2 Chapter Two: Prior Evidence and Baseline Data

Pretest Data: Using Baseline Data to Inform Instruction

Data can be intimidating. Collecting data that is specific to a learning objective and breaking it down in ways that can easily translate to instructional strategies can make the use of data less daunting.

“In its simplest form, data is a collection of facts and statistics that can be used for planning or analysis.” (RethinkEd.com) Data provides teachers with the evidence they need to substantiate the instructional choices they make for their students.  Data can also help teachers monitor students‘ progress as they progress through a unit.

Performance assessments, pre-tests, student conferences, attendance, and test scores are some examples of data sources that help inform classroom, school, and district decisions.

What is baseline data and how is it collected?

Baseline data measures student understanding, knowledge or performance prior to intervention or teaching.  It can be collected through various measures including: percent accuracy, frequency, duration, rate and intervals.

Number/Percent accurate is collected by calculating the number of target responses divided by the total number of opportunities.  This can be recorded as a fraction or percentage.  The chart below shows an example of the data gathered on a small group of students during a math assessment in which the students were presented with four problems.  Other examples of situations where you might collect data on number or percent of accuracy might be:

  • Number of words spelled correctly
  • Percentage of math problems solved correctly
  • Vocabulary words correctly defined
Student Problem #1 Problem #2 Problem #3 Problem #4 Total/Notes
J.D.           +              +             –              + 3/4; M/not labeling; copied incorrectly
A.B.           +              +             –               – 2/4; NM/misidentifying factors
E.T.           +               +             +               + 4/4; E/enrichment
P.K.           –              +             –              – 1/4; NM/basic facts; not labeling
R.W.          +              +              +               – 3/4; E/ copied incorrectly

Key:  NM– did not meet expectations      M: met expectations      E: exceeded expectations

You can record frequency by tracking the number of instances of a behavior. Collect frequency data with counters, tallies or a similar technique.  The chart below shows data collected on a single student throughout her day regarding the her success in following directions without prompting.  Other examples of situations you might record the frequency of something happening could include:

  • Number of words read
  • Number of times a student gets out of their seat
  • Number of times students raise their hands
 Class Period Followed directions without prompting                      Directions Given Total
Math IIII IIIII 4/5
ELA II III 2/3
Science I IIIII 1/5
History III IIIII II 3/7
Specialist IIIII IIIII 5/5
TOTAL 15 25 15/25

Duration is measured by tracking the length of a specific occurrence of a behavior. To record duration, start a timer when the behavior commences and stop the timer when the behavior ceases.  The chart below is an example of data collected on one student to track his time on task during English Language Arts class over a two day period.  Other examples of when measuring the duration of behaviors might be:

  • How long a student engages in tantrum behavior
  • How long a student engages in peer interactions
  • How long a student remains on task
DG’s Time on Task in ELA
Date Start Time
End Time Total Number of Minutes
1/15 8:40 8:47 7
1/15 9:15 9:26 11
1/16 8:40 8:52 12
1/16 9:05 9:37 32  (Webquest w/ partner)
1/17 8:47 8:53 6
1/17 9:22 9:30 8
1/17 9:35 9:40 5
1/19 8:51 9:20 29 (Audio book)
1/19 9:24 9:30 6

Rate is calculated by recording the number of behaviors per unit of time.

  • Number of words read per minute
  • Number of math problems completed per minute
  • Number of tantrums per hour

Interval data can be used when tracking each occurrence of behavior is not possible, or when the start and end time of the behavior is not clear. You can also use interval data to obtain a sampling rather than an exact count. Also you can measure interval data by determining a preset time interval and then marking whether the behavior occurred during that interval.  The chart below shows data collected on a specific behavior for one student in the class he attended just before lunch on one day.  Other examples of situations which might lend themselves to tracking interval data might be:

  • The occurrence of body rocking
  • The occurrence of staying in an assigned area
  • The occurrence of off-task behavior
Wandering from Seat
Time Period Yes No
12:00-12:10 x
12:10-12:20 x
12:20-12:30 x
12:30-12:40 x

Without baseline data, it is difficult to show improvement in learning in a class as a whole or in identified subgroups of that class:  identified students, students ready for additional challenge, students who speak English as a second language, etc.

Baseline data should always serve as a starting point for instruction and can be gathered from prior student work, student assessment scores (classroom or standardized), or other anecdotal data.  It is not recommended that teachers use homework as baseline data, since it is difficult to ascertain whether or not students’ completed the work independently.

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Prior Evidence and Baseline Data in the GSC Lesson Plan

Section two of GSC’s lesson plan depicts the prior evidence and baseline data that justifies the lesson objective and calls for an analysis of this data.  To earn full points on this portion of the lesson plan, the baseline data must be gathered from a pretest directly targeting the skills to be taught in the lesson.  In sequential lessons with objectives that build on one another, new prior evidence might include the data and work samples from the most recent preceding lesson.

Once pretest data has been collected, it must be analyzed to determine an appropriate instructional response for each student.  To do so, consider proficiency in regard to your objective as described above.  Which students are close to proficiency based on their pretest data?  Which students are far from proficiency and will need scaffolded support to move toward mastery?  Complete an error analysis for each student’s pretest mistakes and describe the results in section 2b.  Next, look for patterns in student understanding and misunderstanding in an effort to determine possible student groupings during the lesson.  Consider representing your data in a graph or chart to bring these patterns to light.

GSC’s Aligned Lesson Plan Section

GSC’s Aligned LOFT Evaluation Criteria

To earn full credit for analysis of data, the data analysis must be clearly related to the activities planned in the lesson (see Chapter 4 of this text, Instruction & Activities).  The analysis must identify patterns of concerns (related to the objective) for both the class as a whole and individual students.  The lesson’s data analysis must lead to purposeful planning of instructional strategies and assessments further along in the lesson plan.

GSC’s Aligned Lesson Plan Rubric Criteria

Analysis of Data: What do I do with this data?

You will use the baseline data you have collected to:

  1. Write/revise lesson objective(s)
  2. Identify how to differentiate instruction for individual students and groups of students
  3. Plan instructional activities
  4. Select/create assessments (adapted as needed to meet specific students’ needs, both those needing support and those needing challenge)

 

In order to analyze your student data, it’s helpful to know exactly what you’re looking for: What does proficient mean? What do the students still need to learn?  This process of defining proficiency requires you as a teacher to shift your mindset from scoring (a summative examination) to diagnosing (a formative examination) student performance. Often teachers spend a great deal of time sorting student responses (either by letter grades or by rubric scores) and virtually no time diagnosing what students know and still need to learn. It is diagnostic information that is essential to helping teachers understand what to do next with their students’ instruction.

Error Analysis

Error Analysis to Guide Instruction

Using Error Analysis to Inform Meaningful Instruction

 

Error Analysis Example

Error Analysis Example

The following is an example of how error analysis was applied to work students produced in a formative assessment.  By analyzing where students were making their mistakes or showing misconceptions, the teacher was able to come up with an instructional plans for the subgroups identified in the analysis.

Lesson Objectives

1. Students will be able to organize information to show an understanding of main ideas within a content area text through charting with a Venn Diagram.
2. Students will be able to identify 8 elements for each of two ecosystems: aquatic and grassland and 5 elements that are common to both.

Formative Assessment in Error Analysis Example
After content instruction and guided practice on the elements of ecosystems and how to compare ecosystems, students completed the following as an independent practice:         1.  read information about aquatic and grassland ecosystems (or listened, through the use of an MP3 player, to the information being read),  2. used a highlighter to highlight elements of each ecosystem as they read/listened (green for grassland and blue for aquatic),  3.  identified common elements of both ecosystems by circling them, 4.  recorded all elements on a Venn Diagram.

Work Sample Analysis for Student 1:  Since he identified 13 aquatic elements, this student’s knowledge of that ecosystem is at mastery level, with one exception. He did not identify grass as being an element in both aquatic and grassland ecosystems (identified as only as an element in grassland ecosystem). This misconception was cleared up when I conferenced with him about his work. When examining his understanding of grassland ecosystems, it was found that although he demonstrated understanding of the main idea of the text material by correctly charting elements, his demonstrated knowledge about grassland ecosystems did not meet the lesson objective mastery criteria. His background knowledge about grasslands coupled with the text did not provide enough information for him to build a complete schema.

Future Instruction for Student 1: Additional instruction along with further practice will be provided through a Webquest. The Webquest will provide examples of several grasslands and the elements that make up the grassland ecosystem and the student will practice identifying the elements. His ability to determine common elements (compare) the two ecosystems is not at mastery level either but may be affected by his lack of complete schema about the grasslands. It is apparent that he understands the concept of compare/contrast since he correctly identified 3 elements that were the same.

Work Sample Analysis for Student 2 : Student was only able to identify 2 elements of the grassland ecosystem and 4 elements of the aquatic ecosystem. The elements he identified represented only animal elements although he did identify water as being common to both systems. This student’s difficulty identifying elements may be related to his challenges with memory and incomplete schema of the elements that make up an ecosystem. Despite being given a cue card reminding him of the different types of elements that make up ecosystems (along with examples), he is not identifying elements.

Future Instruction for Student 2: He needs more scaffolding in order to build his schema of the types of elements. Intensive direct instruction along with a structured Webquest activity in which he has to find examples of each type of element within ecosystems will be given. He did demonstrate understanding of the main idea of the text material by correctly charting elements and he was able to identify 4 elements that were common showing he understands the concept of compare/contrast, however, further practice (once he builds his schema of the types of elements) will be needed.

Work Sample Analysis for Student 3: Student was able to identify 13 elements of grassland ecosystems and 10 elements of aquatic ecosystems; she identified 6 elements in common and demonstrated she understood the concept of compare/contrast. The diversity of the elements she identified demonstrated she understood all of the types of elements in ecosystems. Her correct charting of the elements in the diagram demonstrates her understanding of the main idea of the text. The only element needing further probing was her categorization of “people” as being an element of both grassland and aquatic ecosystems. Her explanation showed a well developed schema and creative thought. She said that she had seen a TV program that showed people living in an underwater research station and that she also knew that there are African tribes that live on the grasslands.

Future Instruction for Student 3 (and DK, KS and TC): Students will create a third ecosystem to compare and contrast with grasslands aquatic ecosystems.   Format for sharing this information with the class will also be a chosen activity (imovie, PowerPoint presentation, Webquest or other).

Analysis of Whole Class Learning: 13 out of the 15 students met or exceeded the evaluation criteria. They demonstrated their understanding of the elements of an ecosystem, the main idea of the text material and the concept of compare/contrast by correctly identifying the specified number of elements in both ecosystems and identifying the items that are common to both. The students who did not meet the criteria are included in the error analysis above along with possible reasons for lack of mastery and interventions directed at guiding them toward mastery.

Future Instruction for Whole Class:  Differentiation of instruction within the next teaching cycle will follow the learning needs of the students. Students not meeting criteria will be provided instruction and practice as outlined above (intensive instruction will be focused on getting them to the level of understanding needed to join the next group). Students meeting criteria will be provided instruction and practice to move them to generalization of knowledge of ecosystem elements. Students exceeding the criteria will work on synthesizing and applying their knowledge of ecosystems by creating a new ecosystem.

Student 8 Grassland Elements 8 Aquatic Elements 5 Common Elements Notes
DK * * *
KS * * *
CC + + See notes for student 1 above
HB + + Misunderstood ‘common’
EG + + +
NM See notes for student 2 above
TL + + +
CF + + +
TC * * *
CW + + +
NT + * *
TK + + +
GP * + +
JP + * +
SH * * * See notes for student 3 above
Key: (-) Does not meet criteria,  (+) Meets Criteria, (*) Exceeds Criteria

Note: The students in this category added elements beyond what was found in the text. This demonstrates a very well developed schema of the ecosystems and generalization skills related to their knowledge of ecosystem elements.

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Analysis of Data in the GSC Lesson Plan

 

GSC’s Aligned Lesson Plan Rubric Criteria

 

 

GSC’s Al

GSC’s Aligd Lesson Plan Rubric Criteriaigned esson Plan Rubric Criteria

Professional Learning Network Input

 

Once you’ve analyzed your data and have created your lesson objective, consider which professional resources might be able to help you plan your instruction for your students.  This might include a website, an experienced teacher, professional texts, field experts on social media and school specialists.  You will note constructive feedback from consulting these resources which directly supports your instructional decisions and approach.

View the following video to see how a PLN might analyze student data to inform future instruction.

 

Professional learning network in the GSC Lesson Plan

 

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GSC Lesson Planning 101 by Deborah Kolling and Kate Shumway-Pitt is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License, except where otherwise noted.

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