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Students, Computers and Learning: making the connection

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Students, Computers and Learning: making the connection September 2015 Andreas Schleicher Director for Education and Skills


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The kind of things that are easy to teach are now easy to automate, digitize or outsource


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3 Changes in the demand for skills Trends in different tasks in occupations (United States) Source: Autor, David H. and Brendan M. Price. 2013. "The Changing Task Composition of the US Labor Market: An Update of Autor, Levy, and Murnane (2003)." MIT Mimeograph, June.


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Robotics


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Google Autonomous Vehicle >1m km, one minor accident, occasional human intervention


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Augmented Reality


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A lot more to come 3D printing Synthetic biology Brain enhancements Nanomaterials Etc.


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The Race between Technology and Education Inspired by “The race between technology and education” Pr. Goldin & Katz (Harvard) Industrial revolution Digital revolution Social pain Universal public schooling Technology Education Prosperity Social pain Prosperity


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Digital skills of 15-year-olds


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Strong performance in in digital reading Low performance in digital reading 18 Average performance in digital reading Fig 3.1


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Countries doing better/worse in digital literacy than in print reading? Source: Figure 3.7 Score-point difference Performance that would be expected based solely on print-reading


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Overall browsing activity Average rank of students in the international comparison of students taking the same test form Source: Figure 4.5 The index of overall browsing activity varies from 0 to 100, with: 0 indicating no browsing activity (no page visits beyond the starting page) and; 100 indicating the highest recorded level of browsing activity (page visits) for each test form.


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Classification of students based on their overall browsing activity Source: Figure 4.6 Percentage of students with no browsing activity (limited computer skills or unwilling ness to engage with assessment tasks) No browsing activity: students with no navigation steps recorded in log files Limited browsing activity: some navigation steps recorded, but index of overall browsing activity equal to 10 or lower Moderate browsing activity: index of overall browsing activity between 10 and 75 Intensive browsing activity: index of overall browsing activity higher than 75


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Think, then click: Task-oriented browsing Average rank of students in the international comparison of students taking the same test form Source: Figure 4.7 The index of task-oriented browsing varies from 0 to 100. High values on this index reflect long navigation sequences that contain a high number of task-relevant steps and few or no missteps or task-irrelevant steps.


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Classification of students based on the quality of their browsing activity Source: Figure 4.8 Percentage of students whose Internet browsing is mostly unfocused Mostly unfocused browsing activity: students for whom the sum of navigation missteps and task-irrelevant steps is higher than the number of task-relevant steps No browsing activity: no navigation steps recorded in log files Insufficient or mixed browsing activity: the sum of navigation missteps and task-irrelevant steps is equal to the number of task-relevant steps or lower, and the index of task-relevant browsing is equal to 75 or lower Highly focused browsing activity: index of task-relevant browsing higher than 75


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Explained variation in the digital reading performance of countries and economies Source: Figure 4.9


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Relationship between digital reading performance and navigation behaviour Source: Figure 4.10 Percentile rank


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Strong performance in in computer-based assessment of mathematics Low performance in computer-based assessment of mathematics 26 Average performance in computer-based assesment of mathematics Fig 3.10


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Relative success on mathematics tasks that require the use of computers to solve problems Compared to the OECD average Source: Figure 3.13


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Digital problem solving skills of adults % 28 PIAAC/OECD


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Students’ use of computers


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Access to computers at home Source: Figure 1.1 %


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Access to computers at home: Change between 2009 and 2012 Source: Figure 1.1 % Note: The share of students with at least one computer at home (1) or with 3 or more computers at home (2) is not significantly different in 2009 and 2012.


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Internet access at home (PISA 2012) Source: Figure 1.2 %


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Internet access at home: Change between 2009 and 2012 Source: Figure 1.2 % Note: White symbols indicate differences between PISA 2009 and PISA 2012 that are not statistically significant.


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Bridging the social divide


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Access to the Internet at home and students' socio-economic status Source: Figure 5.2 % 1. The difference between the top and the bottom quarter of ESCS is not statistically significant.


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Early exposure to computers % of students who first used a computer when they were 6 years or younger Source: Figure 5.4 %


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Early exposure to computers, by gender % of students who first used a computer when they were 6 years or younger Source: Figure 5.5 % 1. The difference between boys and girls is not statistically significant.


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Percentage of students with access to the Internet at school, but not at home Source: Figure 5.7 % 1. The difference between socio-economically advantaged and disadvantaged students is not statistically significant.


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Common computer leisure activities outside of school, by students' socio-economic status OECD average values and values for selected countries Source: Figure 5.8


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Relationship among analogue skills, socio-economic status, and performance in computer-based assessments Source: Figure 5.10 Performance in (print) reading Performance in digital reading PISA index of economic, social and cultural status Direct effect: 0.5% Digital reading (Overall effect: 12.0%) Indirect effect: 11.5% Performance in (paper-based) mathematics Performance in computer-based mathematics PISA index of economic, social and cultural status Direct effect: 0.1% Computer-based mathematics (Overall effect: 12.1%) Indirect effect: 12.0%


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Time online


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Time spent on line in school and outside of school Source: Figure 1.5 Percentage of students spending at least 4 hours on line, during weekend days


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Feeling lonely at school, by time spent on the Internet outside of school during weekdays Source: Figure 1.8 1. The difference between moderate and extreme Internet users is not statistically significant.


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Students arriving late for school, by time spent on the Internet outside of school during weekdays Source: Figure 1.9 1. The difference between moderate and extreme Internet users is not statistically significant.


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Technology in teaching and learning


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Number of students per school computer (PISA 2012) Students per computer Source: Figure 2.14


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Number of students per school computer: Change between 2009 and 2012 Students per computer Source: Figure 2.14


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Number of students per school computer Students per computer Source: Figure 2.14


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Number of students per school computer: Change between 2009 and 2012 Students per computer Source: Figure 2.14


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Use of computers at school` Percentage of students who reported engaging in each activity (OECD average) Source: Figure 2.16 %


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Use of ICT at school % of students who reported engaging in each activity at least once a week Source: Figure 2.1 %


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Use of ICT at school % of students who reported engaging in each activity at least once a week Source: Figure 2.1 %


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Index of ICT use at school Source: Figure 2.3 Mean index


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Change between 2009 and 2012 in the share of students using computers at school Source: Figure 2.4


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Students and teachers using computers during mathematics lessons Percentage of students who reported that a computer was used in mathematics lessons in the month prior to the PISA test Source: Figure 2.7


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Relationship between the change in ICT use at school and increased access to laptops at school Source: Figure 2.17


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Relationship between computer use in mathematics lessons and students' exposure to various mathematics tasks Source: Figure 2.18 OECD average


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Computer use and learning outcomes


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Number of computers available to students and expenditure on education Source: Figure 6.1


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Trends in mathematics performance and increase in computers in schools Source: Figure 6.3


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Trends in mathematics performance and increase in computers at school Source: Figure 6.3


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Students who use computers at school only moderately score the highest in reading Source: Figure 6.5 Relationship between students’ skills in reading and computer use at school (average across OECD countries) OECD average Highest score Print reading Digital reading Students with a value above 1 use chat or email at least once a week at school, browse the Internet for schoolwork almost every day, and practice and drill on computers (e.g. for foreign language or maths) at least weekly Most students with a value above 0 use email at school at least once a month, browse the Internet for schoolwork at least once a week, and practice and drill on computers (e.g. for foreign language or maths) at least once a month


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Source: Figure 6.5 Students who use computers at school only moderately score the highest in reading OECD average


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Frequency of computer use at school and digital reading skills OECD average relationship, after accounting for the socio-economic status of students and schools Source: Figure 6.6


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Performance in digital reading, by frequency of browsing the Internet for schoolwork at school After accounting for the socio-economic status of students and schools Source: Figure 6.6


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Students who do not use computers in maths lessons score highest in mathematics Source: Figure 6.7 Relationship between students’ skills in reading and computer use at school (average across OECD countries) Paper-based mathematics Computer-based mathematics Highest score OECD average


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Computer use in mathematics lessons and performance in computer-based mathematics OECD average relationship, after accounting for the socio-economic status of students and schools Source: Figure 6.8


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Digital reading performance, by index of ICT use outside of school for schoolwork Source: Figure 6.9 OECD average


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Frequency of computer use outside of school for schoolwork and digital reading skills OECD average relationship, after accounting for the socio-economic status of students and schools Source: Figure 6.10


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Students who use computer outside of school for leisure moderately score the highest Source: Figure 6.11 Relation between students’ skills in reading and computer use outside of school for leisure (average across OECD countries) OECD average Print reading Digital reading


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Digital reading performance, by index of ICT use outside of school for leisure Source: Figure 6.11 OECD average


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Frequency of ICT use outside of school for leisure and digital reading skills OECD average relationship, after accounting for the socio-economic status of students and schools Source: Figure 6.12


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Teaching practices and computer use in math lessons (OECD average) Source: Figure 2.19


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Students who use computer outside of school for schoolwork moderately score the highest Source: Figure 6.9 Relation between students’ skills in reading and computer use outside of school for schoolwork (average across OECD countries) OECD average Print reading Digital reading


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Mean mathematics performance, by school location, after accounting for socio-economic status Fig II.3.3 75 75 Most teachers value 21st century pedagogies… Percentage of lower secondary teachers who "agree" or "strongly agree" that:


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Mean mathematics performance, by school location, after accounting for socio-economic status Fig II.3.3 76 76 …but teaching practices do not always reflect that Percentage of lower secondary teachers who report using the following teaching practices "frequently" or "in all or nearly all lessons"


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Mean mathematics performance, by school location, after accounting for socio-economic status Fig II.3.3 77 77 Teachers' needs for professional development Percentage of lower secondary teachers indicating they have a high level of need for professional development in the following areas


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78 The potential of technology To gain the benefits of collaborative planning, work, and shared professional development strategies To open up pedagogical options To give extra attention to groups of learners To give learners a sense of belonging & engagement To mix students of different ages To mix different abilities and strengths To widen pedagogical options, including peer teaching To allow for deeper learning To create flexibility for more individual choices To accelerate learning To use out-of-school learning in effective & innovative ways Inquiry, authentic learning, collaboration, and formative assessment A prominent place for student voice & agency


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Expand access to content As specialised materials well beyond textbooks, in multiple formats, with little time and space constraints Support new pedagogies with learners as active participants As tools for inquiry-based pedagogies and collaborative workspaces Collaboration for knowledge creation Collaboration platforms for teachers to share and enrich teaching materials Feedback Make it faster and more granular Automatise data-intensive processes Visualisation Technology can amplify innovative teaching


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Experiential learning E.g. remote and virtual labs, project-based and enquiry-based pedagogies Hands-on pedagogies E.g. game development Cooperative learning E.g. local and global collaboration Interactive and metacognitive pedagogies E.g. real-time assessment Using digital technology


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81 Mobilise innovation


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Education is a heavily personalised service, so productivity gains through technology are limited, especially in the teaching & learning process Impact of technology on educational delivery remains sub-optimal Over-estimation of digital skills among teachers AND students Naive policy and implementation strategies Resistance of teachers AND students Lack of understanding of pedagogy and instructional design Low quality of educational software and courseware Some conclusions


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Some new developments seem to be more promising: Highly interactive, non-linear courseware, based on state-of-the-art instructional design Sophisticated software for experimentation, simulation Social media to support learning communities and communities of practice among teachers Use of gaming in instruction Concerted influence on the ‘education industry’ Some conclusions


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Make costs and benefits of educational innovation as symmetric as possible Everyone supports innovation (except for their own children) The benefits for ‘winners’ are often insufficient to mobilise support, the costs for ‘losers’ are concentrated That’s the power of interest groups Need for consistent, co-ordinated efforts to persuade those affected of the need for change and, in particular, to communicate the costs of inaction Some conclusions


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Given the uncertainties that accompany change, education stakeholders will always value the status quo. Successful innovations… are good at communicating the need for change and building support for change tend to invest in capacity development and change-management skills develop evidence and feed this back to institutions along with tools with which they can use the information Are backed by sustainable financing Teachers need to be active agents, not just in the implementation of innovations, but also in their design Some conclusions


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86 86 Thank you Find out more about our work at www.oecd.org All publications The complete micro-level database Email: Andreas.Schleicher@OECD.org Twitter: SchleicherEDU and remember: Without data, you are just another person with an opinion


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Using log-file data to understand what drives performance in PISA (Case study)


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Relationship between long reaction time on Task 2 in the unit SERAING and low performance in reading Across countries and economies Source: Figure 7.4 % %


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Success from perseverance Percentage of students who succeed on Task 3 in the unit SERAING, by time spent on the task Source: Figure 7.6


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Navigation behaviour in Task 2 in the unit SERAING Source: Figure 7.9


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Quality and quantity of navigation steps in Task 2 in the unit SERAING, by performance on the task OECD average values Source: Figure 7.10 Navigation steps


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