ACRL e-Learning webcast series: Learning Analytics – Strategies for Optimizing Student Data on Your Campus
This three-part webinar series, co-sponsored by the ACRL Value of Academic Libraries Committee, the Student Learning and Information Committee, and the ACRL Instruction Section, will explore the advantages and opportunities of learning analytics as a tool which uses student data to demonstrate library impact and to identify learning weaknesses. How can librarians initiate learning analytics initiatives on their campuses and contribute to existing collaborations? The first webinar will provide an introduction to learning analytics and an overview of important issues. The second will focus on privacy issues and other ethical considerations as well as responsible practice, and the third will include a panel of librarians who are successfully using learning analytics on their campuses.
Webcast One: Learning Analytics and the Academic Library: The State of the Art and the Art of Connecting the Library with Campus Initiatives
March 29, 2016
Learning analytics are used nationwide to augment student success initiatives as well as bolster other institutional priorities. As a key aspect of educational reform and institutional improvement, learning analytics are essential to defining the value of higher education, and academic librarians can be both of great service to and well served by institutional learning analytics teams. In addition, librarians who seek to demonstrate, articulate, and grow the value of academic libraries should become more aware of how they can dovetail their efforts with institutional learning analytics projects. However, all too often, academic librarians are not asked to be part of initial learning analytics teams on their campuses, despite the benefits of library inclusion in these efforts. Librarians can counteract this trend by being conversant in learning analytics goals, advantages/disadvantages, and challenges as well as aware of existing examples of library successes in learning analytics projects.
Learn about the state of the art in learning analytics in higher education with an emphasis on 1) current models, 2) best practices, 3) ethics, privacy, and other difficult issues. The webcast will also focus on current academic library projects and successes in gaining access to and inclusion in learning analytics initiatives on their campus. Benefit from the inclusion of a “short list” of must-read resources as well as a clearly defined list of ways in which librarians can leverage their skills to be both contributing members of learning analytics teams, suitable for use in advocating on their campuses.
student’s opinion of this process
benefits: self-assessment, personal learning, empwerment
analytics and data privacy – students are OK with harvesting the data (only 6% not happy)
8 in 10 are interested in personal dashboard, which will help them perform
Big Mother vs Big Brother: creepy vs helpful. tracking classes, helpful, out of class (where on campus, social media etc) is creepy. 87% see that having access to their data is positive
recognize metrics, assessment, analytics, data. visualization, data literacy, data science, interpretation
INSTRUCTION DEPARTMENT – N.B.
determine who is the key leader: director of institutional research, president, CIO
who does analyics services: institutional research, information technology, dedicated center
analytic maturity: data drivin, decision making culture; senior leadership commitment,; policy supporting (data ollection, accsess, use): data efficacy; investment and resourcefs; staffing; technical infrastrcture; information technology interaction
student success maturity: senior leader commited; fudning of student success efforts; mechanism for making student success decisions; interdepart collaboration; undrestanding of students success goals; advising and student support ability; policies; information systems
developing learning analytics strategy
understand institutional challenges; identify stakeholders; identify inhibitors/challenges; consider tools; scan the environment and see what other done; develop a plan; communicate the plan to stakeholders; start small and build
ways librarians can help
idenfify institu partners; be the partners; hone relevant learning analytics; participate in institutional analytics; identify questions and problems; access and work to improve institu culture; volunteer to be early adopters;
questions to ask: environmental scanning
do we have a learning analytics system? does our culture support? leaders present? stakeholders need to know?
questions to ask: Data
questions to ask: Library role
learning analytics & the academic library: the state of the art of connecting the library with campus initiatives
7 Things You Should Know About First-Generation Learning Analytics. Published:
causation versus correlation studies. speakers claims that it is difficult to establish causation argument. institutions try to predict as accurately as possible via correlation, versus “if you do that it will happen what.”
Here is a preliminary plan. We will not follow it strictly; it is just an idea about the topics we would like to cover. Shall there be points of interest, please feel free to contribute prior and during the session.
Keeping in mind the ED 610 Learning Goals and Objectives, namely:
Understand and demonstrate how to write literature review in the field of the C&I research
Understand the related research methods in both quantitative and qualitative perspectives from the explored research articles
Understand how to use searching engine to find meaningful articles
Interpret and do critical thinking in C&I research articles
lets review our search and research skills:
How do we search?
Google and Google Scholar (more focused, peer reviewed, academic content)
What is a DOI? A Digital Object Identifier (DOI) is assigned to electronic journal articles (and selected other online content) to specifically and permanently identify and access that article. Most of the standard academic citation formats now require the inclusion of DOIs within a citation when available.
How to find a DOI: Most current academic journal articles include a DOI (usually listed on the first page of the article). Most library databases list a DOI with the record for recent academic journal articles. Most non-academic articles (including magazine and newspaper articles) as well as many older academic journal articles do not have a DOI. Crossref.org provides a DOI Lookup service that will search for a DOI based on citation information (author’s last name, journal name, article title, etc.).
How to access an article via a DOI: Use the CSU Stanislaus Library DOI Look-up for options provided by the library, including access to the full-text via the publisher’s site or a library database service when available. Other, general DOI look-up systems (CrossRef & DOI.org) usually link to the article’s “homepage” on the publisher’s site (which usually include a free abstract but full-text access is restricted to subscribers).
– the first goal of this technology instruction is to figure out the current state of technology in K12 settings.
* split in groups * using each group member’s information and experience about technology in general and technology in school settings, use the flow chart above and identify any known technology, which can improve the process of each step in the flow chart.
* reconvene and compare results among groups. Find similarities and discrepancies and agree on a pool of applicable technology tools and concepts, which can improve the process reflected in the flow chart.
Example how to meet the requirements for the first goal: 1. based on your technological proficiency, how can you aid your study using system thinking/systems approach? the work ahead of you is collaborative. What collaborative tools do you know, which can help the team work across time and space? Skype, Google Hangouts for audio/video/desktopsharing. Google Drive/Docs for working on policies and similar text-based documents.
Work on the following assignment:
Trends in technology cannot be taken separately from other issues and are closely intertwined with other “big” trends :
keeping in mind this interdependence / balance, please work in groups on the following questions. Using the available links above and the literature they lead to, as well as your own findings, please provide your best opinion to these questions:
when planning for a new building and determining learning spaces, what is the percentage of importance, which we place on technology, in relation to furniture, for example?
how much do teachers have a say in the planning of the building, considering that they had worked and prefer “their type” of learning space?
who decides what technology and how? how one rationalizes the equation technology = learning spaces = available finances?
how much outsourcing (consulting) on any of the components of the equation above one can afford / consider? How much weight the strategic planning puts on the consulting (outsourcing) versus the internal opinion (staff and administrators)?
how “far in the future” your strategic plan is willing / able to look at, in terms of technology – learning spaces?
How to stay current with the technology developments:
real-time impact on curriculum structure, instruction delivery and student learning, permitting change and improvement. It can also provide insight into important trends that affect present and future resource needs.
Big Data: Traditionally described as high-volume, high-velocity and high-variety information.
Learning or Data Analytics: The measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.
Educational Data Mining: The techniques, tools and research designed for automatically extracting meaning from large repositories of data generated by or related to people’s learning activities in educational settings.
Predictive Analytics: Algorithms that help analysts predict behavior or events based on data.
Predictive Modeling: The process of creating, testing and validating a model to best predict the probability of an outcome.
Data analytics, or the measurement, collection, analysis and reporting of data, is driving decisionmaking in many institutions. However, because of the unique nature of each district’s or college’s data needs, many are building their own solutions.
For example, in 2014 the nonprofit company inBloom, Inc., backed by $100 million from the Gates Foundation and the Carnegie Foundation for the Advancement of Teaching, closed its doors amid controversy regarding its plan to store, clean and aggregate a range of student information for states and districts and then make the data available to district-approved third parties to develop tools and dashboards so the data could be used by classroom educators.22
Tips for Student Data Privacy
Know the Laws and Regulations
There are many regulations on the books intended to protect student privacy and safety: the Family Educational Rights and Privacy Act (FERPA), the Protection of Pupil Rights Amendment (PPRA), the Children’s Internet Protection Act (CIPA), the Children’s Online Privacy Protection Act (COPPA) and the Health Insurance Portability and Accountability Act (HIPAA)
— as well as state, district and community laws. Because technology changes so rapidly, it is unlikely laws and regulations will keep pace with new data protection needs. Establish a committee to ascertain your institution’s level of understanding of and compliance with these laws, along with additional safeguard measures.
Make a Checklist Your institution’s privacy policies should cover security, user safety, communications, social media, access, identification rules, and intrusion detection and prevention.
Communicate, Communicate, Communicate
Students, staff, faculty and parents all need to know their rights and responsibilities regarding data privacy. Convey your technology plans, policies and requirements and then assess and re-communicate those throughout each year.
“Anything-as-a-Service” or “X-as-a-Service” solutions can help K-12 and higher education institutions cope with big data by offering storage, analytics capabilities and more. These include:
• Infrastructure-as-a-Service (IaaS): Providers offer cloud-based storage, similar to a campus storage area network (SAN)
• Platform-as-a-Service (PaaS): Opens up application platforms — as opposed to the applications themselves — so others can build their own applications
using underlying operating systems, data models and databases; pre-built application components and interfaces
• Software-as-a-Service (SaaS): The hosting of applications in the cloud
• Big-Data-as-a-Service (BDaaS): Mix all the above together, upscale the amount of data involved by an enormous amount and you’ve got BDaaS
Use accurate data correctly
Define goals and develop metrics
Eliminate silos, integrate data
Remember, intelligence is the goal
Maintain a robust, supportive enterprise infrastructure.
Prioritize student privacy
Develop bullet-proof data governance guidelines
Create a culture of collaboration and sharing, not compliance.