Using Mobile Computing Strategies to Enhance Student Learning and Motivation in Probability and Statistics

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Using Mobile Computing Strategies to
Enhance Student Learning and

Motivation in

Probability and Statistics

A Proposal for Mobile Computing

S. Kellogg and F. Matejcik, Industrial Engineering


The Need for Change

As a result of a global economy and the
move to lean enterprises, industry has taken a stronger stance in
asking for a different type of engineering graduate that has the leadership and complex thinking skil
ls needed
by today's industry. T
o maintain the nation’s economic competitiveness and imp
rove the quality of life for
people around the world, engineering educators and curriculum developers must anticipate dramatic changes
in engineering practice and a
dapt t
heir programs accordingly [1].
This report from the National Academy of
Engineering,
written by a group of distinguished educators and practicing engineers from diverse backgrounds,
includes various scenarios for the future based on current scie
ntific and technological
trends
. This report and a
companion phase II report [2] identifies som
e of the
ideal attributes of the engineer of 2020

as well as
recommendations for curricular innovations that begin to address these attributes.
Further, this is not an
isolated study.
The Accreditation Board for Engineering and Technology (ABET) implemen
ted a revolutionary
approach to accreditation by focusing on what is learned rather than what is taught, thereby fostering
innovation [3].
Engineering education
research
is rapidly becoming a researc
h discipline in its own right with
substantial support b
y the National Academy of Engineering and the National Science Foundation.
South
Dakota is one of a handful of states that has joined a new consortium to promote development of workforce
skills needed for the 21
st

Century [4].
Over and over, we are heari
ng the same repetitive cries for development
of team skills, leadership skills, a better understanding of business processes, an ability to innovate and think
outside of the box, and an ability to communicate effectively and organize work in a diverse envi
ronment

(see,
for example, [
5
-
8
]
)
.


In short, industry needs engineering graduates that can help solve and implement
solutions to the complex problems industry is faced with today.



Department Transformational Initiative


The industrial engineering
department adopted a long term strategy to respond to these changes and to
develop a more cohesive and systemic approach to engineering education. The holistic learner development
model [
9, 10
] integrates
different aspects of student development [
7
,
11
-
16
]
with
technical skills through a
thoughtful application of
engineering best practices for student engagement

[6
]
.
More specifically, the
department seeks to improve student learning through four pill
ars of undergraduate education that provide an
intentio
nal curricular focus for
student development and engineering education
.
Figure 1 below

is
adapted
from Purdue’
s Engineer of 2020 Initiative and
provides a conceptual framework for skill
development for the
engineer of 2020.


While Figure 1 below display
s the skills and attributes for the engineer of 2020, it provides only a limited
framework for development of these skills. In this regard, the department has
a research based approach on
engineering education, assessment, and best practices. For nearly
a decade the department has adopted a
best practice approach for pedagogies of engagement [6], intellectual diversity [5, 16], and intellectual
development [11
-
13]. Department courses routinely integrate cases, role plays, simulations,
active/collaborativ
e learning,
multi
-
disciplinary team projects,
and service learning components.
In short, the
department has adopted pedagogies that replace lectures and passive learning with active hands
-
on and real
world learning. This has been a huge effort, but
the r
esults have been staggering. Over seven years of data
suggests a new culture that embraces diverse learners, alternative modes of intellectual inquiry, improved
team skills, and better complex thinking skills (see, for example,
http://ie.sdsmt.edu/Employer/Employers.htm

for a summary of this data).




Figure 1. Four Pillars
of

Development of the Engineer of 2020


While department assessment results are quite impressive (intellectual gains are nearly a half
-
step higher than
our peer group), thus far we have only initiated one step of a long term multi
-
step process for true curricular
transformation and continuous im
provement. The next step of this process is to create a dynamically
interactive classroom through
classroom inversion and mobile computing.


Enhancing Student Learning and Motivation in Probability and Statistics


The goal of this project is to expand
on earlier lessons by using elements of classroom inversion and mobile
technologies to facilitate an interactive, content rich, open
-
ended problem solving classroom in probability and
statistics. This particular course is ch
osen for a number of reasons.
First, w
hile significant gains have been
made in some curricular areas, fewer of these active learning elements have been successfully incorporated in
the more
quantitative courses that stress technical skills. While the development effort is substantial,

it is also
likely that gains through classroom inversion and active learning are likely to be substantial as well.

A second reason for this course is that
,

while important for most engineers,

for the industrial
engineering and the engineering management

disciplines, a solid foundation of statistical reasoning is critical.
While Fundamentals of Engineering (FE) analysis and course assessments indicate that, in general, student
technical skills are good, a weakness remains in students’ abilities to transl
ate information and skill sets from
one Carnegie unit (context) to another. This is particularly problematic for industrial engineering students who
complete 6 credits in probability and statistics, but have difficulty translating that information to indu
strial
engineering applications in simulation, quality control, stochastic models, work measurements, and human
factors. Indeed, long term tracking over 6 years utilizing the Fundamentals of Engineering and a Concepts
Inventory show little, if any, gains
in statistical reasoning (see Figure 2 below).
Conceptual difficulty in
probability and statistics is not limited to SDSMT students. Difficulties in learning basic concepts in probability
and statistics and the implications for open
-
ended design and anal
ysis are well established [17].




Figure 2. Probability and Statistics Concept Inventory Results (2003, 2008)



A wholly self
-
serving reason for selection of probability and statistics is that the department has already
developed a number of
simulations and interactions for this area (
http://ie.sdsmt.edu/ProbWeb/Prob.html

).
Doing a full classroom inversion for a course of this nature requires an enormous effort and is beyond the
scope of
this rfp. Nevertheless, we believe that substantial progress and subsequent gains can be made by
leveraging much of the work already in place.


Finally, conceptual gains in probability and statistics
,

is
of significant interest to the industrial enginee
ring
and broader engineering community. Indeed, there is now a whole host of research in the engineering
community focused entirely on understanding, and ultimately correcting, student misperceptions in a variety
of disciplines [18,

19]
. Results gained f
rom this project and lessons learned
from it
will likely be of significant
interest to the engineering community. If the project demonstrates even a modicum of success, results will be
incorporated into a large multi
-
institutional NSF cyber
-
infrastructure

proposal.


The basic project may be summarized in Figure 3 below.




Figure 3. Project Summary for Enhancing Student Learning and Motivation in Probability and Statistics


Phase I: Inverting the Classroom


The first phase of this project is to create a content rich interactive set of materials for classroom inversion.
In
the traditional classroom, students are assigned a section of a textbook to read before class, though quite of
few of them do not. Follow
ing this passive, if it occurs at all, interaction with the material, students attend
Results of Concepts Inventory(Spr 03)
0
0.2
0.4
0.6
Probability
Counting
Expectations
Distributions
Definitions
Results of Concepts Inventory(Fall 08)
0.0
0.2
0.4
0.6
Probability
Counting
Expectations
Distributions
Definitions
class where they are passive recipients of a lecture. Following the lecture, students are assigned homework. It
is on their own, interacting with their homework, where
students first engage actively with the course
material.



In the
inverted

classroom, we move the active content engagement from post
-
class to in
-
class. The first
systematic attempts at classroom inversion
came from economics education [20
]. In the earl
y attempts, the
focus was on creating an environment where “events that have traditionally taken place
inside

the classroom
now take place
outside

the classroom and vice

versa”
.
Using mobile technologies not only expand learning to
beyond the confines of
the classroom, but provide students with the true flexibility they desire to control the
time, place, and pace of their learning [21].


Phase I of the project will be accomplished in the first year of 2010 and will ultimately replace use of a
text with

open content that will help to shift the learning environment by providing opportunities to find,
evaluate, and put new information to use [22]. Phase I will deal with exploration and evaluation of open
source content materials
such as Carnegie Mellon Un
iversity’s Open Learning Initiative and MIT’s
OpenCourseWare as well as expansion and development of existing interactions. A final deliverable will be a
collection of materials, references, and interactions that allow faculty and students to build a comm
unity of
professional practice.


Phase II: Development of Active Learning Kits


Phase II of the project will be accomplished in the summer of 2011 and will focus on building hands
-
on and
open ended problems
for

probability and statistics.
Hands on kit
s will allow students to measure, analyze, and
define appropriate experimental methods for applications in engineering reliability, manufacturing, human
engineering, and quality control. Open ended problem sets will allow students to build on the resource
s and
information library established in phase I to explore research methodologies and alternative experimental
designs to solve real world
open
-
ended problem sets
.

To the extent possible,
students will make extensive use
of the IE Professional Handbook [
23] and model eliciting activities [24] to
fully engage students in
practical
open
-
ended

problem sets in an environment that encourages use of mobile technologies to identify variables,
explore alternative experimental designs, and to seek and evaluate add
itional information

to solve the
problem
.


Assessment


Assessment of the project will measure self
-
efficacy, attitude,
typology,
and conceptual understanding. It is
not uncommon for student beliefs in their ability to use math and science to solve engineering diminishes over
time spent. If successful, an inverted approach to probability and statistics will lead to greater confiden
ce and
understanding of how to translate basic concepts from one engineering context to another. Measurement will
include a pre and post measurement of self
-
efficacy.

Student attitudes towards probability and statistics will be measured using an online
Student
Assessment of Learning Gains (SALG)
survey [25].
The
new NSF sponsored inventory
specifically designed to
address a number of issues of inte
rest to undergraduate research will be explored for possible inclusion.

Student typology is critical for
certain types of learning. While it is neither reasonable nor practical to
develop different protocols for different typologies, it would be useful to
know which type of student values
the inverted format over the traditional format. The department utili
zes the HBDI profile [16]. Profiles will be
correlated with student attitude measures and conceptual gains.

Student conceptual understanding of probability and statistics is limited in all engineering disciplines and
is critical for industrial engineeri
ng and engineering management students. Limited student conceptual
understanding of critical material can not only slow gains of technical content but limit intellectual growth as
well. Hestenes attempt to understand student conceptions (or misconceptio
ns) utilizing the force concepts
inventory [26] has led to a whole series of
useful
inventories
to aid our understanding of conceptual gains. We
will utilize and pre and post assessment utilizing the concepts inventory for probability and statistics [19].


Several sections of Introduction to Probability and Statistics are offered each year. The material
developed under this project will be available for use by all interested faculty in mathematics and industrial
engineering. In addition to a pre and pos
t assessment of conceptual understanding, the project and course
structure is ideally suited to allow for an experimental and control group. Given appropriate understanding
and support of the mathematics faculty, we propose offering two sections in one ye
ar by one of the project
investigators.

If an experimental/control analysis is not available locally, then the investigators will seek
options through the Oglala Lakota College or through other researchers at other universities with similar
interests in
this area.


Work Plan




Year 1

Year 2



Task

Summer

Fall

Spring

Summer

Fall

Spring

Follow
-
on

Phase I
















Define Important Concepts














Review Open Content














Develop Basic Material















Link interactions















Build Library















Pilot Phase I Course













Phase II














Develop hands
-
on















Develop open
-
ended
problems















Expand library resources















Expand pre
-
class
interactions















Pilot Phase II Course













Assessment
















Concepts Inventory














Salg














HBDI (from IENG 241)














Analysis













Dissemination













NSF Proposal
















Figure 4. Gantt Chart for Proposed Work


Dissemination/Broader Impact



At the very least, curricular modifications and assessment results will be published through appropriate
sources recognizing both the discipline area (probability and statistics) and the delivery media. No less than
one paper will be submitted to the Amer
ican Society for Engineering Education, the Frontiers in Education
conference, EduCause,
Advances in Engineering Education, or similar outlet. In addition, the department is
actively seeking a multi
-
department/multi
-
institutional NSF funded project to con
duct rigorous research
o
n
the
use
classroom inversion and mobile technologies as a mechanism for active engagement in probability and
statistics, engineering management, structures, and materials engineering. Assessment results and lessons
learned from th
is project will be incorporated in a formal NSF STC proposal.




Appendix A

Budget


Budget Request

We are requesting $51,500 for the two year project duration. Budget items by year follow.


Budget Item

Year 1

Year 2

Total

Salary





Dr. Kellogg (1 month summer)

$0

$0

$0


Dr. Matejcik (1.5 month summer)

$11,250

$11,250

$22,500

Students





U
ndergraduate (2 @ 400 hrs @ $10
)

$8,000

$8,000

$16,000


Programmer (400 hrs @ $10)


$4,000

$4,000

Assessment





SALG

$0

0

$0


Self
-
efficacy

$0

0

$0


Concept Inventory

$0

0

$0

Device Zoo





PDA, IPad, Smart Phone

$3,000


$3,000

Dissemination





Conference (1 faculty, 1 student)

$3,000

3000

$6,000

Total Budget

$25,250

$26,250

$51,500



Justification

Drs. Kellogg and Matejcik will
devote 1.5 to 2.5 months in the summer towards this project. This project
capitalizes on considerable development work already accomplished for online support and interactions. Dr.
Kellogg has full funding an
d can receive no additional funds for this project. Development work will be
accomplished during the summer. Assessment and library expansion will occur during the academic year.

Dr. Matejcik will receive 1.5 months of summer support each summer.
Work

accomplished during the
academic year will be covered by reduced teaching load.

Undergraduate students will be employed to find and evaluate open content and open source interactions.
Students will also find, evaluate, and help develop mobile library r
esources. Finally, students will be used to
review course material. Student programmers will be used to develop interactions for alternative mobile
devices.

Life is easier if one recognizes that
different hardware/software configurations do not always
work as planned.
The mobile zoo will be used to test material, library resources, and online interactions on different mobile
technologies.

The project will support one faculty member and one student to at least one professional conference each
year for

dissemination purposes.


References



[1]

The Engineer of 2020: Visions of Engineering in the New Century
, National Academy of Engineering,
National Academy Press, 2004.


[2]

Educating the Engineer of 2020: Adapting Engineering Education to the New Century, National Academy
of Engineering, National Academy Press, 2005.


[3]

Accreditation Board for Engineering and Technology

web site
,
http://www.abet.org/history.shtml


[4]

Partnership for 21
st

Century Skills web site,
http://www.p21.org/



[5
]


Felder, Richard, "Reaching the Second Tier: Learning and Te
aching Styles in College Science Education."
Journal of College Science Teaching, 23
(5), 286
-
290, 1993.


[6
]

Smith, K. Sheppard, S., Johnson, D., and Johnson, R., “Pedagogies of Engagement: Classroom
-
Based
Practices,”
Journal of Engineering Education
, 94 (1), pp. 87
-
102, January 2005.


[7
]

Felder, R.M. and Brent, R., “Understanding Student Differences,”
Journal of Engineering Education
, 94
(1), pp. 87
-
102, January 2005.


[8
]

Genalo, L. J., D. A. Schmidt, and M. Schiltz, “Piaget and Engineering Edu
cation,”
Proceedings of the
American Society for Engineering Education
, June 2004.


[9
]

Karlin, J. and Kellogg, S.,
"Seeing the Forest and the Trees:


Holistic Learner Development,"
Proceedings
of the
Research in Engineering Education Symposium (REES)
, D
avos Switzerland, July 2008.


[
10
]

Karlin, J. and Kellogg, S.,
"Metrics and the Holistic Learner,"
Proceedings of the Research in Engineering
Education Symposium (REES)
, Cairns, Australia, 2009.


[11
]

Perry, W. G., Jr.,
Forms of Intellectual and Ethical
Development in the College Years
, Holt, Rinehart and
Winston, Inc., New York, 1970.


[12
]

King, P. M. and K. S. Kitchener,
Developing Reflective Judgment
, Jossey
-
Bass Publishers, San Francisco,
1994.


[13
]

Lynch, C. L. , S. K. Wolcott, and G. E. Huber
, “Steps for Better Thinking: A Developmental Problem Solving
Process,”
http://www.WolcottLynch.com
, 2002.


[1
4
]

Belenky, M.F., B.M. Clinchy, N
.R. Goldberger, and J.M. Tarule,
Women’s Ways of Knowing: The
Development of Self, Voice and Mind
, New York: Basic Books, Inc.
, 1986.


[15
]

Zull, J.E.,
The Art of Changing the Brain
,
Stylus Publishing, Sterling, VA, 2002


[
16
]

Herrmann, N.,
The Creative Brain
, The Ned Herrmann Group: Brain Books, 1995.


[17]

Garfield
, J. and Ahlgren, A., “Difficulties in Learning Basic Concepts in Probability and Statistics:
Implications for Research, Journal for Research in Mathematics Education, 19 (1), pp. 44
-
63, 1988.


[18]

Streveler, R. A., Olds, B. M., Miller, R. L., and Nelso
n, M. A., “
Using a Delphi Study to Identify the Most
Difficult Concepts

for Students to Master in Thermal and Transport Science
,”
Proceedings
of the
American Society for Engineering Education
, Nashville, TN, 2003.


[19]

Allen, K., Stone, A., Reed
-
Rhoads,
T., and Murphy, T., “The Statistics Concepts Inventory: Developing a
V
alid and Reliable Instrument,”
Proceedings of the American Society for Engineering Education,
Salt Lake
City, UT, 2004.


[20]

Lage, M.J., Platt, G. J., and Treglia, M., “Inverting the
Classroom: A Gateway to Creating an Inclusive
Learning Environment,” Journal of Economic Education, 31(1), 30
-
43, Winter 2000.


[21]

Mellow, P., “The Media Generation: Maximise Learning by Getting Mobile,”
Proceedings of the
Australian Society for Com
puters in Learning in Tertiary Education
, 469
-
476, 2005.


[22]

New Media Consortium and the Educause Learning Initiative,
The 2010 Horizon Report
, The New Media
Consortium, 2010.


[23]

IIE Professional Handbook


[24
]

Diefes
-
Dux, H., Moore, T., Follman
, D., Zawajewski, J., and Imbrie, P.K., “A Framework for Posing Open
-
Ended Engineering Problems: Model Eliciting Activities,”, Savannah GA, 2004.


[25]

Student Assessment of Learning Gains web site,
http://www.sa
lgsite.org/



[26]

Hestenes, D., Wells, M., Swackhamer, G., “Force Concepts Inventory,”
The Physics Teacher
, Vol. 30, 141
-
158, March 1992.