some and not others ? Do we encourage all students in an equitable manner ? These are just a few examples of observable soft data .
Some say soft data speaks to the heart of instruction because it can cut to the core or heart of our actions . It peers into what we destroy in our students , what we refuse to see in our actions or in their strengths , and what we tolerate rather than respect ; all of which may be damaging to student success . On the other end of the spectrum , soft data identifies what we accept we need to know more about , are open to learn , as well as celebrate and build .
In one of my recent doctoral courses , composed solely of site- and district-level administrators , we played a game called The Purple People . For this game , a random selection of students was designated as “ purple people ” and asked to leave the room , while the rest of the class was instructed to ignore them and treat them as if they were outsiders . When the purple people students returned and joined in the next activity , they quickly realized that things had changed , and they were outsiders .
The game lasted 15 minutes , but for every purple person the 15 minutes seemed much longer . During that short time , we temporarily destroyed their will to engage , positive interactions , ability to participate and more . After the activity , some of the randomly designated purple people shared that they didn ’ t bother to engage or look at anyone because they knew they were out . One stated , she decided to act out to get attention , show that she existed and was worth recognizing by sitting on the table .
That day , all my students left with a heightened sense of destructive actions and a keen sense of what it felt and looked like even if they had missed it before . The purpose of this example is to demonstrate observable destructive behavior from the lens of many stakeholders . Multiple aspects of soft data are sometimes missed when the observation focus is narrow . To acquire a complete picture , soft data should include observations of multiple stakeholders in a given setting .
By observing adult actions , we can identify dislikes , sheer tolerance , enthusiasm ,
By observing adult actions , we can identify dislikes , sheer tolerance , enthusiasm , hope and affinity for different students .
hope and affinity for different students . However , gleaning data through observations can lead to subjective findings .
One way to eliminate possible bias is through the use of tools created to observe specific behaviors . For example , if we wanted to observe teacher facial expressions for a particular class , we might start with the seating chart , then icons for various facial expressions . As the teacher interacts with each student the appropriate icon could be placed on each student ’ s cell on the seating chart .
To validate the findings , quantitative data could then be incorporated . Using the example above , this might include a comparison of the icons charted for each student on the seating chart . In yet another classroom , it might include the number of times a particular student is called on , a comparison of wait time offered various children , or the amount of prompting provided to different students .
We can ensure reliability of soft data through a number of methods , including , for example , sample size of students observed , number of teachers observed , number of observations , span of observations , hours observed , as well as observation tools developed to observe particular behaviors . Ensuring reliability and validity of soft data can support findings causally observed and may provide teeth needed for discussions around root causes .
Analyzing curriculum
Soft data can also unearth information about the curriculum . By examining curriculum against the backdrop of student demographics , we can determine if it values our students and their heritages . By examining textbooks , we can verify if they represent the multi-ethnic tapestry of stakeholders through various perspectives . In an age of vast amounts of digital information , this consideration extends to evaluating online resources selected for our students , as well as how students are taught to analyze and discern authors ’ purpose and digital information .
Gathered through myriad settings and observable behaviors , soft data can be measured alongside school goals and objectives to triangulate findings . However , identifying and triangulating soft data are not enough . What generally leads to action is the way in which the data are shared . Hence , once soft data are compiled , the way that we display the information may make all the difference in terms of whether findings are comprehensible , provoke discussion and inquiry , and ultimately lead to action .
Displaying data findings
To display soft data findings , we can use bar graphs , pie charts , tables , line graphs , and other graphics . Bar graphs and tables are used to compare groups and show relationships ; pie charts to display the same data
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