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Teaching and Learning Analytics for the Classroom Teacher

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

Publicado en: Educación
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Teaching and Learning Analytics for the Classroom Teacher

  1. 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. 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. 3. Introduction
  4. 4. Perth, Western Australia
  5. 5. Perth, Western Australia
  6. 6. Curtin University
  7. 7. Curtin University
  8. 8. 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
  9. 9. Joined School of Education @ Curtin University October 2015
  10. 10. 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)
  11. 11. 17 11 2016 23/67 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
  12. 12. 17 11 2016 24/67 Educational Data Analytics Technologies for Data-driven Decision Making in Schools
  13. 13. 17 11 2016 25/67 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
  14. 14. 17 11 2016 26/67 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
  15. 15. 17 11 2016 27/67 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.”
  16. 16. 17 11 2016 28/67 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
  17. 17. 17 11 2016 29/67 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
  18. 18. 17 11 2016 30/67 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]
  19. 19. 17 11 2016 31/67 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]
  20. 20. 17 11 2016 32/67 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.
  21. 21. 17 11 2016 33/67 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)
  22. 22. 17 11 2016 34/67 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].
  23. 23. 17 11 2016 35/67 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
  24. 24. 17 11 2016 36/67 Teaching Analytics: Analyse your Lesson Plans to Improve them
  25. 25. 17 11 2016 37/67 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.
  26. 26. 17 11 2016 38/67 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
  27. 27. 17 11 2016 39/67 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
  28. 28. 17 11 2016 40/67 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
  29. 29. 17 11 2016 41/67 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
  30. 30. 17 11 2016 42/67 Learning Analytics: Analyse the Classroom Delivery of your Lesson Plans to Discover More about Your Students
  31. 31. 17 11 2016 43/67 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. 17 11 2016 44/67 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. 17 11 2016 45/67 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. 17 11 2016 46/67 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. 17 11 2016 47/67 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. 17 11 2016 48/67 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. 17 11 2016 49/67 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. 17 11 2016 50/67 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. 17 11 2016 51/67 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. 17 11 2016 52/67 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. 17 11 2016 53/67 Teaching and Learning Analytics to support Teacher Inquiry
  42. 42. 17 11 2016 54/67 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”
  43. 43. 17 11 2016 55/67 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
  44. 44. 17 11 2016 56/67 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
  45. 45. 17 11 2016 57/67 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
  46. 46. 17 11 2016 58/67 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
  47. 47. 17 11 2016 59/67 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
  48. 48. 17 11 2016 60/67 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.
  49. 49. 17 11 2016 61/67 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
  50. 50. 17 11 2016 62/67 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
  51. 51. 17 11 2016 63/67
  52. 52. 17 11 2016 64/67 WWW2017 Τhe 26th World Wide Web conference 3-7 April 2017 Perth, Western Australia Digital Learning Research Track http://www.www2017.com.au/
  53. 53. 17 11 2016 65/67 ICALT2017 Τhe 17th IEEE International Conference on Advanced Learning Technologies 3-7 July 2017 Timisoara, Romania, European Union
  54. 54. 17 11 2016 66/67 谢谢 Thank you !!! www.ask4research.info
  55. 55. 17 11 2016 67/67 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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