Defining higher order thinking skills in the context of science education was a relatively tricky. Whilst much of the literature refers to higher order skills, it is not always clearly defined. We know from Bloom's taxonomy that more complex thinking skills include: evaluation, synthesis and creation or in the revised taxonomy: creating, evaluating, analysing. Yet what do these mean necessarily, in the science classroom? What do they look like? If we are to assess them, what is the standard against which we are assessing?
Key Higher Order Thinking Skills
With these questions in mind, I went through the relevant curricula for my state, for the VCE (Victoria Certificate of Education) and pulled out the key skills they focused on, for the areas of physics, chemistry, biology and psychology.
These are the terms I collected (I have highlighted key terms):
Psychology
analyse and interpret data, and draw conclusions consistent with the research question
evaluate the validity
and reliability of research investigations including potential confounding
variables and sources of error and
bias
apply understandings to both familiar and new contexts
evaluate the validity and reliability of psychology-related
information and opinions presented in the public domain
Biology
evaluate experimental procedures and reliability of data
collect, process and record information systematically; analyse and synthesise data; draw conclusions consistent with the
question under investigation and the evidence obtained
apply understandings to familiar and new contexts; make connections between concepts; solve problems
analyse and evaluate the reliability of information and
opinions presented in the public domain
Physics
collecting,
processing, recording, analysing, synthesising and evaluating qualitative and
quantitative data
draw conclusions
consistent with the question under investigation and the information collected,
identifying errors and evaluating
investigative procedures and reliability
and accuracy of data
select first-hand and second-hand data and evidence to demonstrate how physics concepts, theories and
models have developed and been modified over time
Chemistry
draw conclusions
consistent with the question under investigation and the information collected;
evaluate procedures and reliability
of data
identify and address possible sources of uncertainty
make connections between concepts; process information;
apply understandings to familiar and new
contexts
use first and second-hand data and evidence to demonstrate how chemical concepts and theories
have developed and been modified over time
An emerging picture
The common theme that emerges from these curricula is that students need to be taught the higher order thinking skills of analysing and interpreting scientific information to draw logical, valid conclusions; synthesising and processing data in a sensical way; and applying understanding to both familiar and new contexts.
What has become apparent to me over the course of this change project, is that it is not necessarily clear to teachers how they are to set about teaching and assessing such skills in their students. We do not have a coherent, regular process to incorporate the formal teaching of these skills to students. Part of this, in my opinion, stems from teachers not having these skills clearly defined. That is now a major focus of this project, to enable teaching and learning. The conversations with my peers about these skills have been useful professional development. Just by reflecting on how we teach and assess higher order thinking, we are starting to make our actions align with our intentions.
That we do not have a formal plan for teaching these skills reminds me of this blog post by Grant Wiggins, author of Understanding by Design (UbD). He refers to inferencing, a higher order skill, of drawing reasoned conclusions from evidence, with a quote that suggests that it cannot be taught, when of course it can. This is a particular skill that needs to be taught in the science classroom. The other skills mentioned above also need to be taught.
I have attached a table below - that begins to define what these higher order skills are, and how they can be taught and assessed. Let me know what you think!
An emerging picture
The common theme that emerges from these curricula is that students need to be taught the higher order thinking skills of analysing and interpreting scientific information to draw logical, valid conclusions; synthesising and processing data in a sensical way; and applying understanding to both familiar and new contexts.
What has become apparent to me over the course of this change project, is that it is not necessarily clear to teachers how they are to set about teaching and assessing such skills in their students. We do not have a coherent, regular process to incorporate the formal teaching of these skills to students. Part of this, in my opinion, stems from teachers not having these skills clearly defined. That is now a major focus of this project, to enable teaching and learning. The conversations with my peers about these skills have been useful professional development. Just by reflecting on how we teach and assess higher order thinking, we are starting to make our actions align with our intentions.
That we do not have a formal plan for teaching these skills reminds me of this blog post by Grant Wiggins, author of Understanding by Design (UbD). He refers to inferencing, a higher order skill, of drawing reasoned conclusions from evidence, with a quote that suggests that it cannot be taught, when of course it can. This is a particular skill that needs to be taught in the science classroom. The other skills mentioned above also need to be taught.
I have attached a table below - that begins to define what these higher order skills are, and how they can be taught and assessed. Let me know what you think!
HOTS
|
Explanation
|
Teaching activities
|
Assessment
|
Analysing and
interpreting information
|
Students being exposed to quantitative data and being
asked to draw conclusions
Students being exposed to qualitative data and being asked
to draw conclusions
Students drawing valid, logical, reasoned conclusions
|
Students regular handling data; from practice questions
and from experiments
Students being asked to observe patterns or trends in
quantitative data
Students practicing drawing rational conclusions
Students being provided with explicit examples of
illogical and irrational conclusions and having these explained
|
Test questions that provide scenarios for students to
interpret
Students being given experimental results where errors
have been made during the experiment and they have to interpret the effect on
the outcome
Students being asked about a range of possible conclusions
drawn about an experiment and needing to describe them as valid/invalid and
provide a rational explanation
|
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