Smart Educational Decision Support Systems for School Complexity Leadership: A Research Agenda for...

University of Piraeus

Department of Digital Systems

Centre for Research and Technology – Hellas (CE.R.T.H.)

Information Technologies Institute (I.T.I.)

D. G. Sampson EDEN Open Classroom 2015 Conference

Advanced Digital Systems and Services for Education and Learning (ASK)

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Smart Educational Decision Support Systems for School Complexity Leadership: A Research Agenda for School

Analytics

Demetrios G. Sampson Senior and Golden Core Member IEEE

Professor, Department of Digital Systems, University of Piraeus, GREECE (until September 2015)

Research Professor, School of Education, Curtin University, AUSTRALIA (from October 2015) Adjunct Research Fellow, Information Technologies Institute, Centre for Research and Technology, GREECE

Editor-in-Chief, Educational Technology and Society Journal Past Chair, IEEE Computer Society Technical Committee on Learning Technology

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… about the speaker… • Research Professor of Learning Technologies, School of Education, Curtin University, AUSTRALIA (from October 2015)

• Professor, Department of Digital Systems, University of Piraeus, GREECE (on a non-paid leave from 1/10/2015)

• Founder and Director, Research Program on Advanced Digital Systems & Services for Education and Learning (since 1999)

• Adjunct Research Fellow, Information Technologies Institute, Centre for Research and Technology, GREECE (since 1999)

• Co-Editor-in-Chief, Educational Technology and Society Journal (Ranked #3 in Educational Technology – Scholar Google)

• Past Chair, IEEE Computer Society Technical Committee on Learning Technology (2008-2012)

• Senior and Golden Core Member, IEEE Computer Society

• Co-author of 365 publications with at least 1700+ citations (h-index:21)

• Received 8 times Best Paper Awards in International Conferences on Learning Technologies

• Guest Editor of 31 Special Issues in International Journals

• Member of Editorial Board, 23 International Journals in Learning Technologies

• Keynote/Invited Speaker on 70 International and/or National Conferences in Learning Technologies

• General and/or Program Committee Chair in 40 International Conferences in Learning Technologies

• Program Committee Member in 400+ International and/or National Conferences in Learning Technologies

• Project Director, Principle Investigator and/or Consultant in 65 research projects with external funding 14M Euro (1991-2016)


http://tinyurl.com/qcfoj2c







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Overview

• The Need: • Technology-Supported School Complexity Leadership

• The Challenge: • Smart Data-driven Educational Leadership Decision

Support Frameworks and Systems

• Towards Building Solutions: • A Research Agenda for School Analytics








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The Need:

Technology-Supported School Complexity Leadership








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Schools in 21st Century

• Geographically distributed hubs that deliver locally the centrally designed state curriculum, as the sum of isolated teacher-led classrooms, preparing students for a national final exam

or

• Accredited (at different levels) autonomous educational organizations that compete for providing better educational services to the society at large (not only for their students)








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Are Digital Technologies an enabler for Large Scale School Education Reforms?

• Large Scale investments in digital technologies infrastructure purchasing and installing technologies in schools.

• Sporadic and isolated efforts from individual teachers to innovate through reflective practice

• Yet, only opportunistic educational added-value and transformative impact at school education …

WHY ?








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Schools are social Complex Adaptive Systems

• Schools are social Complex Adaptive Systems (CAS): Comprise a wide range of agents (e.g., students, teachers, administrators, leaders, parents, policies, infrastructure, etc) which co-exist, interplay and constantly evolve at different layers of the “System”, influenced by the actions of other agents

• As such, schools as organizational entities require an holistic re-conceptualization from their stereotypical monolithic consideration: “every-school-is-(should-be)-the-same”








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• Two core school organizational layers: o Micro Layer: teaching, learning and assessment practices occurring either

within the physical educational organization premises or beyond them

o Meso Layer: organizational development processes of the educational organization

• Operating within an overarching “macro” level (policies, legislation, etc)

Macro Layer

Meso Layer

Micro Layer

• Policy Makers/Society

• Legislations/Accountability

• School Leaders

• “Educational“ Intelligence Processes

• School Teachers, Students

• Teaching, Learning & Assessment Processes








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• School system agents operating in the two identified layers (micro, meso) constantly produce educational data that repeatedly generate feedback loops to these agents and to the “System” as a whole

• These feedback loops and the collective behaviors of the agents result in the formulation of the “System” status in a process known as emergence

• The status of the School System at any point is not just a linear sum of its constituent parts but isforged in a complex manner by the characteristics and interactions of its agents.








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School Complexity Leadership (SCL)

• In this context, School Complexity Leadership (SCL) posits that credible and efficient strategic planning and outcomes can not be devised by a single agent (e.g. the headmaster), but are mainly the result of the actions and interactions of all key “System” agents (e.g., teachers, parents, etc)

• Thus, school leaders and school teachers can benefit from holistic and smart data-driven decision support mechanisms which are based on the collection, analysis, and interpretation of institution-wide educational data, otherwise hidden within “black boxes”.








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The Challenge:

Smart Data-driven Educational Leadership Decision Support

Frameworks and Systems








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Why Smart Data-driven Education Decision Support Systems for School Complexity Leadership ?

• Effective school complexity leadership decisions need to be driven by evidence and rich educational data generated at both layers (micro, meso), that monitor the System’s emergence state and generate meaningful feedback loops towards achieving improvements.

• Thus, a wide range of educational data need to be collected from an institution-wide perspective. These educational data:

• are continuously generated so they need to be collected in a timely manner in order to be meaningful for supporting leadership decisions

• need to be intelligently processed and visualized to monitor current emergence state and then, identify and recommend meaningful feedback loops

• Performing these tasks manually and, at the same time, transparently and collectively by the school leaders is beyond any realistic expectation.








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Reflective School Complexity Leadership comprises tasks which span both school organizational layers and exploit institution-wide educational data

Core School Leadership Tasks Examples of data types used School Layer

Learning Process (Design and) Monitoring • Teaching method employed • Educational resources used

Micro Learning Process Evaluation • Level of use of educational resources

• Learner assessment results

Learner Performance Monitoring • Attendance rates • Level of engagement

Learner Performance Evaluation • Learner assessment results

Curriculum Planning • Learner assessment results • School available resources

Meso

Teaching Staff Management (and Recruiting) • Teaching methods employed

• Competence management • Professional Development activities Teaching Staff Professional Development

District Stakeholder Accountability • Student retention rates

Infrastructural Resource Management • School physical / digital infrastructure

Financial Resource Management • School budget

Learner Data Management • Learner demographics

S. Sergis and D. Sampson, “Data Driven Decision Making For School Leadership: A Critical Analysis Of Supporting Systems”, in Ronghuai Huang, Kinshuk, and J.K. Price (Eds.), "ICT in education in global context: comparative reports of K-12 schools innovation", Springer, 2015








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Current School Leadership Decision Support Systems • Ideally: aim to harvest, analyze and/or visualize diverse educational data

in a timely manner towards generating actionable insights for school leadership

• We have reviewed 70 existing School Leadership Decision Support Systems:

o The majority of current DSS focus on assisting school leaders monitor school elements related to externally mandated accountability requirements (e.g., students' summative assessment score data and/or retention rates)

o "Business" managerial tasks (e.g., management and strategic orchestration of the schools' finances and infrastructure) are captured in isolation to other educational leadership tasks

o Very limited focus is given to micro level educational data for driving internal school improvement (e.g. a detailed formative and reflective evaluation of the teaching practices employed by the teachers).

S. Sergis and D. Sampson, “Data Driven Decision Making For School Leadership: A Critical Analysis Of Supporting Systems”, in Ronghuai Huang, Kinshuk, and J.K. Price (Eds.), "ICT in education in global context: comparative reports of K-12 schools innovation", Springer, [Submitted for Publication]








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Limitations of Current School Leadership Decision Support Systems

• Do not adequately accommodate the full spectrum of the core school complexity leadership tasks

• Do not explicitly consider the interrelations between the collected educational data from the two organizational layers (micro/meso) and the manner in which these affect the overall school performance

• Do not exploit current trends in teaching and learning analytics.

Mainly Management rather than Leadership “tools”








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Towards Building Solutions:

A Research Agenda for School Analytics








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Existing Analytics frameworks and systems for supporting Educational Decision Support

• Two core Analytics strands have been put forward for supporting Educational Decision Support:

o Academic Analytics: addressed at Higher Education Institutions for supporting mainly meso layer leadership processes (e.g., management of infrastructure, finances, faculty data, etc)

o Learning Analytics: addressed at all types of Education Institutions for supporting mainly micro layer leadership processes (e.g., profiling students, provision of feedback to teachers and students, etc)

S. Sergis and D. Sampson, "School Analytics: A Framework for Supporting School Complexity Leadership", in J. M. Spector, D. Ifenthaler, D. Sampson and P. Isaias

(Eds.), "Competencies, Challenges and Changes in Teaching, Learning and Educational Leadership in the Digital Age", Springer, 2015








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Limitations of Existing Analytics frameworks and systems

• Academic Analytics: Limited level of exploitation of the meso layer educational data in combination towards capturing their inter-relations and how this affects overall system performance

• Learning Analytics: Limited level of accommodation for capturing educational data related to micro layer system agents beyond the students (e.g., profiling and activity logging for the teacher, teaching practices utilized, physical context affordances etc.)

• Both: exploit available educational data from the different layers in isolation








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School Analytics Framework








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Education Decision Support Systems for School Complexity Leadership based on School Analytics

• School Analytics can provide a backbone framework for designing school leadership

analytics systems to:

o Collect, analyze and process granulated educational data from all organizational layers (e.g., open-up classroom “black box”) [define “emergence status”]

o identify causal links between school organizational inputs and outputs (e.g., correlate student outcomes to specific teaching practices employed within specific learning contexts)

o generate evidence-based recommendations for strategic holistic school improvement based on the inter-relations of the collected educational data (e.g., targeted teacher professional development based on processing combined data from the teaching practice and student performance) [recommend “feedback loops”]








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Recent Publications within this Research Agenda

• S. Sergis and D. Sampson, "School Analytics: A Framework for Supporting School Complexity Leadership", in J. M. Spector, D. Ifenthaler, D. Sampson and P. Isaias (Eds.), "Competencies, Challenges and Changes in Teaching, Learning and Educational Leadership in the Digital Age", Springer, 2015

• S. Sergis and D. Sampson, "Data Driven Decision Support For School Leadership: Analysis Of Supporting Systems", in Ronghuai Huang, Kinshuk, and Jon K. Price (Eds.), "ICT in education in global context: comparative reports of K-12 schools innovation", Springer, 2015

• S. Sergis, P. Zervas and D. Sampson, “A Holistic Approach for Managing School ICT Competence Profiles Towards Supporting School ICT Uptake”, International Journal of Digital Literacy and Digital Competence (IJDLDC) 5(4), 33-46, 2015

• S. Sergis and D. Sampson, "From Teachers’ to Schools’ ICT Competence Profiles", in D. Sampson, D. Ifenthaler, J. M. Spector and P. Isaias, (Eds.), Digital Systems for Open Access to Formal and Informal Learning, Springer, ISBN 978-3-319-02263-5, Chapter 19, pp 307-327, 2014

• P. Zervas, K. Chatzistavrianos and D. Sampson, "Towards Modeling Teachers’ ICT Competence Profile in Europe", in R. Huang, Kinshuk and J.K. Price (Eds.), ICT in Education in Global Context, Springer, 2014

• P. Zervas and D. Sampson, "Supporting Reflective Lesson Planning based on Inquiry Learning Analytics for Facilitating Students’ Problem Solving Competence Development: The Inspiring Science Education Tools", in Ronghuai Huang, Nian-Shing Chen and Kinshuk (Eds.), "Authentic Learning through Advances in Technologies” Springer, 2015

• S. Sergis and D. Samson, “Learning Objects Recommendations for Teachers based on elicited ICT Competence Profiles”, IEEE Transactions on Learning Technologies, 2015








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From 1/10/2015 Research Professor of Learning Technologies

School of Education, Curtin University Perth, Western Australia, Australia








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Curtin University

#271 in the world

(top 2%) #11 in Australia

according to

Academic Ranking of World

Universities 2015








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School of Education @ Curtin University








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School of Education @ Curtin University

Courses: Undergraduate, Postgraduate, Higher Degree by Research Courses • 4 Bachelor of Education (Early Childhood Education), (Primary Education), (Secondary

Education), (Conversion Course) •2 Language Bachelor of Arts (Humanities) Chinese, Japanese

•Graduate Diploma in Education (Secondary Education) •Graduate Certificate in Teaching English to Speakers of Other Languages (TESOL)

•Master of Arts (Applied Linguistics) •Master of Education

•Master of Science (Science and Mathematics Education) •Master of Philosophy (Education) •Doctor of Philosophy (Education)

•Doctor of Education

SoE is also home to the internationally esteemed Science and Mathematics Education Centre (SMEC). SMEC has one of the largest postgraduate programs in the world in

science and mathematics education, with students from all over the world.








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City of Perth, Western Australia

#8

Best Place to live in the World

according to The Economist

Intelligence Unit - EIU's latest Global Liveability Ranking


https://www.facebook.com/TheEIU












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… stay in contact …

e-mail: [email protected]

twitter: @dem-sampson Slideshare:

http://www.slideshare.net/Demetrios_Sampson

www.ask4research.info


mailto:[email protected]

http://www.slideshare.net/Demetrios_Sampson

http://www.ask4research.info/

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Smart Educational Decision Support Systems for School Complexity Leadership: A Research Agenda for...

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