GEOG 885
Advanced Analytic Methods in Geospatial Intelligence

Syllabus

PrintPrint

GEOG 885: Advanced Analytic Methods in Geospatial Intelligence

Summer 2024

You can print the entire syllabus by clicking on the "Print" link in the upper right-hand corner of this page. You can also just print the course schedule--there is a link for a printer-friendly version of the schedule there. That being said, it is essential that you read the entire document as well as the material covered in the Orientation. Together these serve the role of our course "contract."


GEOG 885 Prerequisite

There are no prerequisites for this course.


Instructors

The instructor rotates by semester. Review the "Meet the Professor" page in the orientation to learn more about your instructor for the semester. 

  • David Jimenez (Summer 2024)

    • Mobile: (575) 618-7989 (The country code for the United States is 1)
    • Email: Please use the course e-mail system - see the Inbox tab in Canvas (in an emergency my PSU email is dxj13@psu.edu).
    • Availability: Please call or e-mail me to schedule a time that is convenient for you. Please note I am located in the Mountain Standard Time zone.
  • Leanne Sulewski

    • Email: Please use the course e-mail system - see the Inbox tab in Canvas.
    • Availability: Please call or e-mail me to schedule a time that is convenient for you.
  • Steve Handwerk

    • Mobile: (717) 303-7962 (The country code for the United States is 1)
    • Email: Please use the course e-mail system - see the Inbox tab in Canvas.
    • Availability: Please call or e-mail me to schedule a time that is convenient for you.
    • Greg Thomas

      • Office: (814) 867-1471 (The country code for the United States is 1)
      • Email: Please use the course e-mail system - see the Inbox tab in Canvas (in an emergency my PSU email is gat5@psu.edu).
      • Availability: Please call or e-mail me to schedule a time that is convenient for you. Please note I am located in the Eastern Time zone.


    Class Support Services

    Penn State Online offers online tutoring to World Campus students in math, writing, and some business classes. Tutoring and guided study groups for residential students are available through Penn State Learning.


    Course Overview

    GEOG 885 explores the challenges and opportunities created by combining human expertise with computational analysis methods in the field of geospatial intelligence (GEOINT). The course focuses on the science and technology of human-machine collaboration using geospatial artificial intelligence (GeoAI) in GEOINT and the professional and ethical concerns that must be considered as we move forward in this rapidly evolving field. Students completing this course will be able to explain and apply Structured Analytic Techniques (SATs), automation methods, and GeoAI tools in combination to solve geospatial intelligence problems. Students will create analysis workflows that ensure the efficiency, credibility, and accuracy of analytical insights. SATs are evaluated by students to gauge their ability to improve the quality and rigor of analysis. Students will also learn how to apply emerging GeoAI tools to summarize data and perform analytical tasks that have typically required human intelligence. GEOINT plays an increasingly critical role in supporting decision-making across a broad range of industries, from defense and intelligence to environmental monitoring and urban planning. The amount of geospatial data available today is overwhelming, but by leveraging the strengths of both humans and machines, we can gain deeper insights into high-dimensional spatial data and more effectively solve geographic problems. The course does not require any technical background, and it is open to students from all disciplines.

    Course Objectives

    Students who excel in this course are able to:

    • LO-1: Apply the geospatial intelligence process including problem spatialization, recording, discovering, tracking, comprehending, and communicating analytic results.
    • LO-2: Contrast the strengths and limitations of the human and machine in geospatial analysis.
    • LO-3: Explain the professional and ethical considerations surrounding machine-driven analysis, automation, and GeoAI in geospatial intelligence analysis.
    • LO-4: Elaborate about the application of human cognitive techniques (Structured Analytic Techniques), computational thinking, GeoAI and automation in geospatial analysis.
    • LO-5: Compare the potential impact of human-machine collaboration on decision-making across different applications.
    • LO-6: Apply critical thinking and problem-solving skills to analyze complex geospatial intelligence problems using a human-machine collaborative approach.
    • LO-7: Defend the results of a geospatial analysis to decision-makers while safeguarding trust, credibility, and accuracy of analytic insights.
    • LO-8: Articulate an understanding of emerging trends and future directions in human-machine collaboration for geospatial intelligence analysis.

    What will be expected of you? 

    Like any upper-level course, you will be challenged to move beyond the knowledge and skills that you bring to the class. You can expect to be busy; as rough estimate, you should allow 12-15 hours per week for class assignments. You'll be glad to know that you don't need to show up for class at a certain time! All you need to do is complete assignments before the published deadlines each week.

    We have worked hard to make this the most effective and convenient educational experience possible. How much and how well you learn is ultimately up to you. You will succeed if you are diligent about keeping up with the class schedule and if you take advantage of opportunities to communicate with us, as well as with your fellow students.


    Required Course Materials

    In order to take this course, you need to have the required course materials and an active Penn State Access Account user ID and password (used to access the online course resources). All (other) materials needed for this course are presented online through Canvas. If you have any questions about obtaining or activating your Penn State Access Account, please contact the Outreach Helpdesk.

    Required Textbooks

    There is no required textbook for this course.

    Required Software

    ArcGIS Online StoryMap will be used and is free for registered students. 

    Note: You need administrative rights on your computer in order to properly install the course software.

    Using the Library

    Just like on-campus students, as a Penn State student, you have a wealth of library resources available to you!

    As a user of Penn State Libraries, you can...

    • search for journal articles (many are even immediately available in full-text)
    • request articles that aren't available in full-text and have them delivered electronically
    • borrow books and other materials and have them delivered to your doorstep
    • access materials that your instructor has put on Electronic Reserve
    • talk to reference librarians in real time using chat, phone, and e-mail
    • ...and much more!

    To learn more about their services, see the Library Information for Off-site Users.


    Assignments and Grading

    Students earn grades that reflect the extent to which they achieve the learning objectives listed above. Opportunities to demonstrate learning include the following, and grades will be based on percentages assigned to each of several components of the course as follows:

    • 9 Case Study Discussions: (30%)
      Each week, we will provide you with a real-life case study along with some resources to get you started thinking about it. You will read the documents, do your own research, and discuss your thoughts, and some specific prompts with your classmates.
    • 2 Reflection Papers: (20%)
      These are 500 word essays of your thoughts about an article, video, concept, etc. expressing your personal experiences and/or thoughts about the concepts put forth in lessons 2 and 8.Capstone: (Total of 50% divided among several types of assignments described below)
    • 1 Capstone (50%)
      The capstone challenges you to work collaboratively and apply the GEOINT process tasks and double-loop approach that is introduced in Lesson 1. You will explore the complexities of human-machine teaming using a real-world problem and investigate the case from two different methodological perspectives. It is split into four different types of assignments.
      • 6 weekly individual written responses to a prompt related to the capstone project. (25%)
      • 6 weekly team project note contributions in the form of an ArcGIS StoryMap. The goal here is to help you progressively work on, and receive feedback on, the final StoryMap and video presentation. (5%)
      • 1 final team video presentation related to the problem using a revised StoryMap your team created throughout the semester. (20%)

    Note about Discussions

    • Acceptable discussion participation
      • Offers solid analysis, without prompting, to move the discussion forward
      • Demonstrates deep knowledge of the topic and the question
      • Actively "listens" to other participants
      • Offers clarification and/or follow-up that extends the conversation
      • Refers back to specific parts of the text
    • Unacceptable discussion participation
      • Offers little commentary
      • Is ill-prepared with little understanding of the text and question
      • Offers no commentary to further the discussion
      • Distracts the group by offering off-topic questions and comments
      • Ignores the discussion
    Assignments and Grading
    Activity Lesson Effort Weight
    Mini-Case Study Discussions 1,2,3,4,5,6,7,9 Individual 30%
    Reflection Papers 2,8 Individual 20%
    Capstone Project Individual Contribution 3,4,5,6,7,9 Individual 25%
     Capstone Project Team Progress Notes 3,4,5,6,7,9 Project 5%
    Team Presentation 10 Project 20%

    Letter grades will be based on the following percentages:

    Grading Scale
    A 90.0% or above
    A- 88.0-89.9%
    B+ 85.0-87.9%
    B 80.0-84.9%
    B- 78.0-79.9%
    C+ 75.0-77.9%
    C 70.0-74.9%
    D 60.0-69.9%
    F 59.9% or below

    Class participation will be considered in grading for those whose final course grade is close to the next letter grade.

    Late Assignment Policy

    Generally speaking, I do not accept late submissions; however, if you are experiencing exceptional circumstances, please contact me. The earlier you contact me to request a late submission, the better. Requests will be considered on a case-by-case basis. Generally, late assignments will be assessed a penalty of at least 10%.

    Curve

    Grades will not be curved in this course.

    Academic Integrity and AI-Generated Content

    According to Penn State policy G-9: Academic Integrity, an academic integrity violation is "an intentional, unintentional, or attempted violation of course or assessment policies to gain an academic advantage or to advantage or disadvantage another student academically." For this course, you are expected to complete all course work entirely on your own, and you may not assist other students with assignments, quizzes, or other assessments, with the exception of group assignments where teammates are expected to collaborate. You may not use generative technology such as ChatGPT or other AI chatbots to compose or revise your work. You may not submit false or fabricated information, use the same academic work for credit in multiple courses, or share instructional content. Students with questions about academic integrity should ask their instructor before submitting work. 

    Students facing allegations of academic misconduct may not drop/withdraw from the affected course unless they are cleared of wrongdoing (see G-9: Academic Integrity). Attempted drops will be prevented or reversed, and students will be expected to complete course work and meet course deadlines. Students who are found responsible for academic integrity violations face academic outcomes, which can be severe, and put themselves at jeopardy for other outcomes which may include ineligibility for Dean's List, pass/fail elections, and grade forgiveness. Students may also face consequences from their home/major program.

    Gradebook

    All grades are posted to Grades in Canvas. To view your grades during the semester, go to GEOG 885 in Canvas and click on the Grades tab


    GEOG 885 Course Schedule

    Printable Schedule

    Below you will find a summary of the learning activities for this course.

    Lesson 0: Orientation
    Date: Week 0
    Objectives:

    At the end of this lesson you will be able to:

    • Demonstrate proficiency in the use of the Canvas learning management system
    • Employ appropriate learning skills in distance education
    • Communicate with other students and the instructor through a Canvas discussion forum
    Readings:
    • All Orientation Materials
    Assignments: Participate in a personal introduction discussion forum.

    Lesson 1: Course Introduction, Ethics and Standards in Intelligence Analysis

    Date: Week 1
    Objectives:

    At the end of this lesson you will be able to:

    • Describe the importance of professional standards and ethical considerations in geospatial intelligence analysis using automation and GeoAI and explain how their use can create unintended consequences and amplify biases in data. (LO-3)

    • Explain key ethical principles that should guide geospatial intelligence analysis. (LO-3)

    • Evaluate a case in geospatial analysis where ethical considerations were or were not considered. (LO-3, LO-6)

    • Compare the potential risks associated with using machines in geospatial intelligence analysis with common human biases. (LO-3, LO-4)

    Readings:
    • Online content
    • Designated readings
    Assignments:
    1. Mini-Case Study Discussion

     Lesson 2: Humans and Machines

    Date: Week 2
    Objectives:

    At the end of this lesson you will be able to:

    • Define the concept of Structured Analytic Techniques (SATs) and explain their underlying principles, methods, and applications in geospatial analysis. (LO-4)

    • Analyze the strengths and limitations of SATs in intelligence analysis, and identify key factors that contribute to their effectiveness, such as accuracy, reliability, and efficiency. (LO-4)

    • Define the concept of human-machine collaboration in geospatial intelligence and explain its importance in enhancing the quality and efficiency of geospatial intelligence analysis. (LO-2, LO-3, LO-5)

    • Describe the current state of geospatial intelligence and the emerging role that automation and GeoAI play in the field. (LO-4, LO-7)

    • Evaluate the potential benefits and challenges of human-machine collaboration in geospatial intelligence. (LO-2, LO-5)

    • Analyze a case study of human-machine collaborations and formulate effective strategies using SATs to promote human-machine collaboration in geospatial intelligence. (LO-2. LO-4, LO-5, LO-6) 

    Readings:
    • Online content
    • Designated readings
    Assignments:
    1. Mini-Case Study Discussion
    2. Reflection paper
    Lesson 3: Problem Spatialization
    Date: Week 3
    Objectives:

    At the end of this lesson you will be able to:

    • Explain the role and importance of spatializing the problem in the geospatial intelligence analysis process. (LO-1)

    • Describe the process of spatializing the problem, including the tools and technologies. (LO-1, LO-8)

    • Evaluate the potential benefits and risks of including machines in the problem spatialization process. (LO-1)

    • Analyze a case study of human-machine collaboration where an improper understanding of the spatial qualities of the problem resulted in a poor analytic outcome. (LO-5)

    • Formulate effective strategies that employ SATs to enhance the understanding of the spatial aspects of a problem when working in a human-machine team. (LO-3)

    Readings:
    • Online content
    • Designated readings
    Assignments:
    1. Mini-Case Study Discussion
    2. Capstone Project Individual Contribution (L3): Problem Spatialization
    3. Capstone Project Team Progress Note (L3): Team Organization
    4. Optional Course Assessment Survey
    Lesson 4: Recording Spatial Data
    Date: Week 4
    Objectives:

    At the end of this lesson you will be able to:

    • Explain the role and importance of recording in the geospatial intelligence analysis process. (LO-1)
    • Describe the process of recording, including the tools and technologies. (LO-1, LO-8)
    • Evaluate the potential benefits and risks of including machines in the recording process. (LO-1)
    • Analyze a case study of human-machine collaboration where an improper understanding of recording resulted in a poor analytic outcome. (LO-5)
    • Formulate effective strategies that employ SATs to enhance the understanding of the spatial aspects of a problem when working in a human-machine team. (LO-3)
    Readings:
    • Online content
    • Designated readings
    Assignments:
    1. Mini-Case Study Discussion
    2. Capstone Project Individual Contribution
    3. Capstone Project Team Progress Note
    Lesson 5: Spatial Discovery
    Date: Week 5
    Objectives:

    At the end of this lesson you will be able to:

    • Explain the role and importance of discovery in the geospatial intelligence analysis process. (LO-1)
    • Describe the discovery process, including the tools and technologies. (LO-1, LO-8)
    • Evaluate the potential benefits and risks of including machines in discovery. (LO-1)
    • Analyze a case study of human-machine collaboration where poor discovery resulted in a poor analytic outcome. (LO-5)
    • Formulate effective strategies that employ SATs to enhance discovery when working in a human-machine team. (LO-3)
    Readings:
    • Online content
    • Designated readings
    Assignments:
    1. Mini-Case Study Discussion
    2. Capstone Project Individual Contribution
    3. Capstone Project Team Progress Note
    Lesson 6: Tracking Phenomena in Space and Time
    Date: Week 6
    Objectives:

    At the end of lesson 6 you will be able to:

    • Explain the role and importance of tracking in the geospatial intelligence analysis process. (LO-1)
    • Describe the tracking process, including the tools and technologies. (LO-1, LO-8)
    • Evaluate the potential benefits and risks of including machines of tracking. (LO-1)
    • Analyze a case study of human-machine collaboration where poor tracking resulted in a poor analytic outcome. (LO-5)
    • Formulate effective strategies that employ SATs to enhance tracking when working in a human-machine team. (LO-3)
    Readings:
    • Online content
    • Designated readings
    Assignments:
    1. Mini-Case Study Discussion
    2. Capstone Project Individual Contribution
    3. Capstone Project Team Progress Note
    4. Optional Course Assessment Survey
    Lesson 7: Comprehending Results
    Date: Week 7
    Objectives:

    At the end of lesson 7 you will be able to:

    • Explain the role and importance of comprehension in the geospatial intelligence analysis process. (LO-1)
    • Describe the comprehension process, including the tools and technologies. (LO-1, LO-8)
    • Evaluate the potential benefits and risks of including machines in comprehension. (LO-1)
    • Analyze a case study of human-machine collaboration where poor comprehension resulted in a poor analytic outcome. (LO-5)
    • Formulate effective strategies that employ SATs to enhance comprehension when working in a human-machine team. (LO-3)
    Readings:
    • Online content
    • Designated readings
    Assignments:
    1. Mini-Case Study Discussion
    2. Capstone Project Individual Contribution
    3. Capstone Project Team Progress Note
    Lesson 8: Competing Human & Machine Methodologies 
    Date: Week 8
    Objectives:

    By the end of this lesson you will have:

    • Apply human-machine collaboration techniques to analyze and interpret geospatial data in a real-world scenario. (LO-6, LO-7)
    • Evaluate the effectiveness of different human-machine collaboration techniques in improving the quality and rigor of geospatial analysis. (LO-6, LO-7)
    • Develop a comprehensive analysis report that effectively communicates insights and recommendations based on the results of the human-machine collaboration. (LO-6, LO-7)
    • Evaluate the ethical implications of using advanced machine learning algorithms in geospatial intelligence analysis and propose strategies to address these ethical considerations. (LO-6, LO-7)
    • Demonstrate proficiency in using structured analytic techniques and artificial intelligence tools to analyze and interpret geospatial data in a collaborative environment. (LO-6, LO-7)
    Readings:
    • Online content
    • Designated readings
    Assignments:
    1. Reflection Paper (with a Discussion component)
    Lesson 9: Communicating Insights
    Date: Week 9
    Objectives:

    At the end of this lesson you will be able to:

    • Explain the role and importance of communicating insights in the geospatial intelligence analysis process. (LO-1)
    • Describe the spatial communication process, including the tools and technologies. (LO-1, LO-8)
    • Evaluate the potential benefits and risks of including machines in communications. (LO-1)
    • Analyze a case study of human-machine collaboration where poor communication of insights resulted in a poor analytic outcome. (LO-5)
    • Formulate effective strategies that employ SATs to enhance communicating insights when working in a human-machine team. (LO-3)
    Readings:
    • Online content
    • Designated readings
    Assignments:
    1. Mini-Case Study Discussion
    2. Capstone Project Individual Contribution
    3. Capstone Project Team Progress Note
    Lesson 10: Capstone Report and Presentation
    Date: Week 10
    Objectives:

    At the end of this lesson you will be able to:

    • Apply human-machine collaboration techniques to analyze and interpret geospatial data in a real-world scenario. (LO-6, LO-7)
    • Evaluate the effectiveness of different human-machine collaboration techniques in improving the quality and rigor of geospatial analysis. (LO-6, LO-7)
    • Develop a comprehensive analysis report that effectively communicates insights and recommendations based on the results of the human-machine collaboration. (LO-6, LO-7)
    • Evaluate the ethical implications of using advanced machine learning algorithms in geospatial intelligence analysis and propose strategies to address these ethical considerations. (LO-6, LO-7)
    • Demonstrate proficiency in using structured analytic techniques and artificial intelligence tools to analyze and interpret geospatial data in a collaborative environment. (LO-6, LO-7)
    Readings:
    • No reading assignment
    Assignments:
    1. Capstone presentation

    Course Policies

    Penn State E-mail Accounts

    All official communications from Penn State are sent to students' Penn State e-mail accounts. Be sure to check your Penn State account regularly, or forward your Penn State e-mail to your preferred e-mail account, so you don't miss any important information.

    Academic Integrity

    This course follows the procedures for academic integrity of Penn State's College of Earth and Mineral Sciences. Penn State defines academic integrity as "the pursuit of scholarly activity in an open, honest and responsible manner." Academic integrity includes "a commitment not to engage in or tolerate acts of falsification, misrepresentation, or deception." In particular, the University defines plagiarism as "the fabrication of information and citations; submitting others' work from professional journals, books, articles, and papers; submission of other students' papers, lab results or project reports and representing the work as one's own." Penalties for violations of academic integrity may include course failure. To learn more, see Penn State's Academic Integrity Training for Students

    Course Copyright

    All course materials students receive or to which students have online access are protected by copyright laws. Students may use course materials and make copies for their own use as needed, but unauthorized distribution and/or uploading of materials without the instructor’s express permission is strictly prohibited. University Policy AD 40, the University Policy Recording of Classroom Activities and Note Taking Services addresses this issue. Students who engage in the unauthorized distribution of copyrighted materials may be held in violation of the University’s Code of Conduct, and/or liable under Federal and State laws.

    For example, uploading completed labs, homework, or other assignments to any study site constitutes a violation of this policy.

    Accommodations for Students with Disabilities

    Penn State welcomes students with disabilities into the University's educational programs. Every Penn State campus has an office for students with disabilities. The Office for Student Disability Resources website provides contact information for Campus Disability Coordinators at every Penn State campus. For further information, please visit the Office for Student Disability Resources website.

    In order to receive consideration for reasonable accommodations, you must contact the appropriate disability services office at the campus where you are officially enrolled. You will participate in an intake interview and provide documentation. See documentation guidelines at Applying for Services from Student Disability Resources. If the documentation supports your request for reasonable accommodations, your campus’s disability services office will provide you with an accommodation letter. Please share this letter with your instructors and discuss the accommodations with them as early in your courses as possible. You must follow this process for every semester that you request accommodations.

    Change in Normal Campus Operations

    In case of weather-related delays or other emergency campus disruptions or closures at the University, this online course will proceed as planned. Your instructor will inform you if there are any extenuating circumstances regarding content or activity due dates in the course due to these delays or closures. If you are affected by a weather-related emergency, please contact your instructor at the earliest possible time to make special arrangements.

    Reporting Educational Equity Concerns

    Penn State takes great pride in fostering a diverse and inclusive environment for students, faculty, and staff. Acts of intolerance, discrimination, or harassment due to age, ancestry, color, disability, gender, gender identity, national origin, race, religious belief, sexual orientation, or veteran status are not tolerated (Policy AD29 Statement on Intolerance) and can be reported through Educational Equity via Report Bias.

    Counseling and Psychological Services

    Many students at Penn State face personal challenges or have psychological needs that may interfere with their academic progress, social development, or emotional well-being.  The university offers a variety of confidential services to help you through difficult times, including individual and group counseling, crisis intervention, consultations, online chats, and mental health screenings.  These services are provided by staff who welcome all students and embrace a philosophy respectful of clients’ cultural and religious backgrounds, and sensitive to differences in race, ability, gender identity, and sexual orientation.  Services include the following:

    Counseling and Psychological Services at University Park  (CAPS): 814-863-0395
    Counseling Services at Commonwealth Campuses
    Penn State Crisis Line (24 hours/7 days/week): 877-229-6400
    Crisis Text Line (24 hours/7 days/week): Text LIONS to 741741

    Military Personnel

    Veterans and currently serving military personnel and/or spouses with unique circumstances (e.g., upcoming deployments, drill/duty requirements, disabilities, VA appointments, etc.) are welcome and encouraged to communicate these, in advance if possible, to the instructor in the case that special arrangements need to be made.

    Connect Online with Caution

    Penn State is committed to educational access for all. Our students come from all walks of life and have diverse life experiences. As with any other online community, the lack of physical interaction in an online classroom can create a false sense of anonymity and security. While one can make new friends online, digital relationships can also be misleading. Good judgment and decision-making are critical when choosing to disclose personal information to others whom you do not know.

    Technical Requirements

    For this course, we recommend the minimum technical requirements outlined on the World Campus Technical Requirements page, including the requirements listed for same-time, synchronous communications. If you need technical assistance at any point during the course, please contact the IT Service Desk (for World Campus students) or Penn State's IT Help Portal (for students at all other campus locations).

    Internet Connection

    Access to a reliable Internet connection is required for this course. A problem with your Internet access may not be used as an excuse for late, missing, or incomplete coursework. If you experience problems with your Internet connection while working on this course, it is your responsibility to find an alternative Internet access point, such as a public library or Wi-Fi ® hotspot.

    Mixed Content

    This site is considered a secure web site, which means that your connection is encrypted. We do, however, link to content that isn't necessarily encrypted. This is called mixed content. By default, mixed content is blocked in Internet Explorer, Firefox, and Chrome. This may result in a blank page or a message saying that only secure content is displayed. Follow the directions on our Technical Requirements page to view the mixed content.

    Deferred Grades

    If you are prevented from completing this course within the prescribed amount of time for reasons that are beyond your control, it is possible to have the grade deferred with the concurrence of the instructor, following Penn State Deferred Grade Policy 48-40. To seek a deferred grade, you must submit a written request (by e-mail or U.S. post) to the instructor describing the reason(s) for the request. Non-emergency permission for filing a deferred grade must be requested before the beginning of the final examination period.  It is up to the instructor to determine whether or not you will be permitted to receive a deferred grade. If permission is granted, you will work with the instructor to establish a communication plan and a clear schedule for completion within policy.  If, for any reason, the coursework for the deferred grade is not complete by the assigned time, a grade of "F" will be automatically entered on your transcript.

    Diversity, Inclusion, and Respect

    Penn State is “committed to creating an educational environment which is free from intolerance directed toward individuals or groups and strives to create and maintain an environment that fosters respect for others” as stated in Policy AD29 Statement on Intolerance. All members of this class are expected to contribute to a respectful, welcoming, and inclusive environment and to interact with civility.

    For additional information, see:

    Attendance

    This course will be conducted entirely online. There will be no set class meeting times, but you will be required to complete weekly assignments with specific due dates. Many of the assignments are open for multiple days, so it is your responsibility to complete the work early if you plan to travel or participate in national holidays, religious observances or University approved activities.

    If you need to request an exception due to a personal or medical emergency, contact the instructor directly as soon as you are able. Such requests will be considered on a case-by-case basis.

    Mandated Reporting Statement

    Penn State’s policies require me, as a faculty member, to share information about incidents of sex-based discrimination and harassment (discrimination, harassment, sexual harassment, sexual misconduct, dating violence, domestic violence, stalking, and retaliation) with Penn State’s Title IX coordinator or deputy coordinators, regardless of whether the incidents are stated to me in person or shared by students as part of their coursework. For more information regarding the University's policies and procedures for responding to reports of sexual or gender-based harassment or misconduct, please visit Penn State's Office of Sexual Misconduct Prevention & Response website.

    Additionally, I am required to make a report on any reasonable suspicion of child abuse in accordance with the Pennsylvania Child Protective Services Law.


    Disclaimer

    Please note that the specifics of this Course Syllabus can be changed at any time, and you will be responsible for abiding by any such changes. All changes will be communicated to you via e-mail, course announcement and/or course discussion forum.