Johns Hopkins University
School of Education
Data-Driven Decision Making for Schools and Organizations
893.632.9M (3 Credits) Spring Semester 2012
Instructor:
Richard Smart
Faculty Associate
JHU School of Education
Teacher & Summer School AP
Howard County Public School System
(410) 313.6945
rsmart1@jhu.edu
Richard_smart@hcpss.org
Skype: Richard Smart (location: Columbia, MD)
Dates:
Location:
Face to Face dates:
Monday January 23, 2012 – Monday April 30, 2012.
Online using ELC (http://cte.jhu.edu) and other tools linked from
ELC.
Monday January 30. CTI Rockville, Lab 2, 5pm – 7:30pm
Course Overview
The increasing impact of a knowledge economy and globalization has been a catalyst to
the fields of knowledge management and organizational decision-making. This course
is designed to introduce knowledge management concepts into an educational context
and to provide an in depth focus on data-driven decision making in educational
organizations and institutions. The models, tools, techniques, and theory of data-driven
decision-making that can improve the quality of leadership decisions are examined
through solution-based scenarios. Students investigate how decisions and strategies are
developed and how tacit or explicit knowledge can be identified, captured, structured,
valued and shared for effective use. Course topics include leadership and strategic
management relative to organizational decision-making, power and politics,
managerial and organizational structures, strategy formulation, organizational
learning, and decision support systems. A related intent is to develop an understanding
of data-mining metrics that can be used to make predictive models that support systemic
change.
Course Objectives
At the conclusion of this course, students are expected to be able to:
(1)
Identify and explain the different concepts and approaches within knowledge
management
(2)
Demonstrate an understanding of the human characteristics of generating,
communicating and using knowledge
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(3)
(4)
(5)
(6)
(7)
(8)
(9)
Understand the models and tools that are used for creating, collecting,
codifying and sharing information
Identify the knowledge and information environments of organizations
Explore how leadership can strengthen and support the achievement of the
strategic objectives of specific organizations
Identify the data-based decision-making principles for selecting and/or
developing appropriate models for predicting change
Understand the ethical issues and problems inherent in knowledge
management, information sharing, and decision-making
Develop a solution for a current organizational problem that reflects an in
depth understanding of data mining applications which can be used to support
decision-making.
Present the organizational solution using persuasive technology (compelling
visualizations and illustrations that are audience sensitive)
Course Required Readings
All required readings are available on the ELC.
Course Recommended Readings
Bruce, A. & Langdon, K. (2000). Learning to lead. Dorling Kindersley, London, UK.
Bruce, A. & Langdon, K. (2000). Making decisions. Dorling Kindersley, London, UK.
Friedman, T. (2005). The world is flat. Farrar, Straus and Giroux, New York, NY.
Groth, R. (2000). Data mining: Building competitive advantage. Prentice-Hall, Inc.,
Upper Saddle River, NJ.
Harris, A. (2005). Crossing boundaries and breaking barriers: Distributing leadership in
schools.
Harvard Business Review (2004). Harvard business review on decision making. Harvard
Business School Press, Boston, MA.
Levitt, S. & Dubner, S. (2005). Freakonomics: A Rogue Economist Explores the Hidden
Side of Everything. HarperCollins Publishers, New York, NY.
Lindner, K. (2005). Crunch time: 8 steps to making the right life decisions at the right
time. Gotham, NY.
McLean Report (2007). Data Mining in Education: Take the Longitudinal Way.
McREL’s balanced leadership framework. (2005). Leadership. 35(1), 34-35.
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Moody, L. & Dede, C. (2007). Models of Data-Based Decision Making: A Case of the
Milwaukee Public Schools. Data-Driven School Improvement, eds. Mandinbach, E. &
Honey, M. Teachers College, Columbia University. 233-254.
Streifer, P.A. (2002). Using data to make better educational decisions. Scarecrow Press,
Inc., Lanham, MD.
Yamashita, K. & Spataro, S. (2004). Unstuck: A tool for yourself, your team, and your
world. Portfolio, NY, NY.
Witten, E.B. & Frank, E. (2000). Data mining: Practical machine learning tools and
techniques with java implementations. Morgan Kaufmann Publishers, San Diego, CA.
Religious Observance Accommodation Policy
Religious holidays are valid reasons to be excused from class. Students who must miss a
class or examination because of a religious holiday must inform the instructor as early in
the semester as possible in order to be excused from class and to make arrangements to
make up any work that is missed.
Statement of Academic Continuity
Please note that in the event of serious consequences arising from the H1N1 flu pandemic
and/or in other extraordinary circumstances, the School of Education may change the
normal academic schedule and/or make appropriate changes to course structure, format,
and delivery. In the event such changes become necessary, information will be posted on
the School of Education web site.
Classroom Accommodations for Students with Disabilities
If you are a student with a documented disability who requires an academic adjustment,
auxiliary aid or other similar accommodations, please contact Jennifer Eddinger in the
Disability Services Office at 410-516-9734 or via email at
soe.disabilityservices@jhu.edu.
Statement of Diversity and Inclusion
Johns Hopkins University is a community committed to sharing values of diversity and
inclusion in order to achieve and sustain excellence. We believe excellence is best
promoted by being a diverse group of students, faculty, and staff who are committed to
creating a climate of mutual respect that is supportive of one another’s success. Through
its curricula and clinical experiences, the School of Education purposefully supports the
University’s goal of diversity, and, in particular, works toward an ultimate outcome of
best serving the needs of all students in K-12 schools and/or the community. Faculty and
candidates are expected to demonstrate a commitment to diversity as it relates to
planning, instruction, management, and assessment.
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IDEA Course Evaluation
Please remember to complete the IDEA course evaluation for this course. These
evaluations are an important tool in the School of Education’s ongoing efforts to improve
instructional quality and strengthen its programs. The results of the IDEA course
evaluations are kept anonymous—your instructor will only receive aggregated data and
comments for the entire class. Typically, an email with a link to the online course
evaluation form will be sent to your JHU email address approximately 85% of the way
through the course. Thereafter, you will be sent periodic email reminders until you
complete the evaluation. The deadline for completing the evaluation is normally one
week after the last meeting of class. Please remember to activate your JHU email account
and to check it regularly. (Please note that it is the School of Education’s policy to send
all faculty, staff, and student email communications to a JHU email address, rather than
to personal or alternative work email addresses.) If you are unsure how to activate your
JHU email account, if you’re having difficulty accessing the course evaluations or you
haven’t received an email reminder by the day of the last class, or if you have any
questions in general about the IDEA course evaluation process, please contact Rhodri
Evans (410-516-0741; idea@jhu.edu).
Assignments
1. Student led data discussion (25 points) – Develop and facilitate one whole
group activity focused on a data set appropriate for an assigned course topic. *Use
the Course Topic Presentation Template for this assignment which is available in
the ELC. (Introduced in Session 1, due as assigned.)
2. Midterm Exam (30 points) – Complete an open-book exam to demonstrate your
knowledge and understanding of important concepts presented in the course.
(Session 4)
3. Data Based Decision-Making (DBDM) Plan (35 points) – Develop a decision
making plan that is directly related to a current data driven or systemic
educational issue reflecting an area of interest and/or expertise. (Session 10)
4. Final Exam (30 points) – Complete an open-book exam to demonstrate your
knowledge and understanding of important concepts presented in the course.
(Session 11)
5. Course Participation and Reading Reflections (30 points) – Have an active
presence in course discussions, and complete course activities as noted in the
activity directions to maximize your learning. Complete weekly personal
reflections after reading the required course readings for each topic area
(Maximum of 300 words).
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Evaluation and Grading
Grading will be based on a scale of 125 points. Points will be totaled and a percentage
will be calculated to determine a final grade.
100 – 95% = A
79 – 77% = C+
94 – 90% = A76 – 73% = C
89 – 87% = B+
72 – 70% = C86 – 83% = B
69 – below = F
82 – 80% = BI = Incomplete
An incomplete will be recorded if work to be turned-in is justifiably late (serious
illness, death in the family, etc.). Unexcused absence is not a valid reason. The
grades of D+, D, and D- are not awarded at the graduate level.
Instructional Methodology
This course will require the ability to analyze and synthesize information across various
disciplines. These include business, education, medicine, and social sciences. In-class
interaction and active discussion on the ELC are important. The emphasis is on
exploration and discovery of new knowledge with guidance by the instructors.
Course Protocols
Communication
How should I contact the course instructor?
Feel free to email your instructor with comments, questions, and concerns. You will
receive a response within 24-48 hours. Note: only you and the instructor have access to
your individual discussion forum. This is also a good place to share questions and
concerns.
Whom should I contact if I am having technical difficulties?
Direct technical questions to techsupport@mail.cte.jhu.edu. Please let your instructor
know if you are having significant technical difficulties.
How will I know about changes to the course?
Frequently, you will find new announcements posted in the ELC which contain
information about current course activities that you are working on and any changes to
the course. Please check announcements every time that you log into the ELC. The
instructor will also provide updates during the face-to-face meeting sessions.
How should I communicate with others in this course?
You should communicate often with your classmates and with the instructors. The
majority of communication outside of the face-to-face course meetings will take place
within the ELC discussion forums. When you have a question about an assignment or a
question about the course, please contact the instructor.
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Assignments
How should assignments be submitted?
The assignment or activity directions will indicate where assignments will be posted.
Individual assignments are always posted to your individual discussion forum in the ELC
unless otherwise indicated.
When will completed assignments be returned?
Assignments will be returned to you within seven days following the due date, depending
on the length of the assignment. You will receive feedback in your team forum or your
individual discussion forum.
What is the policy for late assignments?
You are expected to contact your instructors in advance if you think you cannot meet an
assignment deadline. However, if an assignment is late and prior arrangements have not
been made with the instructors, the assignment score may be affected by 10% or more.
Participation
What are the participation requirements during the online portion?
You are expected to log into the ELC at least two times a week. It is your responsibility
to read all announcements and discussion postings within your assigned forums. You
should revisit the discussion multiple times over the week to contribute to the dialogue.
What are the requirements for attendance to face-to-face meetings?
You must attend all face-to-face course meetings. If you cannot attend, you must inform
the instructors as soon as possible. Unexcused absences will result in a reduction of your
overall course grade.
Attendance
Participation in lectures, discussions, and other activities is an essential part of the
instructional process. Students are expected to attend class regularly; those who are
compelled to miss class meetings should inform their instructors of the reasons for
absences. Faculty often include classroom participation and attendance in student
grading and evaluation. The instructor will clearly communicate expectations and grading
policy in the course syllabus. Students who expect to miss several class sessions for
personal, professional, religious, or other reasons are encouraged to meet with their
academic advisers to consider alternative courses prior to registration.
Examinations
A student who must miss an examination should notify the instructor. If the absence is
justifiable, the instructor may permit a deferred examination.
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Tentative Course Calendar
Online class meetings are shaded below.
Date
Orientation
Session (Online)
January 23 –
January 29
Session 1 – F2F
Topics and Outcomes:
Introduction to Data-Driven Decision
Making
 Review syllabus
 Introduce course assignments
 ELC overview
Introduction to Data-Driven Decision
Making (See Above)
January 30
Data-Driven Decision Making
 Consider the meaning of ―data‖
 Identify the purpose of managing
data
 Introduce different models of
DDDM
Session 1a –
January 30February 5 (@
11:59pm)
Session 2 –
February 6-12 (@
11:59pm)
As above
Due
Introductions forum
post
Course questions
forum post(s)
Sign Up for Student
Led Data
Discussions
Reading Reflection
(Due Jan 30)
Online discussion
Knowledge Creation and Management
 Knowledge creation and knowledge
transfer
 Impact of organizational culture on
knowledge creation and transfer
Data Mining Strategies
Session 3 –
Online
 Identify data-mining techniques and
February 13-19 (@
strategies
11:59pm)
 Review correlation models
 Identify how data-mining
techniques are influenced by
mandated reporting requirements
for NCLB
 Hands-on Data Exercise
Midterm Exam (Online and OpenSession 4 –
book)
Online
February 20 –
February 26
(@ 11:59pm)
Reading and plan preparation week
Reading Week
February 27 –
 Create proposal for major
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Reading Reflection
(Due February 6)
Reading Reflection
(Due February 13)
Midterm Exam
(Submit on the ELC)
DUE: February 26,
2012
DBDM Plan
Proposal (Submit
Date
March 4
(@ 11:59pm)
Topics and Outcomes:
course project.
 Office hours with instructor
(F2F, skype or phone).
Survey Creation, Analysis and
Performance Assessment
 Identify elements of survey creation
and analysis
Due
to forum on ELC)
Due: March 4
States and AYP: Using Data to
Facilitate Change
 Identify how schools and states use
standardized tests to measure
student progress
 Determine how leaders can
facilitate decision making
 Explore methods for evaluating and
analyzing data
Reading Reflection
(Due March 12)
Session 7 –
Online
March 19 - 25
(@ 11:59pm)
Session 8 –
Online
March 26 – April 1
(@ 11:59pm)
Data Ethics
 Hidden Traps in Decision-Making
 Ethical considerations
Reading Reflection
(Due March 19)
Using Data to Persuade: The Art of
Identify persuasive technologies and
presentations Captology
 Methods of communicating data is
a personal and compelling way
 Look at the ways in which data
presentation reflects the needs of
the person presenting (data can be
presented in a way to support
conclusions)
Reading Reflection
(Due March 26)
Spring Break
April 2 – April 9
Reading and plan preparation week
 Optional office hours with
instructor (F2F, skype or
phone).
Leadership and Situated DecisionMaking
 The Effective Decision: The
Hidden Traps Identify the role of
leadership in the decision making
process
 Distributed Leadership
N/A
Session 5 –
Online
March 5 - March
11
(@ 11:59pm)
Session 6 - Online
March 12 – March
18 (@ 11:59pm)
Session 9 –
Online
April 10 -April 15
(@ 11:59pm)
8
Reading Reflection
(Due March 5)
Reading Reflection
(Due: April 10 – NB
this is a Tuesday)
Online Discussion
Date
Session 10 Online
April 16 - April 22
(@ 11:59pm)
Session 11 –
Online
April 17 – 29
(@11:59pm) This
session overlaps
the previous.
Topics and Outcomes:
 Role of data in education
 Reflect on the content of the
readings to identify impact of these
concepts on current leadership and
DDDM
Data Based Decision-Making Plan
Presentations
 Comment on presentations
posted on ELC
 Complete course review
Final Exam (Online and Open-book)
9
Due
Data Based
Decision Making
Plan Presentations
Data Based
Decision Making
Plan
DUE: April 16, 2012
Final Exam
(Submit on the ELC)
DUE: April 29, 2012
Tentative Course Matrix
Week Start
Date
1/23
1/30
2/6
2/13
2/20
2/27
3/5
3/12
3/19
3/26
4/2
4/9
4/16
4/23
M
Tu
O
1-R
2-R
3-R
4
*
5-R
6-R
7-R
8-R
*
*
10–Pr/Pl
11
O
1a
2
3
4
*
5
6
7
8
*
9-R
10/11
11
W
Th
F
Sa
O
1a
2
3
4
*
5
6
7
8
*
9
10/11
11
O
1a
2
3
4
*
5
6
7
8
*
9
10/11
11
O
1a
2
3
4
*
5
6
7
8
*
9
10/11
11
O
1a
2
3
4
*
5
6
7
8
*
9
10/11
11
Su
O
1a
2
3
4-M
*-Pl
5
6
7
8
*
9
10/11
11-F
Bolded days are days when assignments are due. Numbered days indicate the particular
session your online activities should address. See Chart Legend for more details.
Chart Legend:
R = Reading Reflection
Pl = Part of Data Based Decision-Making Plan Due
M = Midterm Exam Due
* = No Required Online Discussion
Pr = Data Based Decision-Making Plan Presentation Due
F = Final Exam Due
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