Big data in education allows for analysis of large sets to see patterns to create new changes/address any problems, etc. Big data analyses, of both retrospective and prospective data, can help us focus on improving learning/education for students.
Further, with regard to technology, big data can help us to understand the functioning of learning systems. It can propel us to analyze academic performance and review how technology is assisting in learning (including in helping performance, as well as improving efficiency, effectiveness, and more), and as such, can identify gaps for future technologies to address.
See more here: https://forbytes.com/blog/use-of-big-data-in-education/
Thanks for sharing this resource Emma. I think that big data is going to be so important in personalizing learning for students in the future. I particularly liked how the article separated educational data mining and learning analytics and how each can be utilized with the help of data!
Hello Mike and Emma,
As an educator, the idea of data in education scares me. I think that it is such a big topic that I have trouble even beginning to understand if uses and usefulness.
For example, in the article, it says that faculties are better able to : “Better understand the strengths and weaknesses of your students. A teacher gets the opportunity to analyze the heap of information as a whole and draw unambiguous conclusions. They recognize the problem and take action to prevent more complex issues. After a thorough analysis, the program is easier to adjust and get more personalized. Hence, it enhances student performance and achievement.”
I understand what this means, but I suppose I am left pondering, when would I spend the time looking through all the data that I can collect? I can collect data on my students’ performances on Quizlet lists and EducationPerfect (an online curriculum that I can use to supplement my lessons) but will I? Would those scores (data sets) surprise me or do I already know this information from all of the formative productions as well as interactions with the students? Thoughts? Ideas?
Thank you,
Maurice
Hey Maurice and Mike!
Oooh, great point. I wonder if maybe it would be more helpful, then, for much larger datasets… such as an entire university’s population?
I totally get what you mean when you say that scores of your own class wouldn’t be that surprising.
Hi Maurice,
Thought I would jump in here as that is the exact challenge I am facing with my teachers right now. Right now, we just finished our first major external assessment (we have a weird school year).
The Grade 1 teachers in my school echoed the same thing you said about already knowing the data results and thus questioned the validity of some of our assessments. They are correct to question this in my opinion. Why waste instructional time (which is arguably more valuable than assessment time) to complete an assessment that just proves what they already know? My answer to them was we do need data to drive some instructional and curricular changes but we need to look at what is truly valuable to teachers as well.
I also asked them to look at what data is most useful to them and what we think we can live without. I think we have a tendency as administrators to think “more data is always better” but we forget that the ultimate purpose of this data is to better serve our students. We can’t exactly act on that if we are always collecting and never working on acting on our results. Less is sometimes more when it comes to educational data.
Hi Benjamin,
Your comment resonates with me because my teachers are currently in the midst of their major external assessments. I’m not sure if we’re referring to the same assessments, but in Alberta, the government assigned grade 1-4 teachers the task of assessing students in literacy and numeracy. The purpose was to identify any specific points where learning loss occurred due to the pandemic and other interruptions.
The data we received was informative, and at the school level, we were able to implement various interventions to address the needs of students who had significant learning gaps. So, as you mentioned, we are definitely taking action based on our results.
However, the frustration lies in the lack of response or action at higher bureaucratic levels beyond the school districts. This valuable data should trigger a broader provincial response to address these gaps. Instead, there seems to be a push for an advanced curriculum, which may inadvertently reinforce the existing learning gaps that have already been identified.
In light of this, I would argue that sometimes less is more when it comes to analyzing and responding to educational data.
From the above comments, I think that we can see a particular mindset that is prevalent: that big data is meant to be a resource that the classroom teacher benefits from. While I think that this is an important aspect of big data and standardized assessments, I think that the current reality of big data is its use at the highest levels of administration. Ministry officials and Superintendents need to be able to make decisions based on data, and the bigger the dataset the better the decisions. Or so the thinking goes! Moreover, if the ways in which this data was used were available to teachers, they may try to influence the outcome by manipulation of data at the root level.
Within my own classes, I hope to automate substantial amounts of my assessment through a LMS, and tracking other graded material by using the feedback options. Through this, I hope to gain insight into my own teaching and classes. By extending the data over multiple years, it may become even more valuable. I teach my subject area (music) at a K-12 school, so some students could be in a dataset for a full 13 years.
Hi Emma,
It’s interesting that you raised up about big data in the use of education. My group assignment is actually keenly related to this. I have to look at the from different sides.
On a positive note, I praise the potential of personalized learning through big data analysis as it provides valuable insights into students’ learning patterns, preferences, and progress. On the flip side, big data powered by algorithms has the potential to foster narrow-minded thinking due to the way it often feeds similar content. Which is the opposite to learning, learning is supposed to be inclusive, open-minded, and iterative.
How do we balance it out? Can we train the algorithms to be more inclusive?
Hi Emma,
Thanks for your insightful post about Big Data in learning. I think that when we talk about Big Data and learning analytics, it’s often in the context of digital quizzes or tracking mouse clicks and time spent on a page in an LMS. However, I believe there’s a much broader opportunity horizon for Big Data in education, especially when we start considering wearable technology.
Imagine integrating heart rate monitors, brain wave monitors, smartwatches for notifications, and audio-visual recorders into the learning environment. These devices could offer a wealth of data for reflective practice, both for teachers and students. They could provide insights into behaviors, give feedback on focused learning time (similar to how we monitor different sleep cycles), and assist in managing behavior and classroom dynamics.
While this might sound a bit ‘big brothery’ and certainly raises privacy and ethical considerations, the potential benefits are profound. As a parent, student, and teacher, the insights and accountability these tools could offer are invaluable. For instance, if I, as a teacher, recorded my day, I could receive feedback on my tone of voice or clarity of speech. It would allow me to reflect on my interactions: Was I too fast, slow, unclear, or perhaps unintentionally unkind? Did I inadvertently provide inaccurate information? Having a ‘news feed’ of my day to review my performance would be incredibly useful.
Similarly, for my child, such a system could provide real-time insights into their behavior at school, track their literacy progress, and even recommend books tailored to their current reading level. This data-driven approach would be far more meaningful than knowing they spent 15 minutes on one page of an online course and only 30 seconds on another. It’s about moving beyond surface-level analytics to a deeper understanding of the learning process, tailored to individual needs and behaviors.
In essence, this is about harnessing the power of Big Data, AI, and machine learning to transform the educational experience into something more personal, reflective, and effective. It’s an exciting horizon, and I’m eager to see how it unfolds in the realm of education.
Your comment made me consider all the ways that big data can be used within education. The areas that I was not immediately considering was the administrative element (curriculum design, student success forecast, and student aptitude assessment).
https://link.springer.com/chapter/10.1007/978-981-16-9447-9_54/figures/1
From all the comments above, I gather big data is useful and important in education, as well as educational technology developments. This is especially the case after COVID 19, where much of the learning had to be transitioned online, and from this, much more valuable data were able to be collected. Though there are privacy and ethical concerns, in general, I do believe big data and learning analytics are very important for the future of education. Reading the report and its prediction on how big data can propel education forward, I think one big use of big data for educators is predictive analytics and personalized learning. Patterns can help educators tailor their teaching in the future, and adapt their teaching to individual needs. Big data is specifically very important to learning technologies specialists for the future design of educational technologies. They can leverage the analytics to design and optimize edTech tools to again, adapt to student needs, or even create gamified learning platforms. And finally, I think administrators and policymakers can use big data, and understand its trends, to improve institutional performance, create better policies and more efficient resource allocations.
Thanks Jennie for your comments on big data I would agree with you on the potential for big data and the availability of this data as more technology is used and online presence increases. In looking at how this data can be analyzed I am interested to see the potential utilization of AI in taking mass amounts of this data and helping to analyze this data to hopefully make it more efficient for users in decision making. I found this blog that talks a little about it.
https://www.luzmo.com/blog/ai-data-analysis#:~:text=AI%20algorithms%20will%20search%20for,years%20to%20analyze%20big%20data.