Early drafts of the present accreditation
standards on assessment referred to a desire by the commission that
there be an ongoing dialog about learning and assessment. Dialog as in
a meaningful exchange of information. Assessment has tended to be a
monologue, of which I continue to be guilty. Yet I do not know how to
start a dialog if no one says anything. So I continue my monologues on
the assessments I am doing in my own classrooms.

MS 150 Statistics remains a fairly traditional chalk and talk lecture
style course. The course coverage well echoes industry standard texts.
The topics are typical of many community college introductory
statistics courses, and the course articulates well to other
institutions. Assessment remains driven by quizzes, tests, a midterm,
and a final. The model of assessment of course level student learning
outcomes is one in which performance on student learning outcomes is
aggregated to generate higher level assessments. This is one of the
models that I outlined in the SLOAP presentation in the spring of 2006.

Individual item analysis of quizzes and tests is mapped to outcomes on
the course outline. The aggregated average performance on all outcomes
is then used as a way of reporting "internal" accomplishment of the
program learning outcome. This first table details the item analysis on
instruments since term start:

Src |
Q |
Description |
l |
Outref |
SLO |
Corr |
Corr% |

Pre | P1 | level of measure | 1 | 1 | Calculate basic statistics | 2 | 5% |

Pre | P2 | sample size | 1 | 1 | Calculate basic statistics | 5 | 12% |

Pre | P3 | mean | 1 | 1 | Calculate basic statistics | 9 | 21% |

Pre | P4 | median | 1 | 1 | Calculate basic statistics | 13 | 31% |

Pre | P5 | mode | 1 | 1 | Calculate basic statistics | 4 | 10% |

Pre | P6 | min | 1 | 1 | Calculate basic statistics | 34 | 81% |

Pre | P7 | max | 1 | 1 | Calculate basic statistics | 34 | 81% |

Pre | P8 | range | 1 | 1 | Calculate basic statistics | 10 | 24% |

Pre | P9 | standard dev | 1 | 1 | Calculate basic statistics | 3 | 7% |

Pre | P10 | coef var | 1 | 1 | Calculate basic statistics | 0% | |

Pre | P11 | slope | 5 | 5 | Perform a linear regression and make inferences based on the results | 4 | 10% |

Pre | P12 | intercept | 5 | 5 | Perform a linear regression and make inferences based on the results | 4 | 10% |

Pre | P13 | slope-intercept eqn | 5 | 5 | Perform a linear regression and make inferences based on the results | 1 | 2% |

Pre | P14 | predict y given x | 5 | 5 | Perform a linear regression and make inferences based on the results | 6 | 14% |

Pre | P15 | predict x given y | 5 | 5 | Perform a linear regression and make inferences based on the results | 4 | 10% |

q01 | 1234 | level of measure | 1 | 1 | Calculate basic statistics | 10 | 20% |

q01 | 5 | calculate percentage | 1 | 1 | Calculate basic statistics | 23 | 46% |

q01 | 6 | completed homework | 1 | 1 | Calculate basic statistics | 35 | 70% |

q01 | 78 | remembered weight, bfi | 1 | 1 | Calculate basic statistics | 48 | 96% |

q01 | 9 | perform percent multiplication | 1 | 1 | Calculate basic statistics | 47 | 94% |

q01 | 10 | recall fact | 1 | 1 | Calculate basic statistics | 47 | 94% |

q01 | 11 | recall fact | 1 | 1 | Calculate basic statistics | 44 | 88% |

q01 | 12 | body fat classification | 1 | 1 | Calculate basic statistics | 36 | 72% |

q02 | 1 | level of measure | 1 | 1 | Calculate basic statistics | 14 | 29% |

q02 | 2 | min | 1 | 1 | Calculate basic statistics | 46 | 96% |

q02 | 3 | max | 1 | 1 | Calculate basic statistics | 42 | 88% |

q02 | 4 | range | 1 | 1 | Calculate basic statistics | 34 | 71% |

q02 | 5 | bin width | 2 | 2 | Represent data sets using charts and histograms | 34 | 71% |

q02 | 6 | frequency table | 2 | 2 | Represent data sets using charts and histograms | 9 | 19% |

q02 | 7 | histogram chart | 2 | 2 | Represent data sets using charts and histograms | 10 | 21% |

q02 | 8 | shape of distribution | 2 | 2 | Represent data sets using charts and histograms | 4 | 8% |

The above data is aggregated in the following table which is using the reduced set of outcomes found in the proposed MS 150 outline:

Outref |
Students will be able
to: |
Sum |
Count |
Avg |

1 | Calculate basic statistics | 11.35 | 22 | 52% |

2 | Represent data sets using charts and histograms | 1.19 | 4 | 30% |

3 | Solve problems using normal curve and t-statistic distributions including confidence intervals for means and hypothesis testing | 0 | 0 | 0% |

4 | Determine and interpret p-values | 0 | 0 | 0% |

5 | Perform a linear regression and make inferences based on the results | 0.45 | 5 | 9% |

PSLO | define mathematical concepts, calculate quantities, estimate solutions, solve problems, represent and interpret mathematical information graphically, and communicate mathematical thoughts and ideas. | 18.06% |

Note that these proposed outcomes are also on the current outline as course level outcomes. The proposed outline simplifies the outline by removing the plethora of individual specific learnings, performance of which is documented in the first table above. This revision to reduce to a set of five broader outcomes is in line with the direction the curriculum committee has been taking over the past twelve to eighteen months.

Note that in the table above the

The aggregate accomplishment of the PSLO over time is depicted in the following chart:

Note that at term start (pre is the pretest) the students were already able to perform some basic statistical calculations.

In the world of I, P, D this course has the difficulty of being a solo shot at statistics for all students in associate degree programs. The course introduces concepts, students practice them, and then demonstrate proficiency on internal quizzes and tests. While some students may go on to the third year business program and take Business Statistics, this cannot be said to be the P or D for MS 150 as that is a wholly separate program with its own entrance requirements.

I will be using statistics in SC 130 Physical Science, but MS 150 Statistics is not a pre-requisite nor will it likely ever be a pre-requisite course. On the contrary, SC 130 is a good place to introduce concepts such as standard deviation. The two courses well reinforce each other, with either one being good "practice" for the other.

My hope ultimately is that others will find a useful assessment idea here or there among the tremendous amount of chaff I generate.

As always I long for IRPO to bring back information from the field - from alumni, employers, and the community - on whether MS 150 alumni are a) using statistics, b) able to effectively use statistics, and c) what they need to do that the course did not prepare them to do. This would provide critical triangulation information.