William Cope
Wei Liu
Pinakini Shah
Greg Jack
Leah Goldberg
Nell C Rundle
Bahar AnahmiasNew Learning MOOC’s Updates
Education and big data
Let's talk about Big Data in Education, according to the papers.
First, a definition: in education, ‘big data’ are: 1) the purposeful or incidentalrecording of interactions in digitally-mediated, network-interconnected learning environments; 2) the large, varied, immediately available and persistent datasets that are generated; and 3) the analysis and presentation of the data generated for the purposes of learner and teacher feedback, institutional accountability, educational software design, learning resource development, and educational research (Cope & Kalantzis, 2015a).
Now, what its happening into this new era? How we can follow up the individual part of our students?
In recent years, a group of organizations including the Bill & Melinda Gates Foundation, the Michael and Susan Dell Foundation, and EDUCAUSE have crafted a definition of “personalized learning” that rests on four pillars:
Each student should have a “learner profile” that documents his or her strengths, weaknesses, preferences, and goals;
Each student should pursue an individualized learning path that encourages him or her to set and manage personal academic goals.
Students’ learning environments should be flexible and structured in ways that support their individual goals.
It's important make feel the students confortables and be heard.
Re-inventing Education for the Digital Age | David Middelbeck | TEDxMünster
https://www.youtube.com/watch?v=ArI6albrkuY
PERSONALIZED LEARNING must be a perpetual, ongoing and CONSISTENT process, that has both end user and enterprise metrics in mind. Unfortunately, many schools and districts have deployed a variety of systems that can gather information on a variety of topics, yet the data-meshing and analysis to define individualized learning goals, risk aversion practices, and timely-responsive interventions have not been a part of the output.
In some instances in the US, private schools in all of their autonomy pull best practices from regional/local districts despite the imbalance of funding. Technology inclusion may be afforded on the front end (student delivery of curricula) but data mining and machine learning are not granted the attention needed to redirect or elevate student achievement.
In attempt to render solution based interventions, I believe that smaller scale funding should/could be sought out to help bring some of the smaller charter/private schools to a viable point of scalable analysis. Rather than starting at district and state levels (US), scale small to large in population based segments.