The ABC learning gains project seeks to address this issue by adopting a framework that allows an examination of three critical factors (affective, behavioural and cognitive) in a suitable and scalable way, to provide broadly-based and accurate information about students’ learning gains. The ABC model of learning gains will be tested through a longitudinal mixed-method study of learning gains by applying an ABC model. This model will be applied across three diverse institutions – The Open University (distance learning), Oxford Brookes University (campus based learning with large portfolio of vocational training), and University of Surrey (campus based learning and around half of students doing placements during their degree) - using principles of learning analytics. This three year longitudinal study builds on previous longitudinal design studies (Calvert, 2014; Richardson, 2012; Rienties & Nolan, 2014). The study will consist of two phases. The goal of Phase 1 is to compare the relative performance of the students’ learning gains using secondary data analysis of pre-existing ABC data for academic years 2013-2015. The goal of Phase 2 is to understand the complexities of learning gains by conducting a follow-up mixed-method analysis of new ABC quantitative and qualitative data for academic years 2015-2017. As such, the ABC learning gains project will scrutinise three forms of data allowing triangulation and considering different dimensions in learning gain. This study will run across three different institutions to access the suitability and scalability of the ABC learning gains in the HE sector in England http://www.open.ac.uk/iet/main/node/1211

1. Case Study: A Longitudinal Mixed-Method
Study of Learning Gain - Applying
Affective-Behaviour-Cognition Framework
at Three Institutions
11-05-2016
@drBartRienties & Dr Jekaterina Rogaten
@learninggains
https://twitter.com/LearningGains

2. ABC learning gains team
Dr Bart Rienties Dr Jekaterina Rogaten Prof Denise Whitelock
Dr Simon Cross Prof Allison Littlejohn
Prof Rhona Sharpe (OB) Dr Ian Scott (OB)
Prof Ian Kinchin (US) Prof Steven
Warburton (US)
Dr Simon Lygo-Baker (US)

3. ABC approach

4. Quantitative
Qualitative
Academic
performance
VLE
Satisfaction
Interviews
Diary
Module
design
Socio-
Demographic
Structure of the ABC project

5. What does the literature say about
Learning gain?
• The concept of learning gain is primarily
used to examine the effect of any particular
educational ‘intervention’
– Web of Science core collection and ERIC
– The time frame of the search 2000 -
2016.
– In total 231 studies were identified of
which 73 studies were selected.
– All learning gains can be classified into
ABC
– Meta-analytic literature review to be
submitted to the Educational Research
Review in June 2016.

6. Example Arts AA100
• The arts past and present
– 30 week, 60 credit, Level 1 module
– Introduces to university-level study in the arts across a range of subject areas - art history, classical studies, English, history,
philosophy, music and religious studies.
• Assignment 01 (10%)
• Assignment 02 (10%);
• Assignment 03 (15%);
• Assignment 04 (20%);
• Assignment 05 (15%);
• Assignment 06 (20%);
• Assignment 07 (10%)
– 25% - BA (Honours) History, 19% - BA (Honours) Humanities, 16.2% - BA (Honours) English Language and Literature, 12.5% - BA
(Honours) English Literature
– Design: 75% Assimilative, 20% Assessment, 4% Productive

7. Participants
3262 students from years 2013/14 and 2014/15
2147 Females (age M=33.2, SD=12.8) & 1115 Males (age M=35.5, SD=14.2)
Motivational orientation:
36.8% - employment and personal development, 13.6% - employment, 23.9% - personal development
Occupational status:
43.9% in full-time employment, 24.2% part-time employment, 13.4% not in paid work, 10.9% unemployed
93.2% White; 19% reported study disability
50% have A level or equivalent, 30.9% have below A level, 14.3% have HE qualification
81.4% were only taking AA100 module

8. Example Arts AA100

9. Example Arts AA100

10. Example Arts AA100

11. Multilevel Modelling
Level 1
Level 2
Level 3
ABC1
Student1
ABC3 ABC1 ABC2 ABC3 ABC1 ABC2 ABC3ABC2
Student2 Student3
Course1 Course2
ABC1 ABC2 ABC3
Student4
ABC1 ABC2 ABC3
Student5
Course3
Learning
Design

15. Assimilative Finding and
handling
information
Communicati
on
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

16. Method – data sets
• Combination of four different data sets:
• learning design data (189 modules mapped, 276
module implementations included)
• student feedback data (140)
• VLE data (141 modules)
• Academic Performance (151)
• Data sets merged and cleaned
• 111,256 students undertook these modules

17. Constructivist
Learning Design
Assessment
Learning Design
Productive
Learning Design
Socio-construct.
Learning Design
VLE Engagement
Student
Satisfaction
Student
retention
Learning Design
151 modules
Week 1 Week 2 Week30
+
Disciplines Levels
Size module
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

18. Cluster 1 Constructive (n=73)

19. Cluster 2 Assessment (n=10)

20. Cluster 3 Productive (n=38)

21. Cluster 4 Social Constructivist (n=20)

22. Week Assim Find Com. Prod Exp Inter Asses Total
-2 -.03 .02 -.02 -.09 .20* -.03 .01 .35**
-1 -.17* .14 .14 -.01 .30** -.02 -.05 .38**
0 -.21* .14 .37** -.07 .13 .08 .02 .48**
1 -.26** .25** .47** -.02 .28** .01 -.1 .48**
2 -.33** .41** .59** -.02 .25** .05 -.13 .42**
3 -.30** .33** .53** -.02 .34** .02 -.15 .51**
4 -.27** .41** .49** -.01 .23** -.02 -.15 .35**
5 -.31** .46** .52** .05 .16 -.05 -.13 .28**
6 -.27** .44** .47** -.04 .18* -.09 -.08 .28**
7 -.30** .41** .49** -.02 .22** -.05 -.08 .33**
8 -.25** .33** .42** -.06 .29** -.02 -.1 .32**
9 -.28** .34** .44** -.01 .32** .01 -.14 .36**
10 -.34** .35** .53** .06 .27** .00 -.13 .35**

23. Model 1 Model 2 Model 3
Level0 -.279** -.291** -.116
Level1 -.341* -.352* -.067
Level2 .221* .229* .275**
Level3 .128 .130 .139
Year of implementation .048 .049 .090
Faculty 1 -.205* -.211* -.196*
Faculty 2 -.022 -.020 -.228**
Faculty 3 -.206* -.210* -.308**
Faculty other .216 .214 .024
Size of module .210* .209* .242**
Learner satisfaction (SEAM) -.040 .103
Finding information .147
Communication .393**
Productive .135
Experiential .353**
Interactive -.081
Assessment .076
R-sq adj 18% 18% 40%
n = 140, * p < .05, ** p < .01
Table 3 Regression model of LMS engagement predicted by institutional, satisfaction and learning design analytics
• Level of study predict VLE
engagement
• Faculties have different VLE
engagement
• Learning design
(communication & experiential)
predict VLE engagement (with
22% unique variance
explained)

24. Model 1 Model 2 Model 3
Level0 .284** .304** .351**
Level1 .259 .243 .265
Level2 -.211 -.197 -.212
Level3 -.035 -.029 -.018
Year of
implementation .028 -.071 -.059
Faculty 1 .149 .188 .213*
Faculty 2 -.039 .029 .045
Faculty 3 .090 .188 .236*
Faculty other .046 .077 .051
Size of module .016 -.049 -.071
Finding information -.270** -.294**
Communication .005 .050
Productive -.243** -.274**
Experiential -.111 -.105
Interactive .173* .221*
Assessment -.208* -.221*
LMS engagement .117
R-sq adj 20% 30% 31%
n = 150 (Model 1-2), 140 (Model 3), * p < .05, ** p < .01
Table 4 Regression model of learner satisfaction predicted by institutional and learning design analytics
• Level of study predict
satisfaction
• Learning design (finding info,
productive, assessment)
negatively predict satisfaction
• Interactive learning design
positively predicts satisfaction
• VLE engagement and
satisfaction unrelated

25. Model 1 Model 2 Model 3
Level0 -.142 -.147 .005
Level1 -.227 -.236 .017
Level2 -.134 -.170 -.004
Level3 .059 -.059 .215
Year of implementation -.191** -.152* -.151*
Faculty 1 .355** .374** .360**
Faculty 2 -.033 -.032 -.189*
Faculty 3 .095 .113 .069
Faculty other .129 .156 .034
Size of module -.298** -.285** -.239**
Learner satisfaction (SEAM) -.082 -.058
LMS Engagement -.070 -.190*
Finding information -.154
Communication .500**
Productive .133
Experiential .008
Interactive -.049
Assessment .063
R-sq adj 30% 30% 36%
n = 150 (Model 1-2), 140 (Model 3), * p < .05, ** p < .01
Table 5 Regression model of learning performance predicted by institutional, satisfaction and learning design analytics
• Size of module and discipline
predict completion
• Satisfaction unrelated to
completion
• Learning design
(communication) predicts
completion

26. 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

27. Next steps
• Ethics
• Data collection
Planned:
• TEA conference (October)
• Modelling cognitive learning gains using
learning analytics
• SRHE conference (December)
• Presentation of the 4-level model for
OU+OB+US students samples
• OEB conference (December)
• Modelling learning gains using learning
analytics
Confirmed:
• Teaching Excellence Framework: Raising Quality Standards (14th July)
• Presentation on Learning Analytics for measuring learning gains
• EARLI conference (17-19th August)
• Presentation of the 3-level model for OU students sample
• HEIR 2016 Conference (7-8 September)
• Presentation on ABC multi-level model OU student sample
• Data storage
• Data analysis
Dissemination

28. Case Study: A Longitudinal Mixed-Method
Study of Learning Gain - Applying
Affective-Behaviour-Cognition Framework
at Three Institutions
11-05-2016
@drBartRienties & Dr Jekaterina Rogaten
@learninggains
https://twitter.com/LearningGains