With learning engineering at the core of the Tools Competition, a critical component of the Phase II proposal for Growth and Transform competitors is a learning engineering plan to support research at scale.
This provides an indication that there’s interest in the broader field for the data and insights your tool is designed to capture—and this data can support researchers to answer critical questions about learning.
Learning engineering is a partnership between technologists, researchers, and educators to use big data in order to: better understand the learning process, develop more effective interventions, and drive evidence-based product innovation.
As a competition, we are looking for tools that capture, analyze, and share robust learning data not just for their own benefit, but importantly contribute knowledge to the field at-large.
In Phase II proposals, competitors are asked to detail how their tool advances learning engineering and to detail a learning engineering plan or partnership to support research external to their team.
What are organizers looking for in a learning engineering plan, broadly?
Broadly, your proposal should tell us about the potential of your tool to support research on a wide scale and the steps you will take to leverage your tool and data to do this. We recognize that competitors join the competition at different phases—for some, this may be the first time you’ve heard of learning engineering, for others you may have a fully instrumented tool. Your learning engineering plan will look different depending on your tool and phase of development.
The Tools Competitions aims to support tools and teams that believe in the importance of learning science research and are prepared to leverage the power of data to iterate their tool and improve the field’s knowledge of learning. We’re looking to see this dedication come through in your plan.
What should be included in a learning engineering plan?
A learning engineering plan will look different for every proposal, and no set structure is required. There is a wide range of what this can look like based on your team and tool.
You should both detail the learning data your tool captures and how you plan to leverage it to support research. Some examples of what this plan might entail include:
- Partnering with researchers who plan to use your tool’s data in their research and describing the research it will support.
- Publishing/sharing data for external researchers to access (e.g., via GitHub). View this helpful resource for more.
- Instrumenting the platform to allow ongoing rapid experimentation and A/B testing. Read more about instrumentation here.
- Developing an open access dashboard for researchers to utilize.
- Working with a researcher to structure your data for usage for research at scale.
- Engaging a researcher to help you understand how your tool can better support research. This may include advising on: structuring or improving data and metrics for effective research; instrumenting your tool; identifying and testing interventions; validating your methodology, etc.
- Identifying numerous researchers who can attest to your tool’s potential to address important research questions and detailing how.
The strongest plans will support research on an ongoing basis. Further, researchers you support as part of your plan must be external to your immediate organization or team. This demonstrates that there is interest and demand for your tool and dataset in the wider research community.
What do you mean by learning data?
Learning data provides rich insights on the learning process. Rather than simply the number of users accessing your platform and their general activity, learning data supports researchers in answering critical questions about learning. This may include data around learner performance, behavior, attitudes, cognitive processes, or other usage and demographic data that can help researchers understand what is working, for which learners, under what conditions.
How can I find and engage researchers?
Get in touch! Identify researchers that might be interested in the type of data your tool will collect—based on their content or technical area of expertise—and conduct outreach.
A great place to start is to identify academic departments focusing on relevant learning outcomes or methodologies. These do not have to be local to your area. You may also find it helpful to refer to conference proceedings (like this one or this one), academic papers, or other major edtech programs for those working in relevant areas.
Send an email describing your tool and project, and inquire about their research objectives and whether they would be open to collaborating. Align together on your goals. You are welcome to build costs for the research partnership into your budget.
If the researcher you contact is unavailable, they may be able to recommend other colleagues or PhD students within their department.
Where and how do I address the learning engineering plan requirements in the proposal?
In the Learning Engineering section of the proposal, competitors at the Growth and Transform levels are asked to detail a plan or partnership demonstrating that their tool will support external researchers (in 350 words or less). Competitors should include how their proposed tool will support research, the names of researcher(s) being engaged, how they will work together or support their research, and the nature of the partnership. As mentioned, you are welcome to include costs for developing a research partnership in your budget.
No formal agreement or official documentation is necessary, and plans and structure for each partnership can vary widely. You are welcome to upload a letter of agreement or other evidence, but this is not required.
If you have any questions about developing a learning engineering plan, please reach out to our team at ToolsCompetition@the-learning-agency.com.
