Four ways to help students develop critical thinking skills

Critical thinking and research skills are among the most valued in higher education, especially in the sciences. As knowledge becomes increasingly easier to acquire via the internet and AI tools, post-graduation outcomes like employment and advanced degree attainment are becoming increasingly dependent on their demonstration.

For better or for worse, this changing landscape has generated a lot of downward pressure on university educators to develop new and innovative ways to train critical thinking and research skills, a mandate that is neither straightforward nor easy to achieve. As one of the people often tasked with these mandates, I would like to share what I consider to be four important elements of successful critical thinking and research learning design. 

1. Break up activities into steps

The first thing to realise about most critical thinking and research skills is that they are actually conglomerations of many simpler ones. Despite instructors often conceptualising tasks like data analysis, research report writing and experimental design as single entities (and presenting them to students as such), closer scrutiny quickly reveals each to consist of multiple steps. 

Successful data analysis, for example, may require students to perform several operations in a specific order: aligning data correctly in a spreadsheet, choosing an appropriate statistical method to apply and employing the correct computer code or toolbar options to execute the selected method. Each step involves a different skill, with a mistake in any one likely preventing the correct result. 

In much the same way, report writing also necessitates multiple steps. Writing a section summarising prior research on a specific topic, for example, can require students to collect information sources, evaluate the relevance of each, extract specific content and arrange that material in a coherent manner before any writing even commences. Each of these steps would, again, require a different skill.

Therefore, to design effective critical thinking and research learning processes, educators must have a clear progression of steps in mind. If the goal is to write about prior research, then the corresponding teaching schedule should contain clear guidance about how students are expected to perform each step: finding, evaluating, extracting and organising relevant content. It is this sequence of steps that should function as the blueprint for any organised approach to critical thinking and research activity design.

2. Provide criteria and examples to help students deal with complexity

One of the things I notice many instructors do after working out a plan is to provide students with a written protocol, assuming the explanations alone will lead to skill mastery. When the skills in question are simple (e.g., clicking toolbar buttons), these explanations may be adequate. More often than not, however, the skills involved will not be so rudimentary, necessitating something more. 

Take, for example, appropriately selecting a statistical method for data analysis. A decision-making skill like this requires students to have clear criteria for how judgements should be made and examples illustrating correct and incorrect applications of the criteria. Some criteria may be more obvious than others, allowing students to learn the skill quickly (for example, if you have two columns in your dataset, use method X), but most will not be so obvious (for example, if a non-ordinal, categorical dependent variable is paired with a continuous independent variable, use method Y). The more complexities a skill entails, the more criteria and examples are necessary to help students through the nuances.

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3. Support deliberate practice through scaffolded learning

At the end of the day, practice ends up being the most critical ingredient to skill mastery. When building an effective learning regimen, two ideas from cognitive load theory become especially useful: the simple-to-complex and low-to-high fidelity strategies. The former advocates for the need to practise individual steps within a process before practising the whole, while the latter advocates for a gradual increase in task difficulty over time to prevent information overload. For instance, when applied to data analysis, practising data alignment on a spreadsheet before practising it together with statistical method selection would be an example of the simple-to-complex, while working with small datasets before large ones would be an example of low-to-high fidelity.

4. Adjust and optimise

There will always be students who pick things up faster than others, sometimes even without the detailed criteria and examples mentioned above. Most learners, however, will likely not be so independent. The activities I design are usually catered to this latter group, which tends to be quite heterogeneous in pre-learning competencies, meaning that different students will need different degrees of practice with each step to get better. To address this diversity, it is critical to design effective assessments and seek regular feedback from students to improve and adjust the learning methods. In many respects, these adjustments are akin to strokes of a chisel used to carve a statue: each one matters and patience is paramount.

Justin Fendos is a senior associate professor in the Department of Biosciences and Bioinformatics at Xi'an Jiaotong-Liverpool University. 

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