Sampson-Keynote@SMTE2019
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The document discusses the importance of educational data literacy (EDL) in enhancing online and blended teaching, highlighting the need for professionals like instructional designers and trainers to acquire new competencies. It introduces the Learn2Analyze initiative, which aims to develop these competencies through a professional development MOOC, integrating theory and practice with educational data analytics tools. The project addresses the lack of existing frameworks and courses focused on EDL, providing resources to support the professional growth of educators in this emerging field.
![Data Literacy for Educators
Data Literacy
for Educator
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]
11](https://image.slidesharecdn.com/sampsonsmte2019slideshare-191105222134/85/Sampson-Keynote-SMTE2019-11-320.jpg)
Sampson-Keynote@SMTE2019
- 1. Educational Data Literacy for OnlineTeaching and Learning The case of the Learn2Analyse Initiative Demetrios Sampson PhD(ElectEng) (Essex), PgDip (Essex), BEng/MEng(Elec) (DUTH), CEng Golden Core Member IEEE Computer Society, Senior Member IEEE Professor, Digital Systems for Learning and Education Department of Digital Systems, University of Piraeus, Greece KeyNote Speech 10th International Conference of Science, Mathematics &Technology Education Mauritius Institute of Education, Reduit, Mauritius 6 November 2019
- 2. Educational Data Literacy The challenge and the opportunity Definitions The value add proposition of the Learn2Analyze project 2
- 3. Educational Data Literacy The challenge and the opportunity 3
- 4. The challenge and the opportunity DigitalTeaching and Learning is a key innovation for both: University Teaching and Learning Professional Development andVocational Training To this end, blended and online courses are nowadays widely used to meet the needs of pre-service higher education and vocational training students and/or in-service professionals. Such courses are typically supported by Course or Learning Management Systems (CMS or LMS) which are web-based systems that handle teaching and learning activities online. Designing, delivering, evaluating and re-designing high quality online or blended courses and learning experiences is at the core of online education and training. 4
- 5. The challenge and the opportunity Designing, delivering, evaluating and re-designing high quality online or blended courses and learning experiences is at the core of online education and training. In this context, two important professional roles in the digital education and training job market, namely: the Instructional Designers, who design and develop online (and blended) courses, and the Trainers or Tutors who support the delivery of these online (and blended) courses, require new professional competences compared to those assumed at the traditional face to face education and training programs. 5
- 6. The challenge and the opportunity A recent advancement in online and blended teaching and learning is Educational Data Analytics (EDA): the use of educational data generated during teaching and learning (including assessment) to better support individual learners' in online and blended courses. As a result, most Course Management Systems are now incorporating Educational Data Analytics tools. However, these tools are not widely used mainly because of the low Educational Data Literacy (EDL) competences of the professionals that could be using them: instructional designers and trainers, or even K12 teachers adopting the flipped classroom model in their teaching 6
- 7. The challenge and the opportunity Educational Data Literacy is a core competence for all education professionals, including school teachers, instructional designers and tutors of online and blended learning course, as well as educational institutions' leaders. Nevertheless, existing professional competence frameworks for educators pay little attention to EDL, missing out the potential of using emerging EDL methods and tools in online and blended teaching and learning - thus there is a need for extending existing professional competence frameworks for educators with new competences to accommodate the emerging field of Educational Data Literacy; there are very limited professional development courses for cultivating EDL competences - thus there is a need for professional development programs that develop and assess Educational Data Literacy competences. 7
- 9. Educational Data Collected and organised to represent all aspects of teaching and learning, including Profiling and Interaction Data Between Students,Teachers, Learning Environment derived from qualitative and quantitative methods 9
- 10. Data Literacy for Educators the ability to understand and use data effectively to inform educational and pedagogical decisions a competence set 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 10
- 11. Data Literacy for Educators Data Literacy for Educator 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] 11
- 12. Educational Data AnalyticsTechnologies Teaching Analytics methods and digital tools to visualize, analyze, and/or assess teaching practice Learning Analytics methods and digital tools to collect, analyze and report student-related educational data towards monitoring the learning process Teaching & Learning Analytics to support the process of reflective practice: facilitating teachers to reflect on their teaching design using evidence from the actual delivery to their students 12
- 13. Teaching Analytics:AnalyseTeaching Design for self-reflection and improvement Visualize the elements of a lesson plan Visualize the alignment of a lesson plan to educational objectives / standards Validates whether a lesson plan has potential inconsistencies in its 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 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 mentors to receive feedback 13
- 14. Learning Analytics Collection of learner data during the delivery of a teaching design (e.g., a lesson plan) to build/update individual student profiles. Types of learner data typically are “Dynamic Student Data”: Engagement in learning activities. For example, the progress each learner is making in completing certain 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 learner is viewing/using. Emotional Data, for example stress, boredom, anxiety. mentors to receive feedback 14
- 15. Educational Data AnalyticsTechnologies Descriptive Analytics “what has already happened”: they are related to existing data summarization, namely the visualization of past data Predictive Analytics “what will happen”: they are related to processing existing data for pattern elicitation, so as to make estimations of future trends Prescriptive Analytics “what should we do”: they are related to generating decision-support recommendations for actions to be taken, based on the analysis of existing data 15
- 16. Educational Data Literacy The value add proposition of the Learn2Analyze project 16
- 17. Learn2Analyze: An Academia-Industry Knowledge Alliance for enhancing Online Training Professionals’ (Instructional Designers and e-Trainers) Competences in Educational Data Analytics European Commission ERASMUS+ Key Action 2 “Cooperation for innovation and the exchange of good practices - Knowledge Alliances” Academia – Industry - End User Communities 17
- 18. The European Union 500 million people - 28 countries - a single market Free movement of people, goods, services and capital 7% of theWorld's population 24% of world expenditure on research 32% of high-impact publications 32% of patent applications 18
- 19. Erasmus+ A 14.7 Billion€ program for education, training, youth and sport 2014-2020. 1,750 Billion€ per year for Education &Training Knowledge alliances Structured, long-term cooperation between HEIs and enterprises Deliver new multidisciplinary curricula responding to business needs Facilitate the exchange, flow and co-creation of knowledge between HEIs and enterprises 19
- 20. The value add proposition of the Learn2Analyze project The scope of the Learn2Analyze project is: to enhance existing professional competence frameworks for instructional designers and e-trainers of online courses with new Educational Data Literacy competences for using emerging Educational Data Analytics methods and tools; to develop and to evaluate a professional development Massive Open Online Course (MOOC) for cultivating these competences with emphasis to combining theory and practice through the use of existing educational data analytics tools from world market leaders. 20
- 21. The value add proposition of the Learn2Analyze project The core outcomes of the Learn2Analyze project are: to produce and validate a comprehensive proposal for an Educational Data Literacy Competence Profile for instructional designers and e-trainers of online and blended courses; to design, develop, offer and evaluate a competence-based Professional Development Massive Open Online Course leading to a Certificate of Achievement in Educational Data Literacy; to facilitate building a professional community around Educational Data Analytics with the participants and the graduates of the Learn2Analyze MOOC. 21
- 22. 22
- 23. Educational Data Literacy Roadmap 23
- 24. Learn2Analyze Educational Data Literacy Competence 24
- 25. Learn2Analyze MOOC Learn to Analyze Educational Data and Improve your Blended and OnlineTeaching Start Date: October 21th, 2019 End Date: December 14th, 2019 Pre-Requisites:None Duration: 8 weeks Time Commitment: 68 hours in total Level: Introductory Language: English Cost: None 25
- 26. Learn2Analyze MOOC Learn to Analyze Educational Data and Improve your Blended and OnlineTeaching Target Audience instructional designers and e-tutors of online and blended courses school teachers of blended learning courses (using the flipped classroom model) university students Learning Outcomes cover 100% the set of competences anticipated by the Learn2Analyze Educational Data Literacy competence framework (L2A EDL-CP). 26
- 27. Learn2Analyze MOOC Learn to Analyze Educational Data and Improve your Blended and OnlineTeaching The course is combines theory on collecting, analysing, interpreting and using educational data (including issues related with ethics and privacy), with practice on applying educational data analytics in three different e-learning platforms, namely, Moodle, the eXact Suite and the IMC Learning Suite. Leads to a free Certification of Achievement in Educational Data Literacy upon successful completion. 27
- 28. Learn2Analyze MOOC Module 1 - Orientation Module 2 - Online and Blended Teaching and Learning supported by Educational Data Topic 1 – Educational Data as a key success factor for online and blended teaching and learning Topic 2 – Data is Everywhere (Educational Data Collection) Topic 3 – Adding value to educational datasets (Educational Data Management) Module 3 - Learning Analytics Topic 1 - Using learner-generated data and learning context for extracting learning analytics Topic 2 - Organizing and presenting learning analytics Topic 3 - Interpreting and mapping learning analytics Module 4 -Teaching Analytics Topic 1 - Data sources for supporting teaching analytics Topic 2 - Data ethics and privacy principles for teaching analytics Topic 3 - Applying and communicating educational data and analytics findings Module 5 - Applying Educational Data Analytics with Moodle Topic 1 - Moodle Site Level Topic 2 - Course Level Topic 3 - User Level Topic 4 - 3rd Party Tools in Moodle Module 6 - Applying Educational Data Analytics the eXact Suite Topic 1 - Understanding and supporting course progress via learning reports Topic 2 - Additional learning reports Topic 3 - Beyond the LMS, monitoring and supporting informal learning via eXact Delivery Portal Module 7 - Applying Educational Data Analytics the Learning Suite Topic 1: How educational data is handled in Learning Suite (Overview of main principles and techniques Topic 2: Possibilities of Teaching Analytics in the Learning Suite) Topic 3:Tools of Learning Analytics in the Learning Suite Topic 4: Using Teaching and Learning Analytics tools of the Learning Suite to support Teacher Inquiry Module 8 – Concluding the MOOC 28