Numeric information in graphic forms skills pre-assessment
The second program learning outcome which SC 130 Physical Science addresses is "Students will be able to present and interpret numeric information in graphic forms." The sixteen students in physical science this summer were given six questions which focused on this outcome.
The following table indicates the number of students answering a given question correctly.
Beyond a basic ability to plot data which was presented in an xy table, student abilities range from weak to almost no ability to complete the item correctly.
A colleague's first reaction was that the result is due to the lack of a mathematics pre-requisite on the course. There is no mathematics pre-requisite. Conceivably a student could be in the class who has not passed even developmental level mathematics.
The following grid notes the highest mathematics course the student passed at the college and their performance by question number on the pre-assessment. Note that students who have passed MS 101 Algebra and Trigonometry have either passed MS 100 College Algebra or have placed above MS 100 College Algebra. Four of the sixteen students also completed MS 150 Statistics, their performance in noted in the last four rows.
The instrument asked only about very basic mathematical skills. While the above might appear discouraging, I have seen this sort of result before. Students do not retain even fundamental skills out beyond the end of a course. And while I am personally pleased that the statistics alumni had a higher average (3.25) than the average for students in the other mathematics courses (1.75), I recognize that the small sample size makes this statistically irrelevant.
While motor learning skills are well retained even when not practiced, academic skills are apparently lost fairly rapidly. The majority of these students were in the listed math classes within the past few terms.
In curriculum committee over a half decade ago the question was often asked, "What skills does a graduate have?" My sense was that any measurement near graduation would show the same sort of specific skills loss as is seen above.
Given the background of the students, my hope is that with review the students might come back up to speed on material I have reason to believe they had mastered at one time in the past.
The following table indicates the number of students answering a given question correctly.
| Skill | Correct | Percent |
| Calculate slope from line on graph | 4 | 25.00% |
| Determine units from axis labels | 3 | 18.75% |
| Determine y-intercept from line on graph | 1 | 6.25% |
| Write out the equation of line from graph | 1 | 6.25% |
| Plot xy data given a table labeled x, y | 10 | 62.50% |
| Calculate slope from line on graph | 3 | 18.75% |
Beyond a basic ability to plot data which was presented in an xy table, student abilities range from weak to almost no ability to complete the item correctly.
A colleague's first reaction was that the result is due to the lack of a mathematics pre-requisite on the course. There is no mathematics pre-requisite. Conceivably a student could be in the class who has not passed even developmental level mathematics.
The following grid notes the highest mathematics course the student passed at the college and their performance by question number on the pre-assessment. Note that students who have passed MS 101 Algebra and Trigonometry have either passed MS 100 College Algebra or have placed above MS 100 College Algebra. Four of the sixteen students also completed MS 150 Statistics, their performance in noted in the last four rows.
| math course | q1 | q2 | q3 | q4 | q5 | q6 | sum |
| 100 college alg | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 100 college alg | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 100 college alg | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 100 college alg | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 100 college alg | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 100 college alg | 1 | 0 | 0 | 0 | 1 | 0 | 2 |
| 100 college alg | 1 | 0 | 0 | 0 | 1 | 0 | 2 |
| 100 college alg | 0 | 1 | 0 | 0 | 1 | 1 | 3 |
| 100 college alg | 1 | 1 | 0 | 0 | 1 | 1 | 4 |
| 100 college alg | 0 | 0 | 1 | 1 | 1 | 1 | 4 |
| 100 college alg | 1 | 1 | 1 | 1 | 1 | 1 | 6 |
| 101 alg & trig | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 101 alg & trig | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 101 alg & trig | 1 | 1 | 0 | 0 | 1 | 0 | 3 |
| 106 tech math | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 106 tech math | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 150 statistics | 0 | 0 | 0 | 0 | 1 | 0 | 1 |
| 150 statistics | 0 | 0 | 1 | 1 | 1 | 1 | 4 |
| 150 statistics | 1 | 0 | 0 | 0 | 1 | 0 | 2 |
| 150 statistics | 1 | 1 | 1 | 1 | 1 | 1 | 6 |
The instrument asked only about very basic mathematical skills. While the above might appear discouraging, I have seen this sort of result before. Students do not retain even fundamental skills out beyond the end of a course. And while I am personally pleased that the statistics alumni had a higher average (3.25) than the average for students in the other mathematics courses (1.75), I recognize that the small sample size makes this statistically irrelevant.
While motor learning skills are well retained even when not practiced, academic skills are apparently lost fairly rapidly. The majority of these students were in the listed math classes within the past few terms.
In curriculum committee over a half decade ago the question was often asked, "What skills does a graduate have?" My sense was that any measurement near graduation would show the same sort of specific skills loss as is seen above.
Given the background of the students, my hope is that with review the students might come back up to speed on material I have reason to believe they had mastered at one time in the past.
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