Scaffold learning with decision trees

Marking a set of practice papers on structure and bonding, I faced answers containing a jumble of key phrases used in incorrect contexts. For example, delocalised electrons caused ionic compounds to conduct electricity when molten and intermolecular forces appeared to be the explanation for a wealth of properties. During our lessons, the students had appeared confident. Where had it all gone wrong? The answer: structure and bonding is a complex topic for novice learners and my students were overwhelmed by what they were expected to absorb and retain. Perhaps this experience resonates with you?

The similarities and differences between the bonding in sodium chloride and chlorine may be obvious to us as experts, but baffling to a novice. We may believe we gave a simple explanation for the types of particles, how they form and the forces involved in holding them together, but in fact we overloaded the student with too much information. Once we introduced more than one type of bonding, we overwhelmed their working memory and all details merged into one. So how can we help our students overcome this challenge?

Putting these questions down on paper forced me to organise my thoughts

In the case of structure and bonding, I needed my decision tree to contain a series of questions to scaffold the thinking required to identify the bonding type in the given substance. In creating this, I had to consider not only what decisions I subconsciously made when answering the question, but also what fundamental knowledge learners need. I had assumed, incorrectly, that my students were secure in knowing if an element was a metal or a non-metal. I adapted the decision tree to add hints, but made a note to explicitly recap this knowledge before teaching the unit in the future.

Decision trees can also prove effective when students attempt to solve complex mathematical problems such as those forming part of the amount of substance topic, and the notoriously difficult acids, bases and buffers.

In the latter example, I found that taking the time to distil my thinking into a series of decisions has greatly enhanced my own teaching of the topic. These questions arose:

• Which information do I initially scan for in a question that will then need highlighting when modelling simpler calculations?
• Which fundamental knowledge needs emphasising very early on as it will present a barrier if missing?

Putting these questions down on paper forced me to organise my thoughts and I can use the answers to add clarity to future teaching.

Removing the scaffold

At some point you need to remove the scaffold the decision tree provides. The key is to do this gradually, ensuring understanding at every step.

I was initially concerned about how students would manage without the scaffold. For some students simply seeing the topic summarised in a single schema has helped them organise the knowledge, removed confusion and in turn reduced working memory load. For others, I’ve used spaced retrieval activities to support with knowledge retention. I provide a copy of the decision tree with key words or equations removed (different ones each time) and prompt students to fill the gaps from memory. Careful consideration of the removed words helps focus learners’ attention on the key decisions or knowledge they need for success. As a result, their understanding of these complicated topics has greatly improved.