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Presentations morning session 22 January 2018 HEFCE open event “Using data to increase learning gains and teaching excellence”

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With the Teaching Excellence Framework being implemented across England, a lot of higher education institutions have started to ask questions about what it means to be “excellent” in teaching. In particular, with the rich and complex data that all educational institutions gather that could potentially capture learning gains, what do we actually know about our students’ learning journeys? What kinds of data could be used to infer whether our students are actually making affective (e.g., motivation), behavioural (e.g., engagement), and/or cognitive learning gains? Please join us on 22 January 2018 in lovely Milton Keynes at a free OU- and HEFCE-supported event on Using data to increase learning gains and teaching excellence.

10.30-11.00 Welcome and Coffee

11.00-11.30 Lightning presentations by participants, outlining insights about learning gains


1130-1300 Insights from the ABC-Learning Gains project

Dr Jekaterina Rogaten (OU): Reviewing affective, behavioural and cognitive learning gains in higher education of 54 learning gains studies
Prof Bart Rienties & Dr Jekaterina Rogaten (OU): Are assessment scores good proxies of estimating learning gains: a large-scale study amongst humanities and science students
Prof Rhona Sharpe (University of Surrey) & Dr Simon Cross (OU): Insights from 45 qualitative interviews with different learning gain paths of high and low achievers
Dr Ian Scott (Oxford Brookes) & Dr Simon Lygo-Baker (OU): Making sense of learning trajectories: a qualitative perspective

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Presentations morning session 22 January 2018 HEFCE open event “Using data to increase learning gains and teaching excellence”

  1. 1. 22 January 2018 HEFCE open event “Using data to increase learning gains and teaching excellence” https://twitter.com/LearningGains #learninggainsOU https://abclearninggains.com/
  2. 2. “The HEALTH AND SAFETY messages”  No fire drill today   Toilets  Jennie Lee Building  Live-recording, photos and streaming: so be mindful of what you are saying or doing   Contribute to live-tweeting: @learninggains #learninggainsOU
  3. 3. Lightning presentations Carol Calvert Open University Heike Behle Warwick University Ruslan Ramanau Open University David Reeve JISC Liz Bennett University of Huddersfield Paul Hazel QAA David Boud University of Technology Sydney Selena Killick Open University Fiona Cobb University of London Ed Foster Nottingham Trent University
  4. 4. 4 Why not engage early with inexperienced learners- 4% increase in retention to be won and a lot of student satisfaction Ever Reg'd Regi'd at start Reg'd at 25% fee point TMA01 submitted ICMA41 submitted 2016 1026 943 885 795 778 2017 1042 982 934 841 835 0 200 400 600 800 1000 1200 Numbers on M140J- ( data from Institutional dashboard) 2016 2017 16 more students registered for October 2017 than 57 more submitted most recent assessment in 2017/18 - Immerse in just one module in discipline + - Network with peers + - Work with some tutors + - Brush up/ acquire academic skills + - Try out materials at own pace + - Build confidence about software used Students are able to
  5. 5. Assessment and learning gain A dilemma: >If assessment results aren’t an adequate indicator of student learning, what are they supposed to be? >If they do demonstrate learning, then why bother with other measures of learning gain? A way out: >Take seriously the idea that summative assessment should assure course learning outcomes, and work back from that David Boud Centre for Research in Assessment and Digital Learning, Deakin University University of Technology, Sydney HEFCE open event: Using data to increase learning gains and teaching excellence
  6. 6. Dr David Reeve Head of Information Strategy Jisc Here be GDPR dragons
  7. 7. Ed Foster, NTU
  8. 8. Photo by Annie Spratt on Unsplash
  9. 9. TEF Students’ expectations Responsibility Measurement Positive employment outcome More than employment rates Employability skills More than employability skills Employability Framework
  10. 10. Students’ response on learning analytic dashboards SRHE Scoping Award Aims: • To identify which elements of dashboards design were most valued by students; • To identify students’ learning responses to seeing data presented about themselves via a dashboard; • To identify the potential and limitations of using dashboards with undergraduate students; • To identify questions raised by their use for future research in the area. Key findings: • Students want information about their progress; • Even the weaker ones; • Personal component of getting this feedback is huge and varied; • Move from self-regulated learning to seeing feedback as having 3 dimensions (Sutton 2012): – understanding – being – acting.
  11. 11. Comparative perspectives on learning gains  Learning gains is a appealing construct as it helps to move away from focus on desirable learning outcomes (e.g. higher satisfaction rates and better scores) vs. less desirable to create a richer, more nuanced perspective of learning and its benefits;  Comparative perspective would be useful to explore the generalisability of the construct by comparing views of students and teachers across:  Different academic disciplines;  Different types of online and blended courses (e.g. fully online, most using synchronous or asynchronous tools etc.) ;  Different institutions and systems of higher education.
  12. 12. What if the answer was in the Library all along? Selena Killick @SelenaKillick
  13. 13. Presenters this morning Dr Jekaterina Rogaten Open University Dr Simon Cross Open University Dr Ian Scott Oxford Brookes Prof Rhona Sharpe University of Surrey Dr Simon Lygo-Baker University of Surrey
  14. 14. Reviewing affective, behavioural and cognitive learning gains in higher education. Jekaterina Rogaten Bart Rienties Simon Cross Denise Whitelock Rhona Sharpe Simon Lygo-Baker Allison Littlejohn https://twitter.com/LearningGains #learninggainsOU https://abclearninggains.com/
  15. 15. Research Team Dr Bart Rienties Dr Jekaterina Rogaten Dr Simon Cross Dr Ian Scott Prof Ian Kinchin Prof Denise Whitelock Prof Allison Littlejohn Prof Rhona Sharpe Dr Simon Lygo-Baker Dr George Roberts
  16. 16. Learning gains We define Learning Gains as: Growth or change in knowledge, skills, and abilities over time that can be linked to the desired learning outcomes or learning goals of the course Rogaten, J., Rienties, B, Cross, S., Whitelock, D., Sharpe, R., Lygo-Baker, S., Littlejohn, A. (Submitted: 22-3-2017). Reviewing affective, behavioural, and cognitive learning gains in higher education.
  17. 17. How are learning gains measured: a meta-analysis  The concept of learning gain is primarily used to examine the effect of any particular educational ‘intervention’  There is a gradual increase in studies examining learning gains all across the world  All learning gains can be classified into ABC 53% 16% 21% 10% Behaviour-Cognitive Learning Gains Affective-Behaviour-Cognitive Learning Gains Cognitive Learning Gains Rogaten, J., Rienties, B, Cross, S., Whitelock, D., Sharpe, R., Lygo-Baker, S., Littlejohn, A. (Submitted: 22-3-2017). Reviewing affective, behavioural, and cognitive learning gains in higher education. Affective-Cognitive Learning Gains Year Numberofstudies
  18. 18. What type of learning gains are there Affective learning gains: • Attitude • Confidence • Enjoyment • Enthusiasm for a topic • Feeling comfortable with complex ideas • Interest in a topic • Motivation • Satisfaction • Self-efficacy Cognitive learning gains: • Students’ ability to evaluate and create knowledge • Analytical ability • Autonomous cognition • Critical thinking • Ethical thinking • Creative and higher order thinking Discipline specific skills • Knowledge and understanding of the topic, • Oral and written communication • Problem solving • Scientific reasoning • Statistical and research kills/knowledge Behavioural learning gains: •Ability to work independently •Applied conceptual understanding •Effort and engagement •Leadership skills •Team/group working skills •Practical competence •Resource management •Responsibility •Preparation skills •Time management skills
  19. 19. Methodologies Affective LG Behaviour LG Cognitive LG Pre-post objective - - 31 (21,836) <g> ranged 0.26 to 0.39 comparison between control and treatment (e.g., Andrews et al., 2011; Emke et al., 2016; Georgiou & Sharma, 2015) Pre-post subjective 9 (1,561) <g> = 0.39 (e.g., Beck & Blumer, 2012; Cheng, Liang, & Tsai, 2015; Mortensen & Nicholson, 2015) 1 (114) (Stolk & Martello, 2015) 6 (12,942) <g> = 0.34 (e.g., Hatch et al., 2014; Lim, Hosack, & Vogt, 2012; Stolk & Martello, 2015) Cross-sectional subjective 10 (1,772) M=3.89 (e.g., Gok, 2012; Liu et al., 2014; Moorer, 2009) 12 (4,154) M=3.85 (e.g., Casem, 2006; Gill & Mullarkey, 2015; Gok, 2012) 16 (5,082) M=3.7 (Casem, 2006; Douglass et al., 2012; 2012) Note: 47% off all studies assessed more than one type of learning gains and as such, one sample can fall into more than one category and number of samples in the table not strictly add up to the total number of samples examined in this review.
  20. 20. Computation of learning gains  Raw gain  True gain  The normalised gain  Normalised change  ANCOVA on pre-test and post-test scores  ANOVA on raw gain scores.  ANOVA on residuals  Repeated measures ANOVA
  21. 21. Conclusion  There are variety of approaches have been undertaken to date.  Whilst a range of learning gains have been found there is a lack of consistency in approach.  In general pre-post testing is considered the most appropriate strategy for capturing learning gains.  There are variety of methods of computing learning gains and each computational approach has its disadvantages.  There seems to be limited difference between self-reported gain and more objective measures of learning gains.
  22. 22. Are assessment scores good proxies of estimating learning gains: a large-scale study amongst humanities and science students Dr. Jekaterina Rogaten Prof Bart Rienties https://twitter.com/LearningGains https://abclearninggains.com/
  23. 23. ABC project to measure learning gains? Affect Behaviour Cognition Academic performance VLE Satisfaction
  24. 24. What students think they gain? I think I am more openly critical (in the positive sense) Day to day when I have my book I have very different approach from recording my notes for example [in my new job], there will reports and planning to be drawn and I think that this will be an aspect of my job where I can say yes the OU study and discipline I’ve received from the OU has actually contributed to that. I observe things better, work into deeper and work on the whole picture rather than narrow. I think more logically and more ‘why did that happen, why did that happen’, there is more questioning, instead of just to accept things. I am much better at time management, I am much more organised now and planning things in advance. now I say, ‘you know what, I can do that in future’. I feel more confident and I am happier because I am doing something I have always wanted to be doing and something that interests me I think I can go confidently to speak what I learned. But even to a job that isn’t directly related to this subject area. I could talk about my experiences, my time management, team working, computer skills as I feel much more confident, I can say, ‘actually I have done this’. Which was one of the reasons I wanted to a degree.
  25. 25. Do grades matter? How well do your grades represent your progress? probably in the same way that many other people when they look at their own assignment results and exam results …. I feel that I am doing fairly well but I’d always like to improve myself to my results. I get quite upset when I get around 70s … because I am putting so much effort I want my grades to reflect it. They usually go up. But it is Marginal. 5 marks across all the TMAs that’s the variance, it just varies very slightly Even if it is 1-2 marks I say what did I do differently and I go back to tutor to see what did I do differently. What happened, what caused it? Well there are questions with the text books, exercises. So if I get correct answer, I know I am doing fine. When I say correct answer that’s not the end product that’s the whole answer check through it “I suppose you could say… the skills you learn, like group work, presenting and being able to talk to people… I would say the main way that you think about [achievement], it’s just the grade because… that’s what is going on your CV… and affect what job you get. … I’d say the skills you learn as well as becoming an all-rounded person are quite important as well”.
  26. 26. Do students make learning gains, and what is the power of LA to predict learning gains?  Using assessment results for estimating learning gains has number of advantages:  Assessment data readily available  Widely recognized as appropriate measure of learning  Relatively free from self-reported biases  Allows a direct comparison of research finding with the results of other studies Rogaten, J., Rienties, B, Whitelock, D. (2016). Assessing learning gains, TEA Conference, Tallinn, Estonia
  27. 27. Estimating learning trajectories Level 1 Level 2 Level 3 Grade1 Student1 Grade3 Grade1Grade2Grade3Grade1Grade2Grade3Grade2 Student2 Student3 Course1 Course2 Grade1Grade2Grade3 Student4 Grade1Grade2Grade3 Student5 Course3
  28. 28. 1st year STEM students VPC course 51.6% VPC student 9.1% VPC TMA 40.3% Regression S.E. 2-level S.E. 3-level S.E. Intercept B0 70.81 0.21 69.97 0.31 74.52 4.22 Slope B1 1.06 0.08 1.76 0.11 0.58 1.39 Deviance 211024.410 202908.57 194660.91 X2 change 8115.84** 8247.67** ** p<0.01
  29. 29. 1st year STEM students Model 1 S.E. Model 2 S.E. Fixed Part Intercept 74.52 4.22 75.14 4.21 B1 Slope 0.58 1.39 0.58 1.39 B2 Black -6.86** 0.97 B3 Asian -4.67** 0.79 B4 Mixed/Other ethnicity -2.64** 0.86 B5 Unknown ethnicity -1.05 1.02 B6 HE/PG qualification 1.81** 0.36 B7 Lower than A level / No formal qualification -2.08** 0.36 B8 Unknown qualification -1.80* 0.79 B9 Low SES -1.58** 0.42 B10 Unknown/Not Applicable SES 0.94 0.58
  30. 30. How do STEM students compare to Social Science students  Participants  5,791 Science students of whom 58.2% were females and 41.8% were males with average age of M = 29.8, SD = 9.6.  11,909 Social Science students of whom 72% were females and 28% were males with average age of M = 30.6, SD = 9.9  Measures  Tutor Marked Assessments (TMA)  Across 111 modules
  31. 31. Descriptive statistics: Social Science Social Science Science
  32. 32. How do STEM students compare to Social Science students Social Science Science Variance in students’ initial achievements 6% 33% variance in students’ learning gains 19% 26% VPC module 4% 25% VPC student 56% 51% VPC TMAs 40% 22%
  33. 33. Effect of socio-demographic factors Variable Social Science Beta Model1 Beta Model2 Beta Mode 3 Gender 0.64* Unknown -2.4* Other -6.68** Mixed -3.27** Asian -4.66** Black -7.99** HE qualification 0.29 Lower than A levels -3.11** No formal qualification -6.93** PG qualification 2.62** Variance explained in learning gains 0.1% 3% 3.1% Variable Science Beta Model 1 Beta Model 2 Beta Model 3 Gender 0.29 Unknown 1.76 Other -4.09 Mixed -0.59 Asian -7.31** Black -13.07** HE qualification 2.64** Lower than A levels -4.59** No formal qualification -9.48** PG qualification 8.19** Variance explained in learning gains 0.3% 2.2% 3%
  34. 34. Social Science Science
  35. 35. Social Science Science A levels or equivalent HE Qualification Lower than A levels No formal qualification PG qualification
  36. 36.  University 1  1,990 undergraduate students  University 2  1,547 undergraduate students  20 degree programmes within each university  DV – average grade yearly grade University 1 University 2 Year M SD M SD 1 60.65 7.62 63.75 12.66 2 61.31 6.81 65.64 12.74 3 63.32 6.63 64.12 14.02 Can we use this approach to look across different universities?
  37. 37. Data Analysis: Descriptive Plots University 1 University 2
  38. 38. Data Analysis: Model comparison University 1 Regression S.E. 2-levels S.E. 3-levels S.E. Intercept B0 60.252 0.114 60.2 0.16 60.337 0.687 Slope B1 0.429** 0.027 0.422** 0.024 0.365** 0.111 Deviance 74016.17 68227.62 67983.61 X2 change 5788.548** 244.012** University 2 Intercept B0 64.322 0.325 64.225 0.323 63.626 1.419 Slope B1 0.184 0.251 0.22 0.211 -0.131 0.723 Deviance 33145.79 32600.89 32255.87 X2 change 544.898** 345.028** **p<0.001
  39. 39. Variance partitioning University 1 University 2 Variance at Department level 13.1% 22% Variance between students 59.8% 22% Variance within students (between years) 27.1% 56%
  40. 40. Summary of findings  Although both universities overall showed positive gains, substantial differences were present in variance at departmental level.  Aggregate learning gains estimates can result in misleading estimates of students’ learning gains on a discipline or degree level.  Multilevel modelling is a more accurate method in comparison with simple linear models when estimating students’ learning gains.
  41. 41. Possible implications  Support for subject level TEF  Guidance on where to focus interventions and resources  Visualisation could promote data informed learning design decisions Questions still to answer:  Does grade trajectory reflect students’ learning gains?  Can we make a meaningful comparison between universities?  What impact grade trajectory has on students? Do they need to know their own trajectory and how it compares to others?
  42. 42. Dr. Jekaterina Rogaten Prof Bart Rienties https://twitter.com/LearningGains https://abclearninggains.com/
  43. 43. Rhona Sharpe, University of Surrey Simon Cross, The Open University OU Learning Gain Conference / 22 January 2018 Insights from 45 qualitative interviews with different learning gain paths of high and low achievers
  44. 44. PRESENTATION OVERVIEW Using data from interviews undertaken at the Open University UK and Oxford Brookes University, this presentation will probe the relationship between how students understand and interpret the learning gains they experience and the meaning and significance they give to metrics in use for measuring learning gain. ABC Learning Gains Project
  45. 45. 22/01/2018 Learning Gains As TEF develops we will incorporate new common metrics on engagement … and learning gain, once they are sufficiently robust. Government Green Paper Policymakers are now called on to provide evidence to inform questions such as: How do students’ knowledge, skills and work-readiness change and improve through their experience of higher education?... Traditional measures for evaluating student performance remain essential …but they do not provide all of the evidence needed to address these questions. And this evidence is more important than ever… HEFCE Average gain score needs to be interpreted with caution and there remain many outstanding issues that need investigating. Pascarella et al. 2011.
  46. 46. 22/01/2018 1. How do students and alumni understand and interpret the learning gains they experience? 2. How do students understand and interpret measures of learning gain currently in use? 3. To what extent can these two be reconciled? 4. What is the contribution of learning gains to employability/work readiness? Research questions How do students understand and interpret learning gains?
  47. 47. 22/01/2018 Semi-structured interviews with 19 part-time distance learners resident in the UK lasting 30-60 minutes and 12 alumni from full time courses at campus based university. • Student perception of gain and progress • Cognitive, behavioural and affective change • Relationship between grades and progress • Study expectations • Graduate attributes and employability • Work relevancy and readiness Methodology and participants How do students understand and interpret learning gains?
  48. 48. 22/01/2018 First stage Interim analysis based on review of interviewer notes, second listen to interview and coding of interview transcripts. Part-time distance students How do students understand and interpret learning gains? Student 8 Female High Lowest STEM Student 19 Female Low Medium Arts/Soc.Sci Student 5 Female Low Highest Edn/Lang Student 10 Male High Medium Arts/Soc.Sci Student 12 Female Low Lowest STEM Student 18 Female High Highest STEM Attainment grouping as measured by assessment marks Progress grouping as measured by assessment marks Gender Faculty Sources of quotations used in presentation
  49. 49. 22/01/2018 First stage Interim analysis based on review of interviewer notes, second listen to interview and coding of interview transcripts. Full-time campus alumni How do students understand and interpret learning gains? Alumnus 8 Female 1st Upward Business Alumnus 5 Female 2:1 Upward Health & Life Sciences Alumnus 12 Female 2:1 Upward Business Alumnus 7 Female 2:1 Upward Health & Life Sciences Final degree classification Progress trajectoryGender Faculty Sources of quotations used in presentation
  50. 50. 22/01/2018 The ‘Turning point’ How do students understand and interpret learning gains? “  A wake up call  The biggest game changer  Where it all kind of came into place  Self-realisation  Finding myself Did not enjoy first year but ‘when I was part of the social enterprise, I think that was like the biggest game changer, with me feeling at home at university.. when I was part of the social enterprise, I found my niche, I found really good friends, a really good social network.’ (A5)
  51. 51. 22/01/2018 Turning points / Pivot moments How do students understand and interpret learning gains? Formative feedback Critical self- awareness Independence • LEVEL OF STUDY Goal setting Ownership of Learning Learning design and sequence Work-Life Level of study What Where
  52. 52. 22/01/2018 Critical self-awareness How do students understand and interpret learning gains? “And you do look back and see what you did, you analyse it and you try and implement it in the future and that’s exactly what I did in my second semester and sort of saw the same results.” (A12)  Reviewing feedback and successful study strategies (A12)  Recognising my entrepreneurial spirit, and being able to choose modules in the final year (A8)  Finding out where you belong and what you are good at (A5)  Discovering what motivated me (A7) “Realising within myself that I didn’t want to follow the same path as everyone else.” (A8) .
  53. 53. 22/01/2018 Independence How do students understand and interpret learning gains? “I was really able to kind of understand how much I was able to gain and achieve by just working independently. I really cherished my alone time, for me to explore things on my own and discover things.” (A12)  Self-realisation of ability to study independently (A12)  Experience of working independently on placement applied to final year tasks (A8)  Personal development, emotional maturity “I fitted in my boots a lot better” (A5)  Choosing a topic that I was interested in and motivated to ‘eat up’ the whole journal article (A7) “When I came back in my final year that really helped me to understand how I can work more independently and deliver results at the end.” (A8) .
  54. 54. 22/01/2018 Level of study ‘Student 8’ How do students understand and interpret learning gains? [Initially] it was very much like study for study purposes [but] about a year and half into it, my mind-set changed, it was like I enjoy what I’m doing and it’s giving me something tangible. This year was the first year when … it reflected back on my day job. The outcome was a decision to increase study intensity… I [feel] that I have the knowledge, it’s the time that I was missing… I don’t think for me it’s grade itself is… it’s actually the knowledge. [Getting the grade] is what’s needed this year for me not to give up so that I had an opportunity to do the exam and face the next step. Looking back Current module I have decided that I will care about my grades next year and try to get the highest scores I physically can just to see [how well I can do] if I don’t have that additional 60 credits. Looking ahead
  55. 55. 22/01/2018How do students understand and interpret learning gains? There were things in the first two modules that I already knew [but] when I come to this year [then] I would say probably 95% of what I’ve learned … I’ve never heard of. So, I think this has been a real turning point for me. [This year] was my turning point… Your TMA (continuous tutor marked assessment) is not everything. It’s not, it’s supposed to be what you actually physically know yourself inside… I think that’s really important. Looking back Current module Level of study
  56. 56. 22/01/2018 Work-Life Reference (Points) How do students understand and interpret learning gains? The way I think, the way that I possibly act at times, my life feels different now … I can talk confidently to people. It’s just when you learn something you [then] become aware of, for instance, either news, work itself, everyday life; which again kind of changes your perspective and then it allows you to properly build your own confidence because you understand things Confidence Awareness
  57. 57. 22/01/2018 1. Participants often described their learning gains as turning points/pivot moments. These were seen at different times e.g. at Level 2 (OU), returning from placement year (OBU) or during postgraduate study (OBU). 2. Sometimes turning points resulted in ‘improvement’ according to the metrics we use to measure of learning gain. However, sometimes not. e.g. changes in study strategies, grades, choice of modules/topics. 3. Both OU students and OBU alumni frequently perceive and measure gain with reference to outside work and life context. 4. If the impact of a turning point is not always seen in measures (e.g. grades,) how could they be made visible? 5. Should change/gain be conceived as incremental or paradigm changing? a. Can the same scale measure before and after a turning point? b. Is the presence of a turning point itself a measure of gain? Findings and more questions
  58. 58. Making sense of learning trajectories: a qualitative perspective Dr Ian Scott Dr Simon Lygo-Baker
  59. 59. Methods Semi-structured interviews, with students that had completed diaries, 4 students came forward, 1st and 2nd year students, from a variety of subjects. Students responded to adverts.
  60. 60. Alpha Alpha reported change with regard to use of technology, for example, using collaborative documents, recording lectures also becoming more independent as learning
  61. 61. Alpha’s GPA trajectory
  62. 62. Beta
  63. 63. Beta “Because I’m doing architecture, I think more architecturally about things now… When I look at a building or something, I try and see how it’s connected or what’s underneath it, instead of just seeing what other people see”. “Because I’m doing architecture, I think more architecturally about things now… When I look at a building or something, I try and see how it’s connected or what’s underneath it, instead of just seeing what other people see”.
  64. 64. Beta’s trajectory
  65. 65. Gamma However “I think I probably do a lot more outside of Uni but still relating to my topic… reading around things that maybe won’t help in an essay but I just find interesting anyway, which I didn’t do before.” “The difference from school in that you’d be taught something and you would accept it as right and wouldn’t really think to question it. “ She noted, “I think I work at a different pace to everyone else, it’s quite hard because you’re never doing work at the same time. I think that’s why I never really work with people on the course, but I would like to be able to because I think it would be useful.”
  66. 66. Gamma “It’s so different from school and I’d never experienced anything like it before, and how much more independent you are with your work, how much more it is reliant on you. You have to… be a lot more organised and know what you’re doing.” I’ve done a lot more sort of critical thinking, sort like criticising… the way some studies are done and why they might not be as reliable as others. … I don’t just accept everything and then I might… do more research myself if there is something that I don’t quite agree with or I think I’ve read something different before.”
  67. 67. Gamma’s trajectory
  68. 68. Delta “I’m definitely become more mature.” “I just come up with my arguments and then find the books that I need to, and I’ve learned how to get straight to the point that I need to and then summarise all that information and then just write in an essay as quickly as possible”. “I spend a long time on forums every day.” A Non Gain: “it’s a combination of the fact that, firstly, I’m not working with anyone [else] and, secondly, I don’t speak to that many people in the university and the people I do [talk to] are from this country.” to always kind of question everything”. He used this skill more broadly when reading news media and was more sceptical “unless there’s [sic] sources to back it up”. He related this to the proliferation of “fake news”: “it’s definitely something you’ve got to question… and make sure what you’re reading is actually true”.
  69. 69. Delta’sTrajectory
  70. 70. A quick summary ABC model is a useful anvil to elucidate student’s perceptions on their own learning and what they have gained. For these students no evidence of a link between how students articulate what they have gained and their grade trajectories. All of our case studies could describe gains, be able to think critically and study independently were common themes Some of the informants had clearly shown identifiable personal development e.g confidence, ability work in group, deeper understanding of discipline and people, capacity seek and take in more views. Such gains were not articulated by all participants, reinforces how these students engage in unique ways with their learning and the University.
  71. 71. 22 January 2018 HEFCE open event “Using data to increase learning gains and teaching excellence”. https://twitter.com/LearningGains #learninggainsOU https://abclearninggains.com/

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