Higher Education Experts' Views on Learning Analytics Policy Recommendations for supporting study success: A German case

1. HIGHER EDUCATION EXPERTS‘ VIEWS ON LEARNING
ANALYTICS POLICY RECOMMENDATIONS FOR
SUPPORTING STUDY SUCCESS: A GERMAN CASE
Project Team:
Prof. Dr. Dirk Ifenthaler, Universität Mannheim (ifenthaler@uni-mannheim.de)
Dr. Jane Yin-Kim Yau, Universität Mannheim (jyau@uni-mannheim.de)

2. (1) Theore8cal Background
• Study dropout rate in Germany: 29 % (Heublein et al., 2017)
• PrognosRcaRon of study success or iden8fica8on of risk students
• Learning AnalyRcs (Ifenthaler, 2015)
• Use sta8cally and dynamically generated data from learners and learning
environments
• Analysis und visualisa8on in real-8me; dashboard
• Goal modeling and op8miza8on of teaching-learning processes and
learning environments
• Lack of policy recommendaRons in German higher educa8on
ins8tu8ons (for implemen8on of LA) -> Goal
• Lack of experts views‘ on LA & policy recommenda8ons in Germany

3. (2) Research Ques8on
Do experts of the German higher
educa8on sector confirm and accept
policy recommenda8ons for the
implementa8on of LA for suppor8ng
study success in?

4. (3) Methodology
BMBF-funded, 1/1/17-31/12/18
Aim: to determine whether the policy recommendaRons are supported by
German higher educaRon experts; how ready the insRtuRons are for the
implementaRon of LA
Interview Study (N = 37)
(1) Learning culture, (2) Study success, (3) Technology acceptance,
(4) Understanding of LA, (5) Current LA projects (if any),
(6) Strategies, policies and guidelines, (7) Time and resources,
and (8) Demographic informa8on

5. (4) Interview Study
• Conducted from 06/2018 to 08/2018
• Collected a list of suitable par8cipants – level of exper8se in
educa8onal technology + professional staff in a German HEI
• Contacted them via email to request par8cipa8on. Ini8ated with a
pilot study of 6 academic staff from our university. Main – Skype.
• Demographic informa8on – age 27 to 60; 10 - female; Areas of
exper8se: Info Management, Business/Maths Educa8on, Educa8onal/
Computer/Data Science, Electronic/Mechanical Engineering,
Psychology, Web Technologies
• Transcrip8on/Data Analysis: qualita8ve content data analysis,
searched for evidence to support/reject our policy recommenda8ons

6. (5) Policy recommenda8ons
Established via systema8c review + references of other recommenda8ons
Policy recommenda2ons
1. Develop educa8on leadership
2. Develop individualized prac8ces
3. Develop assessment prac8ces
4. Develop organiza8onal structures
5. Develop a quality assurance process
6. Develop a capacity building ini8a8ves
7. Develop a learning analy8cs curriculum
8. Adherence to data protec8on
9. Enable country-wide discussions
10. Develop a na8onal ethical board
11. Develop a standardized LA system

7. (6) Results: Learn Culture, Study Success, Technology
Learn culture
• internal/learner-perspec8ve
• external/ins8tu8on-perspec8ve
• combina8on of both
• should be mo8va8ng, engaging, ac8ve learners
• individual, team-based, different teaching methods
• different universi8es provide various forms of
teaching, mentoring, seminars, supervision…

8. (7) Results: Learn Culture, Study Success, Technology
Study success defini8on by par8cipants:
• Broader -Finish studies, find a good job, financial
security
• Narrower – finish one course/degree
• Should not be 8ed to examina8ons but learning and
applying knowledge relevant for lifelong learning
• Studying a subject sui8ng to the passion and
personality of the learner
• Shuold be producers rather than consumers of
knowledge

9. (8) Results: Learn Culture, Study Success, Technology
Technology acceptance – staff and students
• requires trust of technology use (e.g. high in CS dept)
• students willing to use if helpful - studies
• deciding factors: performance, expecta8ons, social
influences, facilita8ng condi8ons
• some students reluctant - did not want staff to know
how olen they were using the system
• must be user-friendly, high-quality user interface
• technology must show the quality of mentoring and
feedback capabili8es to gain trust – transparent/ethical.
• Barrier – data protec8on

10. (9) Results: understanding of LA, barriers
“By implemen+ng a detailed learning analy+cs scheme, one is a6emp+ng to
ask the students if they have any difficul+es rela+ng to different aspects of
their university courses. One might indicate factors that one can make
posi+ve teaching/learning impact on and make it easier for the students to
s+ll learn everything that they are required to learn. To iden+fy factors that
make it more difficult for them to learn materials or to focus on their studies
and try to do something about those factors. So LA is about retaining or
increasing the teaching quality and at the same +me enabling more
students to finish their degree.”
Most – had basic understanding of LA
Barriers in the German context:
• Data protec8on, novelty, unclear/unskilled staff + students

11. (10) Results: readiness to adopt LA
• 6/37 expressed their ins8tu8on were ready
• 30/37 expressed they were mentally ready but required
further resources in order to implement LA (benefits
outweigh the costs)
• 1/37 rejects (mobile) technology completely for
learning – only paper-based materials.
• Many German HEIs do not currently have the resources;
some were unfamiliar with LA but willing to invest in it
as they believe study success can be enhanced.
• Barrier is also the data protec8on which limits the
amount of data that can be collected. Limited data
means limited accurate predic8ons on learning process.

12. (11) Valida8on of policy recommenda8ons
Policy recommenda2ons Requirements for adop2on
1. Develop educa8on leadership Train and support researchers
2. Develop individualized prac8ces Train and support staff
3. Develop assessment prac8ces Train and support staff
4. Develop organiza8onal structures Provide suppor8ve structures (workshop)
5. Develop a quality assurance process Train and support staff
6. Develop capacity building ini8a8ves Train and support staff
7. Develop a learning analy8cs curriculum Train and support staff
8. Adherence to data protec8on Become familiar + navigate guidelines
9. Enable country-wide discussions Train and support staff
10. Develop a na8onal ethical board Train and support staff
11. Develop a standardized LA system Train and support staff

13. (12) Conclusion and contribu8ons
• Aim towards a standardized LA system that can
be integrated into any VLE to provide at-risk
predic8on and personalized preven8on and
interven8on strategies.
• More work on ethical and privacy guidelines
suppor8ng implementa8on at HE ins8tu8ons.
• German HEIs are currently not ready to adopt
LA but guidelines have been provided to ensure
successful implementa8on.

14. Thank you for your anen8on!
ifenthaler@uni-mannheim.de
jyau@mail.uni-mannheim.de