Teaching and Learning Strategies

In this article, you will discover teaching and learning strategies.

Equipped with the theories of how people learn, teachers and trainers can choose among different techniques and approaches available to design learning to achieve their teaching objectives. What teaching strategies are the best? The answer is that every teaching strategy, or a combination of strategies, should be the best tool for the task – depending on what is being taught, at what level and in what context, to what audience. Every field, including education, has developed the best practices and strategies within its own domain. Later and throughout the course, we will be looking at some of those practices, related both to teaching/training (or pedagogy in general) as well as the strategies used in domain-specific teaching of genomics and bioinformatics.

Constructivist models include methods and strategies for promoting the development of logical thinking and applied cognitive, affective, and psychomotor skills, by implementing learner-centered, learner-directed teaching. It is especially conducive to teaching science, for example, the use of inquiry-based approaches is suitable to the teaching of the scientific method.

Learning focused on student-led discussion with critical thinking methods has been shown to improve both student efficacy and performance. Amolins et al (2015) conducted one such study: Evaluating the effectiveness of a laboratory-based professional development program for science educators

Experts Teaching/Training Others

You as an expert in your own field will want to teach others the knowledge and skills you have. The content of the expert knowledge in a domain needs to be differentiated from the pedagogical content knowledge that is a base for effective training/teaching. Experts sometimes forget what path they have gone through to becoming experts and even though they are good at their domain, they sometimes need additional knowledge and skills for helping others learn. Those will include awareness about typical difficulties that learners might encounter while learning specific topics and strategies to overcome those, as well as typical paths the learner must traverse in order to climb up the levels from acquiring basic knowledge and understanding towards more transferable and creative thinking.

In this course, we are already dealing with the multidisciplinary nature of genomics and bioinformatics. For example, bioinformatics can be seen as a combination of biology, data science, and computer science. There is already a multitude of content knowledge required to achieve domain expertise, to which acquiring of the pedagogical content needs to be integrated, for the effective teaching/training of others.

There are ways to help with this – you can apply ‘adaptive’ expertise by:

• assessing the limitations of your own knowledge and considering different paths to achieve the training goals;
• having and presenting a bigger picture (to include the purpose and context of your training);
• use of reflective techniques
• not worrying about looking knowledgeable (experts don’t have answers to all the questions!)
• if it helps, use the term: accomplished novice, rather than an expert. This frees the trainers to learn and allow themselves to be taught by their adult learners – as it will often happen in reality.

Teachers/trainers and learners can also take turns in leading the group, and use different prompts for identifying goals, generating ideas, improving existing ideas, etc. Taking turns will also help learners externalise their mental processes.

The use of distributed expertise is a powerful way to conduct training/teaching of multidisciplinary content – team/collaborative work where experts in different domains are working together (sometimes distributed in space and/or time), applying complementary expertise for the most effective learning. For example, supportive learning environments can be designed by education specialists to scaffold active learning, where domain experts and learners can use the available pedagogical tools/techniques and approaches to best conduct their domain-specific training and learning. This model is also suited to responding well to diverse audiences of learners and is a substitute for the ‘all in one person’ expertise.

Motivate Your Learners

The main motivation in the context of domain-specific training such as genomics and bioinformatics will be ‘competence motivation’. Although extrinsic rewards can affect behaviour, adult learners work/learn hard for intrinsic reasons as well.

To motivate your learners:

• consider different learners’ styles and needs: some learners will prefer learning through observing, some through discussing or collaboration with others, or ‘hands-on’ exercise. Accessibility is a very important factor. When designing courses, different learning needs should be recognised and opportunities created for everyone to take part in the process of learning/training. UDL (Universal Design for Learning) principles presented at the link offer a set of concrete suggestions that can be applied to any discipline or domain to ensure that all learners can access and participate in meaningful, challenging learning opportunities.
• introduce social opportunities – feeling that one is contributing towards helping others, or one’s community can be a very powerful motivation
• allow learners to see the usefulness of their learning – and impact the others, especially the local community (including teaching others, presenting learning to others, working effectively in groups).