A Cognitive Load Perspective to Instructional Design for Online Learning

Learners struggle to keep up with the cognitive demands of online learning. Terms referring to the drain of learners' cognitive resources such as “Zoom fatigue” have been around for a while. The instructional design of online courses must consider cognitive factors more than ever. The cognitive load theory (CLT) has major underpinnings for designing online courses. The CLT seeks to optimize the learning process by considering the demands of the learning tasks (intrinsic cognitive load), design of the learning material (extrinsic cognitive load), and activation of learners' cognitive resources (germane cognitive load). Several principles have been proposed to manage each cognitive load type. This chapter will begin by outlining the CLT. Then, well-defined cognitive load effects will be introduced, along with evidence from the field. Next, new frontiers of the theory will be presented. Finally, implications of the cognitive load effects for online learning practices will be discussed.

The Validity of Physiological Measures to Identify Differences in Intrinsic Cognitive Load

A sample of 33 experiments was extracted from the Web-of-Science database over a 5-year period (2016–2020) that used physiological measures to measure intrinsic cognitive load. Only studies that required participants to solve tasks of varying complexities using a within-subjects design were included. The sample identified a number of different physiological measures obtained by recording signals from four main body categories (heart and lungs, eyes, skin, and brain), as well as subjective measures. The overall validity of the measures was assessed by examining construct validity and sensitivity. It was found that the vast majority of physiological measures had some level of validity, but varied considerably in sensitivity to detect subtle changes in intrinsic cognitive load. Validity was also influenced by the type of task. Eye-measures were found to be the most sensitive followed by the heart and lungs, skin, and brain. However, subjective measures had the highest levels of validity. It is concluded that a combination of physiological and subjective measures is most effective in detecting changes in intrinsic cognitive load.

Learning Analytics for Diagnosing Cognitive Load in E-Learning Using Bayesian Network Analysis

A learner’s cognitive load is highly associated with their academic achievement within learning systems. Diagnostic information about a learner’s cognitive load is useful for achieving optimal learning, by enabling the learner to manage and control their cognitive load in the e-learning environment. However, little empirical research has been conducted to obtain diagnostic information about the cognitive load in e-learning systems. The purpose of this study was to analyze a personalized diagnostic evaluation for a learner’s cognitive load in an e-learning system, using the Bayesian Network (BN) as a learning analytic method. Data from 700 learners were collected from Cyber University. A learner’s cognitive load level was measured in terms of three components: extraneous cognitive load, intrinsic cognitive load, and germane cognitive load. The BN was built by representing the relationship among the extraneous cognitive load, intrinsic cognitive load, germane cognitive load, and academic achievement. The conditional and marginal probabilities in the BN were estimated. This study found that the BN provided diagnostic information about a learner’s level of cognitive load in the e-learning system. In addition, the BN predicted the learner’s academic achievement in terms of their different cognitive load patterns. This study’s results imply that diagnostic information related to cognitive load helps learners to improve academic achievement by managing and controlling their cognitive loads in the e-learning environment. In addition, instructional designers are able to offer more appropriately customized instructional methods by considering learners’ cognitive loads in online learning.

APPLICATION OF INQUIRY BASED LEARNING MODEL USING STEM APPROACH TO REDUCE STUDENTS' INTRINSIC COGNITIVE LOAD

This study is an experimental research using a quasi-experimental design with the type of non-equivalent control group pretest-posttest. This research aims to reduce Intrinsic Cognitive Load (ICL) through Inquiry Based Learning (IBL) model based STEM approach. This research was conducted at SMA Negeri 1 Seputih Banyak with the research subjects of class X IPA 1 students and X IPA 4. An data Intrinsic Cognitive Load obtained from essay the pretest-posttest (Task Complexity worksheet). Cognitive load test data analysis was performed using the data normality test, N-gain test and hypothesis test using the Paired Sample T-Test. The experimental class implements the Inquiry Based Learning model based audio and visual using STEM approach while the control class is nonaudio and visual. The results showed by the average value of N-Gain Intrinsic Cognitive Load (ICL) in the experimental class was 0.63 with the moderate category and the control class was 0.18 with the low category. Based on data analysis, can be concluded that there is a positive influence in the form of a decrease in the cognitive load Intrinsic Cognitive Load (ICL) in the experimental class using the Inquiry Based Learning model plus an audio and visual based STEM approach.

The cognitive load of inpatient consults: A convergent parallel mixed methods study using the consult cognitive load instrument.

11027 Background: Consultation is crucial for inpatient care and a primary responsibility of fellows. Understanding the cognitive load associated with the complex skill of consultation would enhance fellow learning. The authors aimed to determine themes describing the fellow experience during consults, align these themes with Consult Cognitive Load (CCL) scores, and identify strategies to manage cognitive load. Methods: The authors studied 16 fellows using mixed methods. Fellows who accepted an invitation completed a consult followed by the CCL, a measure of cognitive load during consults, and an interview. Three authors conducted a thematic analysis. Member checks and triangulation with fellows supported theme trustworthiness. Subsequently, three authors rated the extent and cognitive demand of each theme expressed in each transcript. The authors measured interrater reliability and used Spearman correlation to describe the association of these ratings with CCL scores. The authors examined themes to identify strategies that educators might use. Results: Analysis revealed four themes: “nature and scope,” which conceptually aligned with intrinsic cognitive load (IL); “leveraging resources,” which had elements of both IL and extraneous cognitive load (EL); “extraneous factors,” which aligned with EL; and “drivers,” which aligned with germane cognitive load (GL). Interrater reliability for extent and demand ratings ranged from 0.57 to 0.79. The correlation between “nature and scope” and IL was 0.37, “extraneous factors” and EL 0.71, and “drivers” and GL 0.32. “Leveraging resources” did not correlate with IL (0.06) or EL (-0.09). Potential strategies based on themes included offering level-appropriate assistance to match IL, focusing the fellow’s attention to reduce EL, and providing succinct teaching to promote GL. Conclusions: This study provided deep insight into the fellow consult experience and suggested trustworthy strategies that educators can use to design and guide consult learning. The theme “leveraging resources” merits further exploration.

Making an Effort Versus Experiencing Load

In cognitive load theory (CLT), the role of different types of cognitive load is still under debate. Intrinsic cognitive load (ICL) and germane cognitive load (GCL) are assumed to be highly interlinked but provide different perspectives. While ICL mirrors the externally given task affordances which learners experience passively, germane resources are invested by the learner actively. Extraneous affordances (ECL) are also experienced passively. The distinction of passively experienced load and actively invested resources was inspired by an investigation where we found differential effects of a learning strategy training, which in fact resulted in reduced passive load and increased actively invested effort. This distinction is also mirrored in the active and passive forms for effort in German language: “es war anstrengend” (it has been strenuous) vs. “ich habe mich angestrengt” (I exerted myself). In two studies, we analyzed whether we could distinguish between these active and passive aspects of load by using these phrases and how this distinction relates to the three-partite concept of CLT. In two instructional design studies, we included the active and passive items into a differentiated cognitive load questionnaire. We found the factor structure to be stable, with the passive item loading on the ICL factor and the active item loading on the GCL factor. We conclude that it is possible to distinguish between active and passive aspects of load and that further research on this topic could be constructive, especially for learning tasks where learners act in a more self-regulated way and learner characteristics are taken into account.

Managing LearningThrough Mathematical Literacy Based on Cognitive Load

The object of mind is something that must be taught by the teacher to the student in the form of learning, or we know that the teaching is the process ofinteraction between students, between students and teachers, and learning resources in the learning environment so that with it we as educators need to optimize thestudent's thought process so that students can be optimal for information. There are three sources of how students obtain optimization in the learning process, namely: intrinsic cognitive load (dependingon the difficulty level of amaterial), extraneous cognitive load (depending on the presentation of thematerial) and germane cognitive load (whichis imposed by teaching methods that lead to better learning outcomes). the results showed that the interactive element is well managed and extraneous cognitive load is suppressed to a mable so that it creates a large enough germane cognitive load.

Intrinsic Cognitive Load in Online Learning Model of School Mathematics 1 in Covid-19 Pandemic Period

<p class="JRPMAbstrakTitle">The study aims at determining the emergence of intrinsic cognitive load in online learning models of School Mathematics 1 in Covid-19 pandemic period. This research is a descriptive qualitative one the data of which are obtained from observation sheets, questionnaires and interview results. Validity checking uses the triangulation method. The results of the study show that the intrinsic cognitive load is caused by the interactivity and isolated/interacting elements contained in the learning process. Elements of interactivity are in the form of terms or concepts in Mathematics learning. These terms or concepts, for examples, are the meaning of Knowledge, Standard Measurement, Mathematical Approach, Intertwined Principles, Content, Context, Competence, PISA Learning Concepts, De-conceptualization, Systems Approach, Conceptual Approach, etc. Isolated/interacting elements are seen from looking for examples of implementation in the real world and actualization of events in Indonesia. An example of implementation in the real world is an element that interacts in real situations in the learning practice of Mathematics.</p>

The imagination effect when using textual or diagrammatic material to learn a second language

The imagination effect occurs when students learn better from imagining concepts and procedures rather than from studying them. Cognitive load theory explains the effect by better use of available working memory resources and increased productive, intrinsic cognitive load. The effect has been found in numerous empirical studies. However, in the majority of studies demonstrating the effect, visual/spatial information has been used: the type of information that is believed to be easy for imagining. The reported two experiments investigated if an imagination effect could be obtained using verbal information in the area of learning a second language. The results indicated that while textual material was expectedly more difficult for learning than diagrammatic material, asking learners to imagine textual material did improve performance. Cognitive load theory has been used as a theoretical framework to interpret the results.