This study examined learning gains among humanities and science students using assessment scores from a large sample of over 17,000 students. The researchers found that science students showed more variance in initial achievement and learning gains compared to social science students. Socio-demographic factors like gender, ethnicity and prior education explained more variance in learning gains for science students. The study also compared learning gains across universities, finding substantial differences at the departmental level, suggesting that aggregate estimates can be misleading. Multilevel modeling provided a more accurate method for estimating learning gains compared to simple linear models.

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. Study 1 results slides removed

8. 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

9. Descriptive statistics: Social Science
Social Science Science

10. 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%

11. 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%

12. Social Science Science

13. Social Science Science
A levels or equivalent
HE Qualification
Lower than A levels
No formal qualificatio
PG qualification

14.  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?

15. Study 3 results slides removed

16. 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.

17. 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?

18. Dr. Jekaterina Rogaten
Prof Bart Rienties
https://twitter.com/LearningGains
https://abclearninggains.com/