The pulse of learning analytics understandings and expectations from the stakeholders

While there is currently much buzz about the new field of learning analytics [19] and the potential it holds for benefiting teaching and learning, the impression one currently gets is that there is also much uncertainty and hesitation, even extending to scepticism. A clear common understanding and vision for the domain has not yet formed among the educator and research community. To investigate this situation, we distributed a stakeholder survey in September 2011 to an international audience from different sectors of education. The findings provide some further insights into the current level of understanding and expectations toward learning analytics among stakeholders. The survey was scaffolded by a conceptual framework on learning analytics that was developed based on a recent literature review. It divides the domain of learning analytics into six critical dimensions. The preliminary survey among 156 educational practitioners and researchers mostly from the higher education sector reveals substantial uncertainties in learning analytics. In this article, we first briefly introduce the learning analytics framework and its six domains that formed the backbone structure to our survey. Afterwards, we describe the method and key results of the learning analytics questionnaire and draw further conclusions for the field in research and practice. The article finishes with plans for future research on the questionnaire and the publication of both data and the questions for others to utilize.

[1]  César Hervás-Martínez,et al.  Data Mining Algorithms to Classify Students , 2008, EDM.

[2]  S. Dawson,et al.  Harnessing ICT potential: The adoption and analysis of ICT systems for enhancing the student learning experience , 2010 .

[3]  Thierry Chanier,et al.  How Social Network Analysis can help to Measure Cohesion in Collaborative Distance-Learning , 2003, CSCL.

[4]  Serge Gutwirth,et al.  Profiling the European Citizen, Cross-Disciplinary Perspectives , 2008 .

[5]  Riccardo Mazza,et al.  Exploring Usage Analysis in Learning Systems: Gaining Insights From Visualisations , 2005 .

[6]  Erik Duval,et al.  Dataset-driven research for improving recommender systems for learning , 2011, LAK.

[7]  Louis C. Pugliese,et al.  Action Analytics: Measuring and Improving Performance that Matters in Higher Education. , 2008 .

[8]  Erik Duval,et al.  Visualizing Activities for Self-reflection and Awareness , 2010, ICWL.

[9]  Yves Punie,et al.  Mapping Major Changes to Education and Training in 2025 , 2010 .

[10]  Wolfgang Reinhardt,et al.  AWESOME: A widget-based dashboard for awareness-support in Research Networks , 2011 .

[11]  Erik Duval,et al.  Dataset-Driven Research to Support Learning and Knowledge Analytics , 2012, J. Educ. Technol. Soc..

[12]  Mireille Hildebrandt,et al.  Privacy and Identity , 2006 .

[13]  Ryan Shaun Joazeiro de Baker,et al.  PSLC DataShop: A Data Analysis Service for the Learning Science Community , 2010, Intelligent Tutoring Systems.

[14]  Will N. Browne,et al.  The Role of Algorithms in Profiling , 2008, Profiling the European Citizen.

[15]  P. Winne,et al.  Feedback and Self-Regulated Learning: A Theoretical Synthesis , 1995 .

[16]  Rebecca Ferguson,et al.  Social Learning Analytics , 2012, J. Educ. Technol. Soc..

[17]  Erik Duval,et al.  User Context and Personalized Learning: a Federation of Contextualized Attention Metadata , 2010, J. Univers. Comput. Sci..

[18]  Ramón Fabregat,et al.  Activity-Based Learner-Models for Learner Monitoring and Recommendations in Moodle , 2011, EC-TEL.

[19]  Erik Duval,et al.  Recommender Systems for Technology Enhanced Learning ( RecSysTEL 2010 ) Issues and Considerations regarding Sharable Data Sets for Recommender Systems in Technology Enhanced Learning , 2010 .