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Michigan Assessment Consortium Common Assessment Development Series Module 14 – Presenting the Results of an Assessment

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Developed by Bruce R. Fay, PhD & Ellen Vorenkamp, EdD Assessment Consultants Wayne RESA

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Support The Michigan Assessment Consortium professional development series in common assessment development is funded in part by the Michigan Association of Intermediate School Administrators in cooperation with …

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In Module 14 you will learn about Score types… Standards-based reports… Graphical Representations…

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So, you’ve… Developed a test (for use as a ‘common’ assessment) Pilot / field-tested it (right?) Looked at the field test results (of course) Now what?

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Presenting Your Results Before you present the results of your test, you need to be clear about: Who the audience is Why they are seeing this data? (What?) Why they should care about it? (So what?) What you want them to do as a result of seeing it? (Now what?)

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SCORE TYPES

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A score by any other name Many score types that you may have heard of are really only appropriate for Norm-Referenced Tests (NRT), such as percentile rank, stanine, and grade level equivalent. Your common assessment is a Criterion- Referenced Test (CRT), so lets focus on score types that are appropriate for that.

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Raw Scores Number of items correct or Number of points earned Q? What’s the difference? A! None, if each item has the same point value, otherwise…

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Scaled Score (equal weight) If each test item has the same “weight”, say 1 point (1 if correct, 0 if wrong) then % correct is: The simplest scaled score you can create The same as %points earned Puts the raw score on a scale of 0 – 100

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Scaled Score (unequal weight) If each test item does not have the same number of points (there are weighted and/or partial credit items on the test) then % correct becomes % of total possible points earned You still end up with a 0 – 100 scale

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% Correct Features (Issues) Features A “common” scale, as in “widely used” A “common” scale, as in “the same regardless of raw score points” Intuitively interpretable (maybe) Permits comparisons between different tests Issues Can/will be misinterpreted Can make a 10 point test and a 100 point test appear equally important Widely held belief that scores in certain ranges (60-70, 70-80, etc.) have some inherent meaning

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% Correct Interpretation Q? Is 50% correct good or bad? A!: We don’t know yet. We don’t discuss standard–setting until the next module (15). But most people think it is intuitively obvious that this is a “bad” score.

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Other ways to scale? Yes, but we don’t really need them…

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STANDARDS-BASED REPORTS

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Two kinds of “standards” Content Standards The definition of the content to be learned; what students are to know and be able to do Performance Standards The definition of how good is good enough on a test to determine if, or the extent to which, students know and can do

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Reporting by Content Standards This is our concern in this module The next module (15) deals with performance standards

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Let’s consider… A test covering 5 GLCEs with 5 selected- response items per GLCE, with each item worth 1 point (25 points total). Q? What does a raw score of 20 (a % correct scaled score of 80%) mean? A! It depends

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Depends on What? Student A GLCE 1: 4/5 GLCE 2: 4/5 GLCE 3: 4/5 GLCE 4: 4/4 GLCE 5: 4/5 Student B GLCE 1: 5/5 GLCE 2: 5/5 GLCE 3: 5/5 GLCE 4: 3/5 GLCE 4: 2/5 Same or different?

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How about these two? Student C GLCE 1: 5/5 GLCE 2: 5/5 GLCE 3: 4/5 GLCE 4: 3/5 GLCE 5: 3/5 Student D GLCE 1: 5/5 GLCE 2: 5/5 GLCE 3: 5/5 GLCE 4: 5/5 GLCE 5: 0/5 These 4 examples all have a raw score of 20 (80% correct) but represent 4 different performances by the students.

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Another way to see it GLCEA #A %B #B %C #C %D #D % total

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Scores by “Standard” Remember, we haven’t set performance standards yet, so we really can’t say what these scores mean Even so, 5 out 5 may suggest that a student knows the material and 0 out 5 may suggest that they don’t (depends on item-GLCE match) However…even though this is a CRT, you can’t make instructional decisions without the context of the overall pattern of scores

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Say what? There will often be extreme scores (outliers) that are not representative of most of the scores in a set. Q? What if most of the students scored a 0 or a 1 on GLCE 5 in the example? A! Maybe a picture would help

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GRAPHICAL REPRESENTATIONS Or, I can see clearly now

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Guidelines for Good Graphs Title & Subtitles Data Source and Time Frame Axis Labels Legend Viewable Colors Readability (3-D doesn’t make it better)

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Appropriate Type Bar Graphs Line Graphs Scatterplots Stem & Leaf Pie Charts (evil)

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Results for 25 students (# scoring at each score point for each GLCE)

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The Data Here’s how the spreadsheet is set up GLCE GLCE GLCE GLCE GLCE Note: This will be replaced with a table so it looks better

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Let’s Assume… We have established that 3 out of 5 on each standard is an acceptable standard of evidence that a student understands the GLCE in question (hey, these were hard items) Then students who score a 3, 4, or 5 on the cluster of items for GLCE can be considered “proficient” while students with a 2, 1, or 0 are not.

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Proficiency by Standard (for 25 Students) GLCE# Not Prof% Not Prof# Prof% Prof This is what the previous data looks like in table form. Would a picture help?

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Proficiency by Standard (for 25 Students)

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Here’s the data GLCENPProf Note: this will be replaced with a table so it looks better

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Repeated Measures If you test the same content on more than one occasion, you can look at your test results over time. As an example, lets look at test results for our class of 25 students on a pre-test, two intermediate tests, and a post-test covering the same five GLCEs. We will look only at GLCE 1, with 5 points possible each time.

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The Data – Results for 25 students on 4 tests by score point Score PointsPre-TestTest 1Test 2Post-Test (This is a somewhat idealized example), but interpret it with caution!

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And here’s the picture – Results for 25 students on 4 tests by score point

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The Excel spreadsheet ScorePre-TestTest 1Test 2Post-Test Note: This will be replaced with a table for better viewing

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Conclusions

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Next Module

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