Studio Education is a place for students to study English Literature and Drama, but there is growing momentum within its SALA Programme towards an emphasis on the inclusion of Data Literacy within the Liberal Arts. In today’s world, sometimes defined as the Information Age, skills associated with data of all types are increasingly important to learn, develop and apply. For this reason, there has been a conscious effort to include Data Literacy in the development of new SALA courses. Anthropology & Geography with Writing, Mathematical Explorations and Scientific Explorations are three such academic offerings that incorporate student exposure to data, albeit alongside the more traditional elements of Liberal Arts courses such as reading and writing.

When it comes to the development of Data Literacy, there are four distinct yet integrative skills that are taught to students in an effort increase comfort with data of all types. These skills, discussed in their logical progression, are: (1) Knowing what data to collect; (2) Collecting data; (3) Analysing data; (4) Presenting & explaining data. Each of these skill sets is part of an integrative process that can be used to address questions and problems in nearly any field of study.

Knowing What Data to Collect: 

Data literacy actually begins long before the data in question even exists. Without knowing what type of information is needed to answer a question, it is impossible for meaningful observations and data collection to occur. Essentially, answering different questions requires collecting different information. Should the data be qualitative or quantitative? Are interviews or surveys more useful? These are just two questions that a researcher may ask before ever actually dealing with the data itself. Often, problems in the humanities require a mixed-methods approach where the researcher collects both quantitative and qualitative data. For example, when conducting ethnographic research for her project on The Culture of Wet Markets in Shanghai in Anthropology and Geography class last semester, Kaoru Nishimatsu counted the number of customers at different stall types but also recorded buyer-seller verbal and nonverbal interactions. This allowed her to more accurately describe the disappearing culture with both numbers and words. The subsequent step is then thinking, “What information really matters?” and “How does this data help me answer my question?”

Data literacy involves not only numbers, but qualitative information as well.

Collecting Data:

The ability to select research methods and use collection tools is also very important for any data-literate student. And ultimately, the collection of data requires both soft and hard skills; for example, taking accurate ethnographic notes or operating a Vernier data sensor. In the case of Anthropology and Geography with Writing, students learned ethnographic research techniques associated with conducting interviews, writing surveys and taking observations. Soft skills such as identifying tonal changes in an interviewee or observing body language allow for a refined understanding of qualitative data collection. In Scientific Explorations, the use of computerised sensors provides students with the opportunity to work with the same technology used to collect data in the academic world. For example, this past semester saw students utilise state-of-the-art digital gas pressure sensors to investigate phenomenon such as anaerobic respiration, chemical reaction rate and the ideal gas laws. In a Mathematical Explorations class, data collection may involve taking precise measurements of different natural organisms in search of The Golden Ratio. In each case, the practical skills associated with data collection improve students’ data literacy.

Skills associated with data collection increasingly involve the ability to use electronic sensors.

Analysing Data:

An often-overlooked aspect of Data Literacy, analysis, is perhaps the most important. Generally speaking, people are bombarded with much more information than is valuable on any given day. Therefore, it is important to be able to engage in the ‘sifting and winnowing’ necessary to find truth. In Anthropology and Geography, that may mean teaching students how to physically code their qualitative data and make sense of patterns in speech. It could also mean utilising UN datasets of development metrics to draw conclusions about comparative rates of development in different countries. In Scientific Explorations classes, this means teaching students how to use the plethora of features within Excel to sort and clean quantitative data. In Mathematical Explorations, data analysis may involve analysing the accuracy of mathematical models in relation to real world outcomes. By learning how to analyse data, students eventually understand that data can be manipulated to serve certain purposes, and they thereby become more informed and willing to question conclusions based on the underlying information. 

Presenting & Explaining Data:

Research ultimately serves little purpose unless it can be summarised, applied and presented. It is critically important to understand the best way for data to be displayed and here again, Excel is the practical tool. In Scientific Explorations courses, students may utilise Excel to create different graphs from pHet digital lab data to display how different factors affect projectile motion. Students are directly taught what is necessary to provide clarity to data illustration and begin to intuitively critique data displays they encounter in their everyday lives. Next, the need to verbally describe and explain the patterns is what matters. Students are therefore taught the vocabulary and strategies necessary to accurately describe what is shown in graphs. But description is not enough. The phrase, “but tell me what that means” is one that echoes in SALA classrooms. This simple statement encourages students to go beyond description and provide intelligent explanations. In an Anthropology and Geography course this may mean utilising the concepts of ritual and taboo to explain a graph of basketball players’ pre-shot routines. And in a Scientific Explorations course this may mean using economics to explain why, even though it produces the most rapid rates of respiration, a certain type of sugar is not used in the production of biofuels. Regardless of the specific situation, explaining data is a practice in applicability.

Once data is collected, it must be carefully analysed, displayed and explained.

In many ways, the data literacy process mirrors other processes that are inherent throughout the academic or professional spheres. Take for example the Experimental Method or Design Cycle, both of which are shown below. Not only do these increasingly important processes include data within their steps, they also mirror the aforementioned process of understanding, collecting, analysing and reporting data. All of these processes depend upon various interactions with data and the skills developed through exposure to them.

Data Literacy involves a process similar to The Scientific Method (left) and the Design Cycle (right).

In addition to the more tangible benefits of utilising data in the classroom, an explicit emphasis on data literacy in the classroom also encourages the development of extremely important soft skills. For example, students learn the importance of good organisation and develop strategies for information management when required to work with large data sets. Students also begin to understand the value of properly planning and preparing long-term assignments before fully progressing through them. As one current SALA student, Zhining Zhao, stated, “Working with Excel on data analysis has been academically valuable because it teaches me how to be more logical in my reasoning and planning.” Additionally, students learn valuable lessons about the importance of building on earlier work, the persistence necessary for complex problem solving and so much more.

In summary, in a data-driven world, the importance of data is actually far beyond its numerical value. Rather than only emphasising the technical skills and more narrow knowledge base necessary to work with numbers, SALA courses utilise Data Literacy to foster new ways of thinking, understanding, and approaching the world.

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