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ετπε2017 invited speech-april2017

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Προσκεκλημένη Ομιλία
"Αναλυτική Εκπαιδευτικών Δεδομένων στην Σχολική Πράξη"
5ο Πανελλήνιο Επιστημονικό Συνέδριο
«Ένταξη και Χρήση των ΤΠΕ στην Εκπαιδευτική Διαδικασία»
21-23 Απριλίου 2017





Published in: Education
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ετπε2017 invited speech-april2017

  1. 1. 5ο Πανελλήνιο Επιστημονικό Συνέδριο «Ένταξη και Χρήση των ΤΠΕ στην Εκπαιδευτική Διαδικασία 21 April 2017 Αναλυτική Εκπαιδευτικών Δεδομένων στην Σχολική Πράξη Δημήτριος Σάμψων 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
  2. 2. 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
  3. 3. Introduction
  4. 4. Perth, Western Australia
  5. 5. Curtin University
  6. 6. 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
  7. 7. Joined School of Education @ Curtin University October 2015
  8. 8. EDU1x: Analytics for the Classroom Teacher edX MOOC, Curtin University EDU1x Analytics for the Classroom Teacher 6300 enrollments from 140 countries since October 2016
  9. 9. April 2017 9/55 Educational Data Analytics Technologies for supporting Data-driven Decision Making in Schools
  10. 10. 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
  11. 11. 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
  12. 12. 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.”
  13. 13. 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
  14. 14. 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
  15. 15. 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].
  16. 16. 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]
  17. 17. April 2017 17/55 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]
  18. 18. April 2017 18/55 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.
  19. 19. April 2017 19/55 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)
  20. 20. 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].
  21. 21. 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
  22. 22. April 2017 22/55 Teaching Analytics: Analyse your Lesson Plans to Improve them
  23. 23. 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.
  24. 24. 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
  25. 25. 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
  26. 26. 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
  27. 27. 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
  28. 28. 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?
  29. 29. 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
  30. 30. April 2017 30/55 Learning Analytics: Analyse the Classroom Delivery of your Lesson Plans to Discover More about Your Students
  31. 31. 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
  32. 32. 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.
  33. 33. 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.
  34. 34. 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
  35. 35. 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.
  36. 36. 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
  37. 37. 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
  38. 38. 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 • 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
  39. 39. April 2017 39/55 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
  40. 40. April 2017 40/55 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
  41. 41. 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?
  42. 42. April 2017 42/55 Demonstration of Learning Analytics (2/3)  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
  43. 43. April 2017 43/55 Demonstration of Learning Analytics (3/3)  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
  44. 44. April 2017 44/55 Teaching and Learning Analytics to support Teacher Inquiry
  45. 45. 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”
  46. 46. 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
  47. 47. 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
  48. 48. 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
  49. 49. 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
  50. 50. April 2017 50/55 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
  51. 51. 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.
  52. 52. 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
  53. 53. 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?
  54. 54. 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.
  55. 55. 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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