While many “brick-and-mortar” universities had to rapidly shift online provision during the pandemic, a range of online and distance learning universities have been teaching in blended and online formats for years. Obviously with every single click potentially interesting data might become available about how and perhaps why learners are engaging with learning materials and activities. A blossoming field of learning analytics has emerged since 2011 trying to make sense of these increased data flows. The Open University UK (OU) has been trailblazing innovative learning across the globe for 50 years. Since 2014 the OU has gradually moved from small-scale experimentation to large-scale adoption of learning analytics throughout all 400+ modules and qualifications available within the OU for its 170.000+ online learners. This keynote will explore how you as researcher, practitioner, and/or policy maker could start to use learning analytics to better understand your educational practice. Using examples from small-scale experiments and large-scale adoptions of predictive learning analytics I will explore together with EDEN RW participants which approaches and methods in learning analytics might be useful to consider. No prior knowledge or experience of learning analytics is expected, and join me on a journey of how you could potentially use data from your learners and teachers to further improve and finetune your blended and online provision. Dr. Bart Rienties is Professor of Learning Analytics and programme lead of the learning analytics and learning design research programme at the Institute of Educational Technology at the Open University UK. He leads a group of academics who provide university-wide learning analytics and learning design solutions and conduct evidence-based research of how students and professionals learn. His primary research interests are focussed on Learning Analytics, Professional Development, and the role of motivation in learning. Furthermore, Bart is interested in broader internationalisation aspects of higher education. He has successfully led a range of institutional/national/European projects, and has received a range of awards for his educational innovation projects. He has published over 285 academic outputs, and is the 2nd most published author on Networks in Education in period 1969-2020 (Saqr et al. 2022), the 3rd most cited author on higher education internationalisation in Asia in the period 2013-2018 (Can & Hou, 2021), the 4th most cited author and contributor in Learning Analytics in the period 2011-2018 (Adeniji, 2019), the 5th most published author on internationalisation in the period 1900-2018 (Jing et al. 2020) and the 7th most published author on social network analysis in social sciences in the period 1999-2018 (Su et al. 2020), and the 14th most published author on educational technology in the period 2015-2018 (West & Bodily, 2020).

1. @DrBartRienties
Professor of Learning Analytics
All papers referred to in this presentation can be
accessed via
https://iet.open.ac.uk/people/bart.rienties
Keynote: How can you use
learning analytics in your own
research and practice: an
introductory perspective

2. Agenda for today
1. A basic introduction of learning analytics
2. What approaches are typically used in LA?
3. How have we used learning analytics at the OU?
4. What is next for learning analytics and how can I contribute?

3. Dyckhoff, A. L., Zielke, D., Bültmann, M., Chatti, M. A., & Schroeder, U. (2012). Design and Implementation of a Learning Analytics Toolkit for Teachers. Journal of Educational Technology & Society, 15(3), 58-76.

4. If you are unfamiliar with learning analytics, watch this 3 min short video by Dr Yi-Shan Tsai
(Monash University)
https://www.youtube.com/watch?v=XscUZ8dIa-8&t=161s

5. Agenda for today
1. A basic introduction of learning analytics
2. What approaches are typically used in LA?
3. How have we used learning analytics at the OU?
4. What is next for learning analytics and how can I contribute?

6. Web of Science (15 September 2022). Learning Analytics.

7. Web of Science (15 September 2022). Learning Analytics.

8. 8
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89(December 2018), 98-110. https://doi.org/10.1016/j.chb.2018.07.027

9. 9
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89(December 2018), 98-110. https://doi.org/10.1016/j.chb.2018.07.027

10. Hernández-de-Menéndez, M., Morales-Menendez, R., Escobar, C. A., & Ramírez Mendoza, R. A. (2022). Learning analytics: state of the art. International Journal on Interactive Design and Manufacturing (IJIDeM), 16, 1209–
1230. https://doi.org/10.1007/s12008-022-00930-0
328 OU papers on Learning Analytics can be found here: https://tinyurl.com/2p892rf2
1. Identify good
practice/teachers/modules
2. Alignments between
modules/qualifications
3. Indications of good practice
between/across institutions
1. Support access and inclusion
2. EDI
1. Improved pedagogical awareness
2. Improved data literacy and
confidence
3. Driver for change based upon
evidence
What we have learned in 10 years in terms of benefits?
Case-studies included from Arizona State University (USA), Dublin City University (IRE), Georgia State University (USA), Northern Arizona
University (USA), New York Institute of Technology (USA), The Open University (UK), Open Universities Australia (AUS), Purdue University
(USA), Rio Salado College (USA), Sinclair Community College (USA), Tecnológico de Monterrey (Mex), University of Alabama (USA), University
in Ankara (TUR), University of Maryland (USA), University of Michigan (USA), University of Wollongong (AUS)

11. OU #1 in Europe, #2 in world
OU has Ethics LA policy since 2014
Data Governance
What we have learned in 10 years in terms of challenges?
Actual adoption and sense making
Actual adoption and sense making
LA embedded in design and practice
Good evidence within a module, more
needed across qualifications and
diversity
Hernández-de-Menéndez, M., Morales-Menendez, R., Escobar, C. A., & Ramírez Mendoza, R. A. (2022). Learning analytics: state of the art. International Journal on Interactive Design and Manufacturing (IJIDeM), 16, 1209–
1230. https://doi.org/10.1007/s12008-022-00930-0
328 OU papers on Learning Analytics can be found here: https://tinyurl.com/2p892rf2

12. Agenda for today
1. A basic introduction of learning analytics
2. What approaches are typically used in LA?
3. How have we used learning analytics at the OU?
4. What is next for learning analytics and how can I contribute?

13. Some of LA Methods used at the OU
o Artificial Intelligence (Holmes & Culver, 2019; Rizvi et al., 2018)
o Cluster analysis (Rienties et al., 2019; Tempelaar et al., 2018; Tempelaar, Rienties, et al.,
2020; Tempelaar et al., 2021)
o Decision Trees (Rizvi, Rienties, & Khoja, 2019)
o Eye-tracking (Gillespie, 2022; Rets, 2018; Rets et al., 2022)
o Experimental (Herodotou, Heiser, et al., 2017; Herodotou, Rienties, Verdin, et al., 2019;
Knight, Rienties, Littleton, Tempelaar, et al., 2017; Korir et al., 2022; Mittelmeier et al.,
2018; Rienties et al., 2016)
o Focus groups (Ferguson et al., 2016; Olney et al., 2018)
o Lab-study (Knight, Rienties, Littleton, Mitsui, et al., 2017; Knight, Rienties, Littleton,
Tempelaar, et al., 2017; Mittelmeier et al., 2018; Rienties et al., 2018)
o Learning design (Holmes et al., 2019; Li et al., 2017; Macfadyen et al., 2020; Nguyen et al.,
2018; Nguyen et al., 2017a; Rienties et al., 2023; Rienties, Lewis, et al., 2018; Rienties &
Toetenel, 2016; Toetenel & Rienties, 2016)
o Observation (Murphy et al., 2021; Rets et al., 2021)
o Mixed methods (Korir et al., 2020; Murphy et al., 2020; Thomas et al., 2020; Xue et al.,
2020)
o Process Mining (Rizvi, Rienties, Rogaten, et al., 2019; Rizvi et al., 2022)
o Predictive Learning Analytics (Herodotou, Hlosta, et al., 2019; Herodotou et al., 2021;
Herodotou, Naydenova, et al., 2020; Herodotou, Rienties, Boroowa, et al., 2019;
Herodotou, Rienties, et al., 2017; Herodotou, Rienties, et al., 2020; Herodotou, Rienties,
Verdin, et al., 2019; Hlosta et al., 2017; Huptych et al., 2017; Nguyen et al., 2017b;
Tempelaar, Rienties, & Giesbers, 2015)
o Qualitative research (Murphy et al., 2020; Rets et al., 2021; Xue et al., 2020)
o Surveys (Cross et al., 2016; Richardson, 2009, 2013, 2015; Tempelaar, Nguyen, et al.,
2020)
o Structural Equation Modelling (Tempelaar et al., 2012; Tempelaar, Rienties, Giesbers, et
al., 2015)
o Social Network Analysis (Froehlich et al., 2020; Korir et al., 2020; Nguyen et al., 2017a,
2017b; Rienties et al., 2012)
o Text analytics (Hillaire et al., 2017, 2019; Hillaire et al., 2022; Ullmann & Rienties, 2021;
Ullmann et al., 2019)

14. What we have learned from large scale adoption of
predictive learning analytics at the OU (2014-2022)
Kuzilek, J., Hlosta, M., Herrmannova, D., Zdrahal, Z., & Wolff, A. (2015). OU Analyse: analysing at-risk students at The Open University LACE Learning Analytics Review (Vol. LAK15-1). Milton Keynes: Open University.
Kuzilek, J., Hlosta, M., & Zdrahal, Z. (2017). Open University Learning Analytics dataset. Scientific Data, 4, 170171. doi: 10.1038/sdata.2017.171
Wolff, A., Zdrahal, Z., Herrmannova, D., Kuzilek, J., & Hlosta, M. (2014). Developing predictive models for early detection of at-risk students on distance learning modules, Workshop: Machine Learning and Learning Analytics
Paper presented at the Learning Analytics and Knowledge (2014), Indianapolis.
Accurate? Reliable? Fair?
Who uses
it?
Is it
effective?
Does it lead
to
interventions?
Usability?
Design
improvements
?
Other
institutions
?
Open DATA SET

15. What we have learned from large scale adoption of
predictive learning analytics at the OU (2014-2022)
Kuzilek, J., Hlosta, M., Herrmannova, D., Zdrahal, Z., & Wolff, A. (2015). OU Analyse: analysing at-risk students at The Open University LACE Learning Analytics Review (Vol. LAK15-1). Milton Keynes: Open University.
Kuzilek, J., Hlosta, M., & Zdrahal, Z. (2017). Open University Learning Analytics dataset. Scientific Data, 4, 170171. doi: 10.1038/sdata.2017.171
Wolff, A., Zdrahal, Z., Herrmannova, D., Kuzilek, J., & Hlosta, M. (2014). Developing predictive models for early detection of at-risk students on distance learning modules, Workshop: Machine Learning and Learning Analytics
Paper presented at the Learning Analytics and Knowledge (2014), Indianapolis.
Accurate? Reliable? Fair?
Who uses
it?
Is it
effective?
Does it lead
to
interventions?
Usability?
Design
improvements
?
Other
institutions
?
Open DATA SET
Boroowa, A., & Herodotou, C. (2022). Learning Analytics in Open and Distance Higher Education: The Case of the Open University UK. In P. Prinsloo, S. Slade, & M. Khalil (Eds.), Learning Analytics
in Open and Distributed Learning: Potential and Challenges (pp. 47-62). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-0786-9_4
Herodotou, C., Hlosta, M., Boroowa, A., Rienties, B., Zdrahal, Z., & Mangafa, C. (2019). Empowering online teachers through predictive learning analytics. British Journal of Educational Technology,
50(6), 3064-3079. https://doi.org/10.1111/bjet.12853
Herodotou, C., Maguire, C., McDowell, N., Hlosta, M., & Boroowa, A. (2021). The engagement of university teachers with predictive learning analytics. Computers & Education, 173, 104285.
https://doi.org/https://doi.org/10.1016/j.compedu.2021.104285
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 Proceedings of the
Seventh International Learning Analytics & Knowledge Conference, Vancouver, British Columbia, Canada.
Herodotou, C., Rienties, B., Hlosta, M., Boroowa, A., Mangafa, C., & Zdrahal, Z. (2020). The scalable implementation of predictive learning analytics at a distance learning university: Insights from a
longitudinal case study. The Internet and Higher Education, 45, 100725. https://doi.org/10.1016/j.iheduc.2020.100725
Hlosta, M., Papathoma, T., & Herodotou, C. (2020). Explaining errors in predictions of at-risk students in distance learning education. In I. I. Bittencourt, M. Cukurova, K. Muldner, R. Luckin, & E.
Millán, Artificial Intelligence in Education Cham.
Hlosta, M., Zdrahal, Z., & Zendulka, J. (2017). Ouroboros: early identification of at-risk students without models based on legacy data Proceedings of the Seventh International Learning Analytics &
Knowledge Conference, Vancouver, British Columbia, Canada.
Huptych, M., Bohuslavek, M., Hlosta, M., & Zdrahal, Z. (2017). Measures for recommendations based on past students' activity Proceedings of the Seventh International Learning Analytics &
Knowledge Conference, Vancouver, British Columbia, Canada.
Kuzilek, J., Hlosta, M., Herrmannova, D., Zdrahal, Z., & Wolff, A. (2015a). OU Analyse: analysing at-risk students at The Open University (LACE Learning Analytics Review, Issue.
http://www.laceproject.eu/learning-analytics-review/analysing-at-risk-students-at-open-university/
Kuzilek, J., Hlosta, M., Herrmannova, D., Zdrahal, Z., & Wolff, A. (2015b). OU Analyse: analysing at-risk students at The Open University. Learning Analytics Review, 1-16.
http://oro.open.ac.uk/42529/1/__userdata_documents5_ajj375_Desktop_analysing-at-risk-students-at-open-university.pdf
Kuzilek, J., Hlosta, M., & Zdrahal, Z. (2017). Open University Learning Analytics dataset [Data Descriptor]. Scientific Data, 4, 170171. https://doi.org/10.1038/sdata.2017.171
Olney, T., Walker, S., Wood, C., & Clarke, A. (2021). Are We Living In LA (P)LA Land? Reporting on the Practice of 30 STEM Tutors in their Use of a Learning Analytics Implementation at the Open
University. Journal of Learning Analytics, 1-15. https://doi.org/10.18608/jla.2021.7261
Rets, I., Herodotou, C., Bayer, V., Hlosta, M., & Rienties, B. (2021). Exploring critical factors of the perceived usefulness of a learning analytics dashboard for distance university students.
International Journal of Educational Technology in Higher Education, 18(1), 46. https://doi.org/10.1186/s41239-021-00284-9
Rienties, B. (2021). Implementing Learning Analytics at Scale in an Online World: Lessons Learned from the Open University UK. In J. Liebovitz (Ed.), Online Learning Analytics (pp. 57-77).
Auerbach Publications.
Rienties, B., Clow, D., Coughlan, T., Cross, S., Edwards, C., Gaved, M., Herodotou, C., Hlosta, M., Jones, J., Rogaten, J., & Ullmann, T. (2017). Scholarly insight Autumn 2017: a Data wrangler
perspective. http://article.iet.open.ac.uk/D/Data%20Wranglers/Scholarly%20Insight%20Report%20Autumn%202017/DW_Scholarly_Insight_Report_Autumn_2017.pdf
Rienties, B., & Herodotou, C. (2022). Making sense of learning data. In R. Sharpe, S. Bennett, & T. Varga-Atkins (Eds.), Handbook for Digital Higher Education. Edward Elgar Publishing.
Wolff, A., Zdrahal, Z., Herrmannova, D., Kuzilek, J., & Hlosta, M. (2014). Developing predictive models for early detection of at-risk students on distance learning modules, Workshop: Machine
Learning and Learning Analytics Learning Analytics and Knowledge (2014), Indianapolis.

16. Herodotou, C., Rienties, B., Hlosta, M., Boroowa, A., Mangafa, C., Zdrahal, Z., (2020). Scalable implementation of predictive learning analytics at a distance learning university:
Insights from a longitudinal case study. Internet and Higher Education, 45, 100725.

17. Herodotou, C., Rienties, B., Hlosta, M., Boroowa, A., Mangafa, C., Zdrahal, Z., (2020). Scalable implementation of predictive learning analytics at a distance learning university:
Insights from a longitudinal case study. Internet and Higher Education, 45, 100725.
Amongst the factors shown to be critical to the scalable PLA implementation were: Faculty's engagement with OUA,
teachers as “champions”, evidence generation and dissemination, digital literacy, and conceptions about teaching online.

18. • Eye-tracking combined with think-aloud
protocol of experienced teachers using
PLA
• Most teachers comfortable with main
dashboard, but worried about ethics/data
• Some erroneous interpretations and sense
making of actual data
• Uncertainty about what options to address
identified issues
Gillespie, A. (2022). Teachers’ Use of Predictive Learning Analytics: Experiences from The Open University UK. Doctorate in Education, Milton Keynes.

19. Herodotou, C., Naydenova, G., Boroowa, A., Gilmour, A., & Rienties, B. (2020). How can predictive learning analytics and motivational interventions increase student
retention and enhance administrative support in distance education? Journal of Learning Analytics, 7(2), 72-83. https://doi.org/10.18608/jla.2020.72.4

20. Student Facing Analytics

21. Rets, I., Herodotou, C., Bayer, V., Hlosta, M., Rienties, B. (2021). Exploring critical factors of the perceived usefulness of a learning analytics dashboard for
distance university students. International Journal of Educational Technology in Higher Education. 18 (46).
Mixed method with 22 undergraduate business students
The majority of participants found the Study recommender useful for two
reasons:
a) to remind them of the learning material they had missed, and
b) as a means of directly accessing content (e.g., as opposed to going through the
VLE).
Perceived usefulness was influenced by
• Trustworthiness of learning analytics dashboard
• Peer comparison
• Academic self-confidence
• Change in study patterns
• “Good” vs “not-so-good” students

22. Magic of learning design (does not come easy)
“Research on the relationship between learning design and learning
analytics has also been a focus in European research in recent years. For
example, in their research at the Open University UK, Toetenel and
Rienties combine learning design and learning analytics where learning
design provides context to empirical data about OU courses enabling the
learning analytics to give insight into learning design decisions. This
research is important as it attempts to close the virtuous cycle
between learning design to improve courses and enhancing the
quality of learning, something that has been lacking in the research
literature. For example, they study the impact of learning design on
pedagogical decision-making and on future course design, and the
relationship between learning design and student behaviour and outcomes
(Toetenel and Rienties 2016; Rienties and Toetenel 2016; Rienties et al.
2015).”
Wasson, B., & Kirschner, P. A. (2020). Learning Design: European Approaches. TechTrends, 1-13.

23. Assimilative Finding and
handling
information
Communication Productive Experiential Interactive/
Adaptive
Assessment
Type of activity Attending to
information
Searching for
and processing
information
Discussing
module related
content with at
least one other
person (student
or tutor)
Actively
constructing an
artefact
Applying
learning in a
real-world
setting
Applying
learning in a
simulated
setting
All forms of
assessment,
whether
continuous, end
of module, or
formative
(assessment for
learning)
Examples of
activity
Read, Watch,
Listen, Think
about, Access,
Observe,
Review, Study
List, Analyse,
Collate, Plot,
Find, Discover,
Access, Use,
Gather, Order,
Classify, Select,
Assess,
Manipulate
Communicate,
Debate, Discuss,
Argue, Share,
Report,
Collaborate,
Present,
Describe,
Question
Create, Build,
Make, Design,
Construct,
Contribute,
Complete,
Produce, Write,
Draw, Refine,
Compose,
Synthesise,
Remix
Practice, Apply,
Mimic,
Experience,
Explore,
Investigate,
Perform,
Engage
Explore,
Experiment,
Trial, Improve,
Model, Simulate
Write, Present,
Report,
Demonstrate,
Critique
Conole, G. (2012). Designing for Learning in an Open World. Dordrecht: Springer.
Rienties, B., Toetenel, L., (2016). The impact of learning design on student behaviour, satisfaction and performance: a cross-institutional comparison across 151
modules. Computers in Human Behavior, 60 (2016), 333-341
Open University Learning Design Initiative (OULDI)

24. Toetenel, L., Rienties, B. (2016). Analysing 157 Learning Designs using Learning Analytic approaches as a means to evaluate the impact of pedagogical
decision-making. British Journal of Educational Technology, 47(5), 981–992.

25. Constructivist
Learning Design
Assessment
Learning Design
Productive
Learning Design
Socio-construct.
Learning Design
VLE Engagement
Student
Satisfaction
Student
retention
150+ modules
Week 1 Week 2 Week30
+
Rienties, B., Toetenel, L., (2016). The impact of learning design on student behaviour, satisfaction and performance: a cross-institutional comparison across 151
modules. Computers in Human Behavior, 60 (2016), 333-341
Communication

26. Assessment activities
Constructivist
Learning Design
Assessment
Learning Design
Productive
Learning Design
Socio-construct.
Learning Design
VLE Engagement
Student
Satisfaction
Student
retention
Week 1 Week 2 Week32
+
Communication & Assessment
Nguyen, Q., Rienties, B., Toetenel, L., Ferguson, R., Whitelock, D. (2017). Examining the designs of computer-based assessment and its impact on student
engagement, satisfaction, and pass rates. Computers in Human Behavior. DOI: 10.1016/j.chb.2017.03.028.

27. Nguyen, Q., Rienties, B., Toetenel, L., Ferguson, R., Whitelock, D. (2017). Examining the designs of computer-based assessment and its impact on student
engagement, satisfaction, and pass rates. Computers in Human Behavior. DOI: 10.1016/j.chb.2017.03.028.
69% of what students are
doing in a week is
determined by us, teachers!

28. https://teach4edu4-project.eu/
https://rapide-project.eu/

29. Agenda for today
1. A basic introduction of learning analytics
2. What approaches are typically used in LA?
3. How have we used learning analytics at the OU?
4. What is next for learning analytics and how can I contribute?

30. What are the five main questions for HE in next five years?
1. How to move from proof-of-concept to large-scale adoption?
2. How to provide effective AND inclusive personalised learning
analytics?
3. Who owns the data? What about the ethics?
4. What about professional development of staff and learners?
5. How to balance commercial with HE interests?

31. 31
1. Largest society focused on Learning Analytics (since 2011)
2. 547 members in 2022, newsletter subscription 5400 +
3. 18 Institutional members
4. 140+ scholarship for PhD students and ECRs
5. Dedicated journal included in Web of Science
6. 2021 Google Scholar rankings LAK conference in top 10
7. International Alliance to Advance Learning in the Digital Era (IAALDE)
8. Online resources, webinars, podcasts, trainings
9. Global presence with regional dedicated events
https://www.solaresearch.org/

32. @DrBartRienties
Professor of Learning Analytics
All papers referred to in this presentation can be
accessed via
https://iet.open.ac.uk/people/bart.rienties
Keynote: How can you use
learning analytics in your own
research and practice: an
introductory perspective

33. Boroowa, A., & Herodotou, C. (2022). Learning Analytics in Open and Distance Higher Education: The Case of the Open University UK. In P. Prinsloo, S. Slade, & M. Khalil (Eds.), Learning Analytics
in Open and Distributed Learning: Potential and Challenges (pp. 47-62). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-0786-9_4
Cross, S., Whitelock, D., & Mittelmeier, J. (2016). Does the Quality and Quantity of Exam Revision Impact on Student Satisfaction and Performance in the Exam Itself?: Perspectives from
Undergraduate Distance Learners 8th International Conference on Education and New Learning Technologies (EDULEARN16), Barcelona, Spain. http://oro.open.ac.uk/46937/
Ferguson, R., Brasher, A., Cooper, A., Hillaire, G., Mittelmeier, J., Rienties, B., Ullmann, T., & Vuorikari, R. (2016). Research evidence of the use of learning analytics; implications for education
policy (A European Framework for Action on Learning Analytics, Issue. https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/research-evidence-use-learning-analytics-
implications-education-policy
Froehlich, D., Rehm, M., & Rienties, B. (2020). Mixed Methods Approaches to Social Network Analysis. Routledge.
Gillespie, A. (2022). Teachers’ Use of Predictive Learning Analytics: Experiences from The Open University UK The Open University]. Milton Keynes.
Herodotou, C., Heiser, S., & Rienties, B. (2017). Implementing randomised control trials in open and distance learning: a feasibility study. Open Learning: The Journal of Open, Distance and e-
Learning, 32(2), 147-162. https://doi.org/10.1080/02680513.2017.1316188
Herodotou, C., Hlosta, M., Boroowa, A., Rienties, B., Zdrahal, Z., & Mangafa, C. (2019). Empowering online teachers through predictive learning analytics. British Journal of Educational Technology,
50(6), 3064-3079. https://doi.org/10.1111/bjet.12853
Herodotou, C., Maguire, C., McDowell, N., Hlosta, M., & Boroowa, A. (2021). The engagement of university teachers with predictive learning analytics. Computers & Education, 173, 104285.
https://doi.org/https://doi.org/10.1016/j.compedu.2021.104285
Herodotou, C., Naydenova, G., Boroowa, A., Gilmour, A., & Rienties, B. (2020). How can predictive learning analytics and motivational interventions increase student retention and enhance
administrative support in distance education? Journal of Learning Analytics, 7(2), 72-83. https://doi.org/10.18608/jla.2020.72.4
Herodotou, C., Rienties, B., Boroowa, A., & Zdrahal, Z. (2019). A large-scale implementation of predictive learning analytics in higher education: the teachers’ role and perspective. Educational
Technology Research Devevelopment, 67, 1273–1306. https://doi.org/10.1007/s11423-019-09685-0
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 Proceedings of the
Seventh International Learning Analytics & Knowledge Conference, Vancouver, British Columbia, Canada.
Herodotou, C., Rienties, B., Hlosta, M., Boroowa, A., Mangafa, C., & Zdrahal, Z. (2020). The scalable implementation of predictive learning analytics at a distance learning university: Insights from a
longitudinal case study. The Internet and Higher Education, 45, 100725. https://doi.org/10.1016/j.iheduc.2020.100725
Herodotou, C., Rienties, B., Verdin, B., & Boroowa, A. (2019). Predictive Learning Analytics 'At Scale': Guidelines to Successful Implementation in Higher Education. Journal of Learning Analytics,
6(1), 85-95.
Hillaire, G., Iniesto, F., & Rienties, B. (2017). Toward Emotionally Accessible Massive Open Online Courses (MOOCs) 14th AAATE Congress 2017, Sheffield. http://oro.open.ac.uk/50395/
Hillaire, G., Iniesto, F., & Rienties, B. (2019). Humanizing text-to-speech through emotional expression in online courses. Journal of Interactive Media in Education, 1, 12.
https://doi.org/10.5334/jime.519
Hillaire, G., Rienties, B., Fenton-O'Creevy, M., Zdrahal, Z., & Tempelaar, D. T. (2022). Incorporating student opinion into opinion mining: a student sourced sentiment analysis classifier. In B.
Rienties, R. Hampel, E. Scanlon, & D. Whitelock (Eds.), Open World Learning: Research, Innovation and the Challenges of High-Quality Education (pp. 171-186). Routledge.
Hlosta, M., Papathoma, T., & Herodotou, C. (2020). Explaining errors in predictions of at-risk students in distance learning education. In I. I. Bittencourt, M. Cukurova, K. Muldner, R. Luckin, & E.
Millán, Artificial Intelligence in Education Cham.
Hlosta, M., Zdrahal, Z., & Zendulka, J. (2017). Ouroboros: early identification of at-risk students without models based on legacy data Proceedings of the Seventh International Learning Analytics &
Knowledge Conference, Vancouver, British Columbia, Canada.
Holmes, W., & Culver, J. (2019). Automating the Categorization of Learning Activities, to Help Improve Learning Design. In S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, & R. Luckin,
Artificial Intelligence in Education Cham.
Holmes, W., Nguyen, Q., Zhang, J., Mavrikis, M., & Rienties, B. (2019). Learning analytics for learning design in online distance learning. Distance Education, 40(3), 309-329.
https://doi.org/10.1080/01587919.2019.1637716

34. Huptych, M., Bohuslavek, M., Hlosta, M., & Zdrahal, Z. (2017). Measures for recommendations based on past students' activity Proceedings of the Seventh International Learning Analytics &
Knowledge Conference, Vancouver, British Columbia, Canada.
Knight, S., Rienties, B., Littleton, K., Mitsui, M., Tempelaar, D. T., & Shah, C. (2017). The relationship of (perceived) epistemic cognition to interaction with resources on the internet Computers in
Human Behavior, 73(August 2017), 507–518.
Knight, S., Rienties, B., Littleton, K., Tempelaar, D. T., Mitsui, M., & Shah, C. (2017). The orchestration of a collaborative information seeking learning task [journal article]. Information Retrieval
Journal, 20(5), 480-505. https://doi.org/10.1007/s10791-017-9304-z
Korir, M., Mittelmeier, J., & Rienties, B. (2020). Is mixed methods social network analysis ethical? In D. Froehlich, M. Rehm, & B. Rienties (Eds.), Mixed Methods Approaches to Social Network
Analysis (pp. 206-218). Routledge.
Korir, M., Slade, S., Holmes, W., & Rienties, B. (2022). Eliciting students’ preferences for the use of their data for learning analytics: a crowdsourcing approach. In B. Rienties, R. Hampel, E. Scanlon,
& D. Whitelock (Eds.), Open World Learning: Research, Innovation and the Challenges of High-Quality Education (pp. 144-156). Routledge.
Kuzilek, J., Hlosta, M., Herrmannova, D., Zdrahal, Z., & Wolff, A. (2015a). OU Analyse: analysing at-risk students at The Open University. Learning Analytics Review, 1-16.
http://oro.open.ac.uk/42529/1/__userdata_documents5_ajj375_Desktop_analysing-at-risk-students-at-open-university.pdf
Kuzilek, J., Hlosta, M., Herrmannova, D., Zdrahal, Z., & Wolff, A. (2015b). OU Analyse: analysing at-risk students at The Open University (LACE Learning Analytics Review, Issue.
http://www.laceproject.eu/learning-analytics-review/analysing-at-risk-students-at-open-university/
Kuzilek, J., Hlosta, M., & Zdrahal, Z. (2017). Open University Learning Analytics dataset [Data Descriptor]. Scientific Data, 4, 170171. https://doi.org/10.1038/sdata.2017.171
Li, N., Marsh, V., Rienties, B., & Whitelock, D. (2017). Online learning experiences of new versus continuing learners: a large scale replication study. Assessment & Evaluation in Higher Education,
42(4), 657-672. https://doi.org/10.1080/02602938.2016.1176989
Macfadyen, L. P., Lockyer, L., & Rienties, B. (2020). Learning Design and Learning Analytics: Snapshot 2020. Journal of Learning Analytics, 7(3), 6-12. https://doi.org/10.18608/jla.2020.73.2
Mittelmeier, J., Rienties, B., Tempelaar, D. T., Hillaire, G., & Whitelock, D. (2018). The influence of internationalised versus local content on online intercultural collaboration in groups: A randomised
control trial study in a statistics course. Computers & Education, 118, 82-95. https://doi.org/10.1016/j.compedu.2017.11.003
Murphy, V., Littlejohn, A., & Rienties, B. (2020). Social network analysis and activity theory: A symbiotic relationship. In D. Froehlich, M. Rehm, & B. Rienties (Eds.), Mixed Methods Approaches to
Social Network Analysis (pp. 113-125). Routledge.
Murphy, V. L., Littlejohn, A., & Rienties, B. (2021). Learning from incidents: applying the 3-P model of workplace learning. Journal of Workplace Learning, ahead-of-print(ahead-of-print).
https://doi.org/10.1108/JWL-04-2021-0050
Nguyen, Q., Huptych, M., & Rienties, B. (2018). Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours. Journal of Learning Analytics, 5(3), 120-
135. https://doi.org/10.18608/jla.2018.53.8
Nguyen, Q., Rienties, B., & Toetenel, L. (2017a). Mixing and matching learning design and learning analytics. In P. Zaphris & A. Ioannou (Eds.), Learning and Collaboration Technologies.
Technology in Education: 4th International Conference, LCT 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, July 9-14, 2017, Proceedings, Part II (pp. 302-316). Springer.
https://doi.org/10.1007/978-3-319-58515-4_24
Nguyen, Q., Rienties, B., & Toetenel, L. (2017b). Unravelling the dynamics of instructional practice: A longitudinal study on learning design and VLE activities. Proceedings of the Seventh
International Learning Analytics & Knowledge Conference, Vancouver, Canada.
Olney, T., Rienties, B., & Toetenel, L. (2018). Gathering, visualising and interpreting learning design analytics to inform classroom practice and curriculum design: a student-centred approach from
the Open University. In J. M. Lodge, J. C. Horvath, & L. Corrin (Eds.), From Data and Analytics to the Classroom: Translating Learning Analytics for Teachers (pp. 71–92). Routledge.
https://doi.org/10.4324/9781351113038-6
Olney, T., Walker, S., Wood, C., & Clarke, A. (2021). Are We Living In LA (P)LA Land? Reporting on the Practice of 30 STEM Tutors in their Use of a Learning Analytics Implementation at the Open
University. Journal of Learning Analytics, 1-15. https://doi.org/10.18608/jla.2021.7261
Rets, I. (2018, 22-25 August 2018). Using eye-tracking to research the effects of linguistic text simplification on the reading behaviour of English language learners EuroCALL 2018, Jyväskylä,
Finland.

35. Rets, I., Herodotou, C., Bayer, V., Hlosta, M., & Rienties, B. (2021). Exploring critical factors of the perceived usefulness of a learning analytics dashboard for distance university students.
International Journal of Educational Technology in Higher Education, 18(1), 46. https://doi.org/10.1186/s41239-021-00284-9
Rets, I., Stickler, U., Coughlan, T., & Astruc, L. (2022). Simplification of open educational resources in English: its effect on text processing of English speakers. In B. Rienties, R. Hampel, E. Scanlon,
& D. Whitelock (Eds.), Open World Learning: Research, Innovation and the Challenges of High-Quality Education (pp. 89-102). Routledge.
Richardson, J. T. E. (2009). The academic attainment of students with disabilities in UK higher education. Studies in Higher Education, 34(2), 123-137. https://doi.org/10.1080/03075070802596996
Richardson, J. T. E. (2013). Approaches to studying across the adult life span: Evidence from distance education. Learning and Individual Differences, 26(0), 74-80.
https://doi.org/10.1016/j.lindif.2013.04.012
Richardson, J. T. E. (2015). The under-attainment of ethnic minority students in UK higher education: what we know and what we don’t know. Journal of Further and Higher Education, 39, 278-291.
https://doi.org/10.1080/0309877X.2013.858680
Rienties, B. (2021). Implementing Learning Analytics at Scale in an Online World: Lessons Learned from the Open University UK. In J. Liebovitz (Ed.), Online Learning Analytics (pp. 57-77).
Auerbach Publications.
Rienties, B., Balaban, I., Divjak, B., Grabar, D., Svetec, B., & Vonda, P. (2023). Applying and translating learning design approaches across borders. In O. Viberg & A. Gronlund (Eds.), Practicable
Learning Analytics. Springer Nature.
Rienties, B., Clow, D., Coughlan, T., Cross, S., Edwards, C., Gaved, M., Herodotou, C., Hlosta, M., Jones, J., Rogaten, J., & Ullmann, T. (2017). Scholarly insight Autumn 2017: a Data wrangler
perspective. http://article.iet.open.ac.uk/D/Data%20Wranglers/Scholarly%20Insight%20Report%20Autumn%202017/DW_Scholarly_Insight_Report_Autumn_2017.pdf
Rienties, B., Giesbers, B., Tempelaar, D. T., Lygo-Baker, S., Segers, M., & Gijselaers, W. H. (2012). The role of scaffolding and motivation in CSCL. Computers & Education, 59(3), 893-906.
https://doi.org/10.1016/j.compedu.2012.04.010
Rienties, B., Giesbers, S., Lygo-Baker, S., Ma, S., & Rees, R. (2016). Why some teachers easily learn to use a new Virtual Learning Environment: a Technology Acceptance perspective. Interactive
Learning Environments, 24(3), 539-552. https://doi.org/10.1080/10494820.2014.881394
Rienties, B., & Herodotou, C. (2022). Making sense of learning data. In R. Sharpe, S. Bennett, & T. Varga-Atkins (Eds.), Handbook for Digital Higher Education. Edward Elgar Publishing.
Rienties, B., Herodotou, C., Olney, T., Schencks, M., & Boroowa, A. (2018). Making sense of learning analytics dashboards: A Technology Acceptance perspective of 95 teachers. The International
Review of Research in Open and Distributed Learning, 19(5). https://doi.org/10.19173/irrodl.v19i5.3493
Rienties, B., Lewis, T., McFarlane, R., Nguyen, Q., & Toetenel, L. (2018). Analytics in online and offline language learning environments: the role of learning design to understand student online
engagement. Journal of Computer-Assisted Language Learning, 31(3), 273-293. https://doi.org/10.1080/09588221.2017.1401548
Rienties, B., Tempelaar, D. T., Nguyen, Q., & Littlejohn, A. (2019). Unpacking the intertemporal impact of self-regulation in a blended mathematics environment. Computers in Human Behavior,
100(November 2019), 345-357. https://doi.org/10.1016/j.chb.2019.07.007
Rienties, B., & Toetenel, L. (2016). The impact of learning design on student behaviour, satisfaction and performance: a cross-institutional comparison across 151 modules. Computers in Human
Behavior, 60, 333-341. https://doi.org/10.1016/j.chb.2016.02.074
Rizvi, S., Rienties, B., & Khoja, S. A. (2019). The role of demographics in online learning; A decision tree based approach. Computers & Education, 137(August 2019), 32-47.
https://doi.org/10.1016/j.compedu.2019.04.001
Rizvi, S., Rienties, B., & Rogaten, J. (2018). Investigation of Temporal Dynamics in MOOC Learning Trajectories: A Geocultural Perspective. In C. Penstein Rosé, R. Martínez-Maldonado, H. U.
Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren, & B. du Boulay, Artificial Intelligence in Education AIED 2018: Artificial Intelligence in Education, London.
Rizvi, S., Rienties, B., Rogaten, J., & Kizilcec, R. (2019). Investigating variation in learning processes in a FutureLearn MOOC [journal article]. Journal of Computing in Higher Education, 32, 162–
181. https://doi.org/10.1007/s12528-019-09231-0
Rizvi, S., Rienties, B., Rogaten, J., & Kizilcec, R. (2022). Beyond one-size-fits-all in MOOCs: Variation in learning design and persistence of learners in different cultural and socioeconomic contexts.
Computers in Human Behavior, 126, 106973. https://doi.org/https://doi.org/10.1016/j.chb.2021.106973
Tempelaar, D. T., Nguyen, Q., & Rienties, B. (2020). Learning feedback based on dispositional learning analytics. In M. Virvou, E. Alepis, G. Tsihrintzis, & L. Jain (Eds.), Machine Learning
Paradigms. Intelligent Systems Reference Library (Vol. 158, pp. 69-89). Springer.

36. Tempelaar, D. T., Niculescu, A., Rienties, B., Giesbers, B., & Gijselaers, W. H. (2012). How achievement emotions impact students' decisions for online learning, and what precedes those emotions.
Internet and Higher Education, 15(3), 161–169. https://doi.org/10.1016/j.iheduc.2011.10.003
Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning Analytics in a data-rich context. Computers in Human Behavior, 47,
157-167. https://doi.org/10.1016/j.chb.2014.05.038
Tempelaar, D. T., Rienties, B., Giesbers, B., & Gijselaers, W. H. (2015). The Pivotal Role of Effort Beliefs in Mediating Implicit Theories of Intelligence and Achievement Goals and Academic
Motivations. Social Psychology of Education, 18, 101-120. https://doi.org/10.1007/s11218-014-9281-7
Tempelaar, D. T., Rienties, B., Mittelmeier, J., & Nguyen, Q. (2018). Student profiling in a dispositional learning analytics application using formative assessment. Computers in Human Behavior, 78,
408-420. https://doi.org/10.1016/j.chb.2017.08.010
Tempelaar, D. T., Rienties, B., & Nguyen, Q. (2020). Individual differences in the preference for worked examples: Lessons from an application of dispositional learning analytics. Applied Cognitive
Psychology, 34(4), 890-905. https://doi.org/10.1002/acp.3652
Tempelaar, D. T., Rienties, B., & Nguyen, Q. (2021). Dispositional Learning Analytics for supporting individualized learning feedback [Original Research]. Frontiers in Education, 6(338).
https://doi.org/10.3389/feduc.2021.703773
Thomas, L., Tuytens, M., Devos, G., Kelchtermans, G., & Vanderlinde, R. (2020). Unpacking the collegial network structure of beginning teachers’ primary school teams: A mixed method social
network study In D. Froehlich, M. Rehm, & B. Rienties (Eds.), Mixed Methods Approaches to Social Network Analysis (pp. 139-158). Routledge.
Toetenel, L., & Rienties, B. (2016). Analysing 157 learning designs using learning analytic approaches as a means to evaluate the impact of pedagogical decision-making. British Journal of
Educational Technology, 47(5), 981–992. https://doi.org/10.1111/bjet.12423
Ullmann, T., & Rienties, B. (2021). Using Text Analytics to Understand Open-Ended Student Comments at Scale: Insights from Four Case Studies. In M. Shah, J. T. E. Richardson, A. Pabel, & B.
Oliver (Eds.), Assessing and Enhancing Student Experience in Higher Education (pp. 211-233). Springer International Publishing. https://doi.org/10.1007/978-3-030-80889-1_9
Ullmann, T. D., De Liddo, A., & Bachler, M. (2019). A Visualisation Dashboard for Contested Collective Intelligence. Learning Analytics to Improve Sensemaking of Group Discussion. RIED: Revista
Iboeroamericana de Educación a Distancia (The Ibero-American Journal of Digital Education), 22(1), (Early Access).
Wolff, A., Zdrahal, Z., Herrmannova, D., Kuzilek, J., & Hlosta, M. (2014). Developing predictive models for early detection of at-risk students on distance learning modules, Workshop: Machine
Learning and Learning Analytics Learning Analytics and Knowledge (2014), Indianapolis.
Xue, L., Rienties, B., Van Petegem, W., & Van Wieringen, A. (2020). Learning relations of knowledge transfer (KT) and knowledge integration (KI) of doctoral students during online interdisciplinary
training: an exploratory study. Higher Education Research & Development, 39(6), 1290-1307. https://doi.org/10.1080/07294360.2020.1712679