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The Evolution of Cognitive Load Theory and the Measurement of Its Intrinsic, Extraneous and Germane Loads: A Review


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Cognitive Load Theory has been conceived for supporting instructional design through the use of the construct of cognitive load. This is believed to be built upon three types of load: intrinsic, extraneous and germane. Although Cognitive Load Theory and its assumptions are clear and well-known, its three types of load have been going through a continuous investigation and re-definition. Additionally, it is still not clear whether these are independent and can be added to each other towards an overall measure of load. The purpose of this research is to inform the reader about the theoretical evolution of Cognitive Load Theory as well as the measurement techniques and measures emerged for its cognitive load types. It also synthesises the main critiques of scholars and the scientific value of the theory from a rationalist and structuralist perspective.
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The evolution of Cognitive Load Theory and the
measurement of its intrinsic, extraneous and germane
loads: a review
Giuliano Orru, Luca Longo*
School of Computing, College of Health and Sciences,
Dublin Institute of Technology, Dublin, Ireland
Abstract. Cognitive Load Theory has been conceived for supporting
instructional design through the use of the construct of cognitive load. This is
believed to be built upon three types of load: intrinsic, extraneous and germane.
Although Cognitive Load Theory and its assumptions are clear and well-known,
its three types of load have been going through a continuous investigation and re-
definition. Additionally, it is still not clear whether these are independent and can
be added to each other towards an overall measure of load. The purpose of this
research is to inform the reader about the theoretical evolution of Cognitive Load
Theory as well as the measurement techniques and measures emerged for its
cognitive load types. It also synthesises the main critiques of scholars and the
scientific value of the theory from a rationalist and structuralist perspective.
Keywords: Cognitive Load Theory, Cognitive Load types; Intrinsic Load,
Extraneous Load, Germane Load; Measures; Instructional Design; Efficiency.
1 Introduction
The construct of Cognitive Load (CL) is strictly related to the construct of Mental
Workload (MWL). The former has evolved within Educational Psychology [1], while
the latter within Ergonomics and Human Factors [2]. Despite their independent
evolution within different disciplines, both are based upon the same core assumption:
the limitations of the human mental architecture and the cognitive capacities of the
human brain and its working memory [3] [4]. In a nutshell, as professor Wickens
suggested [5], mental workload is equivalent to the amount of mental resources
simultaneously elicited by a human during the execution of a task. In order to achieve
an optimal performance, the working memory limits should not be reached [3] [4]. If
this occurs, the mental resources are no longer adequate to optimally execute the
underlying task. Within Ergonomics, the construct of Mental Workload has evolved
both theoretically and practically. A plethora of ad-hoc definitions exist as well as
several domain-dependent measurement techniques, measures and applications [1].
While abundance of research exists, the science of Mental Workload is still in its
infancy because any of the proposed measures can generalise the construct itself.
Similarly, within Educational Psychology, despite Cognitive Load Theory (CLT) is one
of the most invoked learning theory for supporting instructional design [6], research on
how to develop highly generalisable measures of Cognitive Load is limited. Also, it is
unclear how its three types of load intrinsic, extraneous and germane can be
measured and how they interact with each other. The aim of this paper is to provide
readers with the theoretical elements underpinning the construct of Cognitive Load.
This is done from an evolutionary perspective of the measurement techniques and
measures emerged from the three types of load, accompanied with a critical discussion
of their scientific value.
The remainder of the paper is structured as follows. Section 2 presents the key
theoretical elements and assumptions of Cognitive Load Theory, as appeared in the
literature. Section 3 focuses on a review of the measurement techniques and measures
emerged for its intrinsic, extraneous and germane loads. Section 4 builds on this review
by emphasising the open debate on the scientific value of CLT. Section 5 highlights
new perspectives and research on CLT and the reconceptualization of its cognitive load
types, as recently emerged in the literature. Section 6 summarises this study with final
remarks suggesting novel research directions.
2 Cognitive Load Theory
Cognitive Load Theory (CLT) is a cognitivist learning theory aimed at supporting
instructors in the development of novel instructional designs aligned with the
limitations of the human cognitive architecture. In a nutshell, this architecture is the
human cognitive system aimed at storing information, retrieving and processing it for
reasoning and decision making [7]. CLT is based upon the assumption of active
processing that views the learner as actively engaged in the construction of knowledge
[8]. In other words, learners are actively engaged in a process of attention to relevant
material and its organisation into coherent structures that are integrated with prior
knowledge [9]. Another premise of CLT is the dual-channel assumption by which
processing of information occurs in two distinct channels: an auditory and a verbal
channel. The former processes auditory sensory input and verbal information while the
latter processes visual sensory inputs and pictorial representations [10]. An essential
component of this architecture is its memory that can store information for short and
long term. According to another premise, the limited capacity assumption of CLT, the
former memory, also referred to working memory, is conscious and limited, while the
latter is unconscious and unlimited [2]. Baddeley [3] and Paivio [4], following Miller
proposal [7] , support the view that when working memory has to deal with new
information, it can hold just seven chunks at a time. However, if these chunks are related
and if they have to be processed, human beings are capable to handle just two or three
at the same time [11]. Expanding the capacity of working memory coincides with
learning [2]. Learning take places by transferring pieces of information from working
memory to long term memory [3] [4]. According to Schema Theory, this transfer of
information allows the construction of knowledge, in long term memory, in the form of
schema [12]. To construct a schema means to relate different chunks of information
from a lower level to a higher level of complexity and to hold them as a single unit that
can be understood as a single chunk of information [12]. In turn, schema can be
retrieved to solve a problem, a task, or more generally to answer a question in
educational contexts. Schema construction is believed to reduce the load in working
memory [2]. The expansion of long term memory can be achieved by a reduction of the
load of working memory. Leaving sufficient cognitive resources in working memory to
process new information is one of the core objectives of educational instructional
design. In fact, if the amount of information that has to be held in working memory lies
within its limits, the learning phase is facilitated. Contrarily, if the amount of
information overcomes these limits, an overload situation occurs and the learning phase
is hampered.
Figure 1: A representation of the mental architecture and the role of Cognitive Load Theory
(CLT) in connection to working memory and schema construction
A core construct within CLT is Cognitive Load (CL), believed to be multidimensional.
Intuitively it can be defined as the mental cost imposed by an underlying cognitive task
on the human cognitive system [13]. It is possible to distinguish two types of factors
that can interact with cognitive load: causal and assessment factors (figure 2). The
formers affect cognitive load while the latter are affected by cognitive load. The causal
factors include:
features of the task (T) such as structure, novelty and pressure;
the features of the environment (E) such as noise and temperature where a task
(T) is executed and their interaction (ExT);
the characteristic of a learner (L) such as capabilities, cognitive style and prior
the interaction between environment and learner characteristics (ExL);
the interaction between task, environment, learner’s characteristics Ex(TxL).
The assessment factors can be conceptualised with three dimensions: mental load,
mental effort and mental performance. Mental load is imposed by the task (T) and/or
by demands from the environment. It is a task-centred dimension, independent of the
subject, and it is considered constant. Mental effort is a human-centred dimension that
reflects the amount of controlled processing (capacity or resources allocated for task
demands) in which the individual is engaged with [13]. It is affected by the task-
environment interaction (ExT), the subject characteristics interaction with the
environment (ExL) and the interaction of the learner with the task in the environment
(Ex(TxL)). Similarly, the level of mental performance is affected by the factors that
affect mental effort [4]. Other factors might affect cognitive load [14] [15] and research
in the field has not produced a comprehensive list yet [16].
Figure 2: Causal factors and assessment factors according to [41].
Starting from the research of Halford et al. [17] on the difficulty in processing
information with multiple elements at the same time during problem solving, Sweller
defined the degree of complexity of these elements as `element interactivity' [18].
Starting from this definition, two types of cognitive load has emerged: the intrinsic and
the extraneous loads. Intrinsic load refers to the numbers of elements that must be
processed simultaneously in working memory (element interactivity) for schema
construction. ‘This type of load cannot be modified by instructional interventions
because it is intrinsic to the material being dealt with. Instead, extraneous cognitive load
is the unnecessary cognitive load and can be altered by instructional interventions' [2].
Sweller stated that the basic goal of Cognitive Load Theory is the reduction of
extraneous load: this is a type of ineffective load that depends on the instructional
techniques provided by the instructional format to complete a task [2]. This view is
supported by Paas and colleagues that refers to extraneous load as the cognitive effect
of instructional designs that hamper the construction of schema in working memory
[19]. Beside, intrinsic and extraneous, Sweller defined another type of load: the
germane load [2]. This is the extra effort required for learning (schema construction).
It is possible to use this effort when intrinsic and extraneous loads leave sufficient
working memory resources. This extra effort increases cognitive load, but it is
connected to learning, thus, it facilitates schema construction. Germane load is the
effective cognitive load and it is the result of those beneficial cognitive processes such
as abstractions and elaboration that are promoted by ‘good’ instructional designs [20].
Reducing extraneous load and improving germane load by developing schema
construction and automation should be the main goal of the discipline of instructional
design. The three types of load emerged within Cognitive Load Theory, and their role,
can be summarised in figure 3.
Figure 3: Definitions and role of the cognitive load types of Cognitive Load Theory.
Sweller and colleagues, with their attempt to define cognitive load within the discipline
of Educational Psychology and for instructional design, believed that the three types of
load are additive. This meant that the total cognitive load experienced by a learner in
working memory while executing a task, is the sum of the three types of load, these
being independent sources of load [2] (figure 4).
Figure 4: Additive definition of overall cognitive load.
Figure 5 depicts the relationship between the three types of cognitive load, as proposed
in [21]. In condition A (overload), cognitive load exceeds the limits of the working
memory of the learner due to an increment in the extraneous load. In turn errors are
more frequent, longer task execution times occur, sometimes even leading to the
inability to perform an underlying task. In condition B there is spare working memory
capacity and the learners can perform optimally on an underlying task. With spare
capacity, CLT proposes to increase the germane load in order to activate learning tasks,
as in condition C.
Figure 5: Relationship between the three types of cognitive load
3 Cognitive load types, measurement techniques and measures
The definition of the types of load within Cognitive Load Theory (CLT) are supported
by empirical studies that have used three different classes of measures:
task performance measures such as error rates, learning times and secondary
task measures;
subjective measures such as self-reporting questionnaires and rating scales;
physiological measures such as eye movements and physical body responses.
Within Educational Psychology, the focus has always been on the first two classes: task
performance and subjective measures. The reason is intuitive since physiological
measurement techniques require special equipment to gather data, trainer operators to
use this equipment and they are intrusive, most of the time not suitable for empirical
experiments in typical classrooms. Additionally, evidence suggests they did not prove
sufficient sensitivity to differentiate the three cognitive load types [13] envisioned in
CLT. As a consequence, the next sections mainly focus on research studies that
employed task performance and subjective measures.
Miwa et al. [21] developed a task-performance based method for cognitive load
measurement built upon the mental chronometry paradigm [22], in line with the
triarchic view of load [2]. The mental chronometry assumes that reaction time can
reveal the quantity of intrinsic, extraneous and germane loads coming from the
corresponding cognitive processes. The hypothesis behind their experiment is that if, in
the 8x8 Reversi game (figure 4), the three types of cognitive load are manipulated by
changing the presentation of the information to the players, it is possible to measure
them by observing their reaction time of players between movements of discs on the
board. To manipulate the intrinsic load, on one hand, an advisor computer agent
provides some hints to participants on the possible subsequent move (low intrinsic load
condition, figure 6 left). On the other hand, in another condition, no hints are provided
(high intrinsic load condition, figure 6 right). In addition to this, in order to manipulate
extraneous load, the white and black discs are changed with two different letters from
the Japanese alphabet. Since these 2 letters are perceptually similar, they are expected
to lead to higher perception and understanding exerted by participants. Eventually, the
germane load is manipulated by altering the instructions presented to participants. In
order to exert more germane load, each participant is requested to report, after the game,
the heuristics learnt to play it. According to this paradigm, if the reaction time, in high
intrinsic load conditions, is longer than the reaction time, in low intrinsic load
conditions, then it can be considered as a valid indicator of intrinsic load. Here, learning
corresponds to the development of effective strategies for moving discs that can lead a
participant to win the game. These strategies imply that players control and regulate
their cognitive processing by meta cognitive perspectives, thus increasing their germane
load [21].
Figure 6: Low and high extraneous load conditions in the 8x8 Reversi game [35].
The research attempt by Miwa and colleagues [21] is indeed useful to investigate the
discrimination between the three types of load and to provide guidelines on how to
design experiments that contribute to the definition of cognitive load. Their preliminary
findings suggest the three types of load are separable. However, it was executed in a
highly controlled environment and not in more natural settings such as in a typical
classroom, thus limiting the generalisability of their findings.
3.1 Subjective measures of cognitive load
Gerjets et al. [23] proposed two experiments on the use of hypermedia environments
for learning on the topic of probability theory. In the first experiment, the validity of
multimedia principles [10] in hypermedia environments has been tested. In the second
experiment, an analysis of the ability of learners to impact their performance according
to their prior experience was performed. A subjective 9-point Likert scale is employed
to measure the three types of load during learning (table 1).
Table 1: Subjective rating scale to measure cognitive load types from [23]. Each item has to be
rated on a 9 point Likert scale (1=extremely easy, 9=extremely difficult)
Type of load
Intrinsic Load
How easy or difficult do you consider probability theory at this moment?
How easy or difficult is it for you to work with the learning environments?
How easy or difficult is it for you to distinguish important and
unimportant information in the learning environments?
How easy or difficult is it for you to collect all the information that you
need in the learning environment?
Germane Load
Indicate the amount of effort you exerted to follow the last example
Depending on the prior knowledge of learners, high intrinsic and extraneous loads
should lead to poor learning outcomes while high germane load should lead to good
learning outcomes. Unfortunately, in this study no evidence of this connection has been
found. Consequently, authors claim that subjective rating scales are valid if learners are
able to distinguish the types of cognitive load. In other words, in order to be sensitive
to the differences in loads, learners should be aware of the cognitive process connected
to the experienced load. To achieve this, training learners on Cognitive Load Theory
can facilitate their understanding of the three types of load. However, this is not an easy
condition to achieve. In fact, for instance, the level of difficulty (intrinsic load) could
be due to the poor instructional design that increase the extraneous load, or due to the
natural complexity of an underlying learning task. A novice learner could find this
distinction really hard to understand and could not be able to comprehend if the own
difficulty in learning can be attributed to the instructional design (extraneous load) or
the complexity of the task (intrinsic load).
Corbalan et al. [24] hypothesises that, in order to prevent cognitive overload, it is
possible to adapt task difficulty and the support of each newly selected learning task to
the previous knowledge and experience of a learner and his/her perceived task load [24].
This can be done by employing some external agent, the learner him/herself, or both.
The hypothesis was tested by employing two subjective rating scales, one for task load
and one for germane load (as per table 2). This hypothesis was tested by performing an
empirical 2x2 factorial design experiment with health sciences students. The design
variables where the factors adaptation (absence or presence of the agent) and the control
over task selection (program control or shared control program/learner).
Table 2: Measurements of Task and Germane load on a 7-point scale from [24]
(1=extremely low, 7=extremely high)
Type of load
Task load
Rate your effort to perform the task
Germane Load
Evaluate the effort invested in gaining understanding of the relationship
dealt with in the simulator task
Findings suggest that, on one hand, the presence of adaptation delivered more efficient
learning and task involvement. On the other hand, shared control produced a higher task
involvement when compared to program task selection. Learning here refers to good
learning outcomes and lower effort exerted in the underlying learning tasks. Task
involvement refers to good learning outcomes and higher effort exerted in the learning
task. Both the cases prevented cognitive overload [24].
Ayres hypothesised that, by maintaining the extraneous and the germane loads
constant, students can identify changes in the `element interactivity’ within problems
by means of subjective measures, and thus successfully quantify the intrinsic cognitive
load [25]. In his study, extraneous and germane loads are maintained constant by not
providing any instructional hint. Learners had to solve a set of four brackets-expansion
problems without any explicit instructions (source of extraneous load) and without any
didactic feedback (source of germane load). The bracket-expansion problems required
a series of four operations in which the level of difficulty increased. Under this
instructional condition, any change in the overall cognitive load is due to change in the
element interactivity (source of intrinsic load). Intrinsic load is measured by a subjective
measure, as depicted in table 3. After each operation, learners had to rate its difficulty.
The hypothesis is that higher intrinsic load should correspond to more errors.
Table 3: Subjective rating scale of intrinsic load on a 7-point scale from [1]
(1=extremely easy, 7=extremely difficult)
Type of load
Intrinsic Load
How easy or difficult you found each calculation?
The authors tested their hypothesis with two experiments. In the first, students had low
prior mathematical knowledge while in the second, participants had a wider range of
mathematical skills. In the first experiment, students could recognise task difficulty
since subjective intrinsic load was highly correlated with the errors committed by
themselves. In the second experiment, although students did not commit many
mistakes, they still could detect differences in task difficulty. These findings support
the high sensitivity and reliability of the employed self-reporting measure. Additionally,
the takeaway of this study is that, by keeping constant two sources of load out of three,
it is possible to get a measure of the remaining dependent load. Transitively, it turns out
that, by keeping constant the extraneous and the intrinsic loads, any change in cognitive
load, irrespective of the measurement technique employed, corresponds to variations in
the dependent variable, the germane load.
Gerjets et al. [26] investigated how to enhance learning through a comparison of
two instructional designs on the same topic: how to calculate the probability of a
complex event. The first design condition included worked out examples, while the
latter included modular worked examples. To measure the experienced cognitive load
of learners in each condition, a modified version of the NASA-Task Load Index was
(table 4) [27]. Readers are referred to [27] for the original version.
Table 4: Modified version of the Nasa-TLX from [26] where each scale ranged from 0 to 100
(low level to high level)
Type of load
Task demands
(Intrinsic Load)
How much mental and physical activity did you require to
accomplish the learning task? (Thinking, deciding, calculating,
remembering, looking, searching).
(Extraneous Load)
How hard did you have to work to understand the contents of the
learning environment?
Navigational demand
How much effort did you invest to navigate the learning
How successful did you feel in understanding the contents?
How much stress did you experience during learning?
Within Cognitive Load Theory, Sweller [2] stated that task demand is caused by the
degree of element interactivity of the task (intrinsic load), while the effort is exerted to
achieve an effective understanding of the instructional material. The navigational
demands are related to those activities not strictly directed to learning. In this line,
Gerjets and colleagues stated that the scale for task demands is aimed at quantifying the
intrinsic load of the instructional material, the effort scale at quantifying the germane
load and the scale for navigational demands is aimed at quantifying the extraneous load
[26]. The hypothesis of the experiment is that the modular presentation of worked
examples can increase the germane load more than their molar as a whole
presentation. Unfortunately, findings did not provide evidence about any increment of
the germane load. As a possible interpretation, the authors suggested that the
instructional explanations provided during the task to increase the germane load, and
the self-explanation derived using worked out examples, created a redundant
information and the illusion of understanding hampered the learning instead of
improving it. However, in a prior experiment, Gerjets et al. [26] have successful
demonstrated that, in an example based learning, the modular presentation of worked
examples can actually reduce the intrinsic load and improves the germane load more
than the molar presentation of the same problem. The modular presentation provides a
part-whole sequencing of the solution procedures whereas the molar presentation
provides the solutions of the procedures as a whole. The segmentation of the
presentation of the worked example led to a decrease in the degree of interactivity as
well as in the number of simultaneous items. In turn, this led to a decrease in the intrinsic
load. According to the authors, these findings are more relevant to novice learners,
whereas the same instructional design could be redundant for more expert learners
because the degree of their expertise increases. Consequently, in the case of expert
learners, the molar presentation of solution procedures is a more appropriate
instructional design. The modified version of the NASA-TLX, employed in this study,
has been applied also in [28]. Here, authors focused on the effects of different kinds of
computer-based graphic representations in connection to the acquisition of problem-
solving skills in the context of probability theory. Despite different experiments, [29]
[26] and [28] did not provide evidence on the reliability and validity of the subjective
rating scale employed. Therefore, it can be only hypothesised that this scale is sensitive
to the three types of load conceived within CLT.
Galy et al. [30] tested the additivity between the intrinsic, the extraneous and the
germane loads by manipulating three factors believed to have an effect on each of them.
In detail, this study assumed that task difficulty is an indicator of intrinsic load, time
pressure of extraneous load and the level of alertness of germane load. The effect on
the experienced overall cognitive load is connected to the manipulation of the
extraneous and intrinsic loads which are respectively estimated by the self-reporting of
notions of tension (time pressure) and mental effort (task difficulty). The level of
alertness is measured by the French paper-and-pencil version of the Thayers’s
Activation-Deactivation Checklist [31]. Questions are listed in table 5. For each word
in the deactivation list, each student had to tick one from the “not at all”, “don't know”,
“little” and “much” labels. These labels are respectively mapped to weights (1, 2, 3 and
4). The responses were counted up to have a measure of four factors: general activation
(GA), deactivation sleep (DS), high activation (HA), and general deactivation (GD).
The GA/DS ratio yielded an alertness index.
Table 5: Self report scales of cognitive loads types from [30]. Intrinsic and extraneous load are
in the scale 0 to 10 (low time pressure/mental effort to high effort/considerable effort)
Type of load
Intrinsic load
Rate the mental effort (task difficulty) you experienced during the task
Extraneous load
Rate the tension (time pressure) you experienced during the task
Germane load
Select one of the following responses (“not at all”, “don't know”, “little”
and “much”) for each of 20 listed adjectives: active, energetic, vigorous,
full of, lively, still, quiet, placid, calm, at rest, tense, intense, clutched up,
fearful, jittery, wide-awake, wakeful, sleepy, drowsy, tired.
The experimental task consisted of a memory recalling activity with 2 digit numbers
(low difficulty) or 3 digit numbers (high difficulty) in four conditions: low difficulty
and low time pressure, low difficulty and high time pressure, high difficulty and low
time pressure, high difficulty and high time pressure. The difference in cognitive load
due to variations in task difficulty and time pressure with respect to the different levels
of alertness can be taken as an indicator of differences in the contribution of germane
load. In low difficulty and low time pressure conditions, germane load is believed to be
substantially inexistent, but in high difficulty and high time pressure conditions, it is
assumed that the learner has to employ specific strategies to execute the memory task
and thus generating germane load. Authors believed that germane load, as a function of
alertness, corresponds to the subject’s capability to select strategies to be employed
while performing the learning task. However, the implementation of these strategies is
determined by the amount of free cognitive resources determined by task difficulty and
time pressure [30]. Consequently, the authors claimed that alertness is a germane load
factor depending on the quantity of working memory resources left by the intrinsic and
extraneous load experienced.
Leppink et al. [32] developed a new instrument for the measurement of intrinsic,
extraneous and germane loads. The authors consider the critique of Kalyunga et al. [33]
about the expertise reversal effect and its consequences on the learning and on the
different types of load. According to this, the same instructional feature may be
associated with germane load for a learner and with extraneous load for another learner,
depending on the level of expertise and on the level of prior knowledge. To develop a
more sensitive instrument to detect changes in cognitive load types, they proposed a
multi-item questionnaire (table 6). Authors conducted experiments in four lectures of
statistics, asking to rate difficult or complex formulas, concepts and definitions using
the scales in table 6. In a number of studies, Leppink and colleagues verified 7
hypotheses regarding the reliability of the new instrument compared with other
instruments, used in the past, to measure intrinsic load [25], extraneous load [34],
germane load [35] and for overall cognitive load [36]. They also tested five hypotheses
connected to the expected relationship between prior knowledge and intrinsic load, and
between prior knowledge and learning outcomes. Through an exploratory analysis, it
has emerged that the reliability of the rating scale was positive, the extraneous load and
the germane load elements were negatively correlated and the elements that were
supposed to measure intrinsic load were not correlated to germane load.
Table 6: Multi-subjective rating scales of cognitive load types from [32] in the scale 0 to 10
(0=not at all, 10=completely).
Type of load
Intrinsic Load
The topic/topics covered in the activity was/were very complex
The activity covered formulas that I perceive as very complex
The activity covered concepts and definitions that I perceived as very
The instruction and/or explanation during the activity were very unclear.
The instruction and/or explanation were, in terms of learning, very
The instruction and/or explanations were full of unclear language.
The activity really enhanced my understanding of the topic(s) covered
The activity really enhanced my knowledge and understanding of
The activity really enhanced my understanding of the formulas covered
The activity really enhanced my understanding of concepts, definitions
Eventually, the elements that were expected to measure intrinsic load had moderate
correlation with extraneous load. The validity of the scales was verified by comparing
the subjective ratings with the learning outcomes assessed by a performance test. As
hypothesised, a high prior knowledge corresponded to a low intrinsic load. Extraneous
cognitive load was higher when a problem was solved by an unfamiliar format and
germane load was higher when a problem was solved by a familiar format. There is
partial evidence that higher germane load, as measured by multiple subjective scales,
lead to higher results on post-task test performance.
Leppink and colleagues [37] criticised their own previous study [32] mentioning the
uncertainty of their multiple subjective rating scales to represent the three different
types of cognitive load. The main reasons of their critique are three: 1) the correlation
between germane load and the learning outcomes, in the task performance, was lower
than expected and not statistically relevant 2) the previous experiments were all focused
on a single topic, namely statistic 3) the manipulations applied in [32] did not lead to
the expected differences in the measurement of the three different cognitive load types
[37]. In summary, their psychometric instrument might have measured only the level of
expectation instead of the actual invested effort devoted in the complexity of the activity
(intrinsic load), its ineffective explanations (extraneous) and its understanding
(germane). To evaluate a more direct relation between the three types of load and the
learning outcomes, a randomized experiment was performed, with bachelor students
who received a description of the Bayes theorem. To measure the three different types
of load, the authors changed the order of the rating scales and added three items to it (as
per table 7). These items were supposed to contribute to the evaluation of the internal
consistency of the theoretical assumption that the three types of load are separated,
additive and independent. Findings suggest that two items improved the internal
consistency of the mental effort for intrinsic and extraneous loads but not for germane
load, suggesting its re-definition [6].
Table 7: Informed subjective rating questionnaire proposed in [39] available to learners while
rating different learning scenarios on a 7-point Likert scale (1=very low, 7=Very high)
Type of load
Intrinsic Load
I invested a very high mental effort in the complexity of this activity.
Extraneous Load
I invested a very high mental effort in unclear and ineffective explanations
and instructions in this activity.
Germane Load
I invested a very high mental effort during this activity in enhancing my
knowledge and understanding.
Zukic and colleagues [38] focused on the assessment of the validity of the
instrument developed in [37] as well as its internal consistency and its capability to
correlate with learning outcomes. In their study, the correlations between intrinsic and
extraneous load and between extraneous and germane load were statistically significant.
A low degree of experienced intrinsic load and a high degree of reported germane load
could explain the improvement of the learning outcomes. Additionally, a regression
analysis verified that the items associated to the germane load could actually explain
the perceived learning. Eventually, a confirmatory factor analysis supported the
development of a three-dimensional model that includes the three types of load. The
main take away of this study is that germane load can be measured as an independent
source of load.
Klepsch et al. proposed an alternative way to measure the three load types reliably
and validly [39]. The novelty of their approach is the use of two forms of ratings:
informed and naïve. According to this, they conducted an experiment with two different
group of learners. The first, the informed rating group, was trained on how to
differentiate the three types of load through a theoretical explanation of CLT and its
assumptions. The second, the naïve rating group, did not receive the training on CLT.
Learners were asked to rate 24 learning scenarios grouped in 5 different domains
(language learning, biology, mathematics, technology and didactics). To detect changes
in the cognitive load experienced by the two groups of learners, only one type of
cognitive load was manipulated at a time. The learners in group one received the
questionnaire in table 8, while those in group two received the questionnaire in table 9
Both the groups received also an additional question on perceived overall cognitive load
adapted from [36]. The participants in the informed ratings group correctly
discriminated intrinsic, extraneous and germane loads in line with the expectations.
However, participants, in the Naïve ratings group, correctly discriminated only the
intrinsic and the extraneous loads but they were not able to differentiate germane load.
Table 8: Informed subjective rating questionnaire proposed in [39] available to learners while
rating different learning scenarios on a 7-point Likert scale (1=very low, 7=Very high)
Type of load
Intrinsic Load
During this task, Intrinsic Load was…
Extraneous Load
During this task, Extraneous Load was…
Germane Load
During this task, Germane Load was…
Table 9: First version of the Naïve rating scales questionnaire proposed in [39] 7-point Likert
scale (1=completely wrong, 7=absolutely right)
Type of load
Intrinsic Load
For this task, many things needed to be kept in mind simultaneously
This task was very complex
Germane Load
For this task, I had to highly engage myself
For this task, I had to think intensively what things meant.
Extraneous Load
During this task, it was exhausting to find the important information
The design of this task was very inconvenient for learning
During this task, it was difficult to recognize and link the crucial
A reliability analysis of the scales was executed, by task, using the Cronbach alpha
measure based on the formula presented in [40]. This allowed to compute the mean of
several given alpha values based on sampling distribution. The validity of the measure
was analysed by comparing the ratings of learners with the expectations for each type
of load for each task. A very low reliability was detected for all the tasks, in the informed
ratings group, this being an indicator of the capability of learners to differentiate the
types of load separately. However, in the naïve ratings groups, reliability was high,
suggesting how the three types of load were not clearly separable. In particular, germane
load was the dimension that was not discriminable across the two groups. Starting from
this unsatisfying finding, the authors developed a new scale for germane load (table 10).
Table 10: Second version of the Naïve rating questionnaire proposed in [39] with a new scale
for the germane load on a 7-point Likert scale (1=completely wrong, 7=absolutely right)
Load type
Germane Load
I made an effort, not only to understand several details, but to
understand the overall context
My point while dealing with the task was to understand everything
The learning task consisted of elements supporting my
comprehension of the task
Subsequently, they evaluated the overall new questionnaire with a larger sample. A new
experiment was conducted with a group of students who received 8 tasks, one at a time,
designed to induce more or less germane load. Here, in contrast to the first study, and
in line with the idea of doing experiments in more realistic learning environments, each
learning task was designed to induce changes in the three types of load. For intrinsic
load, the degree of interactivity of the tasks was manipulated. For extraneous load,
different learning formats were considered, some employing text and pictures together,
some individually and some with additional non-relevant information. Eventually,
germane load was manipulated by creating tasks aimed at eliciting different degrees of
deeper learning processes. A reliability and validity analysis, conducted as in the first
experiment, confirmed that it is possible to measure the three types of load separately,
in line with the triarchic theory of load [2].
3.2 Task performance and self-reported measures
Deleeuw and Mayer tested the separability of the three types of load in a multimedia
lesson on the topic of electric motors [41]. Two experiments were executed: one with a
pre-question on the content of the lesson aimed at motivating learners to focus on deeper
cognitive processing, and one without. Authors manipulated extraneous load providing
redundant instructional designs to learners. Similarly, they manipulated intrinsic load
through changes on the complexity of the sentences that explained the lesson.
Eventually, they examined the differences in the germane load by comparing students
with high scores on a test of problem solving transfer, against students with lower
scores. The authors evaluated the sensitivity of the response time to a secondary task
during learning for measuring the extraneous load, the effort ratings during learning for
measuring the intrinsic load, and the self-reported difficulty rating, after learning, for
measuring the germane load (as per table 11). In details, the secondary task consisted
of a visual monitoring task where learners had to recognise a periodic change of colour
and to press the space bar each time this colour change took place.
Table 11: Subjective rating scales and secondary task reaction time proposed by [41] on a 9-
point Likert Scale (1=extremely low, 9=extremely high for intrinsic load and 1=extremely easy,
9=extremely difficult for germane load)
Types of load
Intrinsic Load
Your level of mental effort on this part of the lesson.
Germane Load
How difficult this lesson was.
Extraneous Load
Measured by the response time to a secondary task.
At each of eight points in an animated narration, the background colour
slowly changes (pink to black).
Learner is required to press the spacebar of the computer as soon as the
color changes.
The findings of the experiment supported the triarchic theory of cognitive load [2].
Students who received redundant information needed longer reaction time than students
who did not receive redundant instructional design. The explanation about the electric
motor has been provided by learners using different sentences with different levels of
complexity. The scale for intrinsic load reflected higher effort for high complexity
sentences and lower effort for low complexity sentences. Students who reported a lower
and a higher transfer reflected their difficulty by the rating scale provided: low transfer
reflected high difficulty, high transfer reflected low difficulty. Thus, the authors showed
that these different measures of load (reaction time, effort and difficulty) are sensitive
to different types of load (extraneous, intrinsic, germane) [41]. The three different
variables analysed (redundancy and complexity of statement, high or slow capacity of
transfer to solve a problem) are strongly correlated with the three different types of load,
thus providing evidence for their good sensitivity. Eventually, authors recommended a
replication of their research study in other contexts and with different students because
the measurement of the three cognitive load types might be often intrusive, creating an
artificial learning situation. In addition, the study did not account for the prior
knowledge of learners, (most of them had a low prior knowledge) as an important
variable that could influence the overall perception of load.
Cerniak et al. [34] hypothesised how the split attention effect, proposed by Sweller
[2], could be mediated not only by a reduction of extraneous cognitive load but also by
an increase of germane load (germane load explanation) [42]. An experiment,
conducted in a learning context on physiological processes of a nephron, was aimed at
testing the above the research hypothesis. Authors employed the reaction time on a
secondary as a task performance measure, in order to detect variations in the overall
cognitive load between learners who received an integrated format of instructional
designs and learners who received a split source format. The former learners were
expected to experience less overall load because the integrated format was believed to
decrease their extraneous load. The latter learners were expected to experience more
overall load due to the split attention effect believed to increase their extraneous load,
as suggested in [43]. In the experiment, learners had to press the space bar of the
keyboard of a computer every time a stimulus appeared on the screen (for example the
change of a colour). The longer time required to react to this secondary task, the higher
cognitive load exerted on the primary task. Eventually, subjective ratings were applied
to measure the three types of load, as per table 12.
Table 12: Subjective rating scales for the cognitive load types proposed by [34] on a 6-point
Likert Scale (1=not at all, 6=extremely)
Type of load
Intrinsic Load
How much difficult was the learning content for you?
Extraneous Load
How difficult was it for you to learn with the material?
Germane Load
How much did you concentrate during learning?
Findings showed that there is no difference in the overall cognitive load between
learners who received the split source format and those who received the integrated
source format. As a consequence, the former learners increased their extraneous load
and decreased their germane load, whereas the latter learners decreased their extraneous
load and increased their germane load. This confirms that the extraneous and germane
loads partially mediate the split attention effect. However, authors brought forward a
critique whereby there could be a possible confusion between the two different
questions designed for intrinsic and extraneous loads. Learners could have the
impression to answer the same question. In fact, in a new learning context, learners
might not be able to identify the source of difficulty that means the content or the
instructional material delivered. The authors spotted a high correlation between the
extraneous and the germane loads through an analysis of the learning outcomes.
However, they did not state that these measures of loads were aimed at tackling different
working memory resources. As a consequence, the relation between learning processes
and working memory capacity was not demonstrated.
4 Synthesis and observations on the scientific value of Cognitive
Load Theory
According to the literature review conducted in the previous sections, it appears evident
that the three types of load envisioned in Cognitive Load Theory intrinsic, extraneous
and germane have been mainly measured by means of subjective rating scales. This
has more a practical explanation because self-reporting scales are easier to use and they
do not influence the primary task when compared to secondary task measures. They can
be administered post-learning tasks and they are aimed at representing a perceptual
subjective experience of a learner for an entire learning session. This is in contrast to
secondary task measures which, even if more sensitive to variations of cognitive load,
they are more intrusive since they alter the natural execution of a learning task. A
number of researchers brought forward critiques on Cognitive Load Theory in relation
to its theoretical clarity [44] [45] and its methodological approach [46]. According to
these critiques, the assumptions of CLT appear circular because its three types of load
are believed not to be empirically measurable. Empirical research is based on observed
and recorded data and it derives knowledge from actual experience rather than from a
theory, a belief or a logic coming from first principles ‘a priori’. This is the case of
subjective rating scales aimed at measuring the cognitive load types. Regardless of the
way these scales relate to the evaluation of the different cognitive load types, all of them
underlie the phenomenon they are pretending to measure in their premises or
suppositions, namely the definitions of intrinsic, extraneous and germane loads (figure
7, left). In other words, the premises of CLT its cognitive load types are believed
to be confirmed by the data coming from their measurements circularly, without
empirical evidence.
In addition, the fact that human cognitive processes, related to the same instructional
design, can be regarded as germane load in one case and as extraneous load in another
case, it means that CLT can account for nearly every situation [45]. This critique also
refers to the `Expertise Reversal Effect' [47]. In fact, on one hand, some instructional
design, such as written explanations followed by a graphic element to enhance its
understanding, can be useful for a novice learner, by reducing the extraneous load and
increasing the germane load. On the other hand, the same graphical aid can be useless
for an expert learner because it can reduce germane load and increase extraneous load.
In fact, for an expert, it can be redundant to read instructional designs just registered
and automatized in own memory, hampering understanding and learning. Depending
on the degree of expertise, the same instructional design can lead to germane or
extraneous load, emphasising the circularity of CLT (figure 7, right). The theoretical
differences regarding the types of load are based on the subjective experienced load of
learners, implying that they are able to differentiate them by their own. This issue, as
discussed above, depends on the way the questions are formulated, and on the
familiarity of learners on the different cognitive load types, and their prior knowledge.
All these variables are not easy to monitor and control, they can create confusion on the
source of the supposed experienced load. Under a strict scientific view, the evaluation
of this supposed load does not come from the experience of the learners, rather from
the principles of the theory is based upon.
Figure 7: The circularity of the load types of Cognitive Load Theory (left) and the ‘expertise
reversal effect’ by which different cognitive processes can be regarded differently (right)
To analyse the scientific value of CLT, two different methodological approaches
have been followed: the rationalism of Karl Popper [48] [49] and the structuralist
approach of the theories of Joseph Sneed [50]. Under the former approach, it is not
possible to consider CLT scientific because its basic principles, namely the three
different types of load, cannot be tested by means of any experimental method,
consequently they are not falsifiable [46] (Figure 8, left). To be scientific, the measures
should be sensitive to the different types of load. From a strict rationalist point of view,
a measure is scientific if it does not presuppose the assumptions that it shall measure in
its rationale [46]. However, as previously discussed, most of the subjective rating scales,
conceived for the cognitive load types, contain the variables they pretend to measure.
This implies that the logic of the questions influences the logic of the answers. In turn,
the measures of the loads can be obtained ‘a priori’, by setting the questions to validate
the theory they are pretending to verify, and not through any authentic experience of
cognitive load. CLT should provide empirical evidence about the cognitive load types.
Unfortunately, this has not convincingly emerged in the literature of Educational
Psychology, Instructional Design and Cognitive Load Theory, justifying the scepticism
regarding the possibility to measure the three different types of load.
Figure 8: The rationalist view of Cognitive Load Theory (left) and the structuralist view (right)
The second methodological approach to analyse CLT is based upon structuralism
[51] [50] [52] [53]. Under its logic, the scientific value of the theoretical principles of
CLT does not depend on their empirical validity. Rather it depends upon their
effectiveness to form the ground of the structure of a theory that consents to derive
specific predictions on how detailed instructional manipulations can affect learning
outcomes [46]. The structuralist analysis considers the fundamental assumptions of
CLT as theoretical axioms. The empirical content of these axioms is valid in the context
of the theory if they contribute to expand the theory itself (Figure 8, right). Regardless
whether it is possible to validate some research predictions or not, these predictions can
still expand the theory. In fact, CLT has been extensively adopted for the design of
several new instructional formats, expanding its boundaries [46]. As discussed in the
previous sections, several research experiments have been performed in different
learning contexts. In each of this, the intrinsic, extraneous and germane loads have been
manipulated, individually or in pair by employing the traditional experimental/control
group design. In turn, the cognitive load of learners and their learning outcomes where
analysed [6]. If this analysis showed that learning has been actually facilitated, and
statistical power held, then it means that a new instructional design was conceived as it
actually promoted one or more types of load. Similarly, starting from the study of the
‘Goal free effects’ compared to the traditional ways to solve a problem (means
analysis), Sweller and his colleagues have produced various novel findings and
approaches to inform instructional design. Yet, Plass et al. [54] provided a complete list
of CLT effects such as the ‘Worked completion effect’ [55], the ‘Split attention effect’
[56] the ‘Redundancy effect’ [57], the ‘Modality effect’ [58], the ‘Expertise reversal
effect’ [47] and the ‘Collective working memory effect’ [11]. As a consequence,
according to a structuralist point of view, Sweller stated that CLT has been developed
and evolved as a consequence of these contributions and experiments [6]. They defend
the fact that the three types of load were not elaborated a priori, rather they have been
developed according to experimental findings that are falsifiable in their nature. In fact,
it is still possible to replicate the experiments and obtain opposite findings. However,
what cannot be considered falsifiable is only the definition of the three types of load
employed in different experiments because the measures adopted are not considered
scientific. In short, Sweller and colleagues strongly support the view that CLT is
actually built upon empiricism [59]. As educational psychologist, Sweller and Chandler
[60] share the same ultimate goal in the context of cognition and instruction: the
generation of new, helpful instructional techniques aimed at improving learning.
5 Reconceptualization of cognitive load types
As a consequence of the critiques related to the theoretical development of CLT and
after several failed attempts to find a generally applicable measurement technique as
well as the development of measures for the three different types of load, the theory has
been re-conceptualised using the notion of element interactivity. This refers to the
numbers of elements that must be processed simultaneously in working memory for
schema construction and their interactions [18]. In this update of CLT, the element
interactivity now defines the mechanisms not only of intrinsic load, but also of
extraneous load [6]. In detail, the extraneous load is related to the degree of interactivity
of the elements of the instructional material used for teaching activities, and
instructional designs should be aligned to this. These designs should not focus on
enhancing the number of items to be processed by learners, otherwise the resulting load
could be considered extraneous. In other words, when instructional designs do not add
instructions that increase the number of elements that must be processed within working
memory, then the germane load of learners can be triggered. In this case, existing
instructions can facilitate the use of working memory allocated for the intrinsic load.
Additionally, germane load is no longer an independent source of load, it is a function
of those working memory resources related to the intrinsic load of the task. In turn,
intrinsic load depends on the characteristic of the task, extraneous load on the
characteristic of the instructional material, on the characteristic of the instructional
design and on the prior knowledge of learners. Eventually, germane load depends on
the characteristics of a learner which equates to the resources of working memory
allocated to deal with the intrinsic load [6] (figure 9).
Figure 9: Redefinition of the cognitive load types and their roles.
The main theoretical contradiction before the reconceptualization of CLT was the
additivity and the compensability of germane and extraneous loads. Here, the critical
point is that, if extraneous load decreases, while keeping intrinsic load constant, then
germane load should increase too. However, the measures for the three cognitive loads
appeared in the last 30 years, confirm that this compensation does not have empirical
evidence: the total cognitive load does not remain constant but changes [6]. After the
reconceptualization (figure 9), germane load is related to that part of working memory
that deals with the degree of element interactivity of the task. It can be promoted by
creating instructional design aligned to it but it also depends on the intrinsic load, and
as a consequence, it is not clearly measurable [6]. In fact, germane load now forms a
balanced whole with extraneous load without creating logical and empirical
contradictions. If intrinsic load remains constant but extraneous load changes, the
overall cognitive load changes too because more or less working memory resources are
devoted to deal with the degree of element interactivity. At a given level of knowledge
and expertise, intrinsic load cannot be altered without changing the content of the
material presented to learners altogether. Extraneous load, instead, can be altered by
changing the instructional procedures. Yet, germane load coincides to those working
memory resources allocated to deal with the degree of element interactivity inherent to
an underlining learning task. Although germane load has now a fundamental role to
deal with intrinsic load, the additivity of CLT still holds in the two remaining theoretical
assumptions: the intrinsic and the extraneous load. According to this
reconceptualisation, most of the critiques related to the circularity of CLT do not longer
stand according to Sweller. Additionally, Sweller and colleagues, as in [59], consider
the unidimensional subjective rating scale of mental effort proposed by Paas et al. [13]
a valid measure of overall cognitive load. In fact, if intrinsic load is kept constant, it is
feasible to measure the extraneous load by only altering the instructional designs
between an experimental and a control group. It is also possible to measure one type of
load keeping the other constant, and the overall load measured would be an indicator of
the modified type of load: extraneous or intrinsic.
6 Final remarks
The measurement of the cognitive load types envisioned in the Cognitive Load Theory
is a critical challenge for its theoretical development and its scientific value. After the
literature review conducted in the previous sections, and after the presentation of the
critiques that brought to the reconceptualization of the cognitive load types, the reader
is left with two possible interpretations. On one hand, germane load is not clearly
measurable by a common and standardised way, consequently its theoretical
independence is denied, and after its reconceptualisation, it is a function of intrinsic
load. On the other hand, there is evidence, triggered by the proposal of novel multiple
subjective rating scales [38] [39], that the three types of load are measurable, even the
most challenging, namely the germane load. Sweller and colleagues believe that
germane load exists but it is not measurable [59]. He suggested that one of the most
reliable way to measure the overall cognitive load is the unidimensional subjective scale
of Mental Effort [13]. However, the fact that unidimensional scale has been widely
employed within Educational Psychology, does not mean it is always the most
appropriate. For example, within the discipline of Human Factors (Ergonomics), there
exist a plethora of empirical studies that all point to the multi-dimensional nature of the
construct of Mental Workload (cognitive load for educational psychologists) [16] [61]
[62] [63] [64] [65]. There is also an emerging body of knowledge, within Computer
Science, that is employing more formal non-linear approaches for modelling mental
workload as a multi-dimensional construct [66] [67] [68]. Similarly, applications of
mental workload as a multi-dimensional concept can be found in Human Computer
Interaction [69] [70] [71] [72].
Learning is a complex process, it is hard to evaluate it mostly because it is perceived as
a subjective one. Similarly, cognitive load it is a complex construct and it is assumed it
can be modelled end evaluated through quantitative criteria to satisfy the empirical
exigencies of scientific research. This is an existing methodological gap and it is the
reason why, so far, there is little evidence of generally applicable subjective
measurement techniques and measures for the three types of load and for overall
cognitive load. According to Klepsch and colleagues, their informed rating scale is a
novelty in CLT research and it seems to be a valid method for measuring the three types
of load [39]. They believe it is the most logical approach because if learners are
informed, then the evaluation of the experienced load can be done with a higher degree
of awareness. However, in our view, this might bring back the issue of circularity,
suggesting that we are leading learners to understand Cognitive Load Theory as well as
its assumptions and influence them to rate their subjective experience to fit our
expectations. Cognitive load is a complex construct and indeed CLT has had a
significant impact for instructional design. Circularity is also an important issue that
should be avoided in favour of empiricism and falsifiability of measures. We believe
that, with advances in technologies and the availability of cheap sensors and non-
invasive instruments for gathering responses of the human brain and bodies,
physiological measures of mental workload might finally shed further light on the
complex but fascinating problem of cognitive load modelling.
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... The concept of 'cognitive load' was coined by Sweller [8] to describe the state of memory storage and processing of information in a human's mind while coping with a particular task or complex situation, and it can serve as an indicator of the degree of difficulty learners experience while engaging in a task [10,13,14]. Researchers distinguished between intrinsic, extraneous, and germane CL [15,16]. The intrinsic cognitive load (ICL) refers to the mental effort one invests in dealing with a task, which varies from person to person according to expertise in the field related to the task. ...
... So now it's OK. 16: My next question is-does the method works for any rational numbers? 17: I am skipping the checking of the case that one, the first equation, is a natural number and the other, the second equation, is not, and get to the case that both numbers are not natural numbers. . ...
... So now it's OK. 16: My next question is-does the method works for any rational numbers? 17: I am skipping the checking of the case that one, the first equation, is a natural number and the other, the second equation, is not, and get to the case that both numbers are not natural numbers. ...
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In this study, we examined aspects relating to the impact of integrating question-asking activities and providing answers to these questions while reading historical mathematical texts on prospective mathematics teachers’ self-reported cognitive load. The research design of the study was quasi-experimental. The study participants included two groups of 20 students each (experimental and control). The experimental group was instructed to ask questions while coping with the texts, whereas the control group received no special instructions. The experimental group participants were asked to think aloud while coping with the texts and audio record themselves. These records were transcribed into written protocols. Both groups had to respond to a self-esteem index questionnaire in which they had to report the level of difficulty they experienced during their attempts to cope with the texts, as an indicator of their sense of cognitive load. This process was repeated at three time points, relating to three different texts. The data were analyzed using quantitative and qualitative methods. Two main observations were obtained: (1) A significant difference was found between the control and the experimental groups regarding the decrease in cognitive load along the time points. Because the only difference between the control and experimental groups was the activity of question-asking, it might be concluded that question-asking affects the reduction in cognitive load. (2) Question-asking supports the assimilation of new information up to a specific limit, depending on the gap between existing knowledge and new information.
... To explore the relationship between speech production and linguistic performance, the current work will be framed using an integrated psycholinguistic model of speech processing (hereafter called the IPL model; Terband et al., 2019) and the cognitive load theory (CLT; Orru & Longo, 2019;Sweller et al., 2019). The IPL model provides a framework of speech processing that combines elements of several theoretical accounts of speech perception and production (e.g., Guenther, 1994;Levelt, 1989; Van der Merwe, 2009) as well as higher levels of linguistic processing. ...
... The CLT, which initially was conceived to support pedagogical design, rests on the assumption of a limited mental capacity that is used in all tasks. The load associated with any given task depends on the task itself (e.g., complexity), the environment in which the task is performed, the characteristics of the person performing the task, and the interaction between these three features (Orru & Longo, 2019). If the load exceeds the limited mental capacity, the result can be the task taking longer to perform, more errors in executing the task, and/or inability to perform the task, that is, a "trade-off." ...
... The narrative context is demanding, and thus, it is thought to carry a higher risk of cognitive overload or trade-off effects (Johnston, 2008). For children with speech and/or language disorders where more effort needs to be allocated to speech-language processing, this might result in a narrative with less complex language and more linguistic errors (Orru & Longo, 2019). In other words, high simultaneous demands might lead to processing trade-offs, where higher loads in one area (e.g., connected speech in a demanding context) might lead to poorer performance in another (e.g., morphosyntactic accuracy or lexical diversity). ...
Background Speech and language are interconnected systems, and language disorder often co-occurs with childhood apraxia of speech (CAS) and non-CAS speech sound disorders (SSDs). Potential trade-off effects between speech and language in connected speech in children without overt language disorder have been less explored. Method Story retell narratives from 24 children (aged 5;0–6;11 [years;months]) with CAS, non-CAS SSD, and typical development were analyzed in Systematic Analysis of Language Transcripts (SALT) regarding morphosyntactic complexity (mean length of C-unit in words [MLCU]), lexical diversity (moving-average type–token ratio [MATTR]), and linguistic accuracy (any linguistic error/bound morpheme omissions) and compared to 128 age-matched children from the SALT database. Linear and mixed-effects logistic regressions were performed with speech accuracy (percent phonemes correct [PPC]) and diagnostic group as predictors of the narrative variables. Results PPC predicted all narrative variables. Poorer PPC was associated with lower MLCU and MATTR as well as a higher likelihood of linguistic errors. Group differences were only observed for the error variables. Comparison to the SALT database indicated that 13 of 16 children with CAS and SSD showed a higher-than-expected proportion of linguistic errors, with a small proportion explained by individual speech errors only. Conclusions The high occurrence of linguistic errors, combined with the relationship between PPC and linguistic errors in children with CAS/SSD, suggests a trade-off between speech accuracy and language output. Longitudinal studies are needed to investigate whether children with SSDs without language disorder show more language difficulties over time as linguistic demands increase.
... Extraneous load refers to the mental resources allocated to objects that do not contribute to learning and schema creation. This part of cognitive load is about the presentation of information and the instructional format, which can increase the total cognitive load while having li le effect on learning; it is the amount of memory consumed by all the hidden programs operating in the background in the system tray [18]. Germane load represents partitioned mental resources for creating and organizing long-term memory schemas, and it is similar to memory usage when loading a program on a computer. ...
... Creating a schema entails connecting disparate pieces of information to move from a lower to a higher level of complexity and keeping them together as a single meaningful whole chunk of information. Figure 1 illustrates a mental architecture model and the function of CLT in working memory and schema formation [18]. Measuring this load is as important as defining the concept of a cognitive load. ...
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In this study, the factors influencing the cognitive load of computer programmers during the perception of different code tasks were investigated. The eye movement features of computer programmers were used to provide a significant relationship between the perceptual processes of the sample codes and cognitive load. Thanks to the relationship, the influence of various personal characteristics of programmers on cognitive load was examined. Various personal parameters such as programming experience, age, native language, and programming frequency were used in the study. The study was performed on the Eye Movements in Programming (EMIP) dataset containing 216 programmers with different characteristics. Eye movement information recorded during two different code comprehension tasks was decomposed into sub-information, such as pupil movement speed and diameter change. Rapid changes in eye movement signals were adaptively detected using the z-score peak detection algorithm. Regarding the cognitive load calculations, canonical correlation analysis was used to build a statistically significant and efficient mathematical model connecting the extracted eye movement features and the different parameters of the programmers, and the results were statistically significant. As a result of the analysis, the factors affecting the cognitive load of computer programmers for the related database were converted into percentages, and it was seen that linguistic distance is an essential factor in the cognitive load of programmers and the effect of gender on cognitive load is quite limited.
... Cognitive load refers to the number of resources the learner has to process in working memory (Souchet et al., 2022), and the information that can be stored in working memory is limited (Souchet et al., 2022). Similarly, it seems that variations in the perception of cognitive load depend on the type of task and the cognitive effort that each learner needs to complete it (Orru & Longo, 2019). More specifically, according to Sweller (2016) in Cognitive Load Theory (CLT), cognitive load refers to the cognitive capacity for processing information during task resolution or problem solving. ...
... As noted above, learning using self-regulated, multi-channel materials with the important concepts underlined may have facilitated students' understanding and mitigated the cognitive load (Alemdag & Cagiltay, 2018;H. C. Liu, 2021;Pi & Hong, 2016;Ponce et al., 2018;Orru & Longo, 2019;Tong & Nie, 2022). Nonetheless, there are underlying differences in information processing. ...
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Monitoring through the use of eye-tracking technology helps in understanding the cognitive load learners experience when doing tasks. This data gives the teacher and the student important information for improving learning outcomes. This study examined whether students' participation in a learning virtual laboratory, with a self-regulated video monitored with eye-tracking, would influence their learning outcomes. It also examined whether students' prior knowledge affected their learning outcomes. Lastly, the study identified clusters related to cognitive load in relevant Areas of Interest vs. non-relevant Areas of Interest. The sample comprised 42 university students of health sciences. The results indicate that participation in the virtual laboratory was related to better learning outcomes. In addition, prior knowledge did not affect cognitive load. A number of different clusters were found related to indicators of cognitive load in relevant and non-relevant AOIs. More applied studies are needed about the effects of monitoring on learning outcomes and on what it means for individualization of learning.
... Cognitive load is the amount of working memory of the brain used to accomplish Fig. 4: VR glasses based view of a student a certain task [43]. Researchers claim and investigate three types of cognitive load [24]. Intrinsic load which is inherent to the complexity of taught contents, Germane load which is the load required to process learning deeply or store it in long term memory, and Extraneous load which is the undesired load from external factors such as interactive learning media, immersion, realism etc [40], all of which are relevant to the context of hybrid and online learning. ...
Educational institutions worldwide were thrust into adopting an online educational models due to the COVID-19 pandemic exposing severe limitations of tools used in online education, much to chagrin of both teachers and students. However, despite these limitations, hybrid educational models garnered significant interest. Holograms have shown potential in addressing several of the limitations of online educational tools that are currently in use. This paper reports the findings of an exploratory pilot study which investigates the benefits and limitations of holograms in hybrid and online educational models. The study compares student’s holographic viewing experience of the teacher in several dimensions such as presence, zoom exhaustion, and fatigue. Results are drawn from the comparison across a holographic display, virtual reality glasses, a video conferencing tool, and a tele-presence robot. The paper concludes with takeaways for the future steps of the study.
... Extraneous cognitive load is when the mental resources invested and processed by the learners are irrelevant to the actual learning. 1 It usually happens when the learning materials have a poor instructional design that requires learners to spend more time, effort, and mental energy than necessary searching for the right information to process. ...
... The instructional design discipline's main goals should be the cognitive load categories implicated in reducing extraneous load and increasing Germane load by developing scheme construction and automation (Orru & Longo, 2019). In other words, cognitive load theory recommends that extraneous load be reduced by reengineering learning activities when the intrinsic complexity of the task remains unchanged (Curum & Khedo, 2021). ...
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The public doubts that learning can be meaningful and in-depth if done online. Moreover, the implementation of online learning still has weaknesses from upstream to downstream. This research focuses on the cognitive load in students when online learning begins to be carried out massively in Indonesia due to the Covid-19 pandemic. This qualitative study aims to identify high school students' experiences in Bantul while learning online during the pandemic. Data were collected for four months through observation, in-depth interviews, and documentation. The collected data were transcribed, coded, and analyzed for themes using cognitive load theory and learning technology. The results illustrate that high school students in Bantul experience extra effort in learning through online platforms due to the novelty of the online learning experience, distractions, subject matter presentation, and the impact of cognitive load on students' learning. This research enriches innovative strategies for managing online learning by learning technology science. It has contributions to the need to train teachers and students to carry out learning in an independent mode. Online learning, when managed by accommodating good theory and practice of learning technology, proves to be a strategic learning mode, especially amidst the challenges of the Covid-19 pandemic.
... This same principle also informs the Cognitive Load Theory (CLT; Sweller, 1988) which focuses on how intrinsic (IL-associated with the learning task itself), extraneous (EL-non-essential load mostly related to task instructions), and germane (GL-imposed by the learner's deliberate use of cognitive strategies for learning) loads impact the working memory of a learner (Sweller, 1988;Van Merriënboer and Sweller, 2010;Leppink et al., 2014;Young et al., 2015Young et al., , 2021Orru and Longo, 2019). Sweller (2010) reconceptualized CLT by introducing the concept of element interactivity. ...
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Mental workload (MWL) is a concept that is used as a reference for assessing the mental cost of activities. In recent times, challenges related to user experience are determining the expected MWL value for a given activity and real-time adaptation of task complexity level to achieve or maintain desired MWL. As a consequence, it is important to have at least one task that can reliably predict the MWL level associated with a given complexity level. In this study, we used several cognitive tasks to meet this need, including the N-Back task, the commonly used reference test in the MWL literature, and the Corsi test. Tasks were adapted to generate different MWL classes measured via NASA-TLX and Workload Profile questionnaires. Our first objective was to identify which tasks had the most distinct MWL classes based on combined statistical methods. Our results indicated that the Corsi test satisfied our first objective, obtaining three distinct MWL classes associated with three complexity levels offering therefore a reliable model (about 80% accuracy) to predicted MWL classes. Our second objective was to achieve or maintain the desired MWL, which entailed the use of an algorithm to adapt the MWL class based on an accurate prediction model. This model needed to be based on an objective and real-time indicator of MWL. For this purpose, we identified different performance criteria for each task. The classification models obtained indicated that only the Corsi test would be a good candidate for this aim (more than 50% accuracy compared to a chance level of 33%) but performances were not sufficient to consider identifying and adapting the MWL class online with sufficient accuracy during a task. Thus, performance indicators require to be complemented by other types of measures like physiological ones. Our study also highlights the limitations of the N-back task in favor of the Corsi test which turned out to be the best candidate to model and predict the MWL among several cognitive tasks.
... Their performance is much better than Low Engagement profile students but lower than Immersive and Organized profile students. As stated by the previous study (Orru & Longo, 2018), the navigational demands related to those activities are not strictly directed to learning when the efforts are exerted to achieve understanding. Thus, these Dabbling profile students also demonstrated an intermediate level of learning self-efficacy. ...
Research evidence indicated that a specific type of augmented reality–assisted (AR-assisted) science learning design or support might not suit or be effective for all students because students’ cognitive load might differ according to their experiences and individual characteristics. Thus, this study aimed to identify undergraduate students’ profiles of cognitive load in AR-assisted science learning and to examine the role of their distinct profiles in self-efficacy together with associated behavior patterns in science learning. After ensuring the validity and reliability of each measure, a latent profile analysis confirmed that 365 Chinese undergraduates carried diverse dimensions of cognitive load simultaneously. The latent profile analysis findings revealed four fundamental profiles: Low Engagement, Immersive, Dabbling, and Organized, characterized as carrying various respective cognitive loads. The multivariate analysis of variance findings revealed different levels of the six AR science learning self-efficacy dimensions across profiles. Low Engagement students displayed the lowest self-efficacy among all dimensions. Organized students recorded better conceptual understanding and higher-order cognitive skills than Dabbling ones. Students with the Immersive profile had the highest science learning self-efficacy. The lag sequential analysis results showed significant differences in behavior patterns among profiles. Among them, profiles with social interaction, test, and reviewing feedback behavior had a significantly higher score for self-efficacy than those patterns mainly based on test learning and resource visits. This finding provides a unified consideration of students’ diverse profiles and can inform interventions for effective design of AR-assisted science learning to match appropriate strategies to facilitate the science learning effect.
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Les outils numériques ont investi notre quotidien professionnel, facilitant le télétravail, mais nous rendant dépendants. Les dernières réglementations du Code du travail français et plusieurs études mettent en avant la dualité entre leurs bénéfices et désavantages pour l'activité des salariés. Pourtant, leur usage n'apparait pas dans la conceptualisation de la qualité de vie au travail. Ainsi, peuvent-ils être réellement qualifiés d'aide et ne sont-ils pas néfastes pour la santé psychologique des salariés ? Nous pensons que la charge mentale de travail est un concept permettant d'aborder nos interrogations. Ainsi, une première étude basée sur des entretiens subjectifs auprès des téléconseillers, témoigne de l'importance de l'évaluation cognitive individuelle des facteurs impactant la charge mentale de travail. La deuxième étude longitudinale porte sur des salariés en situation de télétravail continue et révèle le caractère multifactoriel et dynamique de la charge mentale, au cours du temps. Enfin, la troisième étude met en avant, à l'aide d'un questionnaire, l'importance du contexte d'usage et du niveau d'automatisation des outils numériques sur la charge mentale. Ces résultats participent à l'élaboration individuelle de recommandations pour la qualité de vie au travail des salariés. En effet, le salarié va évaluer l'impact des facteurs contextuels en fonction de son état psychologique du moment. En outre, la prise de recul sur ses pratiques considérant ses ressources et les stratégies d'adaptation pouvant être mises en place est également bénéfique pour les salariés, afin d'obtenir un équilibre entre la demande liée aux tâches dans un contexte donné et leurs ressources cognitives disponibles.
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Past research in HCI has generated a number of procedures for assessing the usability of interacting systems. In these procedures there is a tendency to omit characteristics of the users, aspects of the context and peculiarities of the tasks. Building a cohesive model that incorporates these features is not obvious. A construct greatly invoked in Human Factors is human Mental Workload. Its assessment is fundamental for predicting human performance. Despite the several uses of Usability and Mental Workload, not much has been done to explore their relationship. This empirical research focused on I) the investigation of such a relationship and II) the investigation of the impact of the two constructs on human performance. A user study was carried out with participants executing a set of information-seeking tasks over three popular web-sites. A deep correlation analysis of usability and mental workload, by task, by user and by classes of objective task performance was done (I). A number of Supervised Machine Learning techniques based upon different learning strategy were employed for building models aimed at predicting classes of task performance (II). Findings strongly suggests that usability and mental workload are two non overlapping constructs and they can be jointly employed to greatly improve the prediction of human performance.
Conference Paper
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The NASA Task Load Index (NASA − TLX) and the Workload Profile (WP) are likely the most employed instruments for subjective mental workload (MWL) measurement. Numerous areas have made use of these methods for assessing human performance and thusly improving the design of systems and tasks. Unfortunately, MWL is still a vague concept, with different definitions and no universal measure. This research investigates the use of defeasible reasoning to represent and assess MWL. Reasoning is defeasible when a conclusion, supported by a set of premises, can be retracted in the light of new information. In this empirical study, this type of reasoning is considered for modelling MWL, given the intrinsic uncertainty involved in assessing it. In particular, it is shown how the NASA − TLX and the WP can be translated into defeasible structures whose inferences can achieve similar validity of the original instruments, even when less information is available. It is also discussed how these structures can have a higher extensibility and how their inferences are more self-explanatory than the ones produced by the NASA − TLX and WP.
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Cognitive Load Theory is one of the most powerful research frameworks in educational research. Beside theoretical discussions about the conceptual parts of cognitive load, the main challenge within this framework is that there is still no measurement instrument for the different aspects of cognitive load, namely intrinsic, extraneous, and germane cognitive load. Hence, the goal of this paper is to develop a differentiated measurement of cognitive load. In Study 1 (N = 97), we developed and analyzed two strategies to measure cognitive load in a differentiated way: (1) Informed rating: We trained learners in differentiating the concepts of cognitive load, so that they could rate them in an informed way. They were asked then to rate 24 different learning situations or learning materials related to either high or low intrinsic, extraneous, or germane load. (2) Naïve rating: For this type of rating of cognitive load we developed a questionnaire with two to three items for each type of load. With this questionnaire, the same learning situations had to be rated. In the second study (N = between 65 and 95 for each task), we improved the instrument for the naïve rating. For each study, we analyzed whether the instruments are reliable and valid, for Study 1, we also checked for comparability of the two measurement strategies. In Study 2, we conducted a simultaneous scenario based factor analysis. The informed rating seems to be a promising strategy to assess the different aspects of cognitive load, but it seems not economic and feasible for larger studies and a standardized training would be necessary. The improved version of the naïve rating turned out to be a useful, feasible, and reliable instrument. Ongoing studies analyze the conceptual validity of this measurement with up to now promising results.
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Self-reporting procedures and inspection methods have been largely employed in the fields of interaction and web-design for assessing the usability of interfaces. However, there seems to be a propensity to ignore features related to end-users or the context of application during the usability assessment procedure. This research proposes the adoption of the construct of mental workload as an additional aid to inform interaction and web-design. A user-study has been performed in the context of human-web interaction. The main objective was to explore the relationship between the perception of usability of the interfaces of three popular web-sites and the mental workload imposed on end-users by a set of typical tasks executed over them. Usability scores computed employing the System Usability Scale were compared and related to the mental workload scores obtained employing the NASA Task Load Index and the Workload Profile self-reporting assessment procedures. Findings advise that perception of usability and subjective assessment of mental workload are two independent, not fully overlapping constructs. They measure two different aspects of the human-system interaction. This distinction enabled the demonstration of how these two constructs cab be jointly employed to better explain objective performance of end-users, a dimension of user experience, and informing interaction and web-design.
Described by the philosopher A.J. Ayer as a work of ‘great originality and power’, this book revolutionized contemporary thinking on science and knowledge. Ideas such as the now legendary doctrine of ‘falsificationism’ electrified the scientific community, influencing even working scientists, as well as post-war philosophy. This astonishing work ranks alongside The Open Society and Its Enemies as one of Popper’s most enduring books and contains insights and arguments that demand to be read to this day. © 1959, 1968, 1972, 1980 Karl Popper and 1999, 2002 The Estate of Karl Popper. All rights reserved.
This book constitutes the refereed proceedings of the First International Symposium on Human Mental Workload: Models and Applications, H-WORKLOAD 2017, held in Dublin, Ireland, in June 2017. The 15 revised full papers presented together with two keynotes were carefully reviewed and selected from 35 submissions. The papers are organized in two topical sections on models and applications.
Cognitive Load Theory John Sweller, Paul Ayres, Slava Kalyuga Effective instructional design depends on the close study of human cognitive architecture—the processes and structures that allow people to acquire and use knowledge. Without this background, we might recognize that a teaching strategy is successful, but have no understanding as to why it works, or how it might be improved. Cognitive Load Theory offers a novel, evolutionary-based perspective on the cognitive architecture that informs instructional design. By conceptualizing biological evolution as an information processing system and relating it to human cognitive processes, cognitive load theory bypasses many core assumptions of traditional learning theories. Its focus on the aspects of human cognitive architecture that are relevant to learning and instruction (particularly regarding the functions of long-term and working memory) puts the emphasis on domain-specific rather than general learning, resulting in a clearer understanding of educational design and a basis for more effective instructional methods. Coverage includes: • The analogy between evolution by natural selection and human cognition. • Categories of cognitive load and their interactions in learning. • Strategies for measuring cognitive load. • Cognitive load effects and how they lead to educational innovation. • Instructional design principles resulting from cognitive load theory. Academics, researchers, instructional designers, cognitive and educational psychologists, and students of cognition and education, especially those concerned with education technology, will look to Cognitive Load Theory as a vital addition to their libraries.
Conference Paper
I present a number of looming barriers to a smooth path of progress for cognitive workload assessment. The first of these is the AID’s of workload (i.e., association, indifference, and dissociation) between its various reflections (i.e., subjective, physiological, and performance measures). The second is the manner in which the time-varying change in imposed task demand links to the workload response, and what specific characteristics of the former drive the latter. The third is the persistent but largely unaddressed issue of the meaningfulness of the work undertaken. Thus, does interesting and involving work result in lower workload and vice-versa? If these foregoing and predominantly methodological concerns can be overcome, then the utility of the workload construct can continue to grow. If they cannot be resolved then workload assessment threatens to be ineffective in a world which desperately requires a valid and reliable way to index cognitive achievement.
Conference Paper
I describe below the manner in which workload measurement can be used to validate models that predict workload. These models in turn can be employed to predict the decisions that are made, which select a course of action that is of lower effort or workload, but may also be of lower expected value (or higher expected cost). We then elaborate on four different contexts in which these decisions are made, with non-trivial consequences to performance and learning: switching attention, accessing information, studying material, and behaving safety. Each of these four is illustrated by a series of examples.