# data[].attributes.body
Projected proficiency?

# data[].attributes.body
It will help identify

# data[].attributes.body
1.  Probability of what score the student might get.

2. Very shocked at how low several of the scores were

that students received.  Expected better

# data[].attributes.headline
There is a lot of variance within each class period, with regard to projected scores

# data[].attributes.subject
There is a lot of variance within each class period, with regard to projected scores

# data[].attributes.body
My students need a lot of help.

I have a lot of work to do.

# data[].attributes.body
My students will score low, the does show me where I need to focus

# data[].attributes.body
For my students, the probability percentage means that they have not historically demonstrated proficiency on math assessments.  For me, this means that I have the opportunity and challenge to facilitate growth.

# data[].attributes.body
Differentiate articles by reading level for

students in order to help build reading

competency while learning science.

# data[].attributes.body
My students productivity is low

# data[].attributes.body
Who will struggle with comprehension on higher level articles and who to group with one another

# data[].attributes.body
AHA.... The literacy probabily is a huge factor in the APUSH class since most of the readings are individual reading assignments.

# data[].attributes.body
Base on TVASS scores, students could judge how well students score on literacy related material. Fro there, teachers could implement differentiated instruction, modifications to lesson presentations, use grouping strategies within the classroom, and/or possibly offer more, in depth tutoring sessions for students who need to extra attention

# data[].attributes.body
Students that had data seem proficient. Should allow for better written communication

# data[].attributes.body
My students probability are low because all SPED. This means I have to work harder to raise their probability.

# data[].attributes.body
Seeing the data in a pie chart made it more useful for categorizing my students.

# data[].attributes.body
Chances of passing

# data[].attributes.body
the probability percentage tells us how the student is projected to score. This helps me to know which students are going to need more interventions, as well as who I could possible use as a peer tutor to work with that student. I also can identify students who are not working up to their potential.

# data[].attributes.body
Students projected to meet proficiency are all in one class and students projected not to meet proficiency are another class.

# data[].attributes.body
Ebony Freemon

# data[].attributes.body
1. AHA!: I have students ranging from 6% to 99% in the same. Differentiation for skill will be essential for building literacy.

2. Strategy: Group students based on skill level, whether by skill level or integrated.

# data[].attributes.body
Student's probability of scoring the indicated performance level or above.

# data[].attributes.body
I can use this as a method to group
based on reading ability in a close reading or group exercise. group of 4 (one low, two medium and one high).

# data[].attributes.body
We can use this data to better understand where our students are and what their predicted growth is in order to plan more effectively.

# data[].attributes.body
Probability percentage means the likelihood that students will be proficient in the subject area.

This tells me if someone will need further academic intervention, or if my job is to challenge them to keep them at an advanced level.

# data[].attributes.body
How well they will perform with assignments related to literacy and comprehension. The probability allows me to differentiate my instruction, and to group students based on ability.

# data[].attributes.body
Helps students gauge how well they will perform on material related to literacy comprehension. This will help the teacher gear instruction in directions to meet these needs.