I recently read an interesting article (see weblink above) by Jonathan Rochelle. Mr Rochelle is the head of the product management team for Google’s education outreach arm, called Google for Education.
Expert machine programming/AI development helps machines learn and machines (increasingly) help students learn. The question is, will conventional teaching cope?
Without doubt, machine learning is high growth off a low base. With a good deal more investment-return uncertainty, machine-assisted student learning is high growth off a low installation base.
Meanwhile, in the land of traditional education methods, the effectiveness of human teachers in fostering high learning growth from students is experiencing far more sluggish improvement. Some of the reasons arguably include the following (in no particular order):
(1) a lack of agreement inside schools on what’s causing the attainment gap problem. Is it a shortage of the best teachers, or the best teaching practices? Is it the poor parent-school partnership or the lack of school boundaries?
(2) resistance to learning from the students. Students and their parents may have a different view from the school about the best teaching style, or the best learning style for the student. Are teachers, who are passionate about their subject, making it relevant enough to the students’ future lives?
(3) the need to build suitable physical facilities to support student learning. Will far more conventional classrooms need to become computer suites, perhaps with virtual reality apparatus?
(4) budget funding constraints
(5) confusion on the institutions’ own goals (too many targets?).
As online education software increasingly provides a more complete teaching solution in the classroom, what can human educators do? Start planning now for the changeover (move to a variable cost workforce and shorter shelf-life classroom facilities), immerse students in the online systems world (so student graduates can partner with it later) and offer school curriculum choices in subjects that will be slowest to become obsolete i.e. subjects that remain valued by future employers who hire student graduates.
Lastly, how long before the Chinese equivalent of Google matches Google’s audacious plans for transforming global education?