STEM education can be broken down into a set of four types of learning strategies that will each individually introduce students to different facets of a new concept. These four types are: discovery-based (through hands-on exploration of ideas), enquiry-based (learning through encouraging questioning), data exploration (learning by observing trends), and analysis-based (understanding through breaking into components). Of the four types of learning strategies in STEM education my teacher and I have mostly centered around the strategies of enquiry-based and data exploration learning.
I believe that discovery-based learning would be the ideal case for teaching STEM content to middle school children as they are at the optimal age for understanding general principles behind the specific occurrences they encounter in their lives. By arriving at scientific principles on their own, the students will be able to derive a solid foundation for the concepts on which to build. One potential weakness of discovery-based learning is that students may build an incorrect mental model and might find it incredibly difficult to change their thinking to the correct model. However, with the appropriate lesson to lead the students through discovery-based learning and a sufficient amount of testing, the correct model can be reinforced.
We have utilized enquiry-based learning in lessons regarding genetic inheritance and ecosystem equilibria. As genetic inheritance is a topic that excites students, we found that when a scenario was posed to the students that was relatable to their life experiences, they were driven to ask questions. For example, when given scenarios of pet cloning, or genetic modification, or eye color and hair color, students were very likely to ask many questions such as ‘how were the researchers able to clone the animal?’ and ‘is that why my brother looks like my dad but I look more like my mother?’. We sought to address these questions in a systematic manner – by making students take notes on presentations and post sticky notes with what they learned or more questions. I believe this technique was very effective as many students continue to express interest in genetics and biology after the unit was complete.
Data exploration learning is a technique particularly suitable to the study of weather and climate and evolution. Students were tasked with collecting weather data (high/low temperatures, pressure, etc.) for cities randomly distributed across the planet. Students then plotted the weather data and were able to observe how high and low weather data changes over a span of 3 weeks and that despite daily changes, the temperatures tended to hover over the expected average. During the evolution unit, students were able to collect data on the efficiency of seed collection of Galapagos Finch beaks and were able to directly observe that the different beak sizes and shapes were particularly suited for one type of seed over another. Specifically, students were able to observe that during a drought, birds with larger beaks could out-compete birds with small beaks.