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Overview of Effective Learning Analytics Using data and analytics to support students at Open University

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Begona Nunez-Herran and Kevin Mayles (Data and Student Analytics), Rebecca Ward (Data Strategy and Governance)
-Move towards centralised LA data infrastructure
-Data governance and lessons learned
 
Prof Bart Rienties & PhD students (Institute of Educational Technology)
-What is the latest “blue sky” learning analytics research from the OU?
-Rogers Kalissa: Social Learning Analytics to support teaching (University of Oslo)
-Saman Rizvi: Cultural impact of MOOC learning (IET)
-Shi Min Chua: Why does no one reply to my posts (IET/WELS)
-Maina Korir: Ethics and LA (IET)
-Anna Gillespie: Predictive Learning Analytics and role of tutors (EdD)


Prof John Domingue (Knowledge Media Institute) & Dr Thea Herodotou (IET)
-What have we learned from 5 years of large scale implementation of OU Analyse?
-Where is LA/AI going?

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Overview of Effective Learning Analytics Using data and analytics to support students at Open University

  1. 1. Jisc Learning Analytics Research Group Open University, Milton Keynes, 26th June 2019 Effective Learning Analytics Using data and analytics to support students
  2. 2. “The UK experience”: Health and Safety • No fire drill today ☺ • Toilets • Jennie Lee Building • Wifi: Eduroam or free open iCloud
  3. 3. Leading global distance learning, delivering high-quality education to anyone, anywhere, anytime The Open University Largest University in Europe No formal entry requirements enter with one A-level or less 33% 38% of part-time undergraduates taught by OU in UK 173,927 formal students 55% of students are 'disadvantaged' FTSE 100 have sponsored staff on OU courses in 2017/8 60% 66% of new undergraduates are 25+ 1,300 Open University students has a disability (23,630) 1 in 8 Students are already in work 3 in 4 employers use OU learning solutions to develop workforce
  4. 4. 13,206 learners 6,000 learners 123,000 learners • A pioneering Moodle-based award winning site providing free access to learning – 1,000+ courses, plus articles and videos. • The OU publishes curriculum as short courses onto OpenLearn and creates free Badged Open Courses (BOCs) which reward informal learners with a badge / certificate of achievement. • Links from BBC broadcasting with themed pages developed to support the content of many series. In addition, print resources can be ordered such as posters. 7.8m new learners each year 60m since launch in 2006 Opening Access: OpenLearn
  5. 5. Innovative and engaging content for broadcast audiences and rich resources for students. 2016/17: the OU co-produced 35 series: • Generated 250 million viewing and listening events across channels and platforms. • Directed 1.2 million viewers to OpenLearn. Dedicated channel on YouTube with bite- sized learning. It is the largest educational presence on YouTube in EU with: • Over 1,700 public videos with 50 million views. • Over 166,000 subscribers to our channel, more than any other UK educational institution • Reaching over 3 million learners per year. The OU now engages with learners on Facebook: • Viewed 6.2 million times by 2.5 million users. • We run Facebook live sessions to engage around topical issues. • We will be doing more with Facebook on the DfE Flexible Learning Fund in 2019. Reaching out to millions more 250m views 2016/17 3m learners per year 2.5m in 2017
  6. 6. • 2m transactions a day • 6m quiz questions answered a year • 4500 tutors • Groups of 15-20 • 8-16 hours a week • Home, work, trains, prisons, submarines • 130 degrees and other qualifications • From 450+ modules Study Experience Overview
  7. 7. Adeniji, B. (2019). A Bibliometric Study on Learning Analytics. Long Island University. Retrieved from https://digitalcommons.liu.edu/post_fultext_dis/16/
  8. 8. Adeniji, B. (2019). A Bibliometric Study on Learning Analytics. Long Island University. Retrieved from https://digitalcommons.liu.edu/post_fultext_dis/16/
  9. 9. Update on LA at the OU Begona Nunez-Herran and Kevin Mayles (Data and Student Analytics), Rebecca Ward (Data Strategy and Governance) -Move towards centralised LA data infrastructure -Data governance and lessons learned Prof Bart Rienties & PhD students (Institute of Educational Technology) -What is the latest “blue sky” learning analytics research from the OU? Prof John Domingue (Knowledge Media Institute) & Dr Thea Herodotou (IET) -What have we learned from 5 years of large scale implementation of OU Analyse? -Where is LA/AI going?
  10. 10. STUDENT SUCCESS ANALYTICS 13 O R G AN I S AT I O N AL C APAB I LT I E S Productionised output and MI Strategic analysis Modelling / AI Data collection Data storage and access Technology architecture Learning design and delivery Student lifecycle managemen t Continuous improvemen t and innovation Creation of actionable insight Availability of data Impact the student experience Adapted from Barton and Court (2012) - https://hbr.org/2012/10/making-advanced-analytics-work-for-you
  11. 11. HOW ANALYTICS SUPPORTS STUDENT SUCCESS STUDENT SUCCESS ANALYTICS KPIs STUDENT SUCCESS PRIORITIES DATA & ANALYTICS WILL SUPPORT, ALIGN TO, AND PROACTIVELY DRIVE THE RECRUITMENT AND STUDENT SUCCESS STRATEGY THROUGH CONTINUOUS INNOVATION CONVERSION STUDY CONTINUATION EMPLOYABILITY STUDY ENGAGEMENT WEBSITE APP ALVLECHATBO T PHONECOMMUNICATION CHANNELS DATA ANALYTICS CAPABILITY CRM & DIGITAL (ANALYTICS) INTEGRATION LAYER ENTERPRIS E DATA HUB BI ENABLEMENT DATA SCIENCE ENABLEMENT ANALYTICS INDUSTRIALISATION DATA SECURITY, PRIVACY & GOVERNANCE Build Your Own / Self-serveStrategic Analysis Canned Reporting BUSINESS INTELLIGENCE DIGITAL EXPERIENCE OFFLINE CHANNELS Statutory Reporting ANALYTICS & DATA SCIENCE Modelling, AI & Machine Learning Test & LearnKnowledge Graph Survey Analytics Sentiment Analysis Trigger Based Interventions Digital Journey Optimisation Personalised Prospect Targeting Personalised Recommendations ANALYTICS CAPABILITY 14 SUPPORTED OPEN ENTRY IMPROVED COMMUNICATIONS FLEXIBLE STUDY INTENSITY INCREASED RETURN RATES INCREASED YR3 CONTINUATION RATES INCREASED SATISFACTION IMPROVED PERSONAL OUTCOMES INCREASED PASS RATES IMPROVED CAREER OUTCOMES D ATA & AN ALY T I C S V I S I O N
  12. 12. THE STUDENT SUCCESS ANALYTICS ROADMAP 15 E N T E R P R I S E D A T A H U B P L A T F O R M ( T I T A N ) – D A T A A N A L Y T I C S E N A B L E M E N T 1. BUSINESS INTELLIGENCE 2. STRATEGIC ANALYSIS 3. DATA SCIENCE 4. ANALYTICS INTEGRATION 5. CONTINUOUS INNOVATION Build a unified reporting capability that allows stakeholders to derive insights into the key drivers of Student Success whilst creating more efficient use of analytics resource. E.g. what trends are influencing study intensity, do we need analysis to see what this implies for the OU curriculum and learning experience?... Provide in depth, deep-dive analyses around insights and hypotheses obtained from the BI capability to form the basis of strategic actions. E.g. We are seeing an increase in students studying in a full-time manner, but some are successful and others less so. They manner and subjects studied is clearly a factor in success outcomes… Build advanced targeting capabilities based on the insights derived from the analysis deep dives that can be used in a scalable manner to support student choices and experience. E.g. a machine learning algorithm that recommends study choices and patterns based on past student success trends… Build the means to turn the recommendations from data science techniques into real-life digital experience and CRM interactions. E.g. Industrialise the recommended module choices through production level processes and integration capability into Digital and to F2F channels… Continuously enable the latest data technologies and techniques to enhance existing and new means of improving Student Success. E.g. Build a Knowledge Graph that enriches our understanding of data relationships, improving the effectiveness of our data science targeting accuracy… Deliver a modern data analytics platform that supports all of the analytical capabilities highlighted for the roadmap. It starts by improving the accuracy, stability and understanding of the OU’s data through effective governance, allows the OU to capture and leverage all types of data assets for use in data analytics, delivers high performing and flexible BI functionality, offers the latest in open source data science capability, has the ability to integrate with digital experience and offline CRM channels, and is constantly able to provide the latest in new data analytical technologies… Create foundational information and resource efficiency… Develop in-depth insight to influence strategic direction… Create intelligence to support student learning & decisions… Deliver intelligence through key communication channels… Leverage new analytics trends to improve student success… DRIVES RESULTS & SUPPORTS INNOVATION DETECTING OPPORTUNITY SIZING OPPORTUNITY MAXIMISING OPPORTUNITY REALISING OPPORTUNITY IMPROVING EFFECTIVENESS
  13. 13. 16 PROGRESS AND PLANS DATA GOVERNANCE IN THE OU INITIATE DEFINE DEVELOP EMBED Recognise the need for data governance and gain agreement to create a governance framework for data. Create policy and the decision- making framework. Identify, define and create the core roles and responsibilities. Create enterprise-wide artefacts to support data management. Embed data governance in technology and procure software. Define and implement data governance for all core business processes. Build data quality dashboards. Transition to BAU. • Identified and agreed Senior Information Risk Owner (SIRO) for the OU. • Gained agreement to implement data governance for the university. • Aligned to related areas of work (GDPR…). • Defined the governance bodies for data and information. • Secured resource to implement the next phase of work. • Created the university’s Data Governance Policy and secured approval. • Formed the Data and Information Governance Group and its sub-groups. • Defined data domains and agreed domain owners. • Agreed SMEs to support data domain owners. • Trained data domain owners and local SMEs. • Initiated next phase. • Create core artefacts: data dictionary, MDM approach, reference data approach, BIM. • Procure and implement data governance software. • Implement data governance for single business process, end-to-end, including: • Data quality measures and dashboards • Local processes • Workflows within the software • Local taxonomy • Repeat implementation across core business processes. • Secure, train and embed BAU resources. • Monitor, review and refine. • Implement data governance for non-core/low-priority business processes.
  14. 14. 17 SUPPORTING LEARNING ANALYTICS Higher quality analytics OU Data Strategy Internal taxonomies Improved data quality External alignment DATA GOVERNANCE IN THE OU
  15. 15. 18 SUCCESS FACTORS • Single most important component • Enables top-down messaging, support and enforcement • Local champions help further the cause Senior management support • It isn’t all about the technology, but… • Data governance software makes things easier and technology projects offer a great opportunity • If you need a reason to govern data… GDPR Alignment to technology and compliance • Must be wide-reaching and frequent • Essential to promote the cause – data governance as an enabler, not a barrier Communication • Data governance isn’t expensive but does require some commitment of resource • Half-hearted or incomplete attempts more likely to fail Budget DATA GOVERNANCE IN THE OU
  16. 16. Update on LA at the OU: slightly more “blue” sky research -Rogers Kalissa: Social Learning Analytics to support teaching (University of Oslo) -Saman Rizvi: Cultural impact of MOOC learning (IET) -Shi Min Chua: Why does no one reply to my posts (IET/WELS) -Maina Korir: Ethics and LA (IET) -Anna Gillespie: Predictive Learning Analytics and role of tutors (EdD) Not included: -Quan Nguyen: Impact of learning design on learning analytics -Garron Hillaire: Role of emotions in learning analytics -Josmario Albuquerque: Role of social bias in learning analytics -Pascaline Fresneau: Investigating the implementation of Social Learning Analytics to Increase Online Peer Collaboration -Jenna Mittelmeier: Using Learning Analytics to implement evidence-based interventions to drive ethnic minority and international student success -Simon Knight: Learning analytics for epistemic commitments in collaborative information seeking environment”.
  17. 17. Social Learning Analytics to Support Teaching and Learning Decisions in Online Learning Environments Case study: Bachelors Course Source of Data: Online Discussion Forums Analysis Methods: Social Network & Discourse Analysis Tools: NodeXL & Coh-Metrix Rogers Kaliisa, Department of Education, University of Oslo, Norway
  18. 18. FINDINGS SNA FINDINGS DISCOURSE ANALYSIS FINDINGS Active Students Less Active Students SNA Metrics S13 S3 S4 S12 S 10 S5 S18 S24 S8 S11 Degree centrality 5 2 2 2 3 1 1 1 1 1 Betweenn ess centrality 114 30 30 30 1 5 0.0 0.0 0.0 0.0 0.0 Closeness centrality 0.01 6 0.01 5 0.01 5 0.01 5 0 .015 0.01 0 0.01 0 0.01 0 0.01 1 0.01 4 Discourse Analysis Results No of words 264 212 1006 133 3 73 100 204 114 121 206 Narrativit y 73 73 47 68 5 3 94 37 64 74 64 Deep Cohesion 69 48 37 94 9 9 10 55 32 23 62 Referentia l Cohesion 67 43 62 35 4 6 83 15 31 70 25 Syntax Simplicity 41 19 62 49 2 5 6 68 17 19 50 Word Concreteness 4 41 18 5 1 3 13 10 10 17 42 • SLA provides insight and a richer understanding of the students’ cognitive and social learning Processes. • Students’ network position can determine their discourse features (Kaliisa, Mørch & Kluge, in press) Fig. 1. Sociogram of week one discussions Table 1. Week 1 discourse and SNA metrics results
  19. 19. • To understand how learners from various geo-cultural clusters perform in different MOOC Learning Designs? • Whether it varies with the discipline? • Temporal dynamics in learning trajectories? ➢ Progression ➢ Activity Engagement Duration The Influence of Geo-Cultural Background on MOOC Learning Trajectories; Mapping Divergence and Similarities Saman Rizvi (saman.rizvi@open.ac.uk) https://iet.open.ac.uk/people/saman.rizvi
  20. 20. 28/06/2019The Influence of Geo-Cultural Background on MOOC Learning Trajectories Preliminary Results Saman Rizvi (saman.rizvi@open.ac.uk) https://iet.open.ac.uk/people/saman.rizvi
  21. 21. 5 Types of Comments in FutureLearn Discussion Initiating Post First Reply First Reply Further Reply Initiating Post Initiator’s Reply First Reply Single Post Shi Min Chua: Discursive practices to achieve dialogic learning and online deliberation in MOOC discussions. https://iet.open.ac.uk/people/shimin.chua
  22. 22. 7 Groups of Social Learners Loner 26% Replier 6% Initiator without replying 18% Initiator who respond 6% Active Social Learner 28% Active social learners without turn-taking 15% Reluctant active social learners 1% Language Usage in Initiating Posts Shi Min Chua: Discursive practices to achieve dialogic learning and online deliberation in MOOC discussions. https://iet.open.ac.uk/people/shimin.chua
  23. 23. 26 STUDY ONE STUDY TWO 2 Privacy Questionnaires Vignettes Follow-up Interviews (N=4) University QUESTIONS • Collection of personal data • Data sharing (identifiable and anonymised) • Benefits (w/ & w/o data sharing) Lab Session Company QUESTIONS • Privacy concepts LA Experts (N=12) Delphi Study QUESTIONS • Privacy concepts RQ1: To what extent are students concerned about privacy in the use of analytics? RQ2: How do LA experts’ privacy concepts (thinking) compare to students’ privacy concepts? Maina Korir: research focuses on ethics and privacy in learning analytics. https://iet.open.ac.uk/people/maina.korir
  24. 24. What factors have an impact on how Associate Lecturers at The Open University use Predictive Learning Analytics? Anna Gillespie EdD student year one Objective: An exploration of the experiences of Open University Associate Lecturers (ALs) in their use of Predictive Learning Analytics (PLA) to support distance learning students who are at risk of non-submission of their Tutor Marked Assignment (TMA) Sub questions: To consider how individual beliefs, attitudes and intentions, their knowledge of technology, and the organisational culture/teaching environment impacts on PLA use and whether is it accepted as an effective teaching tool. ALs use Early Alert Indicators (EAI) which is the OU specific PLA dashboard. Theoretical framework: Using the theoretical models of the Theory of Planned Behaviour, (Ajzen, 1991) the Decomposed Theory of Planned Behaviour, (Taylor and Todd, 1995), and the Technology Acceptance Model (Davis, Bagozzi and Warshaw 1989). I propose that a model based on these theoretical positions, but with modifications can help us to understand the role PLA plays in student support and retention and help understand how ALs use PLA Anna Gillespie: Predictive learning analytics and the role of teachers. A.Gillespie@open.ac.uk
  25. 25. Initial study Using a phenomenological qualitative approach, an initial study based on semi structured interviews of N=5 ALs was conducted. At this stage the findings are limited but have revealed that ALs view the use of PLA differently according to the number of modules taught, their attitude to technology and the support offered by their faculty. Findings indicated that attitudes towards using EAI varied according to each AL’s perception of PLA and their belief about its value as a tool to assist in supporting students: “It feels a bit like snooping.... I know the argument is that it is for their own good, but it’s like FB, they are monitoring us and so are Google…. it feels a bit hollow to me.” More positively “Using EAI, I could see when he dipped and rose, so I'd email him to tell him what he needed to do for the TMA and I would see a massive spike in his activity, he didn’t reply to the email but I knew he was reacting to it” Further investigation in the main study will look at a larger sample and also use Eye Tracking technology to give a clear picture of what aspects of the EAI dashboard are most utilised and hopefully mitigate inconsistencies in self reporting to develop a clear picture of the role of PLA in supporting ALs delivering tuition at distance. Anna Gillespie: Predictive learning analytics and the role of teachers. A.Gillespie@open.ac.uk
  26. 26. Prof John Domingue Dr Christothea Herodotou Director of KMi Senior Lecturer, IET 26 June 2019, JISC LA conference OU Analyse: Lessons learnt from predictive analytics so far
  27. 27. OU Analyse History Pres. Scope Delivery 2014B 2 modules, selected course members Excel spreadsheet, sent by email, manually generated 2014J 12 modules, selected course members Automated pred., excel spreadsheet 2015J Made available to tutors in selected modules Dashboard, within OU network 2016J ~1000 users (338 accessed) , 785 tutors (305 accessed) Dashboard on Internet, no VPN needed, grade predictions 2017J, 18B 37 modules, 375 users (240 accessed), 323 tutors (204 accessed) Mostly STEM pilots, user acceptance 2018J 250 modules in OUA, ~3500 users, including mostly tutors, module chairs, staff tutors, cluster managers, SSTs Dashboard combining OUA predictions and SIO Student Probabilities
  28. 28. 31 What does it do? It produces predictions as to whether students are at risk of failing their studies. The model predicts on a weekly basis whether or not a given student will submit their TMA. It uses a traffic light system to pinpoint in red students at risk, in amber those with a moderate probability of failing and in green those who are unlikely to fail. OU ANALYSE https://analyse.kmi.open.ac.uk/#dashboard
  29. 29. 32 What’s under the bonnet? Demographics Pre-Course Results VLE DataAssessments Under Testing - age - ethnicity - gender - highest education - occupation - studied credits - new/continuing student - imd band - region - country - the score - submission - banking of all the available assessments - activities of students grouped per week and activity type (also known as site type in SAS) - also summary activity counts per week (these attributes can grow in dimensionality as the course progress and if it has more activities, a Minimum Redundancy Maximum Relevance (mRMR) algorithm is run to subset the attributes.) - linking to the qualification of a student Changes with the module predicted - number of previous attempts - banked assessments - number of previously passed/ failed modules - number of credits achieved Static Data OU ANALYSE
  30. 30. Prediction scenario – legacy data Course start prediction date Previous presentation (2016) Course start prediction date Known past Future to be predicted Current presentation (2017) Predictive model Machine Learning Prediction All student data Student’s data Wolff, A. et al: Developing predictive models for early detection of at-risk students on distance learning modules, LAK 2014 Kuzilek et. al: OU Analyse: Analysing At-Risk Students at The Open University. LAK 2015 Martin Hlosta, @mhlosta, martin.hlosta@open.ac.uk A1 cutoff A1 cutoff
  31. 31. Under the bonnet - 1 1. Select Data • Only registered students that haven’t submitted, proper time-slice of the data • VLE data grouped by activity type (forum, HTML content, download resources) 2. Preprocess • Proper alignment of TMAs (different days in different presentations) • Dealing with missing information (previous TMA results) • Outliers – e.g. extreme number of clicks • mRMR – Maximum Relevance Minimum Redundancy * – Most important features that are not correlated Martin Hlosta, @mhlosta, martin.hlosta@open.ac.uk * Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis & Machine Intelligence, (8), 1226-1238.
  32. 32. Under the bonnet - 2 3. Train models and vote • Separate training of 6 Machine Learning models (various types – various experts) Martin Hlosta, @mhlosta, martin.hlosta@open.ac.uk kNN Decision Tree NB SVM SUM votes GBM S NS S/NS S/NS S/NS S/NS S/NS
  33. 33. Under the bonnet - 3 3. Scaling up • Replacing by one ensemble model • Parallelisation and deployment >= 300 modules (updated within hours) Martin Hlosta, @mhlosta, martin.hlosta@open.ac.uk SUM votes GBM S NS S/NS
  34. 34. Grades - Beyond submission prediction • For some modules – S/NS is not a problem • Predict score & band • Only students predicted as Submit Martin Hlosta, @mhlosta, martin.hlosta@open.ac.uk Predict S/NS SNS GBR score 0-100
  35. 35. 38 2015-2018: A 4-year account of PLA Over the last 4 years, OUA reached 23,640 students in 231 undergraduate Open University courses.
  36. 36. 39 2017/18: Use of OUA
  37. 37. 28/06/2019 https://juntosglobal.com/focus-on-customer-journeys-not-touchpoints/ The journey so far
  38. 38. 28/06/2019 https://forums.envato.com/t/the-journey-of-becoming-a-musician-share-your- story/77296 The journey so far - 2016: planned for experimental methodologies - Tuition policy changes - 2016, 2017: voluntary participation - Teachers’ resistance - Renegotiation of teachers' employment contracts. - Systematic engagement with OUA
  39. 39. 42 Systematic engagement of teachers with PLA: An ongoing challenge 2015/16 2018/19
  40. 40. 28/06/2019 What facilitated OUA adoption over the years… • Technical developments on OUA • Access through tutor’s homepage • Ongoing generation and dissemination of evidence of impact • Teachers as champions: training, support, share of authentic, practice-based • Educational managers recognising the value and supporting the initiative • Development of teaching and intervention strategy and actively promoted the availability of OUA • Interdisciplinary team dedicating time on the project
  41. 41. 28/06/2019 Herodotou, C., Verdin, B., Boroowa, A., Rienties, B. (2019). Predictive learning analytics ‘at scale’: Guidelines to successful implementation in Higher Education . Journal of Learning Analytics, 6 (1).
  42. 42. 28/06/2019 Improved learning outcomes: Evidence so far Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z., Hlosta, M., (In press). A large-scale implementation of Predictive Learning Analytics in Higher Education: The teachers' role and perspective. Educational Technology Research and Development. Herodotou, C., Hlosta, M., Boroowa, A., Rienties, B., Zdrahal, Z., Mangafa, C. (In press). Empowering online teachers through predictive learning analytics. British Journal of Educational Technology. Herodotou, C., Verdin, B., Boroowa, A., Rienties, B. (2019). Predictive learning analytics ‘at scale’: Guidelines to successful implementation in Higher Education . Journal of Learning Analytics, 6 (1). Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z., Hlosta, M., Naydenova, G. (2017). Implementing predictive learning analytics on a large scale: the teacher's perspective. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, ACM, NY, pp. 267–271.
  43. 43. 28/06/2019 Future directions
  44. 44. Study plan Block 1 Part 1 Part 2 Block 2 Part 1 Part 2 Part 3 Part 1 Block 3 Part 2 Course Study plan Block 1 Part 1 Part 2 Block 2 Part 1 Part 2 Part 3 Part 2 Block 3 Part 1 Year 2014 Year 2015 Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 . . . . . . Course weeks At the Open University (OU) Introduction – OU study plan
  45. 45. Activity of the Top 25% • describes aggregate information about activities of students in some cohort (i.e. excellent) in the past • is calculated for each study material
  46. 46. Student Facing Analytics
  47. 47. Feedback from tutors Dashboard: Tutor View
  48. 48. https://fourweekmba.com/knowledge-graph-linkedin-ai/ https://engineering.linkedin.com/blog/2016/10/building-the-linkedin-knowledge-graph
  49. 49. Thank you Prof John Domingue Dr Christothea Herodotou Director of KMi Senior Lecturer, IET JISC LA event, 26th June
  50. 50. Jisc Learning Analytics Research Group Open University, Milton Keynes, 26th June 2019 Effective Learning Analytics Using data and analytics to support students

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