I am a quantitative person. Data availability for me, is a precious. Thus, in the beginning of the class, it asked everyone to write down the feeling about the data, and to write down a poem for data. All I can think about is if I have data, just like Simpsons lying on the doughnuts in the heaven. But also, I understand the data limitation and data biased. I shared one quote to my break-out group members, which is quite famous in the quantitative analysis “all the models are wrong, but at least you know something”.
From the class, I truly understand the positive of data, to tell the stories, to identify the problems, but more importantly, how to interpret the data if we have the data available. From the data collection process, it may have the problems about the biased collection or only collect the samples, to interpret population. Once we have the data, it also crucial for us to use or analysis the data with reflection. This is why when we reviewed the data from institution, showing the percentages of home-based students and international students from Asian or EU, it tells some stories but also it has the limitations. It all depends on how we interpret the data, and to reflect on our own institutions.
For example, in the different percentages showed based on the ethnicity, groups shared their own thoughts about how the data tells the truth. First, based on the ethnicity, it directly shows the diversity of the university, trying to balance the ethnicity. From the last 6-year time period, the trend indicated gradually increasing marks from the home-based white and BAME students comparing to other students, also it showed an increasing trend of higher percentages of students can get 2:1 in the end. This may lead to other questions, for example, by reviewing the trend of the data, should we review the assessment methods as equity for all of the students or any changes of the assessment methods or criteria leading to this increasing trend. From the existing data of the university, we need to critical review the data. Even though we could interpret the data in a very positive way, for example, the students are more hard-working, more happiness to learn because of the innovative teaching methods. But also it should leave us to think about whether we are using the inappropriate assessment method or criteria for the students. Both positive and potential limitation could both improve the institution in the end.