Resources and Information Services in the Disciplines and Professions
Topic: Data Services in Libraries
Spring 2018 Syllabus
Canvas Login and Tutorials
Canvas Information: Courses will be available beginning January 24th, 6 am PT unless you are taking an intensive or a one-unit or two-unit class that starts on a different day. In that case, the class will open on the first day that the class meets.
You will be enrolled in the Canvas site automatically.
This course offers an introductory overview to Social Science Data Services for supporting quantitative and qualitative research in higher-education settings. The following topics and accompanying questions will guide our exploration in this course:
- What is this thing called Social Sciences Data Services (henceforth shortened to SSDS)?
- Who is, or should be, responsible for SSDS on a college campus? How are boundaries surrounding SSDS responsibility shifting, and what does that mean for the future of social sciences librarianship?
- What professional or educational background does one need to meet these responsibilities?
- Does/should SSDS include just helping researchers find existing data, or also a librarian's active role in data collection/creation, interpretation/analysis, data visualization, and/or preservation/archiving data for future access?
- What types of things count as "data" in the social sciences - just numeric? What about "qualitative" data?
- SSDS - Reference and Instruction
- What resources exist for already-collected (secondary) data and/or statistics? What do librarians and the users need to know to find, access, and use them?
- What about researchers who want to collect their own (primary) data - what role can we play in helping them collect that data?
- How does one go about the "reference interview" when it comes to data services?
- What are approaches/models/tools for teaching different aspects of social science data access, use, and analysis?
- Data Management
- What does data management encompass? Why is it important to the research process, including granting of research funding?
- How does data management fit in with the open access movement?
- What role do/should librarians play in researchers' data management process?
- Assessing Social Science Users' Data Needs
- Who are the users of social science data on campus? How do the user populations converge/diverge in terms of their SSDS needs?
- How do we go about identifying users' SSDS needs? Once we gauge their needs, how do we develop models for meeting those needs?
- SSDS - Marketing our Wares
- How do we demonstrate our value as SSDS providers on campus?
- What challenges will we face in marketing our abilities, and what strategies do we have for overcoming those?
Below are brief descriptions of the course requirements, the CLOs and Core Competencies they support, and their corresponding percentage breakdown of the final course grade. Our Canvas site will include detailed information and grading rubrics for each requirement.
Where indicated as "OPTIONAL COLLABORATIVE EXERCISE" below, students have the *option* to work on that exercise as groups of 2 or more. If students opt for this, each student will receive an individual grade (vs. an overall group grade) for the exercise that, in addition to the instructor's assessment, reflects their group members' evaluations of their effort and contribution.
- Weekly Online Discussions/Reflections: Students will participate in weekly online discussions/reflections stemming from the various course topics and associated content, readings, and/or exercises. These will be structured, and participation is mandatory. For each week, one substantive, thoughtful initial post (a few hundred words) and a minimum of one response to another person's post (around 150 words each) are required. [Supports CLO #1, CLO #2, CLO #3, CLO #4, CLO #5, CLO #6; Core Competency D, I, K, M, N] 15% of final grade
- Reference Interview Exercise and Classmate Feedback: Students will engage in a simulated reference interview with the instructor to gauge a user's specific data need and then direct the user to the appropriate source(s) to explore. Additionally, students will give feedback on at least one classmate's reference interview. [Supports CLO #1, CLO #2, CLO #3; Core Competency I, M] 20% of final grade
- Instruction Plan Exercise: Students will create an instruction plan for a social sciences data/statistical resource or data/statistical literacy concept. As part of the assignment deliverables, students are expected to teach part of their lesson plan either (1) via recorded screencasting, or (2) using Guide on the Side (the instructor will provide you with an account if you choose this option). OPTIONAL COLLABORATIVE EXERCISE. [Supports CLO #1, CLO #2, CLO #4; Core Competency D, I, K, M] 20% of final grade
- Data Management Plan Exercise: Students will, in collaboration with a hypothetical researcher, create a data management plan for a research project that meets the National Science Foundation Directorate for Social, Behavioral, & Economic Sciences (NSF-SBE) guidelines. [Supports CLO #5; Core Competency D, I, M] 15% of final grade
- Needs Assessment and Service and Marketing Plans Exercise: Students will conduct an assessment/environmental scan of the data needs of a social science academic department and create service and marketing plans for developing or expanding data services to meet those needs. OPTIONAL COLLABORATIVE EXERCISE. [Supports CLO #6; Core Competency D, I, N] 30% of final grade
A detailed week-by-week schedule with assignment due dates will be made available on our Canvas site. Note: The course schedule is subject to change with fair notice to students.
Mode of Instruction
This course will be asynchronous with optional attendance to synchronous office hours in Zoom. We will use our Canvas learning management system site for the delivery of course content, access to reading materials, online discussions/reflections, and assignment submissions.
Attendance at the synchronous office hour in Zoom is optional; the office hour is intended for me to answer students’ questions, for students to get to know me and each other, and for students who would prefer to interact in a synchronous environment. If students ask questions during the office hour or via other modes (phone, email) which will benefit the rest of the class, I will post the question and my response to our Canvas site.
Below is the percentage breakdown of the final course grade for each course requirement.
- Weekly Online Discussions/Reflections: 15%
- Reference Interview Exercise and Classmate Feedback: 20%
- Instruction Plan Exercise: 20%
- Data Management Plan Exercise: 15%
- Needs Assessment and Service and Marketing Plans Exercise: 30%
Late Assignments Policy
Points earned for late assignments will be reduced by 20 percent for every 24 hour period between the due date and the submitted date. No incompletes will be assigned. If your life circumstances require you to seek an extension, you must contact the instructor to arrange the extension no later than one week prior to when the assignment is due – and the instructor still reserves the right to not grant an extension. No extensions will be granted for discussion posts or for the reference interview exercise.
Course Workload Expectations
Success in this course is based on the expectation that students will spend, for each unit of credit, a minimum of forty-five hours over the length of the course (normally 3 hours per unit per week with 1 of the hours used for lecture) for instruction or preparation/studying or course related activities including but not limited to internships, labs, clinical practica. Other course structures will have equivalent workload expectations as described in the syllabus.
Instructional time may include but is not limited to:
Working on posted modules or lessons prepared by the instructor; discussion forum interactions with the instructor and/or other students; making presentations and getting feedback from the instructor; attending office hours or other synchronous sessions with the instructor.
Student time outside of class:
In any seven-day period, a student is expected to be academically engaged through submitting an academic assignment; taking an exam or an interactive tutorial, or computer-assisted instruction; building websites, blogs, databases, social media presentations; attending a study group;contributing to an academic online discussion; writing papers; reading articles; conducting research; engaging in small group work.
Course Learning Outcomes
Upon successful completion of the course, students will be able to:
- Use flagship sources to retrieve statistics and raw data in order to answer a wide range of reference and research questions.
- Locate, access, and evaluate a variety of social sciences statistical and data resources.
- Conduct a data reference interview that demonstrates proficiency in (1) engaging in the iterative question-and-answer process of the data reference interview, (2) recommending appropriate source(s) to fill the user's data reference need and explaining the process by which they identified the source(s), (3) explaining jargon when necessary, and (4) communicating in a timely and professional manner.
- Create an effective and well-organized instruction session for a social sciences data or statistical resource and/or data or statistical literacy concept that reflects (1) thoughtful consideration of how to best teach the social science data/statistical resource and/or data or statistical literacy concept, (2) careful consideration of the impact of the mode of teaching on the instruction plan, and (3) follows Hunter's Direct Instruction Lesson Plan Format.
- Create, in collaboration with a hypothetical researcher, a data management plan for a research project that meets NSF-SBE grant funding guidelines.
- Conduct an assessment/ environmental scan of the data needs of a social science academic department and how/if those needs are currently being met by the library and/or other campus entities, and create service and marketing plans for developing or expanding data services to meet those needs.
Core Competencies (Program Learning Outcomes)
INFO 220 supports the following core competencies:
- D Apply the fundamental principles of planning, management, marketing, and advocacy.
- I Use service concepts, principles, and techniques to connect individuals or groups with accurate, relevant, and appropriate information.
- K Design instructional programs based on learning principles and theories.
- M Demonstrate oral and written communication skills necessary for professional work including collaboration and presentations.
- N Evaluate programs and services using measurable criteria.
- Kellam, L., & Thompson, K. (2016). Databrarianship: The academic data librarian in theory and practice. ALA. Available through Amazon: 0838987990
The standard SJSU School of Information Grading Scale is utilized for all iSchool courses:
|97 to 100||A|
|94 to 96||A minus|
|91 to 93||B plus|
|88 to 90||B|
|85 to 87||B minus|
|82 to 84||C plus|
|79 to 81||C|
|76 to 78||C minus|
|73 to 75||D plus|
|70 to 72||D|
|67 to 69||D minus|
In order to provide consistent guidelines for assessment for graduate level work in the School, these terms are applied to letter grades:
- C represents Adequate work; a grade of "C" counts for credit for the course;
- B represents Good work; a grade of "B" clearly meets the standards for graduate level work or undergraduate (for BS-ISDA);
For core courses in the MLIS program (not MARA, Informatics, BS-ISDA) — INFO 200, INFO 202, INFO 204 — the iSchool requires that students earn a B in the course. If the grade is less than B (B- or lower) after the first attempt you will be placed on administrative probation. You must repeat the class if you wish to stay in the program. If - on the second attempt - you do not pass the class with a grade of B or better (not B- but B) you will be disqualified.
- A represents Exceptional work; a grade of "A" will be assigned for outstanding work only.
Graduate Students are advised that it is their responsibility to maintain a 3.0 Grade Point Average (GPA). Undergraduates must maintain a 2.0 Grade Point Average (GPA).
Per University Policy S16-9, university-wide policy information relevant to all courses, such as academic integrity, accommodations, etc. will be available on Office of Graduate and Undergraduate Programs' Syllabus Information web page at: https://www.sjsu.edu/curriculum/courses/syllabus-info.php. Make sure to visit this page, review and be familiar with these university policies and resources.
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