A common belief in the education world is that students learn better with a hands-on, practical, and experiential setting. EDTC6115 has been a practical testament of this belief. The beginning of the course was a little scary, mostly because most of us had not worked with big data before and we had not experienced real-life project-based course as students before. As a result, we weren’t sure we will make it halfway through the course. This feeling was exacerbated after our first live meeting with the professor—the orientation meeting—when she mentioned that the course will require active participation from everyone and that if anyone thought they would loaf about and still succeed, they had better quit immediately. Although most of us were amused by this statement, we were really scared of the journey we were about to undertake. Yet, we tried to remain strong and take the bull by the horns.
The first two weeks passed by like the wind. There were no particular challenges as we were still getting used to the course content and the protocols in those two weeks were no different from those of previous courses.
Then came data meeting #1. Big data got to us for analysis in teams. We weren’t certain what exactly we were supposed to do with the data but we knew what results we needed to get from it; instructions weren’t seeming clear. I remember this was when one or two students dropped the course, leaving us wondering even more whether we would make it through the course, although data meeting #1 required that we wonder rather about students’ data. In my team, I was brave enough to lead data meeting #1 probably simply because in life I am more used to going in the direction that is most difficult—probably to prove myself. In physics, we say that endorphins are secreted when we solve a difficult problem and not when we solve an easy one; thus, we gain more satisfaction solving the difficult problem. Because of this, physicists like me would create a problem where no problem even exists.
By when we got to data meeting #3, things were becoming even messier—probably intentionally designed to be that way. However, more learning had been taking place as well. The PLC parts 1 & 2 activities began to make things more interesting.
Data meeting #4 was the meeting where most was learned in the course. Even though the projects remained messy, we benefited from the professionalism of the professor who took the initiative to schedule a live meeting with our team to explain better how to set SMART learning goals. This was the part where I learned the most holistically valuable tool in the course.
Then came data meeting #5 and the final project. Seeing that the course was about to end, I had gained enough momentum to remain positive and to celebrate my success in the course.
To conclude, I, in particular, have learned more from this course than I would have if it were not designed in a messy, project-based practical nature. This design has proven to be very effective in getting the student to explore and discover; learning a lot more in so doing. I would, however, not use the approach of this course in my classes because my students, being young high school learners, would not appreciate working things out by themselves. Yet, I can say with certainty that this approach will be used in the really motivated classes whenever and wherever I come across them. In addition, the Data Action Model would be introduced to my school team’s during professional development and this approach would also be adopted. AMEN