course in Spring ’07 in preparation for the formal initiation of the major that fall. Enrollments in CPLS 2110 prior

to Fall ’09 were quite small; the first two undergraduate computational science degrees will be awarded during the

2010-2011 academic year.

The Computational Science (CPLS) program at Stockton has close ties to the Physics (PHYS) program. Therefore

an early opportunity to expand the computational content of cognate courses came when CPLS faculty were asked to

teach the classical mechanics course that is a standard part of the undergraduate physics major. The course was duly

renamed PHYS 3220 (Computational Mechanics) and taught by the first author in Spring ’08 and ’09.

the Environmental Science program. Subsequent discussions among the CPLS faculty highlighted the importance of

developing an assessment tool that would give an early warning to students needing to remediate basic mathematics

skills.

PHYS 3220 focuses on Newtonian mechanics at a medium to advanced level. In Spring ’08 the first author

introduced a sequence of computational projects involving mechanics problems of increasing difficulty. The projects

ranged from modeling projectile motion to modeling motion of connected multiple rigid bodies (a complete list is

given in Table 1).

Table 1: Topics of the projects in PHYS 3220 and the associated computational concepts.

topic computational concept

simulation

motion of multiple connected rigid

bodies

solving stiff DAEs

Does renovating an existing course by taking a computational approach detract from the traditional content, which

is after all the raison d’ˆetre of the course? This question motivated a study utilizing the Force Concept Inventory (FCI)

[9], a well-established tool that assesses mechanics knowledge, to look at whether student understanding was affected

by the increased emphasis on computational thinking.

CPLS faculty make extensive use of projects in both CPLS and cognate courses. Project work reflects current

A decided benefit of a pancurriculum rubric is that it reinforces a student’s computational skills through consistent

emphasis on core competencies throughout the four years of undergraduate study.

The second author designed a brief assessment of basic skills that was administered on the first day of the Fall ’09

semester. Questions gauged geometric understanding of the meaning of the slope and y-intercept of a straight line;

recognizing common functions (mx + b, sin x, and e

) when plotted as data sets as they might appear if obtained as

experimental results (i.e., with noise); and associating a (global) minimum, a (local) maximum, a point with positive

Table 2: Survey questions about experience with and attitudes toward computing. Odd-numbered questions Q1, Q3, etc. omit the words in

parentheses; even-numbered questions include them.

(Q1,2) How often have you used a computer when doing an assignment in a (math or science) course?

(Q3,4) How often have you used spreadsheet (e.g., Excel) software in a (math or science) course?

(Q5,6) How often do you use a computer, even if it is not required, to do an assignment in a (math or science) course?

(Q7,8) How often have you found that using a computer helped you understand a concept in a (math or science)

course?

The FCI [9] is a tool developed to help assess facility with and misconceptions about Newtonian thinking as a

way to explain motion and its causes. It consists of 30 conceptual questions in multiple choice format and has been

FCI were not included in the final grade, so the assessment was of low stakes. To further alleviate test anxiety, the FCI

was administered in such a way that the instructor was able to pair the results without identifying individual students.

The aforementioned rubric was first used in PHYS 3220 in the Spring ’09 semester and contained six categories

score of 180. Table 3 contains a sample category. To test the modified rubric the authors created a pool of ten

exemplary projects, five each from the most recent instances of CPLS 2110 (Fall ’09) and PHYS 3220 (Spring ’09).

This pool was then graded by both authors.

Table 3: Example rubric category. The scale is labeled by the category name and followed by guidelines describing excellent (9–10, A range), good

(7–8, B range), satisfactory (5–6, C range), and poor (0–4, D and F range) work.

construction

3

14 of 17 students (82%) answered questions about slope and y-intercept of a line correctly. Given “noisy” data

following exponential, linear, and sinusoidal trends and equations for each function, 14 of 17 students (82%) correctly

matched the exponential data and function, 13 of 17 students (76%) correctly matched the linear data and function,

and 16 of 17 students (94%) correctly matched the sinusoidal data and function. Of the seven students who answered

one or more of these questions incorrectly, three withdrew from the course almost immediately, one withdrew early

in the semester, and the remaining three obtained the three lowest course grades.

On the question involving rates of change and slope at a point on a curve, 13 of 17 students (76%) identified the

minimum with a slope of smallest magnitude and 2 of 17 students (12%) identified the maximum with a slope of

reference to the rubric, the correlation (r = 0.63) between the total scores we assigned to the ten projects is reassuring.

We selected the same project as best and identified the same two projects as least accomplished. Moreover, we con-

curred on four of the top five projects and four of the bottom five projects. We evaluated two projects near the middle

of the score distribution rather differently. The correlation between our rankings is fairly strong (r = 0.76), which is

significant at the 98% level (p = 0.02; two-tailed t-test). This lends support to the notion that experienced teachers of

results and

discussion

data visualization

results and

discussion

introduction

introduction

distinctive

features

learning

accuracy

limitations

math and science

critical analysis

data visualization

formatting

Figure 3: Evolution of the pancurriculum rubric. Categories are arranged in columns corresponding to the original rubric (first column, Sections 2.3

and 3.3), the expanded rubric (second column, Sections 3.3 and 4.3), and the rubric after further refinement (third column, Sections 4.3 and 5).

Arrows between columns show the elaboration and merging of categories as the rubric evolved. Categories in the current rubric are constellated

into the supercategories to the far right.

Table 4: Pancurriculum rubric categories and evaluation criteria.

Table 4: Pancurriculum rubric categories and evaluation criteria (continued).