Chapter 11 delves into example-based learning, an effective method of learning complex tasks without increasing the cognitive load. This method leverages worked examples, a step-by-step explanation to help learners understand and complete specific tasks, this represents a fully guided instruction. Clark and Mayer (2024) note that using worked examples has led to increased learning efficiency and outcomes in various skill domains, including mathematics, solving legal cases, writing, and science. This may be because these skill domains thrive well in explanatory learning environments, which promote fully guided learning instruction. Guided learning instruction may not be as effective in an exploratory or discovery learning environment, which tends to favor a form of less or fully unguided instruction (Kalyuga et al., 2003).
Worked examples can be an effective way to learn, especially for beginners (Kalyuga et al., 2003), since they are specifically designed to reduce extraneous load (Clark & Mayer, 2024). They can provide several benefits for learning, most importantly, they can reduce the cognitive load by reducing the mental effort required for problem-solving. This allows the learner to focus on understanding the steps required for solving problems rather than finding the solution. Another key benefit of worked example is the motivational value it presents to learners by allowing them to observe the worked example before attempting to solve the problem. This helps to enhance learner engagement and improve their confidence. Additionally, worked examples could help learners acquire foundational knowledge more quickly, particularly if it is structured to progressively scaffold learning, enabling a smooth transition to independent problem-solving.
Based on comprehensive research, Clark and Mayer (2024) provide several guidelines for optimizing these benefits. For example, using two or more examples can improve learning outcomes when dealing with complex tasks. Also, incorporating self-explanation questions requiring learners to reflect on the principles behind each step can help enhance comprehension. Another guideline that can further optimize example-based instruction is the use of video examples for complex tasks, with several studies corroborating this. Furthermore, transitioning from worked example to independent problem-solving as the learner gains more knowledge and expertise. This is particularly significant for learners because, according to Kalguya et al. (2003), worked examples can become redundant for an expert learner and can lead to counterproductivity.
Chapter nineteen delves into an area where I had much misinformation, influencing my not-so-positive mindset about computer games. I have raised several teens (immediate and extended family), and I see their interactions with computer games. My observations are that there is a thin line between entertainment and addiction, and this is what I base my opinion on when it comes to computer games. Therefore, it was informative to read chapter nineteen to get a different perspective on computer games. The chapter evaluates the educational potential of computer games to support learning, skill acquisition, and cognitive development. Considering its popularity and potential for engagement, Clark and Mayer (2024) implement an evidence-based analysis to determine when and how computer games can be an effective learning tool, utilizing three key approaches.
The value-added approach examines how particular game features can impact learning outcomes. Comparing basic computer game models with enhanced models equipped with certain features can help identify those features or design elements that improve learning. Clark and Mayer (2024) identified five features that can aid learning outcomes: coaching, self-explanation, pretraining, modality, and personalization. The second approach is the cognitive consequence approach, which investigates whether cognitive skills can be enhanced by playing certain computer games over others. Findings indicate improvements are only limited to skills practiced within the context of the game. The last approach is the media comparison approach, which explores comparing computer games and conventional media like slideshows and tutorials to determine which is more effective for learning specific skills. The findings indicate that games, though not superior to media are effective in specific fields like mathematics, science, and second language learning. Overall, it is best to use games cautiously for learning, it is best to incorporate short, focused games into instructional materials rather than those off the shelf.
Reference
Clark, R., & Mayer, R. (2024). E-learning and the science of instruction: proven guidelines for consumers and designers of multimedia learning. John Wiley & Sons, Inc.
Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The Expertise Reversal Effect. Educational Psychologist, 38(1), 23–31. https://doi.org/10.1207/S15326985EP3801_4