Education Community Attitudes Toward SIS/LMS Solutions

Gartner, Inc. surveyed District Leaders, School Leaders, Technology/IT Leaders, and Teachers in the U.S. K-12 education community to understand their attitudes toward the data housed in SIS and LMS solutions and how this data is currently used to improve classroom practice and student learning.

Instructional Data Collection And Use Plan

Introduction

District and school leaders are striving to make classroom-level student data available in forms that are more readily usable for improving instructional practices. These demands require that districts procure and effectively deploy data systems such as student information systems (SIS), learning management systems (LMS) and assessment systems.

The Bill & Melinda Gates Foundation has provided funding for a project entitled Closing the Gap: Turning SIS/LMS Data into Action.  Some of the objectives of this project were to uncover current attitudes toward the value of existing SIS and LMS solutions, understand the processes and approaches used to select and implement these solutions, and identify recommendations and best practices for not only selecting and implementing solutions, but transitioning districts to a more data-rich culture.

As part of this project, Gartner, Inc. collaborated with American Association of School Administrators (AASA) and the Consortium for School Networking (CoSN) to identify lessons learned by districts across the U.S. in identifying, selecting, and implementing SIS and LMS solutions.  Many districts reflected that it was important to have an understanding of what data should be collected and how that data will be used to impact student achievement.  More importantly, it is critical to have this plan thought through early on in the effort to transition to a data-rich culture.

This plan template and other resources to support an SIS and or LMS implantation and transitioning K-12 school districts toward a more data-rich culture.

Purpose

The purpose of this document is to guide districts through the process of defining what data is critical to making decisions to improve student achievement and determining how to mine and use that data that ultimately strengthens instructional practices

Structure

This document is divided into two primary sections: 1) Guidance on how to create the Data Collection and Use Plan and 2) A Data Collection and Use Plan template, with instructions and a completed example.

How to Use This Plan

This planning tool can be used in the following ways:

  • District leaders can use this plan to:
    • Determine what policies surrounding data may be necessary
    • Think through what data is needed to understand student achievement trends and patterns
  • School leaders can use this plan to:
    • Inform Professional Learning Committee (PLC) topics and discussions
  • Teachers can use this plan to:
    • Communicate what data is needed on a regular basis to understand student needs and personalize instruction
  • Technology leaders can use this plan to:
    • Helps drive SIS and LMS functional and technical requirements
    • Informs decisions about consolidating sources of data and highlights systems with which an SIS and or LMS may need to integrate

Data Collection Planning Guidance

As suggested in the Readiness Activity Planner assistance template, it is important to start with the end in mind.  The catalyst behind selecting and implementing an SIS or LMS solution should be about the data and outputs that result from these solutions and how they contribute to student performance.  SIS and LMS solutions should not be implemented solely for the sake of automating mundane administrative and instructional planning tasks.  To this end, it is critical that districts have a clear plan for not only what data they want an SIS or LMS to store and display, but more importantly, how that data will inform instructional policies and practices.  When crafting a Data Collection and Use Plan, districts should:

  • Include a variety of staff with varying roles in the planning process; collaboration between educational, administrative and technical staff is critical for this task
  • Be realistic about the data you need vs. the data that you can collect
  • Prioritize data needs
  • Start with the end in mind.

Include Staff with Varying Roles in the Planning Process

Data that serves to inform classroom teaching and learning activities and ultimately student achievement can take many forms and be found in a variety of sources.  For example, information about a student’s learning styles play a key role understanding how that student learns or where he/she may encounter learning challenges.  As a result, it is important to have a variety of stakeholders, to participate in data planning.  This participation can take the form of working to develop the plan and its contents or as a reviewer of the final product to ensure that all data has been considered.  Technology staff will play a key role in the process as they will in most cases be the most knowledgeable of existing data and their sources and data capabilities.  Consider including the following staff roles:

Data Plan Content Contributors:

  • Teachers at all levels
  • Principals and other school leaders
  • Instructional Data Coaches
  • School nurses, social workers, psychologists, counselors and other professionals associated with students’ physical and mental health
  • Educational Diagnosticians
  • Educational Technology Staff
  • Small groups of elementary (grades 4-5), middle and high school students

Data Plan Reviewers:

  • District leadership, e.g., superintendent, assessment, curriculum, food services, transportation
  • Staff responsible for student transportation and food services
  • Disciplinarians and interventionists

Be Realistic

When planning for data use, be mindful of constraints that impact your ability to collect and display the desired data.  For example:

  • Is the data you wish to collect protected by laws and government mandates (e.g. Health Insurance Portability and Accountability Act – HIPAA, Children’s Online Privacy Protection Act – COPPA, Family Educational Rights and Privacy Act – FERPA, etc.)?
  • Do you have the resources or technical bandwidth and capacity to store the data? Once collected from every student within the district, will the data be too large to store in your current database/data warehouse (e.g. photos of every student and their parents/guardians)?
  • Do you have the staff to update and maintain the data for accuracy (e.g. you may want daily trend analysis of how assessments in every classroom in every school but teachers may not have the time to conduct such an analysis on a daily basis)?
  • Do you have the ability to display the data on different types of displays (e.g., computer screens, smart phones, tablets, etc.)?
  • Is the data currently collected somewhere? How accurate is that data?

Prioritize Data Needs

When developing your Data Collection and Use Plan, there are a number of different types of data to consider, including but not limited to:

  • Student demographics
  • Formative, benchmark/common and summative assessment results
  • Lesson plans and supporting instructional resources (e.g. articles, video clips, websites, learning standards, digital content, pacing guides, etc.)
  • Personalized learning plans
  • Response to intervention resources

The large variety of data can create a slippery slope.  Districts can fall into the trap of defining too many data components for which they do not have the capacity or capability to store, mine, and analyze.  To avoid this, districts should consider developing a list of criteria that can be used to prioritize which data components are critical to its current and future educational goals.  For example, a district may have 3 primary educational goals: 1) Closing the achievement gap among subgroups of students 2) increasing reading scores, and 3) increasing the graduation rate.  These goals require data that speaks to formative and summative assessments, suspensions, reading assessments, etc. While understanding the percentage of students that start the year in gifted programs vs. those that enter the program in the middle of school year may be telling, it does not directly support district current and future goals.

Districts should consider the following criteria when prioritizing data needs:

  • Does the data element answer a question that directly supports current and future school, district, state, and national education goals?
  • Does the district currently have a means for accurately collecting and displaying the data element across the district?
  • Does the district have a means of securely and accurately storing/maintaining the data?

Answering yes to all of these questions makes the data element a strong candidate for inclusion in the plan while data elements that result in a negative response to 2 or more of these strongly suggests that it should not be included.  The data included in this plan should drive district policies, school guidelines, PLC discussion topics and data analysis, education programs, teacher – student-parent conferences, etc. and ultimately drive SIS/LMS system requirements.

 

Start With the End in Mind

When planning for data use, start by consider what problems you are ultimately trying to solve and what does the success look like; then use this information to inform and guide data discussions.  Consider:

  • What are the instructional questions the data should answer?
  • Which professional learning resources are needed to support the effective use of the data to strengthen classroom practices?
  • Which evidence-based instructional practices will the data will further enable ( e.g., the role of feedback and assessment for learning)?

 

Data Collection and Use Plan

For each piece of data, provide the following information:

  • Description: summarize the data to be collected and used including who provides the data and how often
  • Source: identify where the data is currently held or stored prior to the system implementation, which may be manual, held in individual files , or in a specific system
  • Extraction Method and Frequency: describe how the data will be pulled from the data source and how often
  • Data Owner/Point of Contact: list the name or group responsible for maintaining the data
  • Data User: name or group who will be using the data
  • Intended Use(s): describe how the data will be used specifically to impact student achievement
Table 1: Data Collection and Use Plan Template
Data Description Source Extraction Method and Frequency Data Owner/Point of Contact  Data User  Intended Use(s)
Example: Formative Assessment Results · Class assignment, quiz and test scores, for each student, calculated by all teachers daily Acquired learning management system and supporting database · Gradebook screen/report for each student

· Extracted at the close of each lesson

· Teachers · Teachers

· Students

· Parents

· Counselors

· School leadership

· This data will be aggregated at the class level by teachers to inform student groupings and identify lessons that require additional time to master

· This information will be aggregated monthly by data coaches and department chairs to inform PLC discussions

· This data will be reviewed each marking period by principals to understand trends an patterns in teacher effectiveness, student progress, and understand where additional resources/learning materials may be required

· This data will be aggregated across schools each marking period by district staff to understand where new educational programs and resources/learning materials may be required

Example: Digital Assets · Digital content

· Teacher developed learning audio recordings and/or videos

· Student portfolios

· Learning management system · Refreshed as needed quarterly and/or yearly · District leadership, e.g., curriculum or technology

· School level leadership or technology

· Teachers

· Students

· Parents

· Teachers will use digital assets to meet the individual needs of students based on their academic strengths and/or gaps in their learning.

· Students will use digital assets to meet their individual needs based on their academic strengths and/or gaps in their learning.