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In our exploration of the quantified self, we also considered the data that already exists in education that forms part of the rich dataset about ourselves.
what are common types of data that exist in education?
what are common types of data that exist in education?
Education contains massive amounts of data from a variety of sources, comes in various forms, and is utilized in a multitude of manners.
Some common ways we collect, aggregate, review, and utilize data in education includes student data, collected demographic data, and academic data to name a few. This kind of data is stored in school servers or learning management systems (LMS).
As our focus is on the quantified self, we will focus on 'Personalized' data in an educational sense, which means focusing on the learner.
How is data currently utilized and QUANTIFIED in education? Check out below of some key examples of where data in education is often 'personalized'.
Indentification data
Indentification data
Student data is often quantified in terms of student numbers, age (and date of birth), gender, and many other data points. Examples of student identifying data can be as simple as the following Canva template that illustrates a Student ID card.
To the complex ones in which student identifier codes are codefied to standards that help high schools transfer student and academic data to universities. The Common Education Data Standards provide a guide of an example shown below.
Example of a Student ID card from a Canva template.
A notable issue with quantifying data is that numbers alone often do not mean much. For example, the number 12 only holds strong meaning when contextually placed next to the word 'Grade' or 'Class'.
To categorize data in a logical format, the Common Education Data Standards (CEDS) initiative provides guidelines in creating domains, entities, categories, and elements to systematically quantify data into logical groupings, as to improve the interoperability of data between educational bodies, such as the large transfer of data from high school to university.
An example definition of how a quantifier like a student identification code is shown in the definition above.
For more information about the CEDS , click here: https://ceds.ed.gov/
Assessment Data
Assessment Data
Assessment data is another great example of how data is collected and aggregated for the purposes of education. This data can come in many forms including, but not limited to:
Standardized Test Scores
Assignment Completion Rates and Homework Hand-in Rates
Class Averages and Distribution
Attendance Rate
Report cards have been used throughout history. What did your report card look like as a student? What kind of quantitative data did it include?
For more details on the history of report cards, we recommend the book Report Cards: A Cultural History by Wade H. Morris (2023).
case study example: turnitin
case study example: turnitin
Turnitin is by far one of the most widely used digital services used in academia to ensure academic integrity and checking of plagarism. Turnitin essentially reads a given piece of digital text, and partitions it into given strings of letters, words, symbols, characters, then compares these phrases or sentences to check for similarities in published academia works.
Below is how the Gordon (2023) from the American Public University describes the way Turnitin works and is used in academia:
When a student completes and submits a written assignment such as a Microsoft Word document, the submitted paper goes through Turnitin.
The Turnitin software will then detect plagiarism by checking the document against a vast database of internet data comprised of academic articles, books, websites, and previously submitted student papers.
Turnitin's sophisticated algorithms compare the student's written assignment to these sources in the Turnitin database, to identify similarities that might indicate instances of potential plagiarism.
...
After the check, Turnitin generates an originality report, which indicates the similarity and the percentage of the submitted work that matches the content from the Turnitin database.
However, it's important to note that a high similarity index on the originality report doesn't automatically mean plagiarism has occurred.
(para. 1)
However Meo and Talha (2019) argue that Turnitin should be seen less as an 'originality' verification tool, and rather a 'text matching tool' that uses a quantified manner to analyze and compare texts.
Reflection Question:
When, where, and how did you (or do you) value quantified data as a learner in your education? As a young learner? As an adult?
What trends do you see in education that data is quantified for the 'person' or the 'self' (i.e. the learner)? How do you personally use data in your educational field or setting?
Share your response to this question on the ETEC 523 blog.
Continue to explore:
Continue to explore:
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