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PROGRESS MONITORING BEST PRACTICES
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PROGRESS MONITORING BEST PRACTICES

DESIGN THE PROCESS

01

SELECT YOUR TOOLS

02

KNOW THE FLOW

03

DEVELOP PLANS

04

MONITOR DATA AND FIDELITY

05

ANALYZE THE DATA

06

Clarify the purpose of progress monitoring and design a process that describes how progress monitoring data will be collected and stored, how frequently progress will be monitored across the tiers, and how data will be analyzed and used to make decisions. “Progress Monitoring.” Center on Multi-Tiered Systems of Support, American Institutes for Research, 2022, https://mtss4success.org/essential-components/progress-monitoring. Jeffco Virtual Academy Example: For example: Claudia Rinaldi of LaSalle University shares her process below! https://screencast-o-matic.com/watch/cYfb2Tzwat?fbclid=IwAR0o0TmZ6yDYxsnUnhr0pVAGViZYYlxumYLiy2X6vKpJz_eBg6bqQlkrklo How to do Cycles of Progress Monitoring in MTSSThis video shows you How to do Cycles of Progress Monitoring in MTSS for about 80-100 students in a grade...Screencast-O-Matic Classroom Resources to Support your Work:

Read the following article to learn the four steps in creating your team-level or classroom-level progress monitoring protocol! Example of Primavera Intervention:

  • Hard Stop in Course
Classroom Level Differentiation of Instruction: “Quick Guide for Multi-Tiered Systems of Supports: The Classroom.” Wayne County Literacy Learning Network, Literacy Learning Network, 12 June 2020, https://lln.resa.net/mtss/. Classroom Level Intervention Strategies to Try:

To limit inconsistencies in administration and errors in scoring and data entry, teams develop a clear plan for ensuring the fidelity of the progress monitoring process. These resources highlight considerations for implementing progress monitoring with fidelity and support implementation. “Progress Monitoring.” Center on Multi-Tiered Systems of Support, American Institutes for Research, https://mtss4success.org/essential-components/progress-monitoring. Classroom Level Data Monitoring and Collecting Tools:

Data-Based Individualization: “Progress Monitoring.” Center on Multi-Tiered Systems of Support, American Institutes for Research, https://mtss4success.org/essential-components/progress-monitoring. What Types of Data are Available for DBI?Types of useful, available data in school districts can be categorized as follows:

  1. academic achievement data
  2. non-academic data
  3. program and systems data
  4. perception data
Academic Achievement Data Non-academic DataProgram and Systems DataPerception Data
  • Academic achievement data include information related to the achievement of and progress toward students’ academic goals. Examples of academic achievement data include:
  • benchmark assessments
  • diagnostic assessments
  • formative and summative assessments
  • common grade-level assessments
  • students’ class averages
  • progress monitoring data
  • student work samples
  • portfolios
  • performance tasks
  • Non-academic data are information or factors that impact students’ academic achievement in some way, but are not direct measures of the learning outcomes. Examples include:
  • student attendance
  • teacher attendance
  • office discipline referrals
  • special needs
  • socio-economic status
  • mobility patterns
  • Program and systems data are information related to the structure of the work itself, which impacts achievement and success. Examples include:
  • learning standards
  • instructional expectations
  • curricular resources
  • observation data
  • schedules
  • new teacher mentoring information
  • meeting agendas and minutes
  • professional development plans
  • behavior management plans
  • student support systems information
  • Perception data are information related to culture which impacts success. Perspective and perception affect people’s behavior and decisions. Examples of perception data include:
    • student surveys
    • staff surveys
    • parent surveys
    • community surveys
    • conversations
    • parent engagement information
The lists of data in this article are not exhaustive but are plentiful, reinforcing the fact there are data all around us. With all of this data available, what is next? How do schools go from having data to having success? How Schools can Use DataThe following steps can be followed to effectively use data:
  1. question
  2. determine
  3. gather and analyze
  4. create and implement
  5. review and revise
Cchiaro. “Data-Driven Decision Making in Education.” Graduate Programs for Educators, 23 July 2020, https://www.graduateprogram.org/2020/02/data-driven-decision-making-in-education/. Never underestimate the power of your feedback! Remember you are the MOST important aspect of your classroom! How are you providing RECENT and RELEVANT feedback to your students?

Progress Monitoring becomes a lot easier when we have the right tools! Your first tool to help you progress monitor is The Taxonomy of Intervention Intensity (Fuchs, Fuchs, & Malone, 2017) can be used to select or evaluate an intervention platform used as the validated intervention platform or the foundation of the Data-Based Individualization process. It can also be used to guide the adaptation of intensification of an intervention during the intervention adaptation step of the Data-Based Individualization process. The Taxonomy includes the following dimensions:

  • Strength: the evidence of effectiveness for students with intensive needs;
  • Dosage: the number of opportunities the student has to respond and receive feedback from the teacher;
  • Alignment: how well the intervention matches the targeted academic skills or behaviors of concern, as well as incorporates grade-appropriate standards or behaviors we would expect for a particular context;
  • Attention to transfer: whether the intervention is explicitly designed to help students make connections between the skills taught in the intervention and skills learned in other contexts and environments;
  • Comprehensiveness: how well the intervention incorporates a comprehensive array of explicit instruction principles; and
  • Behavioral or academic support: whether an academic intervention incorporates behavioral strategies that may support students with self-regulation, motivation, or externalizing behaviors that may impact their ability to learn, or whether a behavioral intervention considers academic components as part of the intervention.
The final dimension of the Taxonomy, individualization, focuses on the ongoing use of progress monitoring data and other diagnostic data sources to intensify and individualize the intervention based on student need. This approach mirrors the remaining steps of the DBI process that consist of data collection and modification in an iterative process until improvement is seen. CLASSROOM LEVEL INTERVENTION INTENSITY:
  • Choose to focus on either the Academic or Behavior options below, and then see how the attached Google Sheet could help you track everything!
“What Is the Taxonomy of Intervention Intensity?” What Is the Taxonomy of Intervention Intensity? | NCII, American Institute for Research, 2022, https://intensiveintervention.org/implementation-intervention/taxonomy-intervention-intensity. Digital Data Collection Tools TutorialsData can come in many forms. Tools that give readily available data that helps you to make informed instructional decisions can save you time and help you target specific learning needs. Click the link below to explore a Playlist of tools to explore. Use headphones if you have them to watch instructional videos. Formative Assessment Tools Playlist
  • PearDeck
  • Google Forms
  • Formative
  • Edpuzzle
  • Flipgrid

Using data effectively begins with asking questions. What information is needed to make informed decisions related to the school goals? Questions that immediately come to mind, when the goal is to improve school achievement and overall success, are:

  • What are the year-to-year and month-to-month trends in student performance at each grade level, for each teacher’s class, and each subject area?
  • Which students have learning gaps? In what areas are those gaps? Which skills are needed to meet the needs?
  • What are teachers’ instructional strengths and areas for growth?
  • What do teachers, parents, and students believe about what is and is not working in the school?
  • Which systems and which programs have been fully implemented, partially implemented and not implemented at all?
  • What resources are being used and are they aligned with students’ learning standards?
Cchiaro. “Data-Driven Decision Making in Education.” Graduate Programs for Educators, 23 July 2020, https://www.graduateprogram.org/2020/02/data-driven-decision-making-in-education/.