Page 4 - R_EdQuire White Paper Nov 2017 v3.4
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EdQuire White Paper: Computer	learning	behaviour	in	K-12
                                                                                                    Nov 2017 V3.4
                                                                                                      Page 4 of 15

               Data	collection
               An	edQuire	software	agent	was	installed	on	student	computers,	which	securely	transmitted	student
               computer	usage	data	to	our	secure	cloud	service.	Data	connections	were	established	with	school	time	tabling
               and	student	information	systems.	Data	was	only	collected	during	lesson	times	and	stored	anonymously	with
               student	identifying	data	held	separately	for	security.	Activities	collected	included	applications,	application’s
               window	title,	visited	websites	and	browser	tab	titles.	Keystroke	frequency	and	use	of	clipboard	was	recorded
               (except	in	OS	X).	No	typed	content	was	recorded	except	for	search	engine	text	contained	in	URLs.	Duration
               spent	on	each	website	or	application	lasting	>	½	a	second	was	recorded.	Data	was	automatically	cleansed	of
               operating	system	activities.	Activities	without	user	input	for	2	minutes	were	reclassified	as	Idle.

               Data	real-time	display
               Teachers	were	given	an	internet	console	for	use	in	lessons,	displaying	the	students‘	live	on-taskness	state
               and	history,	allowing	them	to	see	at	a	glance	classroom	engagement	and	identify	outlying,	struggling	or
               unchallenged	individual	students	who	potentially	needed	support	(Figure	1).

               Figure	1.	Teacher	EdQuire	classroom	console.	Horizontal	bars	at	bottom	of	tiles	represent	a	student’s	history
               in	that	lesson.	Grey	colour	indicated	Idle.

               2.2	General	Analysis	method

               Categorizing	educategory	-	education	vs	entertainment	for	student	computer	activities
               Student	computer	usage	activities	were	first	categorized	in	real	time	by	a	machine	learning	algorithm	into
               educational	or	non-educational.	Activities	were	then	automatically	contextualized,	according	to	teacher-
               entered	lesson	keywords	or	subject	topic,	as:
                   •  on	teacher-assigned	resource,
                   •  On-Task	(displayed	green),	on	own	discovered	resource,	OwnTask	(blue),	Off-Task	(red)	or
                      Unclassified/ambiguous	(orange).
               Ambiguous	(orange)	activities	were	resolved	by	opinions	of	educational	experts	and	then	fed	back	into	the
               algorithm	for	learning.	The	accuracy	of	our	machine	learning	algorithm	for	educationally	categorizing
               activities	was	93%	when	compared	to	human	observers.	For	the	purposes	of	this	analysis,	On-Task,	OwnTask
               and	Ambiguous	were	grouped	into	On-Task.

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