The faculty we work with have access to some course data via their LMS or CRM, but are not sure what to do with the data. There are some tools to use, but which ones have low cost and low learning curve that a faculty member could apply to their course, and then aggregate to share with their program?
The use of analytics is a powerful, transformational tool for higher ed to make more informed decisions. However, the implementation of analytics also needs to consider the impact on the culture of the institution as colleges and universities may be at different places with regard to how data is used. In addition to professional development, it may be useful to have a conversation with users regarding their comfort with ...more »
The use of analytics is a powerful, transformational tool for higher ed to make more informed decisions. However, the implementation of analytics also needs to consider the impact on the culture of the institution as colleges and universities may be at different places with regard to how data is used. In addition to professional development, it may be useful to have a conversation with users regarding their comfort with a new approach especially if they previously relied on stand-alone databases. Additionally, consider how data quality and accuracy can be assured and the governance that needs to support analytics. Finally, who are the champions for the use of analytics and how will this tool become part of day-to-day operations?
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Most of the analytics packages appear to be leveraging data primarily from a CMS and a student information system in order to make predictions on student success (and subsequently, early interventions). Unfortunately, at some institutions less and less of actual student activity is taking place in a CMS. Instead, activity is moving towards common blog platforms, university-supported wikis, VoiceThread, within custom content ...more »
Most of the analytics packages appear to be leveraging data primarily from a CMS and a student information system in order to make predictions on student success (and subsequently, early interventions). Unfortunately, at some institutions less and less of actual student activity is taking place in a CMS. Instead, activity is moving towards common blog platforms, university-supported wikis, VoiceThread, within custom content management platforms and so on.
To make better predictions, much research is still required to understand how this suite of tools plays in the learning environment. We already found correlations with certain blogging patterns and GPA growth over time at PSU...but how to integrate this in an analytics effort? We're still working that out.
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An important concern in the growing use of analytics is misuse of data
A major benefit of analytics is to optimize use of resources.
I've phrased this as a statement to make the voting more meaningful. The question around this is one of startup resources. I've been at two institutions where there was a desire to get into analytics, but in both cases we spun our wheels trying to come up with data definitions, which is deceptively hard. Just ask for a count of students sometime and see how many different answers you get. '-) If there was a set of ...more »
I've phrased this as a statement to make the voting more meaningful. The question around this is one of startup resources. I've been at two institutions where there was a desire to get into analytics, but in both cases we spun our wheels trying to come up with data definitions, which is deceptively hard. Just ask for a count of students sometime and see how many different answers you get. '-)
If there was a set of "template" data definitions for some of the more common things in higher education, would that help institutions get started? If so, who should lead/coordinate that effort?
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Analytics is a fast growing trend in Higher Education but what does it really mean? Is it Metrics? Is it Analysis of Metrics? How do we come together as a higher education community to make sure we are all speaking the same language.
The REAL challenge here is that higher education, in general, is intensely "data rich" and sadly "information poor." As others have said, we need to become more astute and informed about what all of this data really means, what it can actually tell us. We need to go to this next level here in order to be able to attack student success and retention challenges (and myriad related issues).
This is coming from the discussion by UMBC on Day 1 of #EDUSprint. Hrabowski talks about needing options to show people the benefits of decision making with analytics. In order to do this we need a culture of trust around the use of the analytics. We also need this data to work on a model that does not require mediation in order to make informated decisions.
At what price can we obtain the data needed for good academic analytics? At my institution's library about one-third of books purchased do not circulate. What do I need to know to better predict what purchases to make, and will that information save me more money or just increase costs?
Analytics, like computers, make good servants but bad masters. How can we use them thoughtfully and productively? Right now, they're cresting the "peak of unrealistic expectations" on the Hype Cycle! (See http://en.wikipedia.org/wiki/Hype_cycle)
As I look through comments from yesterday, I've noticed that some folks are speaking about analytics generally and others specifically about learning analytics. While I think learning analytics are an important focus area, I think the learning analytics discussion needs to be had as part of the larger conversation about analytics generally.
The higher education community doesn’t know how to use data to make decisions.
EDUCAUSE current working definition of analytics is: Analytics is the use of data, statistical analysis, and explanatory and predictive models to gain insights and act on complex issues. Does this resonate with your understanding of analytics?
Is it about my institution, or is it bigger? Backbone institutions like Strive are receiving attention by Stanford Social Innovation Review. They work with multiple stakeholders to choose common metrics that matter. In Cincinnati, this means common measures of retention, graduation and college prep of all students. Do we dare do this together?
moderated Educause group that shares ideas around learning analytics online and face-to-face at the annual Educause conference.
I think things to be shared would be examples of policies around the use of data, pilot study ideas and progress, job descriptions of learning analytics related positions, also solicit conference presenters.
While surveys are a great tool to find how users feel, or what they think (or believe they think), they are not very useful in finding empirical data about users. Yes, this has been touched on in other Ideas, but survey data is a specific example of what people believe learning analytics solely is, while in reality, it is so much more.
EDIT: Didn't realize I left that last sentence incomplete. My apologies.
Quantitative data is much easier to obtain and analyze than qualitative data, but qualitative data (think of text analyses of student blog posts) has more potential to help us understand what and how students are learning.
Finding academic - higher education institutions using any valued analytics for measuring mobile literacy for research. Faculty encouraging BYOD to classes for mobile interactions such as using devices as polling or clickers; faculty sending assignments via content management systems like BB Mobile etc.
Educause-sponsored workshop to provide attendees with a 3- to 5-day deep dive on learning analytics topics and strategic planning to help attendees lead learning analytics projects on their campuses.
Analytics can be applied to nearly all parts of an institution. Student success, future enrollments, tuition revenues, the impact of PP&E projects, and research funding successes are all potential topics for analytics. But the projects require coordination among different parts of the institution AND with the central IT group that has the best access to most of the data. Just because an analytics person finds a way ...more »
Analytics can be applied to nearly all parts of an institution. Student success, future enrollments, tuition revenues, the impact of PP&E projects, and research funding successes are all potential topics for analytics.
But the projects require coordination among different parts of the institution AND with the central IT group that has the best access to most of the data. Just because an analytics person finds a way to improve student retention doesn't mean that faculty will want to implement it. They may have their own analytics project going on that will come to a different conclusion.
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What is a good first project for analytics? What are the top five reports to start with?
We need to be careful not to out source analytics and not include staff and students in the process
There are a lot of data sources and many reporting tools. It seems to me that creating a unified platform through which all these tools and data can be accessed would be a major step in peeling back the layers. This would also help in the discovery of more data points and also allow for more privacy controls.
There have been many attempts to define different types of analytics. In fact, at LAK12 there was a rather heated debate about it. Recently EDUCAUSE published "Analytics in Higher Education: Establishing a Common Language." (full disclosure, I am an author). When we conceptualized and wrote the article I, obviously, felt it was very important to clear up ambiguous terms. However, now I see a lot of energy dedicated ...more »
There have been many attempts to define different types of analytics. In fact, at LAK12 there was a rather heated debate about it. Recently EDUCAUSE published "Analytics in Higher Education: Establishing a Common Language." (full disclosure, I am an author). When we conceptualized and wrote the article I, obviously, felt it was very important to clear up ambiguous terms. However, now I see a lot of energy dedicated to this task so I wonder if it is just time to “get on with the work?”
http://www.educause.edu/library/resources/analytics-higher-education-establishing-common-language
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One of the most important first steps is to begin by assessing your institution’s readiness for analytics, key performance indicators (KPIs), and dashboard tools. The Delta Stage Model, designed by Dr. Thomas Davenport from Babson College, can be quite valuable in this process. The model can be found online or through the Harvard Business Review resources. Your expectations should be framed by which stage of readiness ...more »
One of the most important first steps is to begin by assessing your institution’s readiness for analytics, key performance indicators (KPIs), and dashboard tools. The Delta Stage Model, designed by Dr. Thomas Davenport from Babson College, can be quite valuable in this process. The model can be found online or through the Harvard Business Review resources. Your expectations should be framed by which stage of readiness your institution falls into (based on the Delta Stage Model), with an eye toward evolving from your current stage to the next stage of readiness.
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Containing or lowering costs is an important benefit of analytics.
For quite some time there has been debate about:
1) does LA need a code of ethics?
2) what should it entail?
3) who has the authority to create/inforce said code?
There has been an attempt to crowd source:
(https://docs.google.com/document/d/1bBRCyldBoS0ywDhNIgB9f4k7-hLUzviu3HUX7JBUDzQ/edit?pli=1#heading=h.7gp15irx7mdv
As we develop larger data sources and increase the number of variables that might be considered in predicting student success, we also need to build better Intelligent Systems (with an artificial intelligence approach, http://en.wikipedia.org/wiki/Artificial_intelligence) that will find patterns we never expected, highlight important trends at the appropriate time, and select among likely prediction alternatives. The ...more »
As we develop larger data sources and increase the number of variables that might be considered in predicting student success, we also need to build better Intelligent Systems (with an artificial intelligence approach, http://en.wikipedia.org/wiki/Artificial_intelligence) that will find patterns we never expected, highlight important trends at the appropriate time, and select among likely prediction alternatives. The need to quickly sift through the data and make decisions about that data becomes more important as the size, frequency, and scope of the data increase.
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Utilize data mining techniques (e.g. artificial neural networks, genetic algorithms, gbm, etc.) to develop a generalized model to help discern which students have the highest probability of success. The model would be able to be customized for a given institution using that institution's own historical data.
The main issue we come upon when we talk about student retention and student success is that any system we might use needs data. This requires the faculty members to enter the data as it happens to invoke any type of early warning system. Many of our faculty are not inclined to use the systems we have in place. How do you encourage faculty participation in the use of the tools provided?
Analytics will result in reductions in force
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