What is the significance of the Midterms result?

An overview of our progress

How do we measure our progress in a class that talks about meaningful indicators? Can we really measure it in an objective manner that is representative of the quality of our work?

To answer that question, we have to first consider the purpose of the class and the goals that were set initally. Right now we are at the end of week 7 and it is a good time to reflect on the progress of DMI. The first thing I did was to look back at the entire lesson plan. I tried to sieve out the meaning of the class at hand (despite being a topic that goes on and on continuous for myself) and I found a sentence within the DMI syllabus that caught my attention.

It is my hope that by focusing on creating meaningful information from data, we will help grow your capacity not to just be an independent thinker but also a independent doer (DMI syllabus)

To me, it looks like the intent of the module is to develop a key trait that is embedded into USP’s latest change — courageous. According to the USP website, courageous is the ability to be “willing to consider differing points of view, unafraid to face challenges and to act upon ideas.” In some sense, I have experienced some of this essence within the class itself. From the active class participation to the numerous posts over the various platforms, I find that there are lots of opportunities to share our thoughts and ideas in an active manner.

Having meaningful data alone will not be useful if we are unable to express it in an active form. As a matter of fact, it took me a while to be able to catch up with the class after coming back a week late from winter school in Korea. Courage itself is a trait that requires time and effort in order to gain sufficient confidence into being able to produce work. In retrospect, it ties right back into the intent of the module to develop not just thinkers, but doers who are involved in contributing as a collective effort.

The struggle with keeping up the pace

It sounds rather simple right? What I have experienced here is that it is hard to be able to keep up the pace for each week’s submission. Reasons such as upcoming tests, assignments and activities outside of class are some of the commitments of a university student and I find it a challenge to be able to juggle all at once.

To be able to produce quality work on a consistent basis is one of the greatest challenge I find in this module. Nevertheless, that does not mean that we stop trying just because it is challenging. In a TED talk that I watched by Angela Lee, an education psychologist, she found that the key defining trait in students who do well in school is not intelligence, but rather, it is the grit to perseverance when times are tough. This resonated with me as I found it relatable as a university student.

Defining the dataset

To answer the question that I initially posted, I find that the midterm results are indicative of our progress in a purely objective manner, considering that each activity can be quantified in a numerical manner. There are still many things that the midterm results lack and this would require some form of refinement. Some of which would include:

1. Class participation
2. The amount of time taken for each assignment
3. The distribution of the quality of work based on results (i.e. how do we qualify the number of upvotes when comparing to other forms of measurement such as conducting a tutorial class)
4. The “meaningfulness” of our results

Many of these questions are still left unanswered after a week of the release of the midterm results. However, what I believe is that it would require a collective effort from the class to be able to work together a form of assessment that represents our work. It might be able to capture everything, but the least is that there must be some sort of agreement or consensus with the mode of assessment that is used for the class with regards to our progress.

What is next?

To sum up, I would like to go answer I gave in my second medium article on Finding Meaning in Data.

Data has to be driven by both subjectivity and objectivity. Just like in the data of the Midterm results, the number of count for each activity acts as the objective data we are collecting. Objectivity is the grounding of the data that gives it support and legitimacy. While for subjectivity, it acts as the light house to guide the direction of the data.

As such, based on the results, I do hope that I will be able to gain more insights on both subjectivity and objectivity. Both these components speaks for the progress of myself as well as the class and I hope to be able to contribute to the collective effort in one way or another.