Are assessment scores good proxies of estimating learning gains: a large-scale study amongst humanities and science students
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Are assessment scores good proxies of estimating learning gains: a large-scale study amongst humanities and science students
- 1. 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/
- 2. ABC project to measure learning gains? Affect Behaviour Cognition Academic performance VLE Satisfaction
- 3. 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.
- 4. 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”.
- 5. 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
- 6. Estimating learning trajectories Level 1 Level 2 Level 3 Grade1 Student1 Grade3 Grade1Grade2Grade3Grade1Grade2Grade3Grade2 Student2 Student3 Course1 Course2 Grade1Grade2Grade3 Student4 Grade1Grade2Grade3 Student5 Course3
- 7. 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
- 8. 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
- 9. 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
- 10. Descriptive statistics: Social Science Social Science Science
- 11. 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%
- 12. 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%
- 14. Social Science Science A levels or equivalent HE Qualification Lower than A levels No formal qualification PG qualification
- 15. 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?
- 16. Data Analysis: Descriptive Plots University 1 University 2
- 17. 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
- 18. 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%
- 19. 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.
- 20. 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?
- 21. Dr. Jekaterina Rogaten Prof Bart Rienties https://twitter.com/LearningGains https://abclearninggains.com/
Hinweis der Redaktion
- Assessment = already collected metrics as opposed to new measures
- Level 1 – Grade: repeated measures on students and tell us about students learning trajectory Level 2 – student: between students variations Level 3 – Course: between course variation
- 10 - modules
- 10 - modules
- Student-level intercept-slope correlation - Students with low initial achievements show high learning gains that students with initial high achievements. Module level intercept-slope correlation Students in modules with the low initial achievements show higher learning gains than students in courses with the high initial achievements VPC – is variance partition coefficient. VPC module shows variance that can be attributed to the differences between modules. VPC student shows the mount of variance that can be attributed to the differences between students, and VPC TMA shows variance within each student across assessments. In this last one we can see that it is almost double in Social Science hence further supports that assessments play important role.
- Gender: male-female learning gains gap (male reference group) Other-white gain gap Mixed-White gain gap Asian-White gain gap Black-white gain gap HE qualification - A levels gain gap lower that A levels - A levels gain gap No formal qualification – A levels gain gap PG qualification - A levels gain gap
- On average students showed improvement in standardised grades, although this was only significant for University 1
- University 1 variance was mainly nested between students University 2 had more variance at the departmental level and within students The negative learning gains seen in some students in University 2 does not imply that these students are losing knowledge or ability per se. However it highlights the complexity of factors that have to be taken into account when using students’ academic performance as a proxy for learning gains, such as ‘assessment difficulty’ and ‘learning design’ (Rienties & Toetenel, 2016). We need to understand these if we are to use academic performance as a proxy for learning gains within Higher Education.
- We set out to see if multilevel modelling of assessment data could be a valid and useful proxy for learning gain Aggregate learning gains estimates can result in misleading estimates of students’ learning gains on a discipline or degree level. (or show no gain hiding a more complex picture – University 2) Multilevel modelling is a more accurate method in comparison with simple linear models when estimating students’ learning gains. The simple models are not able to detect differences between modules when looking at the department and degree level performance, whereas multilevel modelling can.
- The HEFCE funded programme of which this project is a part wants to devise a sector wide APPROACH (not method) and characterisation of learning gain, and explore possible proxies. The contribution of the ABC project is that if we are to use LG research for optimising learning (LAK definition) then we need to use a methodology that allows observation of differences in patterns of gain between departments, courses or modules. We have seen that there are subject level differences and so our research supports the introduction of a subject level TEF. Ideally, we’d like to see LG not just used to judge institutions but integrated into curriculum design. Designing for learning is becoming more learner centred – moving from ’what am I going to teach?’ to ‘How are students going to learn?’. A LG methodology such as this could provide data to support course teams to make evidence based decisions (complementing student satisfaction data). For example, do similar modules with different types of learning activities produce different LG trajectories? We know from the field of Learning Design that visualisation is important for course team making decisions, the multi level modelling approach provide data visualisation. Some questions still to answer….