Suggestions for graduated exposure to programming concepts using fading worked examples

With the framework of the cognitive load theory, researchers have shown students in an experimental condition learned mathematics with worked-out examples significantly outperformed those learning mathematics in the control condition. The current consists of two experimental studies with 66 fifth grade students as participants. In Study 1, participants were divided into an experimental and a control condition, learning mathematics with the worked-out example instruction and traditional instruction, respectively. The result shows an aptitude-treatment interaction, that is, the worked-out example method was more beneficial for students for students with lower prior achievement, while the traditional method was more beneficial for students with higher prior achievement in mathematics. In Study 2, students were assigned to three conditions to learn mathematics, a condition with worked-out examples presented with PowerPoint, a condition with worked-out examples presented with hard copies, and a condition with traditional instruction. The results indicated that the students who received instruction with the worked-out example presented via PowerPoint gained significantly higher grades in a posttest of the mathematical achievement test than those who received the traditional instruction.

This article discusses a three-step method that was used in a college calculus course. The three-step method was developed to help students understand the course material and transition to be more independent learners. In addition, the method helped students to transfer concepts from short-term to long-term memory while lowering cognitive load. The three-step method is based off of the theory of worked examples, which falls under cognitive science and cognitive load theory. The article describes cognitive load theory, worked examples, and how worked examples are connected to the three-step method. The quantitative results of the research study show that, on average, participants performed significantly better than non-participants in the course and performed better on the final exam. The article ends with a discussion of the results of the study and why the three-step method helped students in the class.

Aim/Purpose: This study aims to analyze the cognitive load experienced by secondary school students in Biology within m-learning environments and its impact on learning performance. Background: Cognitive load has become a critical issue that schools need to address to ensure students can excel in their learning without being overwhelmed. While principles for reducing cognitive load have been extensively discussed in previous research, studies focusing on mobile learning (m-learning) for Biology among students in Malaysia remain limited. This study employed Cognitive Load Theory (CLT) and Cognitive Theory of Multimedia Learning (CTML) to address this gap. By integrating four key principles—segmenting and pretraining, modality, redundancy, and seductive details—into m-learning tasks using the Successive Approximation Model (SAM1), this study aimed to reduce cognitive load and enhance students’ learning performance. Methodology: This study employed a quantitative approach using a randomized pre-test/post-test quasi-experimental design. Students were randomly assigned to either an intervention group (20 students) or a control group (18 students). The study was conducted over four weeks, comprising a three-week intervention period with a one-week interval. Statistical analyses, including independent t-tests, Mann-Whitney U tests, Quade ANCOVA, and Pearson correlation, were used to analyze the quantitative data. Qualitative feedback was analyzed using thematic analysis. Contribution: This study contributes by providing instructional design strategies that incorporate principles for reducing cognitive load in mobile learning for Biology. It also demonstrates how Cognitive Load Theory (CLT) and Cognitive Theory of Multimedia Learning (CTML) can be effectively integrated. By examining the cognitive load experienced by secondary school students in m-learning environments, the study offers valuable insights for designing and implementing effective instructional strategies. Identifying the factors influencing cognitive load enables educators to develop targeted interventions that enhance learning experiences and optimize performance. Findings: The study indicated that the adoption of mobile learning tasks not only significantly reduced cognitive load but also corresponded to enhanced learning performance. Participants engaging in m-learning experienced lower cognitive load, which was positively associated with superior performance in learning tasks, emphasizing the beneficial impact of mobile learning on cognitive load management and academic achievement. Recommendations for Practitioners: Educators and instructional designers are encouraged to incorporate cognitive load principles into their instructional strategies and learning material design to enhance student performance. Policymakers should consider similar strategies to reduce the cognitive load for students in educational settings to improve learning outcomes. Recommendation for Researchers: Researchers are encouraged to replicate the design elements used in this study when developing mobile or online learning materials to reduce learners’ cognitive load and enhance their performance. They should also consider expanding this research to other topics, subjects, and educational levels to provide further insights and validate the effectiveness of these design elements across different contexts. Impact on Society: The findings of this study have significant implications for society, particularly in addressing mental health and stress issues among the younger generation. By identifying strategies to manage cognitive load and reduce stress in online learning environments, the study provides valuable insights for educators, parents, and policymakers. These strategies can help mitigate the adverse effects of cognitive overload, improve learning experiences, and promote better mental well-being. Additionally, the study’s recommendations can guide the development of more effective and supportive learning environments, contributing to overall societal well-being and academic success. Future Research: Future studies could explore cognitive load beyond the intrinsic and extraneous components focused on in this study, examining additional elements within the frameworks of cognitive load theory and multimedia learning. In addition to using the cognitive load questionnaire, exploring other measurement tools could ensure a more comprehensive understanding of cognitive load. Future research might also consider enriching mobile learning tasks by diversifying subject matter and conducting longitudinal cohort studies. Such studies could provide valuable insights into memory retention over extended periods, aiding in optimizing mobile learning frameworks and enhancing educational experiences.

This study investigates the effectiveness of applying Sweller’s (1988) cognitive load theory (CLT) principles to improve the learning of derivative concepts among first-year university students. The research employed a quasi-experimental pre-/post-test control group design to evaluate the impact of CLT-based instructional strategies–worked examples, visual scaffolds, and structured problem-solving–on students’ achievement and perceived cognitive load. A total of 60 students were randomly assigned to either an experimental group, receiving CLT-based instruction, or a control group, receiving traditional teaching. Data were collected using achievement tests and the cognitive load scale, assessing intrinsic, extraneous, and germane load components. Quantitative data were analyzed using independent samples t-tests and two-way and mixed-design ANOVA, with effect sizes (Cohen’s d, partial η²) reported to determine the magnitude of observed effects. Results indicated that the CLT-based instruction led to significantly higher achievement scores, reduced extraneous cognitive load, and increased germane processing compared to traditional methods. The findings support the theoretical validity of CLT and its practical applicability for mathematics instruction, suggesting that structured visual and example-based learning can optimize students’ cognitive resources and promote deeper understanding of calculus concepts.

The struggles faced by elder learners suffering from age-related cognitive decline are often overlooked by instructional designers. However, existing educational theories that already inform learning strategy development for other populations should also help establish instructional methods used to help elder learners. In this article, cognitive load theory frames an exploration of proposed means to slow or counteract the effects of age-related cognitive decline in elder learners. Attention is given to the ways in which multimedia learning methods adhering to certain principles of cognitive load theory can increase available working memory capacity. Evidence is provided to show that cognitive load theory-based practices can also facilitate one’s activation of prior knowledge and betters one’s attentional control. Additionally, elder learners benefit from tasks that include worked examples and goal-free problems, whereas conventional, goal-oriented problems impose greater extraneous load on an already taxed working memory. The outcomes of the present analysis can also be applied to stroke victims’ rehabilitation plans and may offer implications for individuals suffering from other brain injuries, attention deficit-hyperactivity disorder, or dementia-related illnesses.

Learning mathematical concepts and procedures typically requires extended cognitive effort, presenting a challenge for many children. People can make tracing actions with the index finger, as well as mimic another's movements, with little or no conscious effort. From the perspective of cognitive load theory, such biologically primary actions may facilitate learning biologically secondary concepts and skills requiring extensive cognitive effort, such as mathematics. The present study investigated effects on learning processes and outcomes of students mimicking a teacher's tracing actions from the perspective of an evolutionarily informed cognitive load theory. One hundred and thirteen Grade Two children learned about number lines, either observing a teacher tracing out elements of worked examples with her index finger, or mimicking the teacher's tracing actions with their own index finger. In accord with hypotheses, results indicated enhanced performance due to mimicking on cognitive load, motivation, and post‐test performance. Directions for future research are discussed.

In this article, we discuss cognitive load measurement techniques with regard to their contribution to cognitive load theory (CLT). CLT is concerned with the design of instructional methods that efficiently use people's limited cognitive processing capacity to apply acquired knowledge and skills to new situations (i.e., transfer). CLT is based on a cognitive architecture that consists of a limited working memory with partly independent processing units for visual and auditory information, which interacts with an unlimited long-term memory. These structures and functions of human cognitive architecture have been used to design a variety of novel efficient instructional methods. The associated research has shown that measures of cognitive load can reveal important information for CLT that is not necessarily reflected by traditional performance-based measures. Particularly, the combination of performance and cognitive load measures has been identified to constitute a reliable estimate of the mental efficiency of instructional methods. The discussion of previously used cognitive load measurement techniques and their role in the advancement of CLT is followed by a discussion of aspects of CLT that may benefit by measurement of cognitive load. Within the cognitive load framework, we also discuss some promising new techniques.

Introduction: Patient handoffs remain a significant patient safety challenge. Researchers have used cognitive load theory (CLT) to unpack the complexity of health professions workplace learning tasks including handoffs. 1 When the cognitive load of a handoff exceeds the working memory capacity of the trainee, learning and performance suffer. CLT focuses on the development of instructional strategies that match intrinsic load (IL) to the developmental stage of the learner, decrease extraneous load (EL), and optimize germane load (GL). CLT strategies have traditionally focused on modifying the complexity of the task, the knowledge of the learner, and/or the design of the instruction. CLT researchers have given little attention to how the learner’s emotions influence cognitive load. 2 Yet, we know that emotion modulates learning through numerous cognitive processes including motivation, attention, working memory, and long-term memory. 3 To address this gap, this study explores how emotion influenced the IL, EL, and GL imposed upon the working memory of trainees performing patient handoffs. Methods: From January to March 2019, we administered a cross-sectional survey to 1,807 residents and fellows from a large 24-hospital health system in the United States. Participants completed the survey after performing a handoff. The survey included questions about features of the learner, task, and instructional environment. IL, EL, and GL were measured with the 16-item Cognitive Load Inventory for Handoffs. 4 Emotions were assessed with an 8-item scale based on the circumplex model of affect. 5 The authors used factor analysis to identify the core dimensions of emotion and then explored the relationship between emotion and cognitive load with 3 types of analyses. First, bivariate correlational analyses examined the relationship between the emotion factors and learner, task, and environment features known to predict cognitive load. Second, we conducted univariate regression analyses to investigate whether emotion factor scores predicted IL, EL, and GL. Third, multivariate regression determined whether the addition of the emotion factors to models including these previously established predictors of cognitive load types increased the variance explained (Δ R2). Results: Six hundred and ninety-three (38.7%) of 1,807 residents and fellows completed a survey. Exploratory and confirmatory factor analysis identified 2 dimensions of emotion representing positive activation (labeled invigoration) and positive deactivation (labeled tranquility). The correlational analyses revealed that higher invigoration was associated with higher levels of training and use of a standardized verbal protocol, while lower levels of invigoration were associated with interruptions, difficulty obtaining essential information, and fatigue. Higher tranquility was associated with higher levels of training and lower levels of tranquility with interruptions, difficulty obtaining information, fatigue, longer handoffs, and greater number of patients. In univariate regression analyses, higher levels of invigoration, tranquility, and their interaction were independently associated with lower intrinsic load and extraneous load. The interaction of invigoration and tranquility predicted lower germane load. Finally, in the multivariate models of predictors for each type of cognitive load, the addition of the emotion term increased the amount of variance explained from 0.10 to 0.16 for IL, 0.15 to 0.27 for EL, and 0.11 to 0.14 for GL. Discussion: Emotion significantly influenced the cognitive load experienced by trainees while performing in vivo handoffs. Like studies in the simulation context, the structure of emotion during patient handoffs consisted of 2 factors. Higher invigoration/tranquility predicted lower IL, El, and GL. Traditionally, CLT researchers assumed that emotion primarily affected EL. Our results suggest a much broader impact of emotion. Significance: The study models how emotion and cognitive load can be measured during a workplace learning activity. These results suggest that enhancing invigoration and tranquility will reduce cognitive load and improve learning and performance. This highlights the importance of developing instructional strategies that help trainees regulate their emotions.

Augmented reality and worked examples: Targeting organic chemistry competence

Background:Advancing patient safety during handoffs remains a public health priority. The application of cognitive load theory offers promise, but is currently limited by the inability to measure cognitive load types.Objective:To develop and collect validity evidence for a revised self-report inventory that measures cognitive load types during a handoff.Methods:Based on prior published work, input from experts in cognitive load theory and handoffs, and a think-aloud exercise with residents, a revised Cognitive Load Inventory for Handoffs was developed. The Cognitive Load Inventory for Handoffs has items for intrinsic, extraneous, and germane load. Students who were second- and sixth-year students recruited from a Dutch medical school participated in four simulated handoffs (two simple and two complex cases). At the end of each handoff, study participants completed the Cognitive Load Inventory for Handoffs, Paas’ Cognitive Load Scale, and one global rating item for intrinsic load, extraneous load, and germane load, respectively. Factor and correlational analyses were performed to collect evidence for validity.Results:Confirmatory factor analysis yielded a single factor that combined intrinsic and germane loads. The extraneous load items performed poorly and were removed from the model. The score from the combined intrinsic and germane load items associated, as predicted by cognitive load theory, with a commonly used measure of overall cognitive load (Pearson’s r = 0.83, p < 0.001), case complexity (beta = 0.74, p < 0.001), level of experience (beta = −0.96, p < 0.001), and handoff accuracy (r = −0.34, p < 0.001).Conclusion:These results offer encouragement that intrinsic load during handoffs may be measured via a self-report measure. Additional work is required to develop an adequate measure of extraneous load.

Enhancing patient education in multiple myeloma - The intersection of cognitive load and socio-emotional adaptation theory.

Cognitive load theory, learning difficulty, and instructional design

Transaction analysis, the process of discerning which accounts are impacted (either increased or decreased) by a given transaction, is a fundamental accounting principle. While proficiency in this concept is vital for introductory accounting students, it poses a significant challenge for many. We addressed this hurdle using Excel and Excel VBA to develop a fully automated Journal Entry Learning Object (JELO) spreadsheet designed to aid first-year accounting students in grasping the intricacies of analyzing business transactions. Grounded in cognitive load theory (CLT), our spreadsheet based JELO provides a user-friendly interface that helps students develop their skills without becoming overwhelmed. Cognitive load theory, pioneered by John Sweller, suggests that learning is optimized when instructional materials reduce unnecessary cognitive load, allowing students to allocate their cognitive resources efficiently toward understanding and mastering the task at hand. CLT distinguishes between three types of cognitive load: intrinsic, extraneous, and germane (Sweller, Ayres, &amp; Kalyuga, 2011). Intrinsic cognitive load refers to the inherent difficulty associated with a specific instructional topic (Sweller, J. 2010). In transaction analysis, the intrinsic load is naturally high due to the complexity of understanding the impact of transactions on various accounts. To manage this, JELO has three tiers of transaction questions. Students are allowed to progress to more complex transactions only after demonstrating proficiency with the lower levels. Extraneous cognitive load is generated by inefficient presentation of information to learners (Paas &amp; Sweller, 2014). JELO minimizes extraneous load through its intuitive and interactive design. By providing clear instructions, a logical interface, and eliminating unnecessary information, JELO ensures that students can focus on the essential aspects of transaction analysis without being distracted by irrelevant information. Germane cognitive load refers to the mental effort required to create schemas and integrate new information into existing knowledge structures (Sweller et al., 2011). JELO enhances germane load by offering scaffolded and immediate feedback. This feedback is designed to reinforce correct responses and guide students through their errors with hints and explanations, facilitating deeper cognitive processing and better schema construction. Our goal was to make skill acquisition enjoyable and, more realistically, to reduce the pain of learning transaction analysis. The JELO offers an alternative to traditional “drill and practice” methods by providing an easy-to-use, interactive learning tool that dynamically adjusts the difficulty of its questions to match the student's skill level. This adaptive learning approach ensures that students are continually challenged just enough to promote learning without becoming overwhelmed. Additionally, JELO's feedback mechanism aligns with CLT principles by providing immediate positive reinforcement and corrective guidance. By doing so, it helps students build confidence and competence in transaction analysis, leading to a more effective and engaging learning experience. By incorporating principles of cognitive load theory, JELO ensures that students are neither under-challenged nor overwhelmed. This careful balancing of students’ overall cognitive load facilitates a more effective learning environment, fostering both proficiency and satisfaction in mastering transaction analysis.

Information that is complicated and ambiguous entails high cognitive load. Trying to understand such information can involve a lot of cognitive effort. An alternative to expending a lot of cognitive effort is to engage in motivated cognition, which can involve selective attention to new information that matches existing beliefs. In accordance with principles of least action related to management of cognitive effort, another alternative is to give up trying to understand new information with high cognitive load. In either case, high cognitive load can limit potential for understanding of new information and learning from new information. Cognitive Load Theory (CLT) provides a framework for relating the characteristics of information to human cognitive load. Although CLT has been developed through more than three decades of scientific research, it has not been applied comprehensively to improve the explainability, transparency, interpretability, and shared interpretability (ETISI) of machine learning models and their outputs. Here, in order to illustrate the broad relevance of CLT to ETISI, it is applied to analyze a type of hybrid machine learning called Algebraic Machine Learning (AML). This is the example because AML has characteristics that offer high potential for ETISI. However, application of CLT reveals potential for high cognitive load that can limit ETISI even when AML is used in conjunction with decision trees. Following the AML example, the general relevance of CLT to machine learning ETISI is discussed with the examples of SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and the Contextual Importance and Utility (CIU) method. Overall, it is argued in this Perspective paper that CLT can provide science-based design principles that can contribute to improving the ETISI of all types of machine learning.

Although cognitive load theory research has studied factors associated with motivation, these literatures have primarily been developed in isolation from each other. In this contribution, we aimed to advance both fields by examining the effects of instructional strategies on learners’ experience of cognitive load, motivation, engagement, and achievement. Students (N = 1287) in years 7–10 in four Australian high schools completed survey measures of motivation, engagement, cognitive load, and their teachers’ perceived instructional strategies and motivating style. Results suggest that teachers’ load-reducing instructional strategies were related to lower cognitive load and were positively associated with relative autonomous motivation, engagement, and achievement. Teachers’ motivating styles characterized by autonomy support and structure were also associated with reduced extraneous and intrinsic cognitive load, as well as motivation and engagement. We conclude that by using load-reducing strategies and a motivating style characterized by structure and autonomy support, teachers can reduce students’ cognitive load and improve their self-regulated motivation, engagement, and achievement. In so doing, we discuss a number of future avenues for the joint study of self-determination theory and cognitive load theory, with the aim of refining and extending both perspectives.