School Research 1

Now to look at some applications for school . . .

One immediate thought is to employ a scaffolded approach where students learn to apply new skills in a low-stakes environment that will encourage them to raise doubts and ask questions. Instead of immediately applying skills to a project which requires a finished (& graded) project, we should let them perform some data evaluation as a discrete skill. An analogy would be a student of tennis who practices a hundred serves a day – she is not applying this skill to win a match (a high-stakes endeavor), so the practice session is a low-stakes opportunity to explore styles and tinker with her mechanics until she finds a comfort level with her ability to make good serves. And just as a coach would work with that tennis player, a teacher could provide the equivalent guidance for a student developing data evaluation skills.

As far as particular skills, a primary one would be the ability to clearly state the goal of their research by defining what they are trying to prove or disprove in specific terms. A goal like, ‘Finding out more about the Civil War’ is too vague and general to provide much guidance for most students. Instead, students should be able to enunciate a series of goals like, ‘Where were the majority of Civil War battles fought’, or ‘What Union generals were most effective’, or ‘What were the hardships people living in the Confederate states faced’, and so forth. Students should engage in a form of backwards design wherein they gain a clear understanding of what their finished product should look like before they begin to gather data.

After clarifying particular research goals and sub-goals, students should then embark on a data-gathering journey. This journey may start by brainstorming potential sources of data, and should be marked by numerous roadmap meetings along the way with peers or teachers, at least until they have gained fluency with the process. When a piece of data is found, its content should be studied in light of the goals of the project. If the data does not contain information relevant to the project goals it can be discarded. If it is relevant to the goals, it becomes part of the data that the student must then evaluate for quality.

To repeat, data should first be examined for relevance – if it passes the relevance test, it then gets evaluated for quality, which will be our next entry.