Analytics dashboards, where data about learning is presented in a visual form, are a central element in learning analytics. It is suggested that dashboards can enhance self-regulation of learning process and design of learning experiences, yet more research evidence in real-life settings is needed (Klerkx, Verbert & Duval 2013). In a meta-analysis of research on learning analytics dashboards, Jivet, Scheffel, Drachsler and Specht (2017) noticed that most dashboard designs make learners aware of their learning process but fail to use this awareness to improve cognitive, behavioral or emotional competencies. They suggest that “different tools should complement dashboards and be seamlessly integrated in the learning environment and the instructional design” (Jivet et al. 2017).

One widely used tool category to support learning process and evaluation of learning are concept maps (Novak 2008), mind maps (Buzan 1996) and other node-link type knowledge mapping tools. These different formats can be used in complementary ways to enhance motivation, attention, understanding and recall (Eppler 2006). They can also be used to visualize curriculum content and structure (Willcox & Huang 2017). Using methods of social network analysis and graph theory, data can be quantified (McLinden 2013) and then used in learning analytics activities.

In this workshop we introduce a method, where a learning analytics dashboard is co-created by teachers and students using concept maps. Each course’s learning content is described with 5-15 key concepts picked by the teacher of the course. These concepts, arranged according to the curriculum structure, form a concept map template that is given to students. During the course of their studies, students self-assess their knowledge level and emotional reactions towards these concepts with icons in the concept map. Students are also encouraged to find cross-links, i.e. relations, between concepts in different courses and name those connections.

Concept map data is regularly quantified to allow further analysis. Aggregated data from multiple concept maps allows us to build a learning analytics dashboard that focuses on knowledge mastery and allows students, teachers and administration to gain insight about how knowledge building happens on course, degree and institution level. The data can then be used in instructional design and curriculum design.

Our work has started from a need to create a dynamic feedback system that would result in weekly feedback rounds between students and teachers. Further development of this method, technology and analysis leads towards more adaptive learning solutions, linking this theme to Grand Challenges for Engineering, i.e. to Advance Personalized Learning (Chase 2008).



Buzan, T., & Buzan, B. (1996). The Mind Map Book: How to Use Radiant Thinking to Maximize Your Brain’s Untapped Potential.

Chase, V. (edit.) (2008). Grand Challengers for Engineering. (pp. 45-47) National Academy of Sciences, on behalf of the National Academy of Engineering. Available: 

Eppler, M. J. (2006). A comparison between concept maps, mind maps, conceptual diagrams, and visual metaphors as complementary tools for knowledge construction and sharing. Information visualization, 5(3), 202-210.

Plattner, H. (2010). An Introduction to Design Thinking. Available:

Jivet, I., Scheffel, M., Drachsler, H., & Specht, M. (2017). Awareness is not enough: pitfalls of learning analytics dashboards in the educational practice. In European Conference on Technology Enhanced Learning (pp. 82-96). Springer, Cham.

Klerkx, J., Verbert, K., & Duval, E. (2014). Enhancing learning with visualization techniques. In Handbook of research on educational communications and technology (pp. 791-807). Springer New York.

McLinden, D. (2013). Concept maps as network data: analysis of a concept map using the methods of social network analysis. Evaluation and program planning, 36(1), 40-48.

Novak, J. D., & Cañas, A. J. (2008). The theory underlying concept maps and how to construct and use them.

Willcox, K. E., & Huang, L. (2017). Network models for mapping educational data. Available: