Analyzing Assessment Data - Task 3

Analyzing Assessment Data Introduction In this paper I have used a data set created for me on hypothetical students to practice analyzing and evaluating data at an individual, subgroup, and whole group level. From the data I have created tables for their summative proficiency scores, discussed the methods used, and then disaggregated the data based on subgroups. I then evaluated and analyzed the data in order to draw conclusions about individual progress and that of the whole group. Proficiency Scores For this section I am including the data set that was created for me for hypothetical students. I then created a table which includes each student, their summative proficiency score, and the method used to determine that score. Student Summative Proficiency Score (Method) 1 4.00 (Linear function) 2 2.74 (Power function) 3 2.96 (Power function) 4 4.00 (Power function) 5 3.00 (Linear function) 6 1.83 (Linear function) 7 3.01 (Power function) 8 2.67 (Linear function) 9 4.00 (Power function) 10 3.83 (Linear function) 11 4.00 (Power function) 12 3.23 (Power function) 13 3.08 (Linear function) 14 3.92 (Power function) 15 3.58 (Power function) 16 2.83 (Linear function) 17 2.87 (Power function) 18 3.83 (Linear function) 19 3.85 (Power function) 20 4.00 (Linear function) Scoring Methodology To determine students’ summative proficiency score there are numerous methods to assist an educator. There are methods that use mounting evidence, mathematical models, and technology. For this task I chose to use technology to help me determine the proficiency score through the Marzano True Calculator from Empower Learning (2020). Additionally, I used the best fit score from the results on the calculator. The best fit score then gave me which method was used and I added that into the table above. Disaggregated Data In this section I have disaggregated the data in order to look more closely at smaller groups of students. This will help me identify overall proficiency of the class, any aberrant patterns or outliers, and student learning at the subgroup level. The first table shows each student and where they fall within the subgroups. The tables thereafter have the data disaggregated according to each subgroup. Subgroup Student # 1 1, 6, 7, 10, 15, 19 2 1, 4, 11, 14 3 2, 5, 6, 8, 17 4 3, 5, 7, 9, 10, 11, 12, 13, 14, 15, 17, 20 5 2, 3, 4, 6, 8, 16, 18, 19 Subgroup 1 Test 1 Test 2 Test 3 Test 4 Test 5 Test 6 Test 7 Test 8 Student 1 2.0 2.0 3.0 3.0 4.0 3.0 4.0 4.0 Student 6 0.0 0.0 1.0 1.0 0.0 1.0 2.0 2.0 Student 7 0.0 1.0 1.0 2.0 2.0 2.0 3.0 3.0 Student 10 1.0 1.0 2.0 2.0 3.0 3.0 3.0 4.0 Student 15 0.0 0.0 1.0 1.0 2.0 2.0 3.0 4.0 Student 19 1.0 2.0 3.0 3.0 3.0 4.0 3.0 3.0 Subgroup 2 Test 1 Test 2 Test 3 Test 4 Test 5 Test 6 Test 7 Test 8 Student 1 2.0 2.0 3.0 3.0 4.0 3.0 4.0 4.0 Student 4 1.0 2.0 3.0 3.0 4.0 4.0 4.0 4.0 Student 11 1.0 2.0 3.0 3.0 3.0 4.0 4.0 4.0 Student 14 1.0 2.0 3.0 2.0 3.0 4.0 4.0 3.0 Subgroup 3 Test 1 Test 2 Test 3 Test 4 Test 5 Test 6 Test 7 Test 8 Student 2 0.0 0.0 1.0 1.0 2.0 2.0 3.0 2.0 Student 5 0.0 1.0 2.0 2.0 1.0 2.0 3.0 3.0 Student 6 0.0 0.0 1.0 1.0 0.0 1.0 2.0 2.0 Student 8 1.0 2.0 2.0 1.0 1.0 2.0 3.0 3.0 Student 17 0.0 1.0 1.0 2.0 2.0 3.0 3.0 2.0 Subgroup 4 Test 1 Test 2 Test 3 Test 4 Test 5 Test 6 Test 7 Test 8 Student 3 1.0 2.0 2.0 3.0 2.0 2.0 3.0 3.0 Student 5 0.0 1.0 2.0 2.0 1.0 2.0 3.0 3.0 Student 7 0.0 1.0 1.0 2.0 2.0 2.0 3.0 3.0 Student 9 1.0 2.0 2.0 3.0 3.0 4.0 4.0 4.0 Student 10 1.0 1.0 2.0 2.0 3.0 3.0 3.0 4.0 Student 11 1.0 2.0 3.0 3.0 3.0 4.0 4.0 4.0 Student 12 0.0 1.0 1.0 2.0 3.0 2.0 3.0 3.0 Student 13 0.0 1.0 0.0 1.0 2.0 2.0 3.0 3.0 Student 14 1.0 2.0 3.0 2.0 3.0 4.0 4.0 3.0 Student 15 0.0 0.0 1.0 1.0 2.0 2.0 3.0 4.0 Student 17 0.0 1.0 1.0 2.0 2.0 3.0 3.0 2.0 Student 20 0.0 1.0 2.0 2.0 3.0 3.0 4.0 4.0 Subgroup 5 Test 1 Test 2 Test 3 Test 4 Test 5 Test 6 Test 7 Test 8 Student 2 0.0 0.0 1.0 1.0 2.0 2.0 3.0 2.0 Student 3 1.0 2.0 2.0 3.0 2.0 2.0 3.0 3.0 Student 4 1.0 2.0 3.0 3.0 4.0 4.0 4.0 4.0 Student 6 0.0 0.0 1.0 1.0 0.0 1.0 2.0 2.0 Student 8 1.0 2.0 2.0 1.0 1.0 2.0 3.0 3.0 Student 16 0.0 1.0 2.0 2.0 2.0 2.0 2.0 3.0 Student 18 1.0 2.0 3.0 3.0 1.0 3.0 4.0 4.0 Student 19 1.0 2.0 3.0 3.0 3.0 4.0 3.0 3.0 Student 20 0.0 1.0 2.0 2.0 3.0 3.0 4.0 4.0 Pattern of Scores When analyzing data, it is important to note any patterns that are seen across the board or within subgroups. An educator may notice that the majority of students all did poorly on one specific assessment, this could indicate that the assessment was not well written or that instructions were not clear. Another example could be that a group of students did exceptionally well with an item or on an assessment, therefore indicating that they need to be challenged. Upon analyzing the summative scores for the whole group, there were patterns that stuck out to me. The majority of the class showed proficiency at the end, having a summative score of 3.0 or higher. There were fourteen out of twenty students with proficient summative scores, five that were on the higher end of a 2.0, and only one that scored below a 2.0. Additionally, all students had scores of 0.0 or 1.0 on their first assessment, with the exception of one student who scored a 2.0. This shows that overall, there was growth within the whole group as there were no students who scored below a 2.0 on their last two assessments, and only one student whose summative proficiency score was below a 2.0. This is all evidence of an increase in growth over time. However, student 2, student 6, and student 8’s scores showed a pattern that they had not grown as much as the others and indicate a lack of learning due to multiple scores of 0.0 and 1.0. These students also had the lowest summative proficiency scores. This tells me these students would need additional interventions to help with their understanding. On the other hand, students 1, 4, 11, 14, and 19 showed rapid growth and proficiency by test 3, indicating that they may benefit from some enrichment activities. Outliers When analyzing the data and focusing on outliers, there were a few that I noticed. One example that I found was student 18. This student showed consistent growth from test 1 where the student scored a 1.0, to test 4 where they had grown to a 3.0, then on test 5 dropped back down to a 1.0. The scores thereafter were back to proficient, scoring a 3.0 and then two 4.0’s. This shows me that this is an outlier in the data set. This student could have had an off day, maybe not had enough sleep, or any other distraction to make the score drop that significantly after steadily increasing with all the other tests. Therefore, the one drop did not indicate that this student was not progressing with their learning. Another example of an outlier may be student 5. This student was showing growth with each of the first tests, then stayed consistent with scores of 2.0 until test 5 where they scored a 1.0, but then went back to 2.0 and finished out the last two assessments with scores of 3.0. Although this student did start off with two low scores, their growth was evident by the consistency until that one drop back down to 1.0. I also believe this may be an outlier because the student finished strong with two scores of 3.0. A student with a similar situation to this is student 14. This student showed rapid growth going from a 1.0 to a 2.0 to a 3.0, then there was a random drop on test 4 back to a 2.0. However, their scores stayed consistently proficient for the remainder of the tests and this student ended up with a summative proficiency score of 3.92, proving that the drop may have been an outlier in the data set. Subgroups After disaggregating the data, I can take a deeper look and draw conclusions at the subgroup level. The students were placed into different subgroups and with this information I created a table for each subgroup with all of their scores in order to analyze their learning. I will first look at subgroup 1. This group had six students, all of which scored low on the first assessment. Three out of the six scored a 0.0, two scored a 1.0, and one scored a 2.0. By assessment 8 all students had proficiency scores of 3.0 or higher, with the exception of student 6 who scored a 2.0. Out of this group, student 6 is the only one who did not show significant growth; therefore, this student will need more supports put in place. As for the rest of the group, they started off low and made significant growth over time and now show proficiency. The second subgroup had only 4 students. Upon looking at this group I can conclude that this group demonstrated rapid growth and significant learning. Their beginning scores were all 1.0, aside from student 1 who scored a 2.0 on the first assessment. By test 3, all these students scored a 3.0. Their scores stayed at 3.0’s and 4.0’s, for the remainder of the assessments. Student 14 did drop back to a 2.0 for assessment 4, however this seemed to be an outlier in the data set because the scores jumped right back up to 3.0’s and 4.0’s after. This group’s overall scores indicate they could benefit from enrichment opportunities as they grew quickly and achieved summative proficiency scores of 4.0, 4.0, 4.0, and 3.92. The third subgroup had five students and after examining their data, I can conclude that they had some growth, but it was very slow. These students all stayed between scores of 0.0 and 2.0 all the way up until assessment 7. By the last assessment two students scored a 3.0 and three of them scored a 2.0. Due to the very slow growth, I took a closer look at the summative proficiency scores to help me determine their overall growth as well. These students’ summative scores were, 2.74, 3.0, 1.83, 2.67, and 2.87. With all of this in mind I feel as though this group of students could use some extra support, or even another opportunity to demonstrate proficiency, as it seems as though they were starting to get it but not quite there. Student 5 did have a summative proficiency score of 3.0 and their last two assessment were also 3.0’s, so this student may or may not need the extra support. Subgroup 4 was the largest of the subgroups with twelve students. The students in this group demonstrated proficient growth at a moderate rate. Seven of the twelve began with a 0.0 score and the other five scored a 1.0, then by the last assessment five out of the twelve scored a 4.0, six scored a 3.0, and one scored a 2.0. Their summative proficiency scores all ranged from 2.87 to 4.00. These students overall have shown that they have successfully learned the content throughout the unit and are mostly ready to move forward. There were only two students, student 3 and student 17, whose summative proficiency scores were below a 3.00, therefore these students may be able to join the students in subgroup 5 for the little extra support in order to demonstrate proficiency. In addition, knowing this is the largest subgroup, the educator could be reassured that overall, they were successful in delivering the content, as this group had twelve out of the class total of twenty students. Finally, subgroup 5 contained nine students and with this group, the scores were kind of all over the place. As with most of the class, these students all started at 0.0 and 1.0, but by the last assessment there were many differing scores. Three out of the nine scored 4.0, four scored 3.0, and two scored 2.0. Overall, these scores show growth, but it is hard to pinpoint a rate with this group as some grew quickly, some more slowly, and some stayed fairly consistent throughout. Upon looking at their summative scores the lowest was student 6 with a 1.83 and then there were two with a score of 4.00. From this subgroup I can conclude that there are a couple students who are ready for enrichment opportunities as well as students who will need extra interventions and more opportunities to grow with this content. When I look at the class as a whole, the evidence shows that there was overall growth and that most students are ready to move on. Fourteen out of the twenty students had summative proficiency scores of 3.00 or higher, five of those having scores of 4.00. This leaves six students who need to be retaught, receive extra support, and/or be given more opportunities to demonstrate proficiency with the content. After closely analyzing the summative proficiency scores and the disaggregated data in the subgroups, I am able to see where there were patterns, outliers, strengths, and areas of need. All of this would help me to form groups and determine where I would need to go with my instruction in order to differentiate and move all students forward appropriately.