Building maker mindsets through learning analytics

Constructionism is making a comeback, and maker-centered learning is experiencing an emerging role in education (Halverson & Sheridan, 2014). Learning through making, however, is a switch for our instructionist-led, standardized-test-affected youth. Constructionism (Papert, 1993) is the core framework underlying maker ed curricula, and constructionist learning environments are full of ill-defined, ill-structured problems — a scary prospect for many students.

Design process to the rescue

It is not enough to understand one must “build in order to think” (Brown, 2009). However, relying on processes that can be found in design thinking and engineering design can help! They have a knack for making ill-defined problems manageable. The design thinking process has a typical series of iterative phases to it: Discover – Define – Ideate – Prototype – Test. Together these offer an opportunity to build a variety of competencies collectively known as the maker mindset. Maker-centered learning cultivates confidence, creative problem-solving, and collaboration skills — all critical for today’s workplace.

Learning analytics assist

While learning analytics started out focused on online courses and tutoring systems, multimodal learning analytics has expanded our ability to understand more complex and open-ended learning experiences. This has been done by adding video and audio data to the mix (Berland, Baker, & Blikstein, 2014; Blickstein & Worsley, 2016). There are many other data sources that could contribute insights, even and perhaps especially, by the students themselves. What if there was an app that enabled students to become more metacognitive about their budding maker competencies? This app could be accessed via mobile or smartwatch and could capture a number of data points involved in the design thinking process. Students could use the default settings which contained the typical design thinking phases or could edit the app to customize it to capture a number of other competencies. For example: Goal setting, asking questions, planning, prioritizing, monitoring, collaborating, asking for help, giving feedback, refining, and reflecting could all be captured at a 20,000-foot level, or in exquisite detail. The possibilities are endless!

The journey map below begins to get at critical aspects of the lesson design by proactively considering emotions and barriers to success, lesson planning, and learning analytics design to capture learning via a few of the Beyond Rubrics’ Maker Elements (i.e., skills and dispositions), in this case, social scaffolding and productive risk-taking.

References

Berland, M., Baker, R. S., & Blikstein, P. (2014). Educational data mining and learning analytics: Applications to constructionist research. Technology, Knowledge and Learning, 19(1-2), 205-220.

Beyond Rubrics. (n.d.). Retrieved October 29, 2019, from https://makered.org/beyondrubrics

Blikstein, P., & Worsley, M. (2016). Multimodal Learning Analytics: a methodological framework for research in constructivist learning. Journal of Learning Analytics, 3(2), 220-238.

Brown, T. (2009, September 30). Tim Brown urges designers to think big. Retrieved January 14, 2018, from https://youtu.be/UAinLaT42xY

Halverson, E. R., & Sheridan, K. (2014). The maker movement in education. Harvard educational review, 84(4), 495-504.

Ochoa, X., & Worsley, M. (2016). Editorial: Augmenting Learning Analytics with Multimodal Sensory Data. Journal of Learning Analytics, 3(2), 213-219. https://doi.org/10.18608/jla.2016.32.10

Papert, S. (1993). The children’s machine: Rethinking school in the age of the computer. New York, NY: BasicBooks. (Chapter: A word for learning, p. 82-105)