Invited Public Lecture Faculty of Education, The University of Hong Kong 17 November 2016

1. Invited Public Lecture
Room 204, Runme Shaw Building, HKU
Faculty of Education, The University of Hong Kong
17 November 2016
Teaching and Learning Analytics
for the Classroom Teacher
Professor Demetrios G. Sampson
PhD(ElectEng) (Essex), PgDip (Essex), BEng/MEng(Elec) (DUTH), CEng
Golden Core Member, IEEE Computer Society
Editor-In-Chief, Educational Technology & Society Journal
Chair IEEE Technical Committee on Learning Technologies
Professor, Learning Technologies | School of Education
Curtin University, Australia

2. Presentation Overview
 Introduction
 Educational Data for supporting Data-Driven Decision
Making in School Education
 Teaching Analytics: Analyse your Lesson Plans to
Improve them
 Learning Analytics: Analyse the Classroom Delivery
of your Lesson Plans to Discover More about Your
Students
 Teaching and Learning Analytics to Support Teacher
Inquiry

3. Introduction

4. Perth, Western Australia

5. Perth, Western Australia

7. Curtin University

8. Curtin University

12. School of Education
Offers programs that embrace innovation in education theory and
practice since 1975, with the aim of preparing highly competent
graduates who can teach and work in a fast-changing world
The main provider of Teacher Education in Western Australia: 45%
WA school graduates 1000 new UG students annually
The dominant online provider of Teacher Education in Australia,
with over 2000 students through Open Universities Australia.
Recognised within Top 100 Worldwide in the subject of Education
by QS World University Rankings by Subject 2015/16

21. Joined School of Education @ Curtin University
October 2015

22. 20 years in Learning Technologies and
Technology Enhanced Learning
• 17 years in Academia and Research: School of Education, Curtin University, Western Australia / Dept of
Digital Systems, University of Piraeus, Greece / Information Technologies Institute, Centre of Research and
Technology - Hellas Greece (since January 2000)
• 3 in Industry: Research & Innovation Director/Consultant in Educational Technology industry and Greek
Ministry of Education (September 1996 – December 1999)
• Ph.D. in Electronic Systems Engineering , University of Essex, UK (1995)
• Diploma in Electrical Engineering , Democritus University of Thrace, Greece (1989)
• 67 Research & Innovation projects with external funding at the range of 15 Million€
• 390 research publications in scientific books, journals and conferences with at least 3740 citations and h-index 28
according to Scholar Google (November 2016) [40% during the past 5 years]
• 9 times Best Paper Award in International Conferences in LT and TeL
• Keynote/Invited Speaker in 72 International/National Conferences [60% during the past 5 years]
• Supervised 150 honours and postgraduate students to successful completion.
• Chair of the IEEE Computer Society Technical Committee on Learning Technologies (2008-2011, 2016-today)
• Editor-in-Chief of the Educational Technology and Society Journal (listed #4 in Scholar Google Top
publications of Educational Technology (https://goo.gl/kHa6vk);
• Founding Board Member / Associate Editor and then Steering Committee Member of the IEEE
Transactions on Learning Technologies (listed #11 in the same Scholar Google list)

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EDU1x: Analytics for the Classroom Teacher
edX MOOC
EDU1x Analytics for the Classroom Teacher
Curtin University
October-December 2016
More than 2500 enrollments from over 127 countries

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Educational Data Analytics Technologies
for
Data-driven Decision Making
in Schools

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School Autonomy
• School Autonomy is at the core of Education System Reform Policies
globally for achieving better educational outcomes for students and
more efficient school operations
• Schools are allowed more freedom in terms of decision making
– For example curriculum design and delivery, human resources management and
infrastructure maintenance and procurement
• However, increased school autonomy introduces the need for robust
evidence of:
– Meeting the requirements of external Accountability and Compliance to
(National) Regulatory Standards
– Engaging in continuous School Self-Evaluation and Improvement

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What is Data-driven Decision Making
 Data-driven Decision Making (DDDM) in schools is defined as[1]:
“the systematic collection, analysis, examination, and interpretation of
data to inform practice and policy in educational settings”
 The aim of data-driven decision making is to report, evaluate and
improve the processes and outcomes of schools

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What are Educational Data? (1/2)
• Educational data can be broadly defined as[2]:
“Information that is collected and organised to represent some
aspect of schools. This can include any relevant information
about students, parents, schools, and teachers derived from
qualitative and quantitative methods of analysis.”

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What is Educational Data? (2/2)
• Educational Data are generated by various sources, both internal and
external to the school, for example[2]:
• Student data
– such as demographics and prior academic performance
• Teacher data
– such as competences and professional experience
• Data generated during the teaching, learning, and assessment processes
– both within and beyond the physical classroom premises, such as lesson plans,
methods of assessments, classroom management.
• Human Resources, Infrastructure, and Financial Plan
– such as educational and non-educational personnel, hardware/software, expenditure.
• Students’ Wellbeing, Social and Emotional Development
– such as support, respect to diversity and special needs

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Video: How data helps teachers
 Data Quality Campaign
‒ Non-profit U.S. organisation to promote the use of
educational data in school education
 Outline: How a teacher can use educational data to
improve teaching practice [1:51].
https://www.youtube.com/watch?v=cgrfiPvwDBw

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Data Literacy for Teachers (1/4)
 Data Literacy for teachers is a core competence defined as[3]:
“the ability to understand and use data effectively to inform
decisions”
• It comprises a competence set (knowledge, skills, and attitudes)
required to locate, collect, analyze/understand, interpret, and act upon
Educational Data from different sources so as to support improvement
of the teaching, learning and assessment process[4]

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Data Literacy for Teachers (2/4)
Data Literacy
for Teachers
Find and collect
relevant
educational data
[Data Location]
Understand what
the educational
data represent
[Data
Comprehension]
Understand
what the
educational
data mean
[Data
Interpretation]
Define instructional
approaches to
address problems
identified by the
educational data
[Instructional
Decision Making]
Define questions
on how to
improve practice
using the
educational data
[Question
Posing]

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Data Literacy for Teachers (3/4)
• Data Literacy for teachers is increasingly considered to be a core
competence in:
– Teachers’ pre-service education and licensure standards. For example, the
CAEP Accreditation Standards, issued by the Council of Accreditation of Educator
Preparation in USA.
– Teachers’ continuing professional development standards. For example, the
InTASC Model Core Teaching Standards, issued by the Council of Chief State School
Officers in USA.
• Overall, data literacy for teachers involves the holistic ability, beyond
simple student assessment interpretation ("assessment literacy"), to
meet both continuous school self-evaluation and improvement needs,
as well as external accountability and compliance to regulatory
standards.

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Data Literacy for Teachers (4/4)
• Despite its importance, Data Literacy for Teachers is still not widely
cultivated and additionally, a number of barriers can limit the
capacity of teachers to use data to inform their practice[5]:
Access to educational data
• Lack of easy access to diverse data from different sources internal and external to
the school system
Timely collection and analysis of educational data
• Delayed or late access to data and/or their analysis
Quality of educational data
• Verification of the validity of collected data - do they accurately measure what
they are supposed to?
• Verification of the reliability of collected data - use methods that do not alter or
contaminate the data
Lack of time and support
• A very time- and resource-consuming process (infrastructure and human
resources)

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Data Analytics technologies (1/2)
 Data analytics refers to methods and tools for analysing large sets of
different types of data from diverse sources, which aim to support and
improve decision-making.
 Data analytics are mature technologies currently applied in real-life
financial, business and health systems.
 However, they have only recently been considered in the context of Higher
Education[6], and even more recently in School Education[7].

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Data Analytics technologies (2/2)
• Educational data analytics technologies to support teaching and
learning can be classified into three main types:
• Refers to methods and tools that enable those involved in educational design to
analyse their designs in order to reflect on and improve them prior to the delivery
• The aim is to better reflect on them (as a whole or specific elements ) and improve
learning conditions for their learners
• It can be combined with insights from their implementation using Learning
Analytics
Teaching
Analytics
• Refers to methods and tools for “the measurement, collection, analysis and reporting
of data about learners and their contexts, for purposes of understanding and
optimising learning and the environments in which it occurs”[8]
• The aim is to improve the learning conditions for learners
• It can be related to Teaching Analytics, which analyses the learning context
Learning
Analytics
• Combines Teaching Analytics and Learning Analytics to support the process of
teacher inquiry, facilitating teachers to reflect on their teaching design using
evidence from the delivery to the students
Teaching and
Learning
Analytics

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Teaching Analytics:
Analyse your Lesson Plans
to Improve them

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Lesson Plans
 Lesson Plans are[9]:
“concise working documents which outline the teaching and learning
that will be conducted within a lesson”
 Lesson plans are commonly used by teachers to:
‒ Document their teaching designs, to help them orchestrate its delivery
‒ Create a portfolio of their teaching practice to share with peers or mentors and
exchange practices
 Lesson plans are usually structured based on templates which define
a set of elements[10], e.g.:
– the educational objectives/standards to be attained by students;
– the flow and timeframe of the learning and assessment activities to be
delivered during the lesson; and
– the educational resources and/or tools that will support the delivery of the
learning and assessment activities.

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Teaching Analytics
 Capturing and documenting teaching designs through lesson plans can
be also beneficial to teachers from another perspective; to support
self-reflection and analysis for improvement
 Teaching analytics refers to the methods and tools that teachers can
deploy in order to analyse their teaching design and reflect on it (as a
whole or on individual elements), aiming to improve the learning
conditions for their students

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Teaching Analytics: Why do it?
• Teaching Analytics can be used to support teaching planning, as
follows:
Analyze classroom teaching design for self-reflection and improvement
• Visualize the elements of the lesson plan
• Visualize the alignment of the lesson plan to educational objectives / standards
• Validates whether a lesson plan has potential inconsistencies in its design
Analyze classroom teaching design through sharing with peers or mentors to
receive feedback
• Support the process of sharing a lesson plan with peers or mentors, allowing them to provide
feedback through comments and annotations
Analyze classroom teaching design through co-designing and co-reflecting with
peers
• Allow peers to jointly analyze and annotate a common teaching design in order to allow for co-
reflection

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Indicative examples of Teaching Analytics as part of Lesson Planning
Tools
# Venture Logo Tool Venture Teaching Analytics
1 Learning Designer
London
Knowledge Lab
• Visualize the elements of the lesson plan
Generate a pie-chart dashboard for the distribution of
each type of learning and assessment activities
2 MyLessonPlanner
Teach With a
Purpose LLC
• Visualize the alignment of the lesson plan to
educational objectives / standards
• Generates a visual report on which educational
objective standards are adopted
• Highlights specific standards that have not been
accommodated
3 Lesson Plan Creator StandOut Teaching
• Validates whether a lesson plan has potential
inconsistencies in its design
Generates different types of suggestions for alleviating
design inconsistencies (e.g., time misallocations)
4 Lesson Planner tool
OnCourse Systems
for Education, LLC
• Analyze classroom teaching design through sharing
with peers or mentors to receive feedback
5 Common Curriculum
Common
Curriculum
• Analyze classroom teaching design through co-
designing and co-reflecting with peers

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Indicative examples of Teaching Analytics as part of Learning
Management Systems
# Venture Logo Tool Venture Teaching Analytics
1 Configurable Reports Moodle
• Visualize the elements of the lesson plan
Generates customizable dashboards to analyze a lesson plan in
Moodle
2
Course Coverage
Reports
Blackboard
• Visualize the alignment of the lesson plan to educational
objectives / standards
• Generates an outline of all assessment activities included in
the lesson plan
• Visualises whether they have been mapped to the
educational objectives of the lesson
3
Review Course
Design
BrightSpace
• Visualize the alignment of the lesson plan to educational
objectives / standards
Visualizes how the learning and assessment activities are
mapped to the educational objectives that have been defined
4 Course Checks Block Moodle
• Validate whether a lesson plan has potential
inconsistencies in its design
Validates a lesson plan implemented in Moodle in relation to a
specific checklist embedded in the tool

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Learning Analytics:
Analyse the Classroom Delivery
of your Lesson Plans to
Discover More about Your Students

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Personalized Learning in 21st century school education
• Personalised Learning is highlighted as a key global priority, due to
empirical evidence revealing the benefits it can deliver to students:
Who: Bill and Melinda Gates Foundation and RAND
Corporation
What: Large-scale study in USA to investigate the
potential of personalised learning in school education.
Results: Initial results from over 20 schools claim an
almost universal improvement in student
performance
Who: Education Elements
What: Study with 117 schools from 23 districts in the
USA to identify the impact of personalisation on
students' learning
Results: Consistent improvement in students’
learning outcomes and engagement

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Student Profiles for supporting Personalized Learning
(1/2)
 A key element for successful personalised learning is the
measurement, collection and analysis and report on appropriate
student data, typically using student profiles.
 A student profile is a set of attributes and their values that describe a
student.

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Student Profiles for supporting Personalized Learning
(2/2)
 Types of student data commonly used by schools to build and populate
student profiles[11]:
Static Student Data Dynamic Student Data
Personal and academic attributes of students Students’ activities during the learning process
Remain unchanged for large periods of time. Generated in a more frequent rate
Usually stored in Student Information Systems
Usually collected by the classroom teachers
and/or Learning Management Systems.
Mainly related to:
• Student demographics, such as age, special
education needs.
• Past academic performance data, such as
history of course enrolments or academic
transcripts
They are mainly related to:
• Student engagement in the learning
activities, such as level of participation in the
learning activities, level of motivation.
• Student behaviour during the learning
activities, such as disciplinary incidents or
absenteeism rates.
• Student performance, such as formative and
summative assessment scores.

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Learning Analytics
 Learning Analytics have been defined as[8]:
“The measurement, collection, analysis and reporting of data about learners
and their contexts, for purposes of understanding and optimizing learning
and the environments in which it occurs”
 Learning Analytics aims to support teachers build and maintain informative
and accurate student profiles to allow for more personalized learning
conditions for individual learners or groups of learners
 Therefore, Learning Analytics can support:
‒ Collection of student data during the
delivery of a teaching design
‒ Analysis and report on student data

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Learning Analytics: Collection of student data
• Collection of student data during the delivery of a teaching design (e.g., a
lesson plan) aims to build/update individual student profiles.
• Types of student data typically collected are “Dynamic Student Data”:
– Engagement in learning activities. For example, the progress each student is
making in completing learning activities.
– Performance in assessment activities. For example, formative or summative
assessment scores.
– Interaction with Digital Educational Resources and Tools, for example which
educational resources each student is viewing/using.
– Behavioural data, for example behavioural incidents.

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Learning Analytics: Analysis and report on student data
 Analysis and report on student data aims to provide insights from
the learning process and help the teacher to provide personalised
interventions
 Learning Analytics can provide different types of outcomes, utilising
both “Dynamic Student Data” and “Static Student Data”:
 Discover patterns within student data
 Predict future trends in students’ progress
 Recommend teaching and learning actions to either the teacher or the
student

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Learning Analytics: Strands
• Learning Analytics are commonly classified in[12]:
Descriptive Learning Analytics
• Depicts meaningful patterns or insights from the analysis of student
data to elicit “What has already happened”
• Related to “Discover Patterns within student data” outcome
Predictive Learning Analytics
• Predicts future trends in student progress to elicit “What will
happen”
• Related to “Predict Future Trends in students’ progress” outcome
Prescriptive Learning Analytics
• Generates recommendations for further teaching and learning
actions, supporting “What should we do”
• Related to “Recommend Teaching and Learning Actions” outcome

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Indicative Descriptive Learning Analytics Tools
# Venture Logo Tool Venture Student Data Utilised Description
1 Ignite Teaching Ignite
• Engagement in learning activities
• Interaction with Digital Educational
Resources and Tools
Generates reports that outline the
performance trends of each student in
collaborative project development
2 SmartKlass KlassData
• Engagement in learning activities
• Performance in assessment activities
• Interaction with Digital Educational
Resources and Tools
Generates dashboards on students’
individual and collaborative performance in
learning and assessment activities
3
Learning Analytics
Enhanced Rubric
Moodle
• Engagement in learning activities
• Performance in assessment activities
• Interaction with Digital Educational
Resources and Tools
• Behavioural data
Generates grades for each student based on
customizable, teacher-defined criteria of
performance and engagement
4 LevelUp! Moodle
• Engagement in learning activities
• Performance in assessment activities
• Interaction with Digital Educational
Resources and Tools
• Behavioural data
Generates grade points and rankings for
each student based on customizable, teacher-
defined criteria of performance and
engagement
5 Forum Graph Moodle • Engagement in learning activities
Generates social network forum graph
representing students’ level of
communication

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Indicative Predictive Learning Analytics Tools
# Venture Logo Tool Venture Student Data Utilised Description
1
Early Warning
System
BrightBytes
• Engagement in learning activities
• Performance in assessment activities
• Behavioural data
• Demographics
Generates reports of each student’s
performance patterns and predicts
future performance trends
2
Student Success
System
Desire2Learn
• Engagement in learning activities
• Performance in assessment activities
• Interaction with Digital Educational
Resources and Tools
• Demographics
Generates reports of each student’s
performance patterns and predicts
future performance trends
3 X-Ray Analytics
BlackBoard -
Moodlerooms
• Engagement in learning activities
• Performance in assessment activities
Generates reports of each student’s
performance patterns and predicts
future performance trends
4
Engagement
analytics
Moodle
• Engagement in learning activities
• Performance in assessment activities
Predicts future performance trends
and risk of failure
5
Analytics and
Recommendations
Moodle
• Engagement in learning activities
• Performance in assessment activities
• Interaction with Digital Educational
Resources and Tools
Predicts students’ final grade

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Indicative Prescriptive Learning Analytics Tools
# Venture Logo Tool Venture Student Data Utilised Description
1 GetWaggle Knewton
• Engagement in learning activities
• Performance in assessment activities
• Behavioural data
Generates reports on students’ performance
trends and provides recommendations for
assessment activities
2 FishTree FishTree
• Engagement in learning activities
• Performance in assessment activities
• Interaction with Digital Educational
Resources and Tools
Generates reports on students’ performance
trends and provides recommendations for
educational resources
3 LearnSmart McGraw-Hill
• Engagement in learning activities
• Performance in assessment activities
• Interaction with Digital Educational
Resources and Tools
Generates reports on students’ performance
trends and provides recommendations for
learning and assessment activity pathways as
well as educational resources
4 Adaptive Quiz Moodle • Performance in assessment activities
Provides recommendations for assessment
activities
5
Analytics and
Recommendat
ions
Moodle
• Engagement in learning activities
• Performance in assessment activities
• Interaction with Digital Educational
Resources and Tools
Generates reports on students’ performance
trends and provides recommendations for
educational activities to engage with

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Teaching and Learning Analytics
to support
Teacher Inquiry

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Reflective practice for teachers
 Reflective practice can be defined as[13]:
“[A process that] involves thinking about and critically analyzing one's actions
with the goal of improving one's professional practice”

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Types of Reflective practice
 Two main types of reflective practice[14]:
 Let’s see how combining Teaching and Learning Analytics can support
classroom teachers’ reflection-on-action, through the process of
teacher inquiry
- Takes place while the practice is executed and the
practitioner reacts on-the-fly
- Teaching Analytics and Learning Analytics mainly support
this type of teachers’ reflection
Reflection-in-
action
- Takes a more systematic approach in which practitioners
intentionally review, analyse and evaluate their practice after
it has been performed, documenting the process and results
Reflection-on-
action

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Teacher Inquiry (1/2)
• Teacher inquiry is defined as[15]:
“[a process] that is conducted by teachers, individually or collaboratively, with
the primary aim of understanding teaching and learning in context”
• The main goal of teacher inquiry is to improve the learning conditions
for students

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Teacher Inquiry (2/2)
• Teacher inquiry typically follows a cycle of steps:
Identify a Problem
for Inquiry
Develop Inquiry
Questions & Define
Inquiry Method
Elaborate and
Document Teaching
Design
Implement
Teaching Design
and Collect Data
Process and
Analyze Data
Interpret Data and
Take Actions
The teacher develops specific
questions to investigate.
Defines the educational data
that need to be collected and
the method of their analysis
The teacher defines teaching
and learning process to be
implemented during the
inquiry (e.g., through a lesson
plan)
The teacher makes an effort to
interpret the analysed data and
takes action in relation to their
teaching design
The teacher processes and
analyses the collected data to
obtain insights related to the
defined inquiry questions
The teacher implements their
classroom teaching design and
collects the educational data
The teacher identifies an issue of concern
in the teaching practice, which will be
investigated

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Teacher Inquiry: Needs
 Teacher inquiry can be a challenging and time consuming process for
individual teachers:
‒ Heavy workloads allow limited time for reflection on teaching practice
‒ Increased difficulty when done in isolation from other teachers
 Digital technologies can be used to support teacher inquiry
‒ A synergy between Teaching Analytics and Learning Analytics has the
potential to facilitate the efficient implementation of the full cycle of
inquiry

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Teaching and Learning Analytics
• Teaching and Learning Analytics (TLA) aim to combine:
– The structured description and analysis of the teaching design provided by
Teaching Analytics to help identify the inquiry problem, develop specific
questions to guide inquiry, and to document the teaching design
– The data collection, processes and analytical capabilities of Learning
Analytics to make sense of students’ data in relation to the teaching design
elements, and help the teacher to take action

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Teaching and Learning Analytics to support Teacher
Inquiry
• TLA can support teachers engage in the teacher inquiry cycle:
Teacher Inquiry Cycle Steps How TLA can contribute
Identify a Problem to Inquiry Teaching Analytics can be used to capture and analyse
the teaching design and help the teacher to:
• pinpoint the specific elements of their teaching design
that relate to the problem they have identified;
• elaborate on their inquiry question by defining
explicitly the teaching design elements they will
monitor and investigate in their inquiry.
Develop Inquiry Questions and Define Inquiry Method
Elaborate and Document Teaching Design
Implement Teaching Design and Collect Data
• Learning Analytics can be used to collect the student
data that the teacher has defined to answer their
question.
• Learning Analytics can be used to analyse and report
on the collected data in order to facilitate
interpretation.
Process and Analyse Data
Interpret Data and Take Actions
The combined use of Teaching and Learning Analytics
can be used to map the analysed data to the initial
teaching design, answer the inquiry question and
generate insights for teaching design revisions.

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Indicative Teaching and Learning Analytics Tools
# Venture Logo Tool Venture Description
1 LeMo LeMo Project
• Generates visualisations of the frequency that each
learning activity and educational resource/tool have
been accessed
• Generates dashboards to show the navigation paths that
students took when engaging with the learning activities
and educational resources/tools
2 The Loop Tool
Blackboard /
Moodle
Generates dashboards to visualize how, when and to what
extend the students have engaged with the learning and
assessment activities, as well as with the educational
resources
3 Quiz statistics Moodle
Analyses each assessment activity in terms of various
metrics to support their refinement
4 Heatmap tool Moodle
Generates visual color-coded reports that show how much
each learning/assessment activity or educational
resource/tool was accessed by the students
5 Events Graphic Moodle
Generates dashboards that show the most frequent actions
that the students performed

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Relevant Publications
• S. Sergis and D. Sampson, “Teaching and Learning Analytics: a Systematic Literature Review”, in Alejandro Peña-Ayala (Eds.), Learning
analytics: Fundaments, applications, and trends – A view of the current state of the art, Springer, 2017
• S. Sergis, E. Papageorgiou, P. Zervas, D. Sampson and L. Pelliccione, “Evaluation of Lesson Plan Authoring Tools based on an Educational
Design Representation Model for Lesson Plans”, in Ann Marcus-Quinn and Triona Hourigan (Eds.), Handbook for Digital Learning in K-12
Schools, Springer, Chapter 11, 2017
• I. Pappas, M.N. Giannakos, M. L. Jaccheri and D. G. Sampson, “Understanding Students’ Retention in Computer Science Education: The Role of
Environment, Gains, Barriers and Usefulness”, Education and Information Technologies, Springer, 2017
• M. N. Giannakos, D. G. Sampson, Ł. Kidziński and A. Pardo, “Enhancing Video-Based Learning Experience through Smart Environments and
Analytics”, in Proceeding of the LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning, 2016
• I. O. Pappas, M. N. Giannakos, D. G. Sampson, “Making Sense of Learning Analytics with a Configurational Approach”, in Proceeding of the
LAK2016 Workshop on Smart Environments and Analytics in Video-Based Learning, 2016
• S. Sergis and D. Sampson, "Towards a Teaching Analytics Tool for supporting reflective educational (re)design in Inquiry-based STEM
Education", 16th IEEE International Conference on Advanced Learning Technologies (ICALT 2016), 2016
• S. Sergis and D. Samson, “Learning Objects Recommendations for Teachers based on elicited ICT Competence Profiles”, IEEE Transactions
on Learning Technologies, 2016
• 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, 2016
• 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, 2016
• 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
• 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. 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

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WWW2017
Τhe 26th World Wide Web conference
3-7 April 2017 Perth, Western Australia
Digital Learning Research Track
http://www.www2017.com.au/

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ICALT2017
Τhe 17th IEEE International Conference on
Advanced Learning Technologies
3-7 July 2017 Timisoara, Romania, European Union

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谢谢
Thank you !!!
www.ask4research.info

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References
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