Page 1 - R_EdQuire White Paper Nov 2017 v3.4

EdQuire White Paper: Computer learning behaviour in K-12
Nov 2017 V3.4
Page 1 of 15

EdQuire White Paper:

Computer learning behaviour in year grades K-12

Nov 2017
FIC Technology Pty Ltd and Edquire International Pte Ltd.
Authors: FIC Technology Research Staff,
Author for Correspondence: Dr Michael Cejnar, m.cejnar@edquire.com.au

ABSTRACT: Students and teachers in Australasian K-12 classrooms utilise a wide range of computer
st
resources to facilitate the learning process generally and instil 21 Century skills specifically.

However, we have very limited understanding of how students use their computer time and whether

feedback about the usage can improve learning. Using a background app installed with students’
knowledge, FIC Technology collected into our cloud database, real-time metadata on the computer
application and online resources used by students in classrooms. Student computer activities were

classified and analysed using an Al-based learning analytics algorithm and delivered to class Teachers
a glanceable, colour-coded display showing each student as either On-Task (green) or Off-Task (red).

Using an AI-based learning analytics engine, we classified and analysed educational activities, while
providing teachers with a glanceable colour-coded display of each student being either on-assigned
task (green) or off-task (red).

We studied 549 students (52% girls) across years 7-12 from 4 independent schools, capturing 21,454

lessons where computers used from January to November 2017. On average, students spent on

average, 17% of this time off-task but 20% of students spent 30% of their time off-task. The majority
of computer time (>30%) was spent on word processing. Boys spent marginally more time off-task,

mostly on gaming. Girls were most distracted by streaming videos. Furthermore, the analysis of

Internet searching skills revealed an overwhelming and use of standard Google searches with only
3% of searches using advanced tools.

When we gave students access to their own data, a self-selected group with higher off-task times
viewed the data and markedly reduced their off-taskness and distractibility. Our unique data and

analysis provides new and needed insights into computer use in classrooms and may facilitate

student self-regulation.

Keywords: Student engagement, Digital Learning, Learning Analytics, AI, Formative Feedback, Self-regulation,
Computer learning

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