Slides from a presentation by my PhD student Iwona Czaplinski at the 2017 International Science of Learning Conference (UQ, Brisbane, September 2017).

1. Designing for personalised learning:
Insights from a pilot of Intensive Mode of Delivery
(IMD) in a science unit
Iwona Czaplinski i.czaplinski@qut.edu.au
Team members:
Prof Martin Sillence, Prof Stuart Parsons, A/Prof Matthew Phillips, Dr Christine Devine, Dr Melody de Laat
Mr Brett Fyfield, Mr Richard Evans
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2017 International Conference
on Science of Learning

2. Conference theme: Research to Reality
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Promoting learning: From Reality to Research back to Reality
2017 International Conference on
Science of Learning

3. Reality
Background
• A core, third-year unit, offered for the first time in Semester 1 2015.
Approaches to learning and teaching
• Inquiry-based, experiential learning.
• Strong emphasis on practical, hands-on learning.
Delivery mode
• Standard teaching period (13 weeks).
• Weekly lectorials, workshops and laboratory classes.
• A short field trip planned for the end of semester.
3
The unit
2017 International Conference on
Science of Learning

4. Reality
Week 1 Week 10
4
The problem
2017 International Conference on
Science of Learning

5. Reality
Low student engagement
• Empty classes, low uptake of resources, low interest in field trip
Affected staff morale
• Frustration, fatigue, dissatisfaction; cancelled field trip
Additional costs to the university
• Support/tech staff, equipment, spaces/rooms
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Three levels of challenges
2017 International Conference on
Science of Learning

6. Reality
Key issues
• Assessment-driven students lack of attendance and
participation
• Perceived lack of learning flow between the content taught and the
assessment tasks decreasing student satisfaction
Applied strategy
Capitalise on assessment drive, while avoiding strategy of over-
assessment to force attendance.
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Analysis of student feedback
2017 International Conference on
Science of Learning

7. From Reality to Research
Microculture (Roxå & Mårtensson, 2015)
• interdisciplinary team of specialists representing their relevant
disciplines (i.e. animal biology/ education/ multimedia design)
• located within a meso-level of higher education (i.e. unit level)
(Trowler, 2008)
• not necessarily represented on organizational hierarchies (Heinrich,
2017)
Having significant conversations (Roxå & Mårtensson, 2015)
• shared goal
• professional respect
• mutual trust
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The team, or a microculture
2017 International Conference on
Science of Learning

8. From Reality to Research
Flexible/ personalised learning
• probabilistic approach to learning design
• within e-learning environments
• a “world of learning” (Kirschner, Strijbos, & Kreijns, 2004).
Create the environment offering diverse affordances
• educational, social, technological
(Good, 2007; Czaplinski, 2013)
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Science of Learning
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Overarching principles

9. Back to Reality
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Science of Learning
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Specific objectives
Objective Solution
Enhanced engagement • Deliver the unit in intensive mode (in parallel to other units).
• Strengthen inquiry-based, experiential learning.
• Capitalise on students’assessment-driven attitude to use the
assessment (set up at the end of each intensive day) to retain
attendance.
Flexible,
personalised learning
Providing options for personalising online learning experiences
through:
• implementation of One Note Class Notebook (making personal notes,
creating/ deleting pages, etc.).
• option of working on and off-line.
• collaborative learning online (e.g. peer review of electronic posters),
scaffolding provided by peers and staff.
• information provided using multiple ways of representation (videos,
PDFs, Internet sites, etc.).
Learning flow
(online & face-to-face)
• Online, self-testing quizzes and formative in each module.
• Q&A sessions at the beginning of each module.

10. Back to Reality
2017 International Conference on
Science of Learning
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Schedule
2015
2016

11. Reality
Evaluation strategy
• Post-it surveys after each module, analysed within 48 hours
• Teacher classroom observation sheets
• Paper-based survey(summaryof experience) completed in Module 4
• Standard teacher evaluation surveys, managed by the University
Quick and targeted response
• Necessary changes implemented immediately
– Fruit
– Better communication with students during self-study periods
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Science of Learning
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Agile evaluation methodology

12. Reality
Attendance
• Week 1 (lecture) ~65%
• Week 3 99%
• Week 7 98%
• Week 9 99%
• Week 12 98%
Progression
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Science of Learning
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Effectiveness of the approach
0
5
10
15
20
25
30
1 2 3 4 5 6
Numberof
students
L Fl Fl Pass CR DI HD
Module 1 Module 4
Grade distribution

13. Reality
Staff morale
• Satisfaction
• Excitement
• Challenge
• Fatigue
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Science of Learning
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Effectiveness of the approach

14. From Reality to Research
… more effective for my learning than the
standard weekly lectures and practicals (n=80).
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Science of Learning
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Quantitative data
… was more suited to my lifestyle and preferred study
habits than the standard weekly lectures and practicals
(n=80)
6
25 24
36
7
0
5
10
15
20
25
30
35
40
strongly disagree disagree
neutral agree
strongly agree
11
28
19
25
16
0
5
10
15
20
25
30
strongly disagree disagree
neutral agree
strongly agree

15. From Reality to Research
Were the educational affordances perceived and taken up?
“Bad time management, as well as no contact time (lectures), which I
benefit from.”
“I was confident in first two modules, but not so much in the renal +
resp. because didn’t get the time to read the textbook (my fault) as I
didn’t realise how long it would take to prepare.”
“Required better time management to study prior to an intensive day. I
had to do it in the few days beforehand or I’d forget too much. Trickier
than spreading over multiple weeks.”
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Science of Learning
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Qualitative Data (1)

16. From Reality to Research
2017
Additional sessions:schedule,how to use One Note Class
Notebook,time-management
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Science of Learning
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Time-management

17. From Reality to Research
The additional lectorial on time management was helpful for me:
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Science of Learning
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Student feedback
14
31
29
14
9
0
5
10
15
20
25
30
35
strongly disagree disagree neutral agree strongly agree

18. Evaluation
Were the educational affordances perceived and taken up?
“Only had to come in 4 times, I was tired as hell each time, but having 2
out 3 Thursdays off was good. I didn’t use all the resources available to
me though, because I didn’t allow enough time.”
“I underestimated the time I require to properly retain, recall and
understand content.”
“It was good to be able to study more when I have time to sit down and
comment for a few hours rather than keeping up every week. However,
could also lead to procrastinations.”
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Science of Learning
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Qualitative Data (2)

19. From Reality to Research
Preliminary data analysis
• Coding
• Discursive analysis
(Jørgensen and Phillips,
2002).
• Interpretation through the
model of affordance.
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Methodology
Model of affordance, as re-interpreted by Czaplinski
(Czaplinski, 2013, p. 105), based on Good (2007).
2017 International Conference on
Science of Learning

20. Discussion
Some students started becoming more self-regulated and self-directed
learners while some others were not.
This is a crucial, time-consuming, developmental process.
Requires learners to modify their frames of reference, to be able to
identify and to strategically use the affordances within the particular
situational contexts.
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Science of Learning
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Hypothesis

21. Conclusion
The development of students’ self-regulation and self-direction
(metacognitive) learning strategies and self-efficacy (motivational)
strategies are crucial for flexible, personalised learning to be effective.
This promotes agentic, aware, self-critical and reflective attitudes
towards learning, and enables expertise in learning.
Double expertise: content and learning.
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Science of Learning
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Preliminary conclusion

22. Back to Research
It is a common effort of all
stakeholders included
(not only the learner)
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Science of Learning
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Implications
Expert learner

23. Back to Research
Personalised learning
Support the development of expertise in learning by assisting students
to become agentic, aware, self-critical and reflective learners:
– explicitly teach selected learning strategies?
Transformative teaching
Assist academics in becoming aware of/ familiar with learning
processes and support them in transforming their pedagogies:
– better design learning activities; support microcultures.
Customised delivery
Create a variety of flexible options catering for various needs of diverse
audiences:
– diverse modes of delivery, alternative teaching periods, personalised
assessment tasks, assessment on demand.
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Science of Learning
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Components of the model

24. Back to Research
• Which learning activities positively affect self-regulation, self-
direction, and self-efficacy?
• How should we support teaching staff in developing their
pedagogical approaches to enable the stimulation of the above-
mentioned skills?
• How do we assist students adapt to the proposed pedagogy and
encourage them to be receptive to opportunities for developing their
learning skills, consciously selecting and taking them up?
• To what extent is change at the institutional level necessary to
support the proposed pedagogy?
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Science of Learning
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Research underpinned, evidenced-based practice

25. Outputs
External
HERDSA (Higher Education Research and
Development Society of Australasia), Sydney,
Australia, 27-30 June 2017. Czaplinski et al.
What about me? Staff perspectives on the
implementation of Intensive Mode of Delivery in
an undergraduate science program.
ASCILITE (Australasian Society for Computers
in Learning in Tertiary Education), Toowoomba,
4-6 December. Fyfield & Czaplinski.
By design - facing the academic challenges of
implementing technology enhanced learning in
higher education on example of a third- year
biology unit.
QUT Internal
• Final Report
• Online case
study (staff
& students)
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Science of Learning
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Work in progress