Page 13 - R_EdQuire White Paper Nov 2017 v3.4
P. 13

EdQuire White Paper: Computer	learning	behaviour	in	K-12
                                                                                                    Nov 2017 V3.4
                                                                                                     Page 13 of 15

               Figure	10		Proportions	of	On-Task	and	Off-Task	per	minutes	during	the	pre-	and	post-stage	of	self-regulation.

               Although	limited	in	time	and	numbers,	this	improvement	in	computer	engagement	is	nevertheless	highly
               significant	both	statistically	and	in	effect.	Interestingly,	these	self-selected	students	had	a	significantly	higher
               Off-Task	times	of	33%	compared	to	10%	for	the	whole	group,	suggesting	students’	awareness	or	meta-
               cognition	of	their	distracted	study	habits,	and	seeking	confirmation	from	our	data	and	then	exercising
               effective	self-governance.	We	will	analyse	the	data	from	the	students	who	did	not	use	the	feedback	as	a
               further	control	measure.

               More	data	is	required	to	confirm	this	effect	and,	of	course,	its	persistence.	Experience	shows	that	students
               will	tend	to	game	parameters	presented	to	them,	particularly	if	they	affect	their	scores.	Great	care	will	need
               to	be	taken	to	ensure	that	such	feedback	is	true	and	constructive.	While	reduction	of	Off-Task	behaviour	and
               task	switching	seems	likely	to	lead	to	improved	educational	outcomes,	all	data-based	interventions	will
               require	validation	against	educational	outcomes,	in	whatever	form	they	may	be	defined	by	experts,	before
               scaling	implementation.

               A	particularly	exciting	application	of	this	feedback	and	self-selection	of	students	is	in	the	context	of	the
               second	digital	divide,	separating	students	with	ICT	learning	skills	from	those	lacking	such	skills	and	being
               distracted	by	ICT.	In	the	latter	group	of	students,	not	only	can	our	data	identify	their	lack	of	skill	and	enable
               teacher	intervention,	but	the	self	regulatory	effect	might	would	automatically	apply	most	benefit	to	those
               students	needing	it	most.

               ©	Copyright	2017	|	All	Rights	Reserved	by	FIC	Technology
   8   9   10   11   12   13   14   15