O'Really?

May 5, 2026

Why is Learning So Challenging?

Filed under: Uncategorized — Duncan Hull @ 10:35 pm
Tags: , , , ,

There are plenty of reasons that learning is challenging but there’s one reason that really stands out: feedback. We learn more efficiently when we can get timely, constructive and personalised feedback on our work from somebody who knows what they are talking about. Whatever you are learning, in the finite time you are learning it, answering these three questions will provide feedback to help you progress:

  1. Which skills and knowledge should have the highest priority in your life (both curricular and extra-curricular)?
  2. What skills and knowledge are you learning best and how do you know you are making progress?
  3. What skills and knowledge do you need to improve and how are you going to learn them?

These simple questions often give complex feedback that consumes time and resources, both of which are in limited supply for you and the community that teaches you. You can’t always get what you want, when you need it. That’s why learning is so difficult.

Hosts and winners of the University of Manchester Students’ Union (UMSU) Awards 2026 on stage in the Whitworth Building.

Teaching is Really Challenging Too

If learning is really challenging then it shouldn’t be a surprise to discover that teaching is really challenging too, for the exactly the same reasons. Like you, your teachers are human (honest!) and we make mistakes which we’ll be more likely to learn from if we can get timely, constructive and personalised feedback. We ask ourselves the same three questions above to help us make progress, but like you, our time is finite as well so:

  1. Which knowledge and skills should we give the highest priority to in our teaching? Where do they fit inside, alongside and outside of curricula?
  2. What skills and knowledge are we teaching best and how will we know when our students are making sufficient progress?
  3. What skills and knowledge do we need to teach better and how are we going to assess them?

Right across the higher education sector, it’s a struggle to get feedback on teaching, positive or negative. Students are very busy, have higher priorities, suffer from endless survey fatigue and don’t always recognise the value of giving constructive feedback. Some students are disengaged and don’t believe that their feedback will not be either listened to or acted on. All of this results in unit surveys that typically have response rates so embarrassingly low (less than 1%) that some UK Universities have abandoned using them completely. The tiny amount of data they provide is often meaningless, distorted and unreliable. Yet Universities continue to use them to assess the quality of their teaching and inform decisions about promotions.

So it’s really reassuring to get positive feedback when we are teaching things well. Thanks to the anonymous students who nominated and voted for me for the Excellence in Embedding Employability in the Curriculum award. I almost blubbed uncontrollably on stage like Gwyneth Paltrow when this nomination text was read out by Freya Weetch: 😭

“Duncan Hull is an outstanding Employability Lead who goes above and beyond to prepare students for the world of work. From connecting students with industry to creating exciting opportunities and careers events, he inspires confidence and ambition at every step. His energy, dedication, and genuine passion for student success have made a huge impact, empowering students to step boldly into their future careers. ”

Those very kind and moving words from an anonymous student will help me stay motivated and remind me why I get out of bed in the morning. Thanks to Alexandra (Lexie) Baynes, Krystyna Drewenska, Freya Weetch, Alec Severs, Amrit Dhillon, Bo Ana Murphy, Ben Ward, Katie Jackson and everyone at the University of Manchester Students’ Union (UMSU) for hosting these events, past and present. It felt appropriate to receive this award in the magnificent Whitworth Building where thousands of former students like me have graduated accompanied by their friends, supporters and families. It was also an opportunity to speak in front of the senior leadership of the The University of Manchester including Duncan Ivison, Jenn Hallam, Peter Green, Colette Fagan – thanks to Andrew Mawdsley for recording my fifteen seconds of fame. 🙏

Congratulations and jubilations to my fellow nominees and laureates: Hanan El-Wandi, The Diversify Politics Society, Dr. Pietro Paolo Frigenti, SFHEA, CMktr, The University of Manchester Women In Business Society, The University of Manchester Neurology and Neurosurgery Society, Alejandra Vicente Colmenares, Transforming Assessment Together, Breaking the Barrier to Let a Voice Out, Lei Zeng, The Inclusive Classroom Project, Alan Davies, Harsath Udayakumar, Vuyo Dube, Maria-Michaela Vierita, Matt Dalgliesh, Yuxin Yan, Abdelrahman Shaaban, Ishnoor Kaur, Fiona Chan, Anahita Jayaram, Christian N. Nwosu, Brogan Pritchard, Md Faisal Mahmud, Adella Tobing, The North West Biotech Initiative, Laura Swain, Adam Danquah, The University of Manchester Faculty of Biology, Medicine and Health Maria Mercè Canal, Sohini Biswas, Carl M. Kulimushi, Anna Hood, PhD, Sam Rodgers, Louisa Shirley, Samhita Mukherjee, Lorraine Brobbey, Clara Dawson, Danny Dresner FCIIS, Doron Cohen, Neil Morrison, Saralees Nadarajah, Alison Hassett, Abdullatif Alfutimie, Venus Muscat, Stephen Craig, Sam Thozer, Mariangela D’Acri, Abbie Jones, Hala Shokr, Michele Caprio, Paul Tobin and Dr. Miri Firth PFHEA. Special thanks to Miri wooping loudly and gratifyingly when the winner was announced. 🏆

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What was it Andy Warhol said? “In the future everyone will be famous for fifteen minutes seconds”. Enjoying my fifteen seconds of fame (top left) during evening in the Whitworth Building (top right) for an award (bottom right). Thanks again UMSU

Teaching is a team sport, not an individual one. Teaching professional skills by embedding employability in the curriculum is no different, we’ve had ongoing help from a large team of people from industry and academia. I’d like to thank the employers in our industry club, particularly Arm, Booking.com, BNY the BBC, Bloomberg, Couchbase, IBM, Apadmi, Matillion, Bet365, Amazon Web Services (AWS), Google, Apple, SeeChange Technologies, Morgan Stanley, Roku and many other members of our industry-club.cs.manchester.ac.uk who’ve helped us run a range of events for students in Computer Science. Many of these events have been organised in collaboration with our fantastic student societies UniCS Manchester, The Manchester Intelligence Society (MIS), The Manchester AI Society, RoboSoc (University of Manchester Robotics Society) and MathSoc Manchester who remind us what the joy of learning in a community is all about.

They’ve been a key part of what we’ve managed to offer students alongside Coding Your Future, the Wednesday Waggle with help from Imago Software (with Suzanne Embury), the Masood Entrepreneurship Centre (with Dan Syder) and UoM alumni. None of this would have been possible without ongoing support from my colleagues. Thanks to everyone who leads and delivers on the Herculean task our teaching and assessment Paul Nutter, Andrew Stewart, Steve Pettifer, Gareth Henshall, Stewart Blakeway, Louise Walker, David Petrescu, Sean Bechhofer, Uli Sattler, Andrea Schalk, Markel Vigo, Bijan Parsia, Toby Howard, Afrodite Galata, Tom Carroll, Chris Page and everyone else in the Department of Computer Science at The University of Manchester. 🐝

We’ve also been supported by Professional Services (PS) staff across the University, particularly Mabel Yau, Ruth Maddocks, Cameron Macdonald, Lisa Wright, Nanna Pedley, Caroline Whitehand, Penney Gordon-Lanes, Ben Carter, Amanda Conway, Helen Frost, Anna Lomas, Jenny Sloan, Kelly-Ann Mallon and everyone in the careers service.

Our teaching and research in Computer Science is part of something bigger, that students don’t really see directly: the School of Engineering (SoE) and The University of Manchester Faculty of Science and Engineering. Thanks to leadership and management from academics and administrators alike, including Sarah Cartmell, Sarah Sharples CBE FREng, Carly Peesapati, Akilu Yunusa-Kaltungo (PhD CEng FIMechE FHEA) and many more.

Last but not least, I’d like to thank the Teaching and Scholarship Network (TaSN) who help all staff across the University to improve the quality of teaching and learning, wherever they work and whatever they do. The TaSN is led by Hannah Cobb and Jenni Rose NTF PFHEA with help from Eleanor Aspey, Helen Baxter, Elaine Clark, Karen Lander, Jen McBride, Dr Rachel Parker-Strak, Thomas Rodgers, Reimala Sivalingam, Rachel Studd, Lisa Taylor, Holly Dewsnip and Nick Weise PFHEA IFNTF. If you care about improving teaching, you should come and join us in the TaSN. We host regular events online and in person which anyone from The University of Manchester (and beyond) is welcome to join. Our next TaSN meeting is Thursday 7th May, see the Teaching and Scholarship Network (TaSN).

You can find out more about the UMSU awards, which continue this week with the Arts and Media Awards on Thursday 7th May at manchesterstudentsunion.com/awards.

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The UMSU awards continue on Thursday the 7th May 2026 ❤️

(You can cite this article using DOI:10.59350/1y79e-6mn80, it’s also available at linkedin.com/pulse/why-learning-so-challenging-duncan-hull–i4qlc)

March 7, 2022

Join us to discuss the feeling of learning ❤️ (vs. actual learning) on Monday 4th April at 2pm BST

Filed under: Uncategorized — Duncan Hull @ 4:08 pm

Learning can be an emotional process and we often don’t realise when we are actually learning. When you’re listening to an expert explain something well, it’s easy to mistake the speaker’s smooth delivery for your own understanding. You might feel like you’re learning, but actual learning is often hard work and feels uncomfortable. Join us to discuss actual learning vs. feeling of learning via a paper by Louis Deslauriers, Logan S. McCarty, Kelly Miller, Kristina Callaghan, and Greg Kestin at Harvard University here is the abstract:

We compared students’ self-reported perception of learning with their actual learning under controlled conditions in large-enrollment introductory college physics courses taught using 1) active instruction (following best practices in the discipline) and 2) passive instruction (lectures by experienced and highly rated instructors). Both groups received identical class content and handouts, students were randomly assigned, and the instructor made no effort to persuade students of the benefit of either method. Students in active classrooms learned more (as would be expected based on prior research), but their perception of learning, while positive, was lower than that of their peers in passive environments. This suggests that attempts to evaluate instruction based on students’ perceptions of learning could inadvertently promote inferior (passive) pedagogical methods. For instance, a superstar lecturer could create such a positive feeling of learning that students would choose those lectures over active learning. Most importantly, these results suggest that when students experience the increased cognitive effort associated with active learning, they initially take that effort to signify poorer learning. That disconnect may have a detrimental effect on students’ motivation, engagement, and ability to self-regulate their own learning. Although students can, on their own, discover the increased value of being actively engaged during a semester-long course, their learning may be impaired during the initial part of the course. We discuss strategies that instructors can use, early in the semester, to improve students’ response to being actively engaged in the classroom..

see [1]

Thanks to Uli Sattler and Andrea Schalk for highlighting the paper. All welcome. As usual we’ll be meeting on zoom, details are in the slack channel sigcse.cs.manchester.ac.uk/join-us.

References

  1. Logan S. McCarty; Kelly Miller; Kristina Callaghan; Greg Kestin (2019) “Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom”Proceedings of the National Academy of Sciences of the United States of America: 201821936. DOI:10.1073/PNAS.1821936116 PMC: 6765278 PMID: 31484770
  2. Jill Barshay (2022) College students often don’t know when they’re learning: Harvard experiment reveals the psychological grip of lectures, The Hechinger Report

March 1, 2022

Join us to discuss conversational programming on Monday 7th March at 2pm GMT

Between the traditional division of non-programmers and programmers, there is a third category of conversational programmers. These are people who learn programming so that they can speak in the “programmer’s language” in order to collaborate better with software engineers. Join us to discuss conversational programming via paper by Katie Cunningham et al. [1] This won a best paper award at SIGCSE 2022:

As the number of conversational programmers grows, computing educators are increasingly tasked with a paradox: to teach programming to people who want to communicate effectively about the internals of software, but not write code themselves. Designing instruction for conversational programmers is particularly challenging because their learning goals are not well understood, and few strategies exist for teaching to their needs. To address these gaps, we analyse the research on programming learning goals of conversational programmers from survey and interview studies of this population. We identify a major theme from these learners’ goals: they often involve making connections between code’s real-world purpose and various internal elements of software. To better understand the knowledge and skills conversational programmers require, we apply the Structure Behaviour Function framework to compare their learning goals to those of aspiring professional developers. Finally, we argue that instructional strategies for conversational programmers require a focus on high-level program behaviour that is not typically supported in introductory programming courses.

See [1] below

All welcome. As usual we’ll be meeting on zoom, details are in the slack channel sigcse.cs.manchester.ac.uk/join-us.

References

  1. Kathryn Cunningham, Yike Qiao, Alex Feng and Eleanor O’Rourke (2022) Bringing “High-level” Down to Earth: Gaining Clarity in Conversational Programmer Learning Goals in SIGCSE 2022: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education, Pages 551–557 DOI:10.1145/3478431.3499370

December 23, 2021

Join us virtually in Durham to discuss Computing Education Practice (CEP) on 6th Jan 2022

Filed under: education,Uncategorized — Duncan Hull @ 10:40 am
Tags: , ,
Picture of Durham Cathedral by Mattbuck on Wikimedia Commons w.wiki/4acc

The ACM Computing Education Practice (CEP) conference is aimed at practitioners and researchers in computing education, both within Computer Science departments and elsewhere. The conference provides a platform to share and discuss innovations and developments in the practice of computing education. CEP is a community, not just a series of proceedings; everybody is encouraged to participate even if they are not presenting. We have an exciting programme of talks and workshops scheduled which will be of interest to anyone teaching Computer Science including:

  • Narrowing and Stretching: Addressing the Challenges of Multi-track programming by Steven Bradley and Eleni Akrida, Durham University
  • Automated Code Tracing Exercises for CS1 by Sean Russell, University College Dublin
  • Feedback and Engagement on an Introductory Programming Module by Beate Grawemeyer et al Coventry University 
  • Gender parity in peer assessment of team software development projects by Tom Crick et al Swansea University
  • Promoting Engagement in Remote Computing Ethics Education by Joseph Maguire and Steve Draper, University of Glasgow
  • Co-constructing a Community of Practice for Early-Career Computer Science Academics in the UK by Tom Crick et al 
  • Assessing Knowledge and Skills in Forensics with Alternative Assessment Pathways by Joseph Maguire
  • Little Man Computer + Scratch: A recipe to construct a mental model of program execution by Noman Javed, London School of Economics
  • Application of Amazon Web Services within teaching & learning at a UK University by Dan Flood Coventry University

The conference will be held online on Thursday 6th January 2022. More info and registration at cepconference.webspace.durham.ac.uk/programme. We look forward to seeing you there. 

On behalf of the UK ACM Special Interest Group on Computer Science Education (SIGCSE) uki-sigcse.acm.org/about/

July 30, 2021

Join us to discuss when study turns digital on Monday 2nd August at 2pm BST

Public domain image of Coronavirus by Alissa Eckert and Dan Higgins at CDC.gov on Wikimedia commons w.wiki/ycs

The pandemic has accelerated changes to the way we teach and learn. Join us to discuss the Covid-19 shutdown: when studying turns digital, students want more structure: a paper by Vegard Gjerde, Robert Gray, Bodil Holst and Stein Dankert Kolstø on the effects of the pandemic on Physics Education at a Norwegian University. [1]

In March 2020, universities in Norway and many other countries shut down due to the Covid-19 pandemic. The students lost access to classrooms, libraries, study halls, and laboratories. Studying turned digital. Because it is unclear when this pandemic will cease to affect students and because we cannot know whether or when a new pandemic occurs, we need to find ways to improve digital study-life for students. An important step in this direction is to understand the students’ experiences and perspectives regarding how the digitalization affected their study-life both in structured learning arenas and their self-study. Therefore, we interviewed 12 students in an introductory mechanics course at a Norwegian university in June of 2020. Through a thematic analysis, we identified four broad categories in the students’ different experiences and reflections, namely that digitalization: (a) provides benefits, e.g. the flexibility inherent in online video lectures; (b) incurs learning costs, e.g. students reducing their study effort; (c) incurs social costs, e.g. missing being around other students; and (d) increases the need for structure, e.g. wanting to be arranged in digital groups to solve mandatory tasks. We also found that the 2019 students on average scored significantly better on the final exam than the 2020 students, d = 0.31, but we discuss why this result should be interpreted with caution. We provide suggestions for how to adapt courses to make students’ digital studying more socially stimulating and effective. Furthermore, this study is a contribution to the historical documentation of the Covid-19 pandemic.

All welcome, as usual, we’ll be meeting on Zoom see sigcse.cs.manchester.ac.uk/join-us for details. Thanks to Sarah Clinch for suggesting the paper.

References

  1. Gjerde, Vegard; Gray, Robert; Holst, Bodil; Kolstø, Stein Dankert (2021). “The Covid-19 shutdown: when studying turns digital, students want more structure”. Physics Education56 (5): 055004. doi:10.1088/1361-6552/ac031e

July 5, 2021

Join us to discuss the tyranny of content on Monday 5th July at 2pm BST

Filed under: Teaching,Uncategorized — Duncan Hull @ 11:56 am
Tags: , , , ,
CC-BY-SA image of Bill Gates by Kuhlmann MSC via Wikimedia Commons w.wiki/3W7k

If content is king, then his rule is tyrannical. Bill Gates once remarked that “Content is King” but In the kingdom of education, how much do educators oppressively inflict content on their learners? What can be done to reduce the tyranny of content? We’ll be discussing this via a paper by Christina I. Petersen et al, here’s the abstract:

Instructors have inherited a model for conscientious instruction that suggests they must cover all the material outlined in their syllabus, and yet this model frequently diverts time away from allowing students to engage meaningfully with the content during class. We outline the historical forces that may have conditioned this teacher-centered model as well as the disciplinary pressures that inadvertently reward it. As a way to guide course revision and move to a learner-centered teaching approach, we propose three evidence-based strategies that instructors can adopt: 1) identify the core concepts and competencies for your course; 2) create an organizing framework for the core concepts and competencies; and 3) teach students how to learn in your discipline. We further outline examples of actions that instructors can incorporate to implement each of these strategies. We propose that moving from a content-coverage approach to these learner-centered strategies will help students better learn and retain information and apply it to new situations.

All welcome. As usual, we’ll be meeting on zoom, see sigcse.cs.manchester.ac.uk/join-us for details.

References

  1. Petersen, Christina I.; Baepler, Paul; Beitz, Al; Ching, Paul; Gorman, Kristen S.; Neudauer, Cheryl L.; Rozaitis, William; Walker, J. D.; Wingert, Deb; Reiness, C. Gary (2020). The Tyranny of Content: “Content Coverage” as a Barrier to Evidence-Based Teaching Approaches and Ways to Overcome It. CBE—Life Sciences Education19 (2): ar17. doi:10.1187/cbe.19-04-0079

March 23, 2021

Join us to discuss learning sciences for computing education on Monday 12th April at 2pm BST

Scientist icon made by Eucalyp via flaticon.com

Learning sciences aims to improve our theoretical understanding of how people learn while computing education investigates with how people learn to compute. Historically, these fields existed independently, although attempts have been made to merge them. Where do these disciplines overlap and how can they be integrated further? Join us to discuss learning sciences for computing education via a paper by Lauren Margulieux, Brian Dorn and Kristin Searle, from the abstract:

This chapter discusses potential and current overlaps between the learning sciences and computing education research in their origins, theory, and methodology. After an introduction to learning sciences, the chapter describes how both learning sciences and computing education research developed as distinct fields from cognitive science. Despite common roots and common goals, the authors argue that the two fields are less integrated than they should be and recommend theories and methodologies from the learning sciences that could be used more widely in computing education research. The chapter selects for discussion one general learning theory from each of cognition (constructivism), instructional design (cognitive apprenticeship), social and environmental features of learning environments (sociocultural theory), and motivation (expectancy-value theory). Then the chapter describes methodology for design-based research to apply and test learning theories in authentic learning environments. The chapter emphasizes the alignment between design-based research and current research practices in computing education. Finally, the chapter discusses the four stages of learning sciences projects. Examples from computing education research are given for each stage to illustrate the shared goals and methods of the two fields and to argue for more integration between them.

There’s a 5 minute summary of the chapter ten minutes into the video below:

All welcome. As usual, we’ll be meeting on zoom, see sigcse.cs.manchester.ac.uk/join-us for details. Thanks to this months paper suggestions from Sue Sentance and Nicola Looker.

References

  1. Margulieux, Lauren E.; Dorn, Brian; Searle, Kristin A. (2019). “Learning Sciences for Computing Education“: 208–230. doi:10.1017/9781108654555.009. in In S. A. Fincher & A. V. Robins (Eds.) The Cambridge Handbook of Computing Education Research. Cambridge, UK: Cambridge University Press

May 26, 2020

Join us to discuss blended learning, Monday 1st June at 11am on Zoom

Filed under: education,Uncategorized — Duncan Hull @ 2:41 pm
Tags: ,

7704609288_14d335dd87_cAt our next journal club, on Monday 1st June at 11am, we’ll be discussing blended learning. We’ve picked a subject, but we haven’t picked a paper yet. So, if you know interesting papers on blended learning, post them to the “paper suggestions” channel at  uk-acm-sigsce.slack.com – there are a few suggestions up there already for starters.

If you’ve not got access to the workspace yet, ping me or Alcywn Parker and we’ll add you to the group. Journal Club is part of the Association for Computing Machinery (ACM) Special Interest Group (SIG) on Computer Science Education (CSE) – all welcome!

April 28, 2020

Join us to discuss learning programming languages: Monday 4th May at 11am #sigcsejclub

Filed under: education,engineering,Uncategorized — Duncan Hull @ 10:17 am
Tags: , , , ,

Hieroglyphs_from_the_tomb_of_Seti_I

Hieroglyphs from the tomb of Seti I, by Jon Bodsworth via Wikimedia Commons and the Egypt archive

ACM SIGCSE Journal Club returns Monday 4th May at 11am. The paper we’re discussing this month is “Relating Natural Language Aptitude to Individual Differences in Learning Programming Languages” by Chantel Prat et al published in Scientific Reports. [1] Here’s the abstract:

This experiment employed an individual differences approach to test the hypothesis that learning modern programming languages resembles second “natural” language learning in adulthood. Behavioral and neural (resting-state EEG) indices of language aptitude were used along with numeracy and fluid cognitive measures (e.g., fluid reasoning, working memory, inhibitory control) as predictors. Rate of learning, programming accuracy, and post-test declarative knowledge were used as outcome measures in 36 individuals who participated in ten 45-minute Python training sessions. The resulting models explained 50–72% of the variance in learning outcomes, with language aptitude measures explaining significant variance in each outcome even when the other factors competed for variance. Across outcome variables, fluid reasoning and working-memory capacity explained 34% of the variance, followed by language aptitude (17%), resting-state EEG power in beta and low-gamma bands (10%), and numeracy (2%). These results provide a novel framework for understanding programming aptitude, suggesting that the importance of numeracy may be overestimated in modern programming education environments.

The paper describes an experiment which investigates the relationship between learning natural languages and programming languages and draws some interesting conclusions that provide some good discussion points. Does being good at learning natural languages like English make you good at learning programming language like Python? Do linguists make good coders? We’ll be meeting on Zoom, details will be sent to anyone who registers at sigman2.eventbrite.co.uk

References

  1. Prat, C.S., Madhyastha, T.M., Mottarella, M.J. et al. (2020) Relating Natural Language Aptitude to Individual Differences in Learning Programming Languages. Scientific Reports 10, 3817 (2020). DOI:10.1038/s41598-020-60661-8

 

October 27, 2017

Mirror, mirror on the wall, who is the most viewed of them all?

Filed under: Uncategorized — Duncan Hull @ 6:42 am

Wikipedia is mirror that reflects the world around it. Sometimes the reflections are accurate, other times they get distorted. [1] Either way, we can look at the data in Wikipedia to see which reflections are being looked at the most using powerful analytics tools that are part of the platform.

Two weeks ago, as part of Physiology Friday, I gave a talk examining how biographies of scientists are viewed in Wikipedia, using the crude measure of PageViews.

Melissa Highton from the University of Edinburgh also gave a talk about the Edinburgh Seven, changing the way stories are told and their Wikipedian in Residence scheme.

Our convenor, Andy Mabbett (normally found on a Brompton) gave a talk introducing Wikimedia since our reason for being there was to recruit and train new editors of Wikipedia.

Thanks to the Physiological Society for having us and Anisha Tailor for putting the program together.

References

  1. Samoilenko, Anna; Yasseri, Taha (2014). “The distorted mirror of Wikipedia: a quantitative analysis of Wikipedia coverage of academics”. EPJ Data Science. Springer Publishing. 3 (1). arXiv:1310.8508 doi:10.1140/epjds20

 

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