Learning For Use – Earthquakes

• Imagine how LfU principles might be applied to a topic you teach. Now switch out the My World technology. What other domain specific (and non-domain specific) software might help you achieve these principles while teaching this topic? By domain-specific, we mean software designed for STEM education, and by non-domain specific, we mean software or other forms of technology that could be used generally in multiple domains (eg. Wikis). Other GIS software can be selected for the switch.

Learning for Use (LFU) is a constructive teaching pedagogy that emphasize on constructing and refining relationships between abstract concepts. Distinct from other learning approaches, LFU includes ways in which people can support social emotional states when learning (i.e. motivation). This learning model strives to decrease misconceptions by refining conceptual relationships. Another definitive feature of LFU is that learners must apply their knowledge. More specifically, “learning how to use conceptual knowledge must be part of the learning process, if the knowledge is to be useful.” (Edelson, 2001, p.357) Uniquely, LFU theorists suggests that although this model is relatively linear, it also synergistic in a sense that as students work through their inquiries, they are more motivated to learn. Their reflection also promotes opportunities to expose learning gaps, hence, motivating them to learn.

 

Design

Instead of My World, this design employs a collection of educational tools to support understanding of earthquake’s epicenter and magnitude. Designed for students grade 6 or higher, learners work through labs to understand how to use data from seismograms to determine the location of the epicenter of an earthquake.

Motivating learners

Edelson (2001) supports that students have to become aware of their own learning gaps and should be motivated fill in these knowledge gaps. Here, it activates students’ prior knowledge of earthquakes and exposes misconceptions.

Students are given seismograms to observe and consider. Students design a map as a response to the following scenario: If an earthquake magnitude of 5.0 happened in downtown Vancouver, locate safe areas for emergency responders build an earthquake refuge. Using the earthquake simulator, compare student created maps and actual map. Students redraw new locations for possible areas of safe refuge.

Knowledge Construction

Edelson (2001) claims that an important part of this stage is to allow students to investigate and observe information by using simulations to further decipher the relationships between variables.

In pairs, watch through Virtual Reality Earthquake Simulation (i.e. Pai-away) to develop basic knowledge about earthquakes. Learners also work through a Gizmos lab (i.e. Earthquake 1) to supplement their understanding about seismographs. Students work through the activities guideline to learn strategies to study about the relationship between incoming signals, time difference and distance. Engage in whole class discussion about factors that contributes to an earthquake and signs about the distance from the epicenter.

Knowledge Refinement

Reflection and application are two key features of this part of the learning (Edelson, 2001). Students have to critically analyze information and use observations to support their claim about the relationship between concepts.

Students work through an additional Gizmos lab (i.e. Earthquake 2) to learn about using seismographs to determine the epicenter. Students screenshot their findings and publically share strategies of finding the epicenter of the earthquake.

Knowledge Application

In order to make conceptual relationships between variables more memorable, students has to demonstrate that they can apply their knowledge.

In a small group design data for a hypothetic earthquake and provide graphs and corresponding map to show the epicenter of an earthquake and locations of possible earthquake refuges.

LFU & Big Data

Interestingly, there is a connection between LFU and big data. In essence, My World and other GIS software are databases with inquiry tools. Notably in WISE and LFU, students are asked to analyze information available in a database. They conduct experiments to collect and analyze data. Research in big data inquiring about drug discovery and databases suggests that these tools facilitates collaboration and allows users to use one simple interface to generate hypotheses and possible novel solutions (Ekins, Clark, Swamidass, Litterman & Williams, 2014). Hence, using inquiry methods, these learning spaces allow students to collaboratively learn about a topic.

The Future of LFU

Using Jonassen, Carr & Yueh’s (1998) idea of technology as tools to share cognitive load, imagine the future of LFU. With applications like Science Journal, with a phone’s sensors, users are able to easily collect local data for experiments. This provides hopeful outlook on independent experimentation. Students are no longer reliant on access to collaborative databases. Rather, they can easily and systematically peruse scientific inquiry by using technological tools to generate data, document and annotate findings.

Discussion Directions

Consider the use of Pai-Away VR simulation in this design. How can VR support LFU?

LFU is designed to help learners to become a data analyst. Agree or disagree. Why?

Reference

Edelson, D.C. (2001). Learning-for-use: A framework for the design of technology-supported inquiry activities. Journal of Research in Science Teaching,38(3), 355-385.

Ekins, S., Clark, A. M., Swamidass, S. J., Litterman, N., & Williams, A. J. (2014). Bigger data, collaborative tools and the future of predictive drug discovery. Journal of computer-aided molecular design, 28(10), 997-1008.

Gizmos Labs https://www.explorelearning.com/  

Jonassen, D.H., Carr, C. and Yueh, H.P. (1998) Computers as mind tools for engaging learners in critical thinking. TechTrends, 43, 24-32. http://dx.doi.org/10.1007/BF02818172