Beginning in Spring 2024, teaching faculty in academic departments can apply to designate courses as Data Intensive. This designation has several benefits:

  • Students will be able to search for data intensive classes that help them meet their personal and professional goals
  • Faculty can demonstrate they are teaching data intensive courses and thus serving a university-wide initiative for improving our data intensive programming, in alignment with Boldly Elon’s goal to strengthen students’ data competency and skills
  • Should the pilot be successful; students will be able to pursue badges and/or micro-credentials related to a data intensive curriculum

The committee welcomes applications from faculty teaching courses that are oriented towards either or both quantitative and qualitative data and evidence. Ideally, these courses are developing students into informed, responsible, and thoughtful users and/or producers of data. Being mindful of this, applicants will be asked to provide examples of projects, assignments, and/or any other course materials that meet this objective.

Learning Outcomes for Data Intensive Courses
Each faculty member seeking to designate a course as Data Intensive must demonstrate how they will implement 2 or more of the below student learning outcomes (SLO’s) in their course, and at least 1 of the SLO’s should be consistently integrated and assessed throughout the course:

  1. Students think critically about when and what data are needed, assess data sources appropriate to the information needed, and identify the context in which data are produced and used.
  2. Students access, or collect when appropriate, data and explain the ethical considerations of how this data is collected and used.
  3. Students identify appropriate data-analysis methods for working with a given data set and explain the associated limitations and interpretations.
  4. Students communicate their data-intensive work to a variety of audiences through multiple modalities (written, visual, verbal).
  5. Students identify and proficiently use appropriate software tools to perform at least 2 of the following:
    a. Proof—or “clean”—data to make it amenable to further analysis,
    b. Transform data (e.g. from text to numbers), through coding or another process
    c. Visualize and compare attributes of a data set,
    d. Analyze data for pertinent information, and
    e. Develop models that estimate relationships among variables in the data.

If you are planning on applying you can check out the application questions and rubric below as well. If you have any questions please feel free to reach out at DataNexus@elon.edu.
Apply for a Data Intensive Course Designation.

Application Questions

  • Your name
  • Your department
  • Your Elon email address
  • Department chair’s name and email address (Note: Please have a conversation with your department chair prior to submitting your application.)
  • Is this application for a course-level data-intensive designation or a section-level data-intensive designation?
  • Course Number and Title for Course
  • Course Number and Title for Course
  • When did you last teach this course?
  • When do you (next) plan to teach this course?
  • Is this an existing course at Elon in the Academic Catalog?
  • What type of data do you predominantly use in this course?
  • Upload syllabus and at least one sample project, assignment, examination, etc. that highlights the integration of at least one of the SLOs throughout the course curriculum and the student performance of the SLO (rather than exposure to the SLO). Pdf uploads are preferred.
  • Describe how the SLOs will be included in the proposed course’s curriculum. Explain how course projects, assignments, activities, and so forth will be used to develop and assess student learning.

Rubric

Is this an existing course at Elon in the Academic Catalog? Yes/No If YES continue reviewing, If NO stop and send back to proposer
Is this for a course or section? Course/Section If COURSE continue reviewing, if SECTION does the applicant provide clarity around section designation
Are the SLOs identified in the proposal? Rating 1-5 Reviewers Comments
Is there evidence of the identified SLOs in the course syllabus and sample assessments? Rating 1-5 Reviewers Comments
Does the proposer/syllabus clearly states how one or more of the SLOs is assessed at the individual level? Rating 1-5 Reviewers Comments
Does the proposer/syllabus clearly state how individual students will achieve expected course-level data competency in stated SLO(s)? Rating 1-5 Reviewers Comments