Reconsidering Cognitive Load in Web based Instruction
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Reconsidering Cognitive Load in Web based Instruction

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This study proposes a new instrument to measure cognitive load types related to user interface and demonstrates theoretical assumptions about different load types. In reconsidering established ...

This study proposes a new instrument to measure cognitive load types related to user interface and demonstrates theoretical assumptions about different load types. In reconsidering established cognitive load theory, the inadequacies of the theory are criticized in terms of the adaption of learning efficiency score and distinction of cognitive load types. Since measurement of mental effort does not cover all types of cognitive load, a new way of isolating different loads is required. Previous studies have focused on designing interface to reduce extraneous cognitive load. However, interface may have the potential to enhance germane cognitive load because learners may construct their knowledge schemata with interface layouts.

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  • The main reason why I came to this study was because interface was isolated from other two layers, learning content and pedagogical plan.
  • IntrisinsicThe number of elements or the complexity of materials depends on the learner’s degree of prior knowledgeFor example, a large number of elements may equate to a single element for experienced learners who have a schema for incorporating this information. High levels of element interactivity cause heavy intrinsic cognitive load, since learners must simultaneously interact with various types of new information. Previous studies have applied sequencing and adaptive strategies in order to avoid the heavy intrinsic load and optimize the complexity of learning content based on learner expertise. For example, sequencing methods were used to control intrinsic cognitive load; items or tasks were ordered from simple to complex, or isolated to interacting also adaptive instruction ID spends its time here.ExtraneousJunkTraditional cognitive load research has focused on designing instructional methods to decrease extraneous cognitive load Studies have examined ways to effectively present learning materials on screen with sensory processing, such as the use of dual coding, the integration of multiple resources and the avoidance of unnecessary materials. These methods are related to the design guidelines of user interface, which seek to effectively present elements on computer screen. Germanea cognitive resource in which learners invest their mental effort to enhance understanding of learning content Germane cognitive load promotes learning by encouraging learners to devote cognitive resources to structure knowledge schema through engagement or motivation. schema construction and automation are major learning mechanisms Strategies elaborative sequencing redirecting attentionExplanatory feedbackCompletion problems
  • subjective methods usually involve a questionnaire comprised of a six or nine point Likert scale, with which learners can indicate their level of mental effort, fatigue or frustration.The secondary-task technique (e.g., Brunken, Plass & Leutner, 2003, 2004) uses the performance of a task that is performed concurrently with a primary task (dual-task performance). For example, when learners are reading information on the screen (primary task), they are instructed to press the space bar on the keyboard as soon as they hear a tone (auditory type second task). Typical performance variables are reaction time, accuracy or error rate, and they reflect the cognitive load imposed by the primary task. However, the secondary task may interrupt a primary task. a psychophysiological technique, uses physiological variables, such as eye movements (Amadieu et al, 2009) or neuroimaging (Whelan, 2007), to measure a learner’s cognitive burden. This test requires specific equipment and is difficult to use in an authentic learning context.
  • Though secondary-task and psychophysiological techniques were recently introduced, most researchers favor rating scales as measurement tools since they are simple and non-invasive The cognitive load levels obtained by rating scales have been used to quantify instructional efficiency scores with learning performance. High instructional efficiency occurs when learner performance is higher than learner mental effort. Paas and Van Merrienboer (1993) developed a computational approach to combine measures of mental effort with measures of the associated primary task performance in order to compare the learning efficiency of instructional conditions. a variety of variables have been adapted to the efficiency formula, such as mental effort in learning phase (Kalyuga, Chandler & Sweller, 1998), and a different formula E = P/ME was proposed (Kalyuga & Sweller, 2005; Moreno & Valdez, 2005). More variables, like time to complete instruction (Salden et al., 2004) and mental effort in both the learning and test phases (Tuovinen & Paas, 2004) have been added to the formula.
  • Currently, there is not measure to isolate the types. NextFirst, the efficiency measure was applied during the assessment phase Second, they tried moving it to the learning phase Third, why is lower effort actually bad? Don’t we want more mental effort in learning?Some folks have tried things, like Eveland and Dunwoody’s study (2001) used multiple items to measure cognitive load, but they still didn’t distinguish among them.
  • A functional, communicative and aesthetically appropriate user interface plays an important role in helping learners focus on learning activities. Attractive displays can stimulate the learner’s engagement and improve learning performance reduce extraneous usability could be used as a measure of extraneous for WBIAlso the interface could be used to support the acquisition of learning for a domainEmbed cues in the user interface to support learners’ cognitive processing to build schemata
  • Derived from Reeves et al. (2002) elearning heuristics, which were based on Jakob Nielsen’s usability characteristics.
  • Based on schema construction and schema automation.
  • we conducted three correlation tests. The relationship between intrinsic load and germane cognitive load was not significant in a correlation test (p = .294,r = -.170),and extraneous load and intrinsic loads did not have a significant correlation (p < .539, r = .100). However, there was a significant relationship between germane load and extraneous load (p < .001, r = -.567).
  • Multiple regressionRegression results indicated that the set of independent variables explained 63.4% (p < .001) of the variance in the posttest score, with two of three variables having a significant influence on learning performance. In order of importance, they were germane cognitive load measured by engagement level (β = .686, p < .001) and intrinsic cognitive load measured by pretest score (β = .279, p = .010). Extraneous cognitive load measured by usability level was not a significant predictor of the posttest scores (β = .024p = .848).
  • Even though the germane cognitive load and extraneous cognitive loads measured in this study were related to interface, the significant relationship between them supported the assumption from cognitive load theory. Lower extraneous cognitive load, or higher usability, tends to have higher germane cognitive load. (less crap = more engagement)In other words, well-designed interface can not only reduce extraneous cognitive load, but it can also enhance knowledge construction and automation. The relationship between intrinsic and germane is surprising. We expected that more knowledgeable learners wouldn’t invest as must or ignore the interface cues; therefore, they would rate the schema items lower. But there was not a significant relationship at all. Germane cognitive load was the most significant predictor, even more than intrinsic, which is a little surprising. Extranous load did not influence learning performance. However, extraneous could have an indirect effect on learning performance because extraneous and germane are correlated to one another. An interface could reduce extraneous cognitive load and foster germane cognitive load, which positively influences learning performance. Therefore, an effectively designed interface that enhances germane cognitive load can promote scaffolding of learning. Instructional designers should consider the role of interface as a cognitive aid when designing an interface for an instructional unit.Extraneous and usability are a little puzzling. There was not a significant predictor for extraneous. Designing a good interface could create a neutral environment, but the point here was to create an interface that specifically cued learning. So….I don’t know.
  • Sample size was small (n= 40) and the instructional unit was specific. The proposed instrument is not revolutionary. It is self-reported and consists of subjective survey items. Moreover, two measurements for germane and extraneous loads are limited to only interfaceThe pretest score for intrinsic cognitive load represents overall content knowledge. In future research, data provided by the learner-reported difficulty level rating could contribute to the measurement of intrinsic cognitive load. In addition, the term “engagement level” may confuse readers, and it is not interchangeable with germane cognitive load.

Reconsidering Cognitive Load in Web based Instruction Reconsidering Cognitive Load in Web based Instruction Presentation Transcript

  • Reconsidering Cognitive Load:  Examining the different types & measuring them if we can
    Cognitive Psychology Seminar
    The University of Memphis
    Dr. Michael M. Grant
    University of Memphis
    Dr. Jongpil Cheon
    Texas Tech University
    March 31, 2010
    Michael M. Grant 2010
  • Learning with WBI
    WBI
    Learning Content
    Pedagogical
    Plans
    Isolation
    Interface
    Learner
  • Cognitive Load Theory
    Basic assumptions – limited working memory
    Extraneous cognitive load
    an irrelevant cognitive resource caused by the layout, navigation, structure or medium of instruction.
    an inherent cognitive resource caused by the complexity of learning content.
    Intrinsic cognitive load
    a relevant cognitive resource caused by learners' investment on schema construction and automation.
    Germane cognitive load
    All are additive
    View slide
  • Applications of Cognitive Load Theory
    Extraneous cognitive load
    Traditional CLT research : reducing extraneous load
    New application approaches
    Controlling the complexity of learning contents
    Adaptive instruction with learners’ content expertise
    Fostering germane cognitive load
    Intrinsic cognitive load
    Germane cognitive load
    Schema construction & automation
    View slide
  • Cognitive Load Theory Research
    Learning efficiency score = performance score & mental effort
    (E = efficiency, P = performance, ME = mental effort)
    (P and ME values were standardized into z scores)
  • Limitations
    No measurement to isolate each cognitive load type
    Extraneous cognitive load
    Intrinsic cognitive load
    Germane cognitive load
    Positive
    Negative
    Learning efficiency concept
    Why lower mental effort is preferred?
    Mental effort measurement
    “Please indicate how difficult the instruction/test you just took was by clicking on the appropriate degree of difficulty”
  • Three layers of the interaction between WBI and a learner
    WBI
    Learning Content
    Pedagogical
    Plans
    Connection
    Interface
    Learner
  • New Way to Measure Cognitive Load
    Extraneous cognitive load
    Usability
    Intrinsic cognitive load
    Difficulty of instruction
    Germane cognitive load
    Schema construction and automation
    It may strengthen the theoretical foundation of cognitive load theory.
  • Measuring Intrinsic Cognitive Load
    Prior knowledge
    Score from pretest
  • Measuring Extraneous Cognitive Load
    Usability level
    The menu in the instruction is easy to navigate.
    I can identify easily where I am and where I should go.
    The amount of information on each page was appropriate to understand.
    The information layout and locations are consistent throughout the instruction.
    Graphics or other elements on the pages are not distracting.
  • Measuring Germane Cognitive Load
    Engagement level (Schema construction and automation)
    The interface contributed to my understanding of (learning content).
    The interface helped me to mentally organize the structure of (learning content).
    As I progressed throughout the unit, the interface helped me to relate later concepts to earlier concepts.
    While proceeding throughout the unit, the interface helped me to remember the structure of (learning content).
    When I think about what I just learned, I remember the content in terms of the interface’s layout.
    Pilot study with 52 undergraduate students
    - Reliability score = .944
  • Methodology
    Participants: 40 (43 originally) undergraduates in the Journalism department
    Instructional Units: Introduction to Public Relations
    Data Collection: (a) pretest score (20 items)
    (b) posttest score (same 20 items)
    (c) engagement level of the instruction
    (d) usability level of the interface
    Data Collection Procedure: Phase 1 - Pretest
    Phase 2 – instruction
    Posttest
    Survey on engagement & usability
  • Results
    Reliability test of responses to engagement items & usability items
    Engagement: Cronbach’s Alpha = .914
    Usability: Cronbach’s Alpha = .767
    All items acceptable
  • Results
    Learning Performance
    Significant difference
    (t= - 12.388, p < .001)
  • Results
    Extraneous cognitive load
    Usability score inverted
    M = 4.21 —> M = 1.79
    Intrinsic cognitive load
    Pretest score invertedM = 10.18 —> M = 9.83
    Germane cognitive load
    Engagement level
    M = 4.02
  • Results
    Correlations
    Intrinsic cognitive load
    p = .539
    r = .100
    p = .294
    r = -.170
    Extraneous cognitive load
    Germane cognitive load
    p < .001
    r = -.567
  • Results
    Multiple Regression
    Intrinsic cognitive load
    Β = .279
    Β = .686
    Learning
    performance
    Germane cognitive load
    63.4% explained
    Β = .024
    Extraneous cognitive load
  • Discussion
    Lower extraneous tends toward higher germane
    Prior knowledge was not related to extraneous or germane
    Germane was the most significant predictor of performance
    Indirect impact on performance by extraneous
    Advancement of cognitive load theory
    Using an interface to promote germane engagement has potential
    Extraneous and usability are puzzling
    Practice of instructional design
  • Discussion
    Limitations & Future Research
    Small sample size
    Self-report
    Pretest score represents all prior knowledge
    Specific to Web-based instruction
    Difficult level could be combined to help measure intrinsic load
    Moving toward more sophisticated analysis
  • Michael M. Grant 2010