The cognitive apprenticeship model of teaching uses real-world tasks and activities to facilitate learning. Explore its potential in eLearning and online teaching.
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Have you ever looked at an expert and wondered how they made a difficult task look so easy? It's not just talent but also how they learn. The Cognitive Apprenticeship Model is a teaching approach that helps students develop expertise by learning directly from experienced mentors.
While most traditional models focus on learning by memorization and repetition, the cognitive apprenticeship model of teaching emphasizes doing and learning from experience. In fields like medicine and software development, this approach is even more prominent.
The article below covers the cognitive apprenticeship model of teaching in detail. It also explains how educators can use it to create impactful learning experiences.
The cognitive apprenticeship model of teaching is an approach that emphasizes learning through guided experiences, similar to traditional apprenticeships but focused on cognitive and metacognitive skills. It was developed by Allan Collins, John Seely Brown, and Susan E. Newman in 1989.
The model aims to make expert thinking visible to learners, enabling them to observe, practice, and refine their skills in real-world contexts. In this model of teaching, the key components include modeling, coaching, scaffolding, articulation, reflection, and exploration.
An example of this model is Reciprocal Teaching in Reading, developed by Annemarie Sullivan Palincsar and Ann L. Brown in 1984. Students and teachers take turns leading discussions about text segments. They focus on strategies like questioning, summarizing, clarifying, and predicting. Studies show that this approach improves reading comprehension.
While the basis of apprenticeship is the same in both methods, traditional and cognitive models differ in certain ways. Let's discuss.
Traditional apprenticeships are hands-on and often focus on developing physical skills. For example, a chef's apprenticeship masters knife techniques. The expert shows the skill, and the apprentice practices until they perfect it.
Meanwhile, cognitive apprenticeship hones cognitive and metacognitive skills, which are the thinking processes behind expert performance. Instead of just watching an expert, learners are guided through how to approach problems. They can then analyze information and make decisions.
For example, in a medical training program, a traditional apprentice observes doctors perform surgeries and gradually assists. In a cognitive model, the apprentice would also be involved in discussions on diagnostic reasoning, decision-making strategies, and patient evaluation techniques, making the expert's thought process explicit.
Traditional apprenticeship mainly involves implicit learning. In simple words, apprentices watch and imitate experts over time. The master may not explain every step, expecting the apprentice to pick up techniques through repetition and experience.
Cognitive apprenticeship takes a different approach. It involves explicit instruction, where experts break down their thought processes and problem-solving approaches to make them visible to learners.
In the example above, the doctors would explicitly explain their reasoning behind each decision. The learners understand how to approach similar situations in the future.
Traditional apprenticeships are often informal. Learning happens as needed, based on the tasks at hand, without a structured curriculum.
In cognitive apprenticeship, planned learning experiences help build learner skills. There's a proper curriculum or coursework that apprentices have to complete. The medical training program would have specific sessions on all evaluation techniques and decision-making strategies.
We've mentioned six components of the cognitive apprenticeship model of teaching above. Here's a detailed overview of these components.
In this step, students learn by watching an expert. The instructor demonstrates the skill while explaining it to the learners, who observe how the expert is approaching a task. Since the instructor is thinking out loud, the mental processes are clear to the learners.
Let's say you're teaching a data analytics course. The instructor walks through a dataset and explains how they identify trends and apply statistical models. They narrate their thought process, saying things like, "First, I check for missing values because incomplete data can skew results."
Students then begin practicing the skill based on the instructor's guidance. In this process, the instructor monitors the learners' progress and gives them feedback.
In the data analytics course, the instructor can give students a data set to clean on their own. They also review the learner's work and help them refine their approach.
Scaffolding provides structured support to learners and helps them complete tasks they would not be able to do alone. As they become confident in their abilities, the instructor can slowly remove assistance until the learners can work independently.
At first, the instructor provides a step-by-step checklist for analyzing a dataset. Over time, they remove parts of the checklist, encouraging students to develop their own workflow. Eventually, students must complete an analysis with no external guidance.
The instructor would make the learners verbalize their reasoning and decision-making process, which helps strengthen their understanding. Students clarify why they made the choices they did.
For example, a student presents their data analysis to the class. They explain why they selected a specific algorithm and workflow to analyze their data. The instructor and classmates then ask questions and provide feedback to improve the student's understanding.
In this step, learners compare their work to an expert's approach or discuss it with their peers. The idea behind this practice is to evaluate their work. For example, in the data analysis course, students can review their version with the instructor to notice differences.
At this stage, students apply what they've learned to new and unfamiliar challenges by testing different approaches and developing problem-solving skills. In the course, the final assignment could require students to analyze a new dataset without the instructor's guidance. They now have to select appropriate methods and present their findings.
If you're creating an online course, here's how you can apply the cognitive apprenticeship model of teaching to it.
In your course, show a skill or concept while displaying how experts think. Basically, don't just put text on the screen and call it a day. Instead, create video lectures where an instructor thinks out loud while solving a problem or uses screen recordings to walk students through a task. You can also provide annotated case studies to show expert decision-making in action.
Don't wait until the end of the course to provide feedback to learners. Instead, sprinkle it throughout the course by letting students take assessments and quizzes.
When you use Coursebox, an AI course builder, this becomes very simple. Its AI quiz generator automatically creates quizzes based on your course content. The AI grader then marks these quizzes to provide instant feedback to learners.
Coursebox's AI chatbot further provides hints and guidance to students throughout the course. It is trained on the content of your course so it can provide relevant and helpful responses to student's questions to facilitate learning.
You can also integrate discussion forums into the course to allow students to ask questions and receive feedback from peers.
Make sure learners have enough support at first to grasp the concepts you're teaching. Then, reduce this support as you steer them toward mastery.
For example, start with guided exercises and then transition to self-directed projects. In the first two or three quizzes, you can offer checklists or glossaries. But in the later assessments, take these resources away. You can also use adaptive learning paths, where learners are directed to review specific content based on their quiz performance.
Students must explain their thinking and compare their work to others to solidify their understanding. You can do this by asking them to record short video explanations of their work.
Similarly, encourage students to take part in discussion forums and share their thought processes with peers. You may also include self-reflection prompts in their assignments to give them the push toward metacognition.
Finally, let your students apply their skills in real-world scenarios. You can assign them open-ended projects with multiple possible solutions or provide them access to real case studies.
For example, if you're teaching a sales training course, you can provide students with real data from a business and ask them to create a sales strategy based on that data. It will not only reinforce their skills but also give them a taste of what they can expect in the industry.
As you can see, the cognitive apprenticeship model of teaching is relatively different from its traditional counterpart. It's more interactive and aims to offer better learning outcomes. However, for it to work, you must do it right.
You shouldn't only teach learners how to do something but also why you took the approach or used the method that you did and when these strategies are appropriate. In short, don't just teach the skill but also the thinking behind it.
Coursebox can further facilitate this by letting you add quizzes and assessments to test your learner's knowledge as they're learning. You can then provide them feedback yourself or let the AI grader do this on your behalf.
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