Advanced Community Information Systems Group Chair for Information Systems and Databases Prof. Dr. M. Jarke

Scaling up Technologies for Informal Learning in SME Clusters

Responsive Open Learning Environments

Business perfOrmance imprOvement through individual employee Skills Training

Self-Regulated Learning Nudges

Miloš Kravčík, Ralf Klamma

Miloš Kravčík, Ralf Klamma (2014). Self-Regulated Learning Nudges. International Workshop on Decision Making and Recommender Systems. Bolzano, Italy, September 18-19, 2014.

Motivation Self-Regulated Learning (SRL)

includes one’s control over own cognitive and

meta-cognitive activities

SRL depends on personal decisions

SRL can be influenced by recommendations

Problem Changing preferences of humans

Long-term aims vs. instant gratification

Cognitive biases – the power of context

Balance between

Freedom of choice – motivation

Guidance – effectiveness

Solution Integration of

Personal Learning Environments – flexibility

Recommender Systems – providing nudges (alert

behaviour in a predictable way, easy to avoid)

Learning Analytics – supporting reflection

Evaluation Outcomes: behavioural changes have limits and require long term research

Issues

short term: usability, workload, learning outcome

mid term: adoption of SRL – qualitative evaluation

long term: adoption of SRL – quantitative evaluation

Technology ROLE Project: Responsive Open Learning Environments

http://www.role-project.eu/

ROLE Sandbox

http://role-sandbox.eu/

ROLE Widget Store

http://www.role-widgetstore.eu/

ROLE Software Development Kit

https://github.com/rwth-acis/ROLE-SDK

ROLE: SRL Process Model

ROLE: PLE with Nudges for SRL

Nussbaumer, A., Kravcik, M., Renzel, D., Klamma, R., Berthold, M., & Albert, D. (2014). A Framework for Facilitating Self-Regulation in Responsive Open Learning Environments. arXiv preprint arXiv:1407.5891.

Nussbaumer, A., Dahrendorf, D., Schmitz, H. C., Kravčík, M., Berthold, M., & Albert, D. (2014). Recommender and guidance strategies for creating personal mashup learning environments. Computer Science and Information Systems, 1(11).

Kravčík, M., & Klamma, R. (2011). On psychological aspects of learning environments design. In Towards Ubiquitous Learning (EC-TEL).

Cognitive Biases: Framing Effect

Source: D. Renzel Source: M. Berthold

Identification of Learning Phases

Krenge, J., Petrushyna, Z., Kravcik, M., & Klamma, R. (2011). Identification of learning goals in forum-based communities. 11th IEEE International Conference on Advanced Learning Technologies (ICALT).

Nudges

Learning Analytics

Weeks

40% of „footprints“ align with SRL process model – potential for improvement

Use

rs

Phase 1 & 2: low sentiment, questioner, lot of intents

Phase 3: increasing sentiment, conversationalist

Phase 4: high sentiment, answering person

URCH Discussion forums: preparation for tests in English language

Social Network Analysis: patterns of behavior from user relations

Intent Analysis: classification of goals from content

Sentiment Analysis: detecting subjective information from content

Workplace Learning Projects Learning Layers

http://learning-layers.eu/

BOOST

http://www.boost-project.eu/

Kravcik, M., & Klamma, R. (2012). Supporting Self-Regulation by Personal Learning Environments. 12th IEEE International Conference on Advanced Learning Technologies (ICALT).