I was meant to be working on fell through, so I’ve been asked by my supervisor what I’d be interested in working on. At first I was abit stuck to come up with things, but after some time and thought I feel like I’ve managed to get at least a little more clarity on it. Even though it’s entirely possible I won’t pursue these research interests immediately, and maybe even because of that, I thought it would be useful to record them for the future when I’m similarly stuck. Also, I’m not just going to include research interests, but also just professional interests in general that might not directly be related to research. Because that makes more sense to me and these things could potentially lead to research anyway in theory.

So, my primary interests are:

  • mathematics/ data science/ statistics : education/ communication/ outreach.
  • specifically working one-on-one with students, and also potentially communicating interesting things in other media (youTube??)
  • cool motivating mathematical ideas that can make great learning activities (silver rhombic dodecahedra for example).
  • maths anxiety, mathematics bridging, indigenous mathematics education, women in maths, basically access to mathematics education for people who might be otherwise disadvantaged.
  • domain area motivated data science/ analysis and applied statistics
  • reproducibility, algorithm deep-dive dissection, etc.