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?
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for Idea: Misuse of data is a concern with using analytics
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The idea was posted9 months ago
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This should be easy for faculty to determine. It is analogous to their students have a large amount of research data for their thesis or dissertation. How would faculty advise a student who has reams of data around their topic but "don't konw what to do with it?" I hope the professor would suggest that the student start with the hypothesis or question they are trying to prove. Once you know your driving needs (root questions) you can then "use" the data.
When we start with data we end up trying to find a problem to apply the canned solution against. Of course there are insights the data can provide from a standardized view - like the enrollment and Grade data UMCB presented in their case study. This type of information can be provided by a data-analyst WITH explanations of how to use the data.
So there are two considerations - what standard information can be provided to help in decision making / direction setting? AND, what are the questions driving the analysis? These are both critical areas which data-stores (warehouses, big-data, etc.) can assist in.
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I agree it should be easy, bu MANY faculty do not know what data they have, how to get it out of their LMS. Some do and can, but are not sure what to do after they get the numbers. I am not talking about starting with the data at all, but for those whose LMS are set up to collect specific data. I am also talking about at the course level. I agree with everything you have said except the "This should be easy" part. If it were easy, it would probably be getting done.
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Why don't your faculty know what to do with your data? Do they already have questions they are trying to answer but are struggling because of poor tool usability?
If you are having a tool issue, try looking at the following leaders in the personal BI space. All of these tools make it easier for analysts to mash-up and analyze their data prior to spending the big bucks on the enterprise solutions.
1) Excel 2010 & Microsoft PowerPivot Add-in (free)
2) Tableau
3) Tibco Spotfire
Finally, I'd argue that great tools are not the answer. You can lead a horse to water, but you can't make him drink it.
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Sometimes people just need good examples. Or someone listening to their questions/issues and suggesting ways that data would be relevant. Case studies would probably help - especially from other faculty.
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Linda, EXACTLY! It's a time energy thing; not that faculty can't come up with good questions and what/how/where to find the answers with the data; however, it would be nice to be provided with example sets of "here is the type of data you are already collecting" and "here are the types of questions you can answer with it" and "here is the method to answering each individual question."
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to Shelley and Linda - but wouldn't it be better to say, hey, what are the questions you have? What are the questions you should have? And then determine the information needed to answer them? And if the data/measures needed to provide that information are already being collected - great! If not, you can determine if you should. But by starting with the data already being collected (and not necessarily used well or at all) we restrict our horizons to what we have vs. what we (might) need.
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Sure...I was speaking to your ""here is the type of data you are already collecting" and "here are the types of questions you can answer with it" statement...but I'm happy with the distinction you're making, especially if you build the scaffolding to provide information in response to needs and questions.
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Faculty need more than how-to. They need to develop totally different understandings of assessment, evaluation, and the current technological capacity for information processing and knowledge generation. Most faculty still have "old paradigm" mental models of how to gather, manipulate and use data in any other aspect of their work except research - and sometimes even in their research!! Analytics as a field requires a different type of expertise - and the field needs to figure out how to purposefully develop the capacity of FUTURE faculty as a part of learning in the UG and Graduate programs. And current faculty will need a sustained program of professional development in analytics.
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I think it's important to remember that while faculty (at least those with doctorate degrees) have experience with "data," it's data specific to their field of expertise. So, excepting faculty in education, many faculty members have likely never really seen or looked at education data and really don't know what to do with it. And given that we don't really teach PhDs how to teach, many may not even know what questions to ask. So the idea of examples, how-tos, and professional development is important, as is the understanding that this endeavor, from a faculty perspective, must be a zero sum resource game at some point. Asking faculty to take on even more isn't any more fair than asking IT staff to take on more without additional resources.
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Kyle, I agree. Most faculty do need examples and professional development because they have had little training in educational research.
And I concur about being careful about demands on time. Anyone following news in higher ed has seen the increasing use of ill-paid adjuncts; this group generally is not expected to do more than teach. That, in turn, places more burden on full-time faculty since they must pick up the advising/service duties of adjunct positions. Thus, faculty TIME is becoming one of the most limited (and valuable!) campus resources... but is not yet treated as such.
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A common tactic to help faculty with all this new found data is the usage in their tenure review process. That gets their attention. We spend a lot of time trying to make this information as easy as possible to share and digest, recognizing their time constraints and unfamiliarity with the data. The spread of the LMS itself it similar to the spread of LMS analytics. Faculty saw and shared what other faculty could do with it, and the students see such a benefit they push the faculty to adopt both.
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The work that led to the development of the SNAPP tool (http://moourl.com/snapp) found that academics did need assistance. And it's just not how to read/understand the data, but also to plan and implement what to do in response to what the data reveals.
Some references to that work is contained in some blurb we've written for a grant application (http://davidtjones.wordpress.com/2012/07/25/enabling-academics-to-apply-learning-analytics-to-individual-pedagogical-practice-how-and-with-what-impacts/) which we're hoping will allow us to explore how this can be done.
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David, great points! Based on what I was discussing above (scaffolding processes to help faculty to gather and interpret data), they (we) also want help in applying and responding as well....esp. if education/pedagogy is not our research interest and/or research is not a required part of our job (think CC faculty). Thanks for sharing your blog link!
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Faculty need help in asking the right question. I think it is unfair, for example, to expect a Chemist who knows all about how molecules interact to understand how humans behave. Many instructors at institutions of higher learning have no formal training in teaching and learning. They were not hired for their expertise in human learning but in Chemistry, for example.
What would be most helpful, I think, is a liminal guide, someone who will stand with faculty at the threshold of their data and help them navigate through it, including what questions to ask and where to look for answers.
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Amen. I'd only add to your statement that not only do faculty need help, but ALL of us need help in identifying the right questions to ask. The great part is once we know the right questions...there are lots of places to look for the answers. Existing data-stores which may or may not have analysis already applied against it is a place to start. Unfortunately, many times we "chase data" rather than figure out what we really need/want to know.
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Many of the faculty members I have dealt with are less interested in learning how to do their own analysis than in having an analytic system which gives them, at a glance, an idea of who is struggling. For example, if they could see a red/yellow/green indicator to tell them who is doing well and who isn't in their class, that is sufficient.
Remember that analytics isn;t their principle job. At a research institution, even teaching isn't. We need to provide more complete systems and work with those who ARE interested in this stuff to develop more tools for faculty, not expect faculty to develop tools on their own.
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Sounds great Rebecca. I'm just not sure why we're calling this analytics - seems like a real-time dashboard on students progress. Isn't this just metrics? We've been working on this for years now. Even the Learning Dashboard presented today - seems to be, well, a dashboard. Just wondering why we are calling it something new and treating this all as a new idea?
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For me (and I acknowledge it may only be me) The newness is the ability to move from being able to (for example) delineate just how big a hole we're in, to teasing out potential causes for the hole, and areas to avoid/target/flag as dangers in order to (again for example) get out it.
Moving to predictive and then prescriptive analytics is relatively new - even if we adopt more established visualisation / presentation techniques.
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Analytics works on multiple scales: faculty getting info individual students and faculty getting information on cohort of students. The former is fairly obvious; the latter is big opportunity for improving student success. Mining overall student results, interactions, and other student actions/non-actions can potentially inform faculty member on making small adjustments in future lectures, homeworks, exams, etc. to improve student success. If done well, this should result in overall improvements in student rankings of faculty member.
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