The scientific method is a systematic process that helps minimize bias in research and begins by developing good research questions. Descriptive research questions are based on observations made in previous research or in passing. This type of research question often quantifies these observations. For example, while out bird watching, you notice that a certain species of sparrow made all its nests with the same material: grasses. Descriptive research questions lead to causal questions. This type of research question seeks to understand why we observe certain trends or patterns.
In simple terms, a hypothesis is the answer to your causal question. A hypothesis should be based on a strong rationale that is usually supported by background research.
This is also how machine learning algorithms work not surprisingly. But what is critical here is to understand that learning cannot take place without some baseline to compare the actual result against. There must be a gap of some kind between the outcome we want and what we got. Without that gap, we are simply reinforcing the status-quo. The weakness with implicit learning is it can reinforce behaviors and beliefs that correlate with a result without actually causing it.
We are just repeating those actions that have been followed by the outcome we wanted whether that is by causation or coincidence. In the case of something like learning to ride a bicycle, that is generally OK.
We may learn things that are unnecessary to stay upright on the bicycle 1 , but we will learn the things that are required. In athletics, once the basics are in place, coaches can help shift this learning from implicit to explicit by having you practice specific things with specific objectives. Bluntly, the vast majority of organizations are engaged in implicit, not explicit, learning.
They may do things that are unnecessary but are also doing things that are required. When the organization has to accomplish something that is outside of their current domain of knowledge — beyond their knowledge threshold — those anecdotes break down. The narrative of cause-and-effect in our minds is no longer accurate. The problem with that?
Once we develop those beliefs, we bias heavily to see evidence they are true, and exclude evidence that they are not true. Your email address will not be published.
Notify me of follow-up comments by email. Notify me of new posts by email. This site uses Akismet to reduce spam. Learn how your comment data is processed. Explicit learning is driven by prediction. What is that? If it worked, great. You are Always Making a Prediction Anyway Any action you take, anything you do, is actually a hypothesis. This skill is worth nurturing from an early age because it develops thinking skills in general. In science, it helps learners to reflect on what has happened in practical work when they check their conclusions against their prediction.
Most learners in Stage 2 will be able to say whether what happened was what they expected to happen, or not, at the end of an enquiry. Even if a prediction is not asked for at the start of an enquiry, we can see evidence that learners have subconciously made a prediction. Statements made after observations have been made or results have been collected can be revealing.
When asked to develop their suggestions further and explain why they think an event will happen, they will often find it difficult to verbalise their reasoning. Those who can offer an explanation usually base their prediction firmly on everyday knowledge of their world. For example, their predictions of shadows may be based on their play with a torch at home or when they made a shadow puppet theatre at school. Their explanations are rooted in their own direct experience.
The important thing for us as teachers is to encourage speculation and give opportunities to express the reasons why they think this. As learners become more mature thinkers, their predictions in enquiries can be justified in terms of scientific knowledge and understanding.
The teacher plays a vital role in encouraging this leap to a higher cognitive level, because many learners feel safer in basing predictions on everyday experiences, rather than trying to use their newly acquired scientific ideas. However, the process of applying new knowledge and understanding will help learners clarify their thoughts and can give you — the teacher — high quality assessment information to act upon. The use of a prediction is also a motivating factor at the start of any enquiry.
It is the weighing of evidence gathered against your prediction that is important in science — and remember that sometimes our evidence will be inconclusive! As part of their developing powers of prediction learners will move from speculative guesses on specific instances e. Before tacking anly hands-on research science activity, think about whether there is an appropriate opportunity for learners to make a prediction about what they think will happen or find out. This can be a whole class, group, pair or individual exercise depending on your classroom organisation for that particular activity.
NB Illustrative practical work is designed to elucidate a concept and is tightly controlled in terms of outcomes by the nature of the activity. It will often involve learners following a set of instructions, with limited opportunities for them to make their own decisions.
In group activities, allow time for learners to talk to each other about their predictions. Ask for a group prediction if possible — this often leads learners to justify their predictions to each other.
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