Adopt Signed School Counseling Progress Report For Free

When evaluating interventions through the use of data-driven decision-making, we conceptualize our data in three primary categories: process, perception and outcome data. Process data is descriptive in nature and communicates what happened during the intervention and who was impacted.

Sharing the Data This can help the student set individual academic goals and be responsible for their own learning. Being able to track data, make goals and promote change helps decision makers, improvement teams and school counselors develop the right activities to target struggling areas.

At the beginning of each group or individual session ask students to fill out a pre-counseling self-assessment on the topic you will be working on with them. During their last session, have them complete the same self-assessment. Compare the results to see where progress has been made. Simple as that!

Data Collection: 5 Useful Tools to Track Progress in School... " Data collection is vital to the success of your school counseling program, and let's be real? for job security too! ... Brief Behavior Rating Progress Monitoring Scale. ... Self-Assessments. ... Motivation Assessment Scales and Survey. ... End of the Year Report.

Your goal as a school counselor is to help students take advantage of their strengths and to help them minimize their weaknesses. There are three main types of student data you will be working with. These are academic data, demographic data, and student action data.

Ing to ASCA, school counselors should be collecting data as part of their role in the school setting. Hatch (2014) speaks of three types of data (Process, Perception, and Outcome) collected by school counselors.

At minimum, you should use data to: align the school counseling vision and mission with the school's vision and mission. identify school counseling program goals from baseline data. promote evidence-based interventions. monitor student progress. evaluate and demonstrate program effectiveness.

You can disaggregate the data in a number of ways such as gender, ethnicity, SES, home location (zip code), teacher or counselor assignments, course taking patterns, feeder school patterns, etc..