Academic Analytics

"gut instinct" vs data-driven decisions

AND

Learning that leaves trails

 Suppose you are an EFL teacher and the next agenda in your lesson plan is teaching types of clothes. You have a look at the textbook and you see a list like "vest", "shirt" and "bodywarmer". You prepare a great lesson plan, , go into the classroom, introduce the words and BOOM!: the students find the words confusing. They seem to be ok with the other new words but these devilish three for some reason just don't work! After the lesson you talk with your colleges and  hear smth like this: "Yeah, those three are always a problem". Each of them knows this that but they still follow the book's plan over and over again. The procedure repeats itself, the experience gets reduplicated and the problem persists.

  This story could have been completely different if you have just  looked into another source for guidance. Namely, if you read some research on the topic, you would have found the following:

"It's easier for the human cognitive system to differentiate between two new different items than two similar ones. Therefore, words that are similar  in some aspect (meaning/pronunciation/spelling) shouldn't be introduced together as they present a challenge for memory."

  See how sometimes research beats the "gut instinct" or the "experience"?  This is why we should steer towards a more data-driven decision-making and make a very judicious use of subjective personal experience and instincts. 

  Research is based on data. The more date we have, the more reliable and valid our research is. Now the amount of data we can get in the modern world is simply breathtaking. It has reached to the point of the so-called "Big data". Let's consider the field of education for example. Virtual Learning Environments like Moodle  track every single click that the learners make, thus yielding a complete virtual footprint. We get information about which pages in VLE are never visited by the learners , which of them are most popular, which learners are active and which, on the other hand, are "at-risk" and maybe need guidance. This data is analyzed via Learning Analytics ( the measurement, collection , analyzes of data for the purposes of optimizing learning) and is used to inform changes in the learning and the curriculum. Effective use of readily available data, that's what this is. 

  Now, do you feel a little in a daze? Do you feel like the Earth under your legs is shaking? That's right! The world is changing and moving ahead! Are you coming?