Welcome to Week 11 OER on Big Data & Learning Analytics,
I have prepared a week of activities and informational resources about Big data and Learning Analytics in Higher Education CLICK HERE to access the OER LaunchPad.
The following are the list of activities to complete this week:
If you want to know where your institution stands with data ethics, privacy and policies try this Activity adopted from Every Learner Everywhere Learning Analytics Strategy Toolkit.
I have worked very hard to do this project independently, so if there are any errors or concerns, please feel free to send me a message.
After your have completed all the above activities, please return here with any comments and feedback on how I can improve this website.
21 responses to “Week 11: Big Data and Learning Analytics”
Hi Ana, nice work in creating this OER by yourself! You provided a lot of information and I was able to have a better understanding of big data and learning analytics. Thank you! The part that I found the most interesting was learning about OnTask and Threadz through the UBC Learning Analytics Pilot videos. It helped me to see how big data and learning analytics apply to our institution and now I have a better idea of how they are used by some of our instructors/professors. As for feedback on how to improve your OER, I left some suggestions in the post-survey. Hope you find them useful.
Thank you, It is a wide topic, but I tried to narrow it to something relatable for all of us since most of us participate in this area without knowing how our data is being used. Thanks for your thorough feedback, I really appreciate it and will make the necessary changes.
Hi Ana. You were able to deliver an engaging and comprehensive OER. It was well presented and informative. In the first few pages, I was impressed with various practical avenues an educator in higher education could use Big Data and LA. Threadz reminds me of a tool that I was recently introduced to called Palladio. Among other things, this tool helps to visualize data (via maps and graphs) by viewing commonalities among a whole population or in organized groupings of that population. It also measures the strengths of these relationships. It was developed by Stanford so it may fit into your OER neatly? You can find some information on it at https://hdlab.stanford.edu/palladio/. I’ll note a problem I encountered while completing Activity 1. It seems the form calculated my responses incorrectly (unless I’m misreading it). I’ll also note that some of the pages within the website caused me some confusion as they deviated from your objective of gearing your OER to higher education. For example, you presented a lot of information on your impact and challenges page that wasn’t exclusively related to higher education and provided information that was BC public schools. Question: Mobile phones have afforded a significant increase in cultivating Big Data in recent years. This fact along with the notion that it’s difficult to find a 19–25-year-old without a mobile phone as well as the significant increase in transmission bandwidth emerging from 5G capabilities, what do you see as natural opportunities for higher education that may emerge? For example, do you predict there may be a large push for higher education to produce more learner-centric mobile applications to better analyze and reform learner’s experiences?
Hi, Shawn, I took a look at Palladio, it also reminds me of Tableau but a more complex visualization, in my opinion. I think it definitely fits and thanks for your suggestion! As it relates to the impact and challenges page some information came off (as well as some duplications), I’ll get that sorted, it is difficult not having extra eyes so I appreciate all the feedback.
To answer your question, I see a big opportunity with the emergence of e-learning and m-learning, a few weeks ago the microlearning team did a great presentation on modern learning experiences using the EdApp so that is a perfect example. Not only will this type of learning boost interaction and retention but it is an opportunity to create an optimal learning experience in higher education.
Yes, your challenges are well understood. As you state, having an extra pair of eyes is useful in correcting and organizing projects. I tend to think this is one the biggest advantages to any group work. I’m glad to hear that you found my feedback to be helpful compensation.
Hi Ana, yes kudos again for completing this OER by yourself. Hopefully our comments & feedback are helpful. Job well done! Thanks, Joseph.
Hi Shaun & Ana, thank you for suggesting Palladio & Tableau as options. I explored them both & they look really interesting! Thank you, Joseph.
Hello Ana, you put together a very informative OER, and considering you did it without peer support makes it even better! I left comments in the final survey for suggested improvements, so I do not need to address that here. The information on the aggregation of big data for analytics and parallel computing was my favorite part. I am fascinated with the analytics of big data and the use of map and reduce methods to connect data in meaningful ways. Of course, the organization of the data has the capacity to add complexity to the usefulness and ‘value’ of the data collected. The use of parallel computing of big data brings up concurrency issues when looking at efficiencies for analysis. I guess the inclusion of meta data has the capacity to alleviate a lot of the potential issues with organizing and cleansing data. I also appreciated the section on privacy especially when considering the moves in Canada, parts of the US, and Europe to protect users from intrusions of data mining, potential invasions of privacy, and classifications. It is an interesting issue and there are two sides to it. I know in the context of Canadian education we find ourselves limited in the ability to utilize analytics tools because of data residency and regional laws regarding it; however, considering the morality of big business, perhaps these restrictions and struggles are worth our attention.
Thank you for putting in the effort to put this together. What a mammoth task to undertake on your own. Good work!
Thanks for sharing your thoughts, I really appreciate your feedback on the survey as well!
I do agree with your comment on privacy, it is also a growing concern that also affects higher education research.
Hi Ana, wow! I can’t believe you did this OER on your own. That’s quite the achievement. I really enjoyed your OER and participated in all the activities. This was very useful for me because I’ve been interested in Big Data and Learning Analytics. Thank you! – Mark
Thank you Mark, it was challenging but I learnt a lot in the process:-)
It’s really impressive that you came up with this alone! I thought it’s only fair that I do not miss a single word and participate actively :). I have been thinking about some must-haves in Learning Management Systems (LMS), and I figured out after your OER that one of them must be learning analytics. As I was working through, I started to reflect and dive deeper into understanding more about Learning Analytics in LMS. I really learned a lot from this and thank you for sparking additional questions that I went further into discovering though your informative OER!
Thank you Connie! I am happy you enjoy the overall content:-)
Great OER, packed with information & awesome examples and visuals! 🙂
I particularly enjoyed the “purposes” page and reading about the Purdue University Case Study of “traffic lights” intervention. Whenever I read about data analytic systems, I always think about how I approach courses in an LMS, and 90-95% of the time (basically whenever possible) I download content and work with it offline. So, when instructors are looking at number of logins, or time spent on content, there’s going to be a massive disjunction between what that data shows and what I am actually doing. This case study captured the same concern with a similar problem identified by an instructor who only had 2 assignments registered in the system and therefore the system was unable to collect ample ‘learning activity data’. This highlights a significant challenge of systems like this, which is, all activity must happen within the system for it to be collected, and further suggests changes/challenges to course design. It also suggests that the system better be pretty darn pleasant to work in, since you should (or must) to reap the benefits of the data driven interventions. I really like the overall concept of the “traffic lights” intervention though, which was to collect feedback earlier in the semester and send motivational or informative feedback along with a corresponding traffic light symbol to indicate (essentially) “you’re doing great, keep it up”, or ” there may be some risks here”. I was quite impressed with the description of the different kinds of data collected and how they are weighted to predict behaviour/outcomes more accurately. It’s definitely easy to see how the technology is becoming more refined, and further clarified the support teachers will need to understand, redesign their teaching and implement technologies like this effectively in their classes.
Thanks so much!
Thanks for an informative OER. I really liked that you provided a very balanced overview of big data and learning analytics. I have tended to be very hesitant of the use of big data given how it has been collected in the private sector, but in working through your content and exercises I found myself considering the benefits in establishing broader trends. I focused on this in the Padlet activity rather than critiquing learning analytics as I have in the past. I would also add that I really liked your page on Market Trends especially given the nature of this course. I think it was really important that you shed light on some of the key actors in the sector and what they are doing. Tableau is being used by some of my colleagues at UBC and it is interesting to see what will come of it. Thanks for including some thoughts on the pandemic as well – some colleagues and I carried out surveys with students last year in the transition period to online and it was certainly helpful to have that data even it was a small sample size. Compliments on having put together this OER on your own.
Hi Ana, good job! I enjoyed navigating through your OER & completing the activities relating to Big Data & LA. I appreciated learning about this topic (especially the videos on ONTASK & THREADZ) and also discussing the benefits vs. challenges of LA. Similar to Paul’s comments (above), I would be interested to learn more about how privacy laws vary across different countries & how this may impact LA. I would also be interested in monitoring trends amongst different educational institutions (i.e. private vs. public schools) and across different geographical areas (i.e. North America vs. Asia). All in all, a very good OER & my only suggestion (as per my post-survey comment) is 1-2 more padlets on topics of interest, as you may be able to generate good discussion and/or extract useful information from participants! Thank you, Joseph.
Bravo on an excellent OER. I enjoyed the information provided most especially the examples on LA tools. Last fall, I enrolled in ETEC 565 A – understanding learning analytics and since then I have been very much interested in exploring different LA tools. Particularly, how the data collected is presented to an end user. I learned one of the challenges educators face with using LA tools is usually around making sense of the data. A lot of LA tools fail to provide an easy to understand visualization of student learning data. I am not sure if you explored Threadz, which is used within Canvas, and I think it does a good job in presenting LA data with good visuals.
I am familiar with Threadz and that is exactly what the Epistemic Network Analysis presented on the OER reminded me of!
HI Ana, you created an amazing OER. I really like the content of the learning analytic and anticipate the use of LS in the classroom. Learning analysis technology is concerned with monitoring and predicting students’ academic performance, identifying potential problems, and recommending targeted improvement strategies and educational decision-making for the teaching process. Learning analysis technology is focus on the essence of education to improving teaching. Thank you, Ana.
I too want to congratulate you on a great solo OER. Learning Analytics is so valuable in the future of education and is pretty fundamental to make the best version of the other markets we’ve explored. I really liked reading about the learning analytics ventures in the market already.
One challenge I want to emphasize of learning analytics, that I don’t believe was mentioned is, institutional cultural. From my understanding, this is a significant barrier that UBC is facing in their implementation of Learning Analytics.
Thank you all for participating in Big Data OER, Ive read all your positive feedback and have applied them where necessary! Really appreciate it…