Example for reflection storytelling 

 Challenge or Selection in storytelling : Dealing with an ever growing amount of data and measures of 

 quantitative assessment ͘ /t is tough to choose data to be presented , And this Concerns that deep   understandings will bring unwanted change . It was Reluctance to become data - driven Then we can 

 think of using the right technology . 

 Application of story telling : According to my knowledge Storytelling is use   get deep understanding   into users and build empathy and reach them emotionally . Designers create personas to represent 

 target users and add conflict to stories that reflect their user journeys and problems . brings 

 languages learning alive and also creates a particuipatory and immersive opinion that allows Young 

 Learners to enjoy hearing the language in a dynamic , sometimes stylistic and entertaining way . 

 Approach or Insight for storytelling : 

 * Put deep understandings the top of presentation hierarchy to know the   way of presenting data 

 and to see their   results . 

 * We can consider this is the best way to choose by countless scientific papers . To present the data 

 science results in this way , . 

 * Firstly We have to start with background explaination and then   talk about data sources , use   graphs and tables to highlight all the data properties , then talk about model building , discuss 

 moudling results and finally conclude deep understandings . 

 * In this flow , deep understandings come the last , because a basic explanation is very important   of 

 the data and the model prepares audience for a true understanding of the deep understandings . 

 Make bold statements on the top of visual hierarchy : 

 * : Data : visualizations are very important   defining moments of data stories . 

 * These   are tables and graphs , consisting with visual cues , coordinate systems , scales , surrounding 

 text and more . 

 * Given the usefulness of data visualizations , we can significantly improve data storytelling through 

 better use of data visualizations . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Example for reflection storytelling 

 Challenge or Selection in storytelling : Dealing with an ever growing amount of data and measures of 

 quantitative assessment ͘ /t is tough to choose data to be presented , And this Concerns that deep   understandings will bring unwanted change . It was Reluctance to become data - driven Then we can 

 think of using the right technology . 

 Application of story telling : According to my knowledge Storytelling is use   get deep understanding   into users and build empathy and reach them emotionally . Designers create personas to represent 

 target users and add conflict to stories that reflect their user journeys and problems . brings 

 languages learning alive and also creates a particuipatory and immersive opinion that allows Young 

 Learners to enjoy hearing the language in a dynamic , sometimes stylistic and entertaining way . 

 Approach or Insight for storytelling : 

 * Put deep understandings the top of presentation hierarchy to know the   way of presenting data 

 and to see their   results . 

 * We can consider this is the best way to choose by countless scientific papers . To present the data 

 science results in this way , . 

 * Firstly We have to start with background explaination and then   talk about data sources , use   graphs and tables to highlight all the data properties , then talk about model building , discuss 

 moudling results and finally conclude deep understandings . 

 * In this flow , deep understandings come the last , because a basic explanation is very important   of 

 the data and the model prepares audience for a true understanding of the deep understandings . 

 Make bold statements on the top of visual hierarchy : 

 * : Data : visualizations are very important   defining moments of data stories . 

 * These   are tables and graphs , consisting with visual cues , coordinate systems , scales , surrounding 

 text and more . 

 * Given the usefulness of data visualizations , we can significantly improve data storytelling through 

 better use of data visualizations .
### Output: 