Counting An Assessment Question As Zero Points
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L HTo prevent an assessment item from counting toward the overall class average in the tracker, teachers can remove the standard ID and points possible associated with that item. This will blank out the average for that item so it is not included in calculations, while keeping the student response data. Common appropriate reasons to exclude an item include if it assessed unintended content or had factual errors, while poor student performance or running out of time are not valid justifications.
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Removing Students From The Tracker Roster
To remove a student from the tracker roster, the teacher should not delete the student's information. Instead, the teacher indicates on either the "Student Roster" tab of the Standards Mastery Tracker or the "Roster_Changes" tab of the Reading Growth Tracker that they wish to remove a student. Any request must include a compelling rationale, such as the student being absent for over 50% of instructional days, and refer to the SGA Handbook for more information on appropriate removal scenarios and policy.
NETR - Not Enrolled This Round
The document explains how to use the code "NETR" or "Not Enrolled This Round" in a student assessment tracker. NETR should be used when a student was not enrolled at the time an assessment was given so their blank response is not counted as a zero. It provides appropriate scenarios like when a student joins after an assessment. It also lists inappropriate scenarios like absence or running out of time, which should not use NETR. To use NETR, simply enter it in the student response field and the tracker will ignore that field and not count it toward averages.
Reporting on Mid Year Data - Standards Mastery
1) The document provides guidance for teachers to summarize their mid-year data on student mastery of standards and character traits.
2) Teachers are instructed to check their overall average standard mastery for standards assessed to date and for all standards to gauge student progress and likelihood of end-of-year success.
3) Additional measures like pacing, coverage, and progress toward ambitious goals are to be reviewed and areas of strength or need identified.
Finalizing Standards Mastery Data
The document provides guidance on finalizing end-of-year standards mastery data by outlining several steps to verify the data is accurate and complete. These steps include ensuring the tracker includes the teacher's name and finalized roster, at least two assessments were administered and the data from those assessments was entered for all students and standards, the tracker is error-free, and progress toward mastery goals was calculated. Taking these steps will help ensure students receive an honest and accurate assessment of their learning.
Treating Your Data With Care
This document discusses the importance of treating student data with care when reflecting on end-of-year results. It emphasizes that data must be honest, accurate, complete, and error-free in order to draw valid conclusions about student learning and achievement. Specifically, it provides examples of how to ensure data is an honest reporting of student accomplishments, reflects any changes over the course of the year, contains all necessary information, and has been thoroughly checked for errors or inconsistencies. Treating data with care is crucial for appropriately measuring student outcomes and communicating their progress to students, parents, and other stakeholders.
Step 5: Verify Outcomes
This document discusses the importance of verifying student outcome data before celebrating or reporting results. It emphasizes that data must be honest, accurate, complete and error-free. This involves finalizing rosters, ensuring all standards are assessed with rigor, collecting multiple rounds of data for each student, and double checking for errors. Verifying outcomes is crucial to send the right message about student achievement and treat data with appropriate care.
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To remove a student from the tracker roster, the teacher should not delete the student's information. Instead, the teacher indicates on either the "Student Roster" tab of the Standards Mastery Tracker or the "Roster_Changes" tab of the Reading Growth Tracker that they wish to remove a student. Any request must include a compelling rationale, such as the student being absent for over 50% of instructional days, and refer to the SGA Handbook for more information on appropriate removal scenarios and policy.
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1) The document provides guidance for teachers to summarize their mid-year data on student mastery of standards and character traits.
2) Teachers are instructed to check their overall average standard mastery for standards assessed to date and for all standards to gauge student progress and likelihood of end-of-year success.
3) Additional measures like pacing, coverage, and progress toward ambitious goals are to be reviewed and areas of strength or need identified.
Finalizing Standards Mastery Data
The document provides guidance on finalizing end-of-year standards mastery data by outlining several steps to verify the data is accurate and complete. These steps include ensuring the tracker includes the teacher's name and finalized roster, at least two assessments were administered and the data from those assessments was entered for all students and standards, the tracker is error-free, and progress toward mastery goals was calculated. Taking these steps will help ensure students receive an honest and accurate assessment of their learning.
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Step 5: Verify Outcomes
This document discusses the importance of verifying student outcome data before celebrating or reporting results. It emphasizes that data must be honest, accurate, complete and error-free. This involves finalizing rosters, ensuring all standards are assessed with rigor, collecting multiple rounds of data for each student, and double checking for errors. Verifying outcomes is crucial to send the right message about student achievement and treat data with appropriate care.
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The document provides guidance for teachers to summarize mid-year reading growth data in their assessments. It instructs teachers to record their current overall average reading growth, the percentage of students meeting ambitious goals, and their most recent character average and growth. Teachers are told to note any increases or decreases in character data from the previous round and explain why. The document also notes how to account for students who left the class.
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1. The tracker should include the teacher's full name and have a finalized roster on the goal tab for the chosen reading assessment. Students missing more than 50% of instruction should be removed.
2. The assessment calendar should show that at least four rounds have been administered and entered for all students. Absent students should make up assessments.
3. The tracker should be error-free. Data should be treated with care as it is critical to the teacher's role and students deserve honest, accurate, complete and error-free data.
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The document provides guidance for inputting student data into a standards mastery tracker:
- Only enter students' initials and anonymize their information. Paste any copied information as values to avoid errors. Set calculations to manual to prevent slowdowns. Carefully enter the correct information and save often.
- Fill out tabs in order: student roster, standards, assessment dates. Only enter data into gray fields, not blue or white.
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This document discusses different types of selection test items, including their purpose, characteristics, and limitations. It focuses on multiple choice questions (MCQs), matching items, and true/false items. For MCQs, it describes the components and how to write them effectively. For matching items, it provides guidelines and notes they are best for factual recall. True/false items are outlined as having strengths like efficiency but also weaknesses like guessing. The document aims to help students understand, evaluate, and construct different selection test items.
Assessment tools
This document provides information on various assessment tools, including concept mapping, rubrics, and Hot Potatoes. It discusses concept mapping, including its development, components, and steps. It defines rubrics and discusses their advantages, components, and steps for creation. It describes the Hot Potatoes software suite, which can be used to create interactive exercises like multiple choice, crosswords, ordering, and matching. It provides details on the specific programs in Hot Potatoes - JQuiz, JCloze, JCross, JMix, JMatch, and Masher - and how each one works.
Purposes of Classroom Assessment.Week 3.pptx
Classroom assessment serves several important purposes:
1. It is an ongoing process that promotes learning through interaction between teachers and students.
2. It emphasizes data collection to diagnose problems, monitor progress, and provide feedback to improve student performance.
3. It involves multiple assessment strategies to obtain a variety of student information.
Purposes of Classroom Assessment.Week 3.pptx
Classroom assessment serves several important purposes:
1. It is an ongoing process that promotes greater learning through interaction between teachers and students to collect student performance data, diagnose issues, monitor progress, and provide feedback.
2. It informs and guides teaching and learning by indicating to teachers what students know and still need to learn to plan effective instruction.
3. It provides feedback and incentives to students about their understanding and how to improve.
Clinical evaluation methods by Tanoj Patidar MSc Nursing (MSN)
The document discusses various methods for clinical evaluation of students in nursing education. It describes formative and summative evaluation, as well as specific methods like case studies, practical exams, observational checklists, rating scales, oral exams, and OSCEs. Each method has advantages and disadvantages for objectively and reliably assessing students' clinical skills and competencies. An effective evaluation combines several methods to provide a holistic view of students' abilities.
CLASSROOM ASSESSMENT PRACTICES.ppt
Classroom Assessment is a systematic approach to formative evaluation, used by instructors to determine how much and how well students are learning. CATs and other informal assessment tools provide key information during the semester regarding teaching and learning so that changes can be made as necessary. "The central purpose of Classroom Assessment is to empower both teachers and their students to improve the quality of learning in the classroom" through an approach that is "learner-centered, teacher-directed, mutually beneficial, formative, context-specific, and firmly rooted in good practice" (Angelo & Cross, 1993, p. 4).
Didactic assessment
This power point is all about assessment definition, related concepts, types of assessment, tools and techniques in setting good assessment.
Didactic assessment
This document provides information about an assessment unit on didactic assessment. It includes an introduction to assessment, objectives of the unit which are to develop understanding of assessment methods and apply assessment principles for effective lesson planning. It also describes different types of assessment including formative, summative, and continuous assessment. Various assessment techniques are explained such as open-ended questions, short answer questions, and examples of each. The roles and importance of assessment in the teaching and learning process are highlighted.
roles_of_assessment_in_making_instructional_decisions.pptx
Diagnostic, formative, and summative assessments each serve different purposes in making instructional decisions. Diagnostic assessment evaluates students' prior knowledge and skills before instruction to identify weaknesses. Formative assessment is administered during instruction through tools like questioning and provides feedback to improve teaching and learning. Summative assessment evaluates student achievement after instruction for grading purposes and to gauge curriculum effectiveness. Together, these assessments inform teachers' instructional planning at different stages of learning.
Similar to Counting An Assessment Question As Zero Points (20)
Clinical evaluation methods by Tanoj Patidar MSc Nursing (MSN)
Clinical evaluation methods by Tanoj Patidar MSc Nursing (MSN)
roles_of_assessment_in_making_instructional_decisions.pptx
roles_of_assessment_in_making_instructional_decisions.pptx
More from L H
Aggregate Data
The document discusses aggregate data and descriptive statistics. It covers topics like dispersion, common mistakes in analyzing average data, Simpson's Paradox phenomenon, and properly interpreting statistical findings versus practical significance. Examples are provided to illustrate Simpson's Paradox, where averages can be misleading and hide important details that disaggregating the data by subgroups would reveal. The key lesson is that aggregate data needs to be disaggregated to tell the full story and avoid paradoxical findings.
Figure 1.1
The document provides step-by-step instructions for recreating a column chart in Excel using tracker data. It describes copying the needed data into a new workbook, organizing the data with column labels, selecting "Insert > Column > 2-D Column" to generate the initial chart, and then customizing the chart by adding a title, axis labels, and data labels. The overall process takes the reader through setting up, generating, and formatting the column chart to display the selected tracker data.
Figure 1.2
The document provides step-by-step instructions for copying tracker data from a workbook into a scratch workbook, organizing it to create a column chart in Excel, and customizing the chart with a title, labeled axes, and data labels to visualize student data. It describes copying two columns of data side-by-side, sorting or organizing the data, highlighting the organized data to insert a 2-D column chart, and using Excel's layout menu to add a title above the chart, a rotated vertical axis label, and optional data labels noting "n=" values.
Figure 1.4
The document provides step-by-step instructions for creating a column chart from student data in Excel. It describes copying student performance data from a tracker into a new workbook, sorting the data from lowest to highest mastery, selecting the data and choosing "2-D Column" to generate the chart. It then advises adding labels like the title, axis titles and inserting lines to mark thresholds of 70% and 80% mastery. The overall purpose is to recreate a graphical display of student data in Excel.
Figure 1.5
The document provides step-by-step instructions for recreating a tracker graphic in Excel using assessment data. It describes copying assessment data from a tracker into a new workbook, sorting the data, organizing it into columns for different performance categories, highlighting the organized data, inserting a 2-D column chart using the Insert menu, and adding titles and labels to the chart layout to make it accessible. The overall purpose is to take raw assessment data and transform it visually into a graphical display using Excel.
Figure 1.3
The document provides instructions for copying student assessment data from a tracker into a new workbook, organizing it into columns by category and sorting by growth level, then using this organized data to generate a column chart in Excel. The steps include highlighting the organized data and selecting "Insert" then "2-D Column" to create an initial chart, making the chart wider to see all student initials, and then adding a title and axis labels through the "Layout" menu to make the figure accessible.
Figure 1.9
The document provides instructions for creating a bar graph in Excel using student assessment data. It involves copying assessment scores and student initials from a tracker into a new workbook, sorting the data by score, organizing it with boys on the right and girls on left, highlighting the organized data, selecting "Insert" then "2-D Column" to generate an initial graph, widening the graph to see all initials, and adding titles, labels and captions through the "Layout" menu to make the graph accessible. The graph will visually display and compare students' mastery of standards.
Figure 1.7
The document provides step-by-step instructions for creating a column graph in Excel to display student assessment data. It involves copying assessment data from individual student tabs into a scratch workbook, sorting the data, organizing it into columns for boys and girls, highlighting the data and selecting "Insert" then "2-D Column" to generate a column graph. Additional steps include deleting axis numbers, scaling the vertical axis to 1.0, labeling the values as percentages, and adding titles and labels to make the graph accessible.
Figure 1.8
The document provides step-by-step instructions for recreating a graphic using Excel by:
1) Copying tracker data from assessments into a new workbook and organizing it so that Excel can recognize the numbers and plot them on a graph.
2) Creating a scatter plot graph from the organized data and formatting it by deleting unnecessary numbers and making the data points larger.
3) Adding labels, titles and axis labels to make the graph accessible and clearly labeled.
Figure 1.10
The document provides step-by-step instructions for recreating a column chart in Excel that shows student reading growth over time. It describes copying relevant data from a tracker workbook, organizing the data for the chart, selecting the column chart type from the Insert menu, and customizing the chart by adding a title, axis labels, and data labels to show growth amounts. The overall process involves transferring and formatting data, selecting the appropriate chart type, and fully labeling the chart for accessibility and interpretation.
Figure 1.11
The document provides step-by-step instructions for creating a column chart in Excel by organizing data, selecting the "Insert" menu to choose a 2-D column graph, and adding titles and labels through the "Layout" menu to make the figure accessible. It demonstrates how to take organized data and turn it into a labeled column chart using Excel's graphing features.
Figure 1.12
The document provides instructions for creating a column graph in Excel. It explains that the user must first organize their data so that Excel can generate the desired figure. They then need to highlight the organized data and select "Insert" then "2-D Column" to generate the initial graph. Some additional formatting is then needed, including adding a title by going to "Layout" then "Chart Title", and labeling the vertical axis by going to "Layout" then "Axes Titles".
Data Analysis for One Student
This document provides guidance and examples for analyzing student data and writing a data narrative focusing on one student. It includes an agenda that outlines the objectives of examining data for all students, subgroups, and individually. Examples are provided of how to write a strong student profile section, display student performance data in graphs and charts, and draw appropriate inferences about a student's achievement based on the analysis. Criteria for selecting the individual student to feature are also discussed.
Data Analysis for Subgroups of Students
This document discusses analyzing student data for subgroups. It provides an agenda that includes objectives around teaching context, analyzing data for all students, analyzing data for subgroups, and analyzing data for one student. It also includes a rubric and assessment template for subgroups. The document instructs the reader to read a sample from Kip that analyzes subgroups and consider strong features of the research questions, rationale, and data disaggregation and analysis presented. [END SUMMARY]
Data Analysis for All Students
The document outlines an agenda for a data analysis training which includes:
1) Analyzing data for all students in a class and comparing it to grade-level benchmarks
2) Analyzing subgroups of students based on factors like gender or race
3) Analyzing individual student data
It then provides examples from a sample analysis, focusing on the section analyzing data for all students in the class. The sample analysis graphs the class results, compares them to benchmarks, and analyzes the distribution of scores. The document discusses strengths of the sample analysis' graphics and written explanations.
Teaching Context
The document provides an overview of a sample teaching context section written by Kip Dynamite. It summarizes Kip's teaching context, which clearly describes that he teaches 2nd grade in all subjects at Pioneer Elementary in Preston, Idaho. Kip's class demographics reflect the overall school population and includes 5 family members. The intended audience is described as educated but without insider knowledge of education terminology. Kip's paragraphs are well organized, starting broadly and zooming into greater detail about his specific classroom. The information in Kip's teaching context was found through public sources like the internet and student information systems.
Data Inference
The document outlines an agenda and objectives for a session on data inference. The session will cover descriptive statistics, dispersion, aggregate data, asking the right questions and using the right graphics, and data inference. It will compare basic descriptive statistics and identify limitations, describe mistakes in analyzing average data, explain the purpose of data narratives, and evaluate research questions. The document provides examples of bad inferences from average data and outlines five points to consider in data inference, including that descriptive statistics don't tell the whole story, cutting the data can reveal more, statistical concepts require context, small samples can confound trends, and research questions shape inferences.
The Right Questions and Graphical Displays
The document discusses research questions and how to evaluate them. It provides examples of poor research questions that are biased or leading and lack meaning. A good research question should be minable so it can be thoroughly explored, crisp so it is succinct and easy to understand, and meaningful so there is a clear rationale for why someone should care about the answer.
Dispersion
Here are the steps to create a histogram:
1) Determine the numerical ranges or "bins" to use. Here they are in 20% increments from 0-100%.
2) Count the number of data points that fall into each bin.
3) Draw columns above each bin to represent the frequencies. The column heights show the number of data points in each bin.
Let me know if any part needs more explanation!
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Counting An Assessment Question As Zero Points
- 1. NOT COUNTING ITEMS IN THE TRACKER FILE
- 2. How can you let the tracker know that an assessment item should not count toward the overall class average?
- 3. © 2014 Relay Graduate School of Education. All rights reserved. 3 • An assessment item was completely misaligned to your scope and sequence - the item assessed content that students have not yet been taught - this might occur if you don’t author your own assessments and don’t see them ahead of time • An assessment item was complete junk - you realize (after administering the assessment) that the question was wrong or unanswerable - this is an honest mistake that occurs occasionally Appropriate Scenarios For Deleting An Item
- 4. © 2014 Relay Graduate School of Education. All rights reserved. 4 • Students didn’t do well on an item - we never go back and delete data for items where students performed poorly - that is not an accurate and meaningful measure of learning, it’s cheating students! • Students ran out of time on an assessment - we never go back and arbitrarily delete items from an assessment, just because students didn’t finish the test Inappropriate Scenarios For Deleting An Item
- 5. How does the tracker know if you wish for an item NOT to count?
- 6. © Relay Graduate School of Education. All rights reserved. 6 Simply Remove the Standard ID And Points Possible Delete the number of points possible Delete the association with a standard Once you remove the Standard ID and the points possible, the average will be blanked
- 7. © Relay Graduate School of Education. All rights reserved. 7 Even If You Have Data Entered, It Won’t Count The student response data is intact, but the scores are not counting
Editor's Notes
- AN ACCURATE AND MEANINGFUL MEASURE of student achievmeent IS AN ACCURATE AND MEANINGFUL INDICATION OF THE DEGREE, EXTENT OR QUALITY OF Student achievement. [really emphasize with intonation here] THERE’S A BIG DIFFERENCE BETWEEN AN ACCURATE AND MEANINGFUL MEASURE OF STUDENT ACHIEVEMENT VERSUS any old number.
- AN ACCURATE AND MEANINGFUL MEASURE of student achievmeent IS AN ACCURATE AND MEANINGFUL INDICATION OF THE DEGREE, EXTENT OR QUALITY OF Student achievement. [really emphasize with intonation here] THERE’S A BIG DIFFERENCE BETWEEN AN ACCURATE AND MEANINGFUL MEASURE OF STUDENT ACHIEVEMENT VERSUS any old number.