I. ASCRC General Education Form (revised 3/19/14)
Use to propose new general education courses (except writing courses), to change or
renew existing gen ed courses and to remove designations for existing gen ed courses.
Note: One-time-only general education designation may be requested for experimental courses
(X91-previously X95), granted only for the semester taught. A NEW request must be
submitted for the course to receive subsequent general education status.
Group
II. Mathematics
VII: Social Sciences
(submit
III. Language
VIII: Ethics & Human Values
separate forms X III Exception: Symbolic Systems * IX: American & European
if requesting
IV: Expressive Arts
X: Indigenous & Global
more than one
V: Literary & Artistic Studies
XI: Natural Sciences
general
w/ lab  w/out lab 
education
VI: Historical & Cultural Studies
group
* Require a Symbolic Systems Request Form.
designation)
Dept/Program Forest Management
Course #
FORS 201
Course Title
Forest Biometrics
M115, M121, M122, M151, M162, M171, or M172
Prerequisite
Credits
3
II. Endorsement/Approvals
Complete the form and obtain signatures before submitting to Faculty Senate Office
Please type / print name Signature
Date
Instructor
David Affleck
Phone / Email x4186 / david.affleck@umontana.edu
Program Chair Elizabeth Dodson
Dean
James Burchfield
III. Type of request
New
One-time Only
Renew X
Change
Remove
Reason for Gen Ed inclusion, change or deletion
Description of change
IV. Description and purpose of the general education course: General Education courses
must be introductory and foundational within the offering department or within the General
Education Group. They must emphasize breadth, context, and connectedness; and relate course
content to students’ future lives: See Preamble:
http://umt.edu/facultysenate/archives/minutes/gened/GE_preamble.aspx
Scientists and professionals in every natural resource field must reason from empirical data to reach inferences or
decisions concerning the systems they study and manage. This course emphasizes the utility of empirical data, the
inherent variability of environmental data, and the consequent need for the application of statistical reasoning in
the interpretation of data. The purpose of the course is to introduce students to effective means of visualizing,
describing, and accounting for variation in order to better understand ecological systems and processes.
Using data from a spectrum of environmental applications including forestry, hydrology, and wildlife biology, the
course begins with a development of graphical and numerical methods of data analysis. The laboratory component
parallels this, and gives students the opportunity to apply these methods to uncover pattern and variability in real
data. Students are next introduced to the basic tenets of experimental design and statistical sampling, which, in
turn, motivate a treatment of probability models. The last segment of the course integrates probability modeling
and data analysis into methods for statistical inference. In laboratory sessions students apply these formal
inferential procedures in environmental applications involving techniques such as analysis of variance and simple
linear regression.
V. Criteria: Briefly explain how this course meets the criteria for the group. See:
http://umt.edu/facultysenate/documents/forms/GE_Criteria5-1-08.aspx
1. Rigorously presents a mapping between a real-world
system and a human abstraction of the system.
The processes and heterogeneity of natural systems
are represented in the abstract, as qualitative and
quantitative data. Data are consistently referenced to
meaningful, measurable ecological attributes, yet the
general concepts of data and data analytic methods
are reinforced in order to unify the treatment of
observations from a range of natural resource
applications. Additionally, probability models are
used to abstract the data generating processes and are
made central to the measurement of evidence in
support of alternative statistical hypotheses.
2. Applies analysis, reasoning and creative thinking in
the understanding and manipulation of symbolic
codes.
Data analytic methods are central to the course and
are applied extensively in laboratory sessions. Both
graphical and numerical modes of analyses are
considered. Analyses are not prescribed, however;
students must identify the relevant features of the data
in light of the problems that are presented to them and
then weigh the effectiveness of alternative
approaches.
3. Utilizes alternative methods of communication,
perception, and expression in order to encourage
rigorous thinking.
An important component of this course is the
empirical evaluation of ecological hypotheses and
relationships. This requires, firstly, that the relevant
ecological concepts be communicated in statistical
terms. Patterns and trends perceived through various
analyses of data then must be related back to the
natural systems of interest. Thus, the symbolic
system is used as a tool to facilitate rigorous analysis,
but the results are interpreted and communicated in
terms of concrete ecological relationships.
VI. Student Learning Goals: Briefly explain how this course will meet the applicable learning
goals. See: http://umt.edu/facultysenate/documents/forms/GE_Criteria5-1-08.aspx
1.
Demonstrate an understanding of the symbols and the
transformations of the system.
In laboratory sessions in particular, students will
apply formulas and manipulations of data to calculate
quantities of interest. Students’ understanding of the
need for these transformations will be demonstrated
through their selection of an analytical approach and
through their interpretation of the data summaries.
2.
Relay and interpret information in terms of the given
symbolic system.
Extraction of information from data is, in essence,
the subject of the course. The symbolic system is a
means to an end, with the end being a more thorough
understanding of the natural systems under study.
3.
Apply creative thinking using the symbolic system in
order to solve problems and communicate ideas.
Throughout the course, students confront real
problems in natural resource science and
management. They must determine how to
efficiently collect data, how to effectively analyze
data, and how to draw valid inferences from data in
order to solve these problems.
VII. Assessment: How are the learning goals above measured? Please list at least one
assignment, activity or test question for each goal.
1. Weekly written laboratory assignments utilize real-world data on natural resources. On each of these
assignments, students must conceptualize the variables and their values in terms of the symbolic system in order to
apply appropriate transformations/formulae. For example, on the 3 rd laboratory assignment, students are supplied
with data on black bear dimensions (length, weight). They are then asked to conceptualize the data as continuous
quantitative variables (“x” and “y”) and to apply various operations:
“Explain in words what is meant by each of the following formulas. Then use the black bear data above to
perform the appropriate calculation.
2
1.  xi
2.  yi
3.  xi
4.  (xi – x̄ˉ )2
5.  xi yi ”
2. Symbolic representation of attributes or relationships is a major theme of the course. For example, in the 5th
laboratory assignment, students are supplied with tree biomass and tree size data and then are asked to i) describe
how certain attributes are statistically related (using linear regression) and ii) explain what the regression
coefficients actually mean in terms of tree biology:
“Fit a linear regression of tree biomass on tree basal area and report the values of the slope and intercept
coefficients. Interpret the slope coefficient: What does its value actually mean?”
Carrying out this exercise requires an ability to translate the tree measurements into a symbolic representation,
apply the appropriate mathematical formulae to that symbolic representation, then translate the symbolic results of
those operations back into the real-world domain of tree size inter-relationships.
3. Examination questions are designed to challenge students beyond applications of formulae or interpretations of
data. In particular, these questions are designed to assess whether students have an understanding of the
foundations and limitations of the statistical methods. For example:
“A crew contracted to replant a clearcut claims to have planted 4,500 seedlings per acre. You decide to check
this claim by installing plots at 36 random locations across the cutblock. Across your plots, you observe a mean
of 4,052 seedlings per acre with a standard deviation of 2,247 seedlings per acre.
(a) At the 5% significance level, is there evidence that the planting crew overstated the stocking level?
(b) Explain the need for the significance test in part (b) above. That is, since the sample mean is 4,052 seedlings
per acre, doesn’t that by itself contradict the planting crew’s claim? ”
To properly answer these questions, students must be able to conceptualize the sample data as a single realization
of a population of different possible outcomes. Part (a) requires them to logically develop a statistical hypothesis
testing framework and carry it through to a logical conclusion. Doing so relies heavily on translation of the
problem into the symbolic system of random variables, as well as on back-transformation of the results into a very
practical conclusion regarding operational/contractual compliance. Part (b) pushes them to communicate why
these symbolic systems are used, and how they are relevant to this type of real-world problem.
VIII. Justification: Normally, general education courses will not carry pre-requisites, will
carry at least 3 credits, and will be numbered at the 100-200 level. If the course has more than
one pre-requisite, carries fewer than three credits, or is upper division (numbered above the 200
level), provide rationale for exception(s).
Not applicable.
IX. Syllabus: Paste syllabus below or attach and send digital copy with form.  The syllabus
should clearly describe how the above criteria are satisfied. For assistance on syllabus
preparation see: http://teaching.berkeley.edu/bgd/syllabus.html
See attached.
Please note: Approved general education changes will take effect next fall.
General education instructors will be expected to provide sample assessment items and
corresponding responses to the Assessment Advisory Committee.