Edu1x tutorial-april2017

Teaching and Learning Analytics
to support the
Classroom Teacher Inquiry
Demetrios Sampson
PhD(ElectEng) (Essex), PgDip (Essex), BEng/MEng(Elec) (DUTH), CEng
Golden Core Member IEEE Computer Society, Senior Member IEEE
Chair IEEE Technical Committee on Learning Technologies
Editor-in-Chief, Educational Technology & Society Journal
Professor of Learning Technologies & Director of Research
School of Education | Humanities Faculty
Curtin University, Australia
April 2017

Presentation Overview
 Introduction: School of Education, Curtin University,
Australia
 edX MOOC: Analytics for the Classroom Teacher
 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

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; around 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 2016

Joined School of Education @ Curtin University
October 2015

EDU1x: Analytics for the Classroom
Teacher
edX MOOC, Curtin University
EDU1x Analytics for the Classroom Teacher
6300 enrollments from 140 countries since October 2016

April 2017 9/55
Educational Data Analytics Technologies
for supporting
Data-driven Decision Making
in Schools

April 2017 10/55
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

April 2017 11/55
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

April 2017 12/55
What is 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.”

April 2017 13/55
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

April 2017 14/55
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

April 2017 15/55
Case Study Video: Data - It's Just Part of Good Teaching
 Data Quality Campaign
 Outline: Watch the principal, the teachers and other
school staff of the Sherman Elementary in Rhode Island,
USA presenting their personal experience of how
educational data can be used to improve students'
learning [3:44].

April 2017 16/55
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]

April 2017 17/55
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]

April 2017 18/55
• 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.

April 2017 19/55
• 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)

April 2017 20/55
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].

April 2017 21/55
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

April 2017 22/55
Teaching Analytics:
Analyse your Lesson Plans
to Improve them

April 2017 23/55
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.

April 2017 24/55
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

April 2017 25/55
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

April 2017 26/55
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

April 2017 27/55
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

April 2017 28/55
Demonstration of Teaching Analytics (1/2)
 Meet Michael, a Science school teacher
 Michael has heard a lot about the Flipped Classroom
model, so he wants to try it out in his classroom!
 He designs a lesson plan in Moodle, adopting basic principles of Flipped
Classroom:
– Free up classroom sessions from lecturing and, instead, provide educational resources that
students can study at home
– Use the classroom sessions to engage students in projects and assessments.
 However, since it is his first ‘Flipped’ lesson plan, he wants to do a last check
before he delivers it to his students, to be sure that he has implemented the
Flipped Classroom principles.
 Can Teaching Analytics help him?

April 2017 29/55
Demonstration of Teaching Analytics (2/2)
 Let’s see how the Moodle Configurable Reports tool can help him analyze his
lesson plan and confirm he has followed the design principles

April 2017 30/55
Learning Analytics:
Analyse the Classroom Delivery
of your Lesson Plans to
Discover More about Your Students

April 2017 31/55
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

April 2017 32/55
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.

April 2017 33/55
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.

April 2017 34/55
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

April 2017 35/55
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.

April 2017 36/55
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

April 2017 37/55
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

April 2017 38/55
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
• Performance in assessment activities
Resources and Tools
Generates dashboards on students’
individual and collaborative performance in
learning and assessment activities
3
Learning Analytics
Enhanced Rubric
Moodle
Resources and Tools
• Behavioural data
Generates grades for each student based on
customizable, teacher-defined criteria of
performance and engagement
4 LevelUp! Moodle
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

April 2017 39/55
Indicative Predictive Learning Analytics Tools
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
 Interaction with Digital Educational
Resources and Tools
 Demographics
3 X-Ray Analytics
BlackBoard -
Moodlerooms
4
Engagement
analytics
Moodle
Predicts future performance trends
and risk of failure
5
Analytics and
Recommendations
Moodle
Resources and Tools
Predicts students’ final grade

April 2017 40/55
Indicative Prescriptive Learning Analytics Tools
1 GetWaggle Knewton
 Behavioural data
Generates reports on students’ performance
trends and provides recommendations for
assessment activities
2 FishTree FishTree
Resources and Tools
educational resources
3 LearnSmart McGraw-Hill
Resources and Tools
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
Resources and Tools
educational activities to engage with

April 2017 41/55
Demonstration of Learning Analytics (1/3)
 Remember Michael, the Science school teacher?
 He is now delivering his Flipped Classroom lesson
to his students, using Moodle!
 However, some issues have arisen which he had not predicted. Now that many
learning activities happen outside the classroom he finds it difficult to:
‒ Identify which of his students are facing problems and what are the problems that
they face?
‒ Assess his students more holistically - You see, Michael believes that assessing his
students by only considering their performance in test and quizzes is not enough.
 How can Learning Analytics help him?

April 2017 42/55
 Let’s see how the Moodle Engagement Analytics tool can help him address his
first question and highlight which students need his support and why, by
exploiting the analysis of data collected in Moodle

April 2017 43/55
 Let’s see how the Moodle Learning Analytics Enriched Rubric tool can help
him address his second question, by defining his own criteria for assessing
students based on their engagement and performance throughout the lesson's
activities

April 2017 44/55
to support
Teacher Inquiry

April 2017 45/55
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”

April 2017 46/55
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
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
- Teaching Analytics and Learning Analytics mainly support
this type of teachers’ reflection
Reflection-on-
action

April 2017 47/55
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

April 2017 48/55
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

April 2017 49/55
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

April 2017 50/55
• 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

April 2017 51/55
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.

April 2017 52/55
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

April 2017 53/55
Demonstration of Teaching and Learning Analytics (1/2)
 Let’s check on Michael on last time!
 He has now delivered his Flipped Classroom
lesson and thinks it was a success!
 However, since it was his Flipped Classroom attempt (and he would like to
design more in the future), he is interested to investigate:
‒ Which elements of his lesson plan (especially educational resources and
learning activities for home study) did students prefer or ignore?
 Can Teaching and Learning Analytics help him?

April 2017 54/55
Demonstration of Teaching and Learning Analytics (2/2)
 Let’s see how the Moodle Heatmap tool can help him to address his question
by inspecting how students engaged with each element of his lesson plan.

April 2017 55/55
References
1. Mandinach, E. (2012). A Perfect Time for Data Use: Using Data driven Decision Making to Inform Practice. Educational
Psychologist, 47(2), 71-85.
2. Lai, M. K., & Schildkamp, K. (2013). Data-based Decision Making: An Overview. In K. Schildkamp, M.K. Lai & L. Earl
(Eds.). Data-based decision making in education: Challenges and opportunities. Dordrecht: Springer
3. Mandinach, E., & Gummer, E. (2013). A systemic view of implementing data literacy in educator preparation.
Educational Researcher, 42, 30–37
4. Means, B., Chen, E., DeBarger, A., & Padilla, C. (2011). Teachers' Ability to Use Data to Inform Instruction: Challenges
and Supports. Office of Planning, Evaluation and Policy Development, US Department of Education
5. Marsh, J., Pane, J., & Hamilton, L. (2006). Making Sense of Data-Driven Decision Making in Education. RAND
Corporation
6. Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and
learning analytics: An issue brief. US Department of Education, Office of Educational Technology, 1-57.
7. NMC (2011) . The Horizon Report – 2011 Edition
8. SOLAR (2011) Proceedings of the 1st International Conference on Learning Analytics and Knowledge
9. Butt, G. (2008). Lesson Planning (3rd Edition), New York: Continuum
10. Sergis, S., Papageorgiou, E., Zervas, P., Sampson, D., & Pelliccione, L. (2016). Evaluation of Lesson Plan Authoring Tools
based on an Educational Design Representation Model for Lesson Plans, In A.Marcus-Quinn & T. Hourigan (Eds.),
Handbook for Digital Learning in K-12 Schools (pp. 173-189), Springer, Chapter 11
11. Data Quality Campaign (2014). What is student data
12. Learning Analytics Community Exchange (2014). Learning Analytics
13. Imel, S. (1992). Reflective Practice in Adult Education. ERIC Digest No. 122.
14. Schon, D. (1983). Reflective Practitioner: How Professionals Think in Action. New York: Basic Books
15. Stremmel, A. (2007). The Value of Teacher Research: Nurturing Professional and Personal Growth through Inquiry.
Voices of Practitioners. 2(3). National Association for the Education of Young Children

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