Direct Instruction and Its Extension with a Community of Inquiry:
A Comparison of Mental Workload, Performance and Efficiency
Giuliano Orru and Luca Longo
School of Computer Science,
Technological University Dublin, Ireland
Keywords: Direct Instruction, Community of Inquiry, Efficiency, Mental Workload, Cognitive Load Theory,
Instructional Design.
Abstract: This paper investigates the efficiency of two instructional design conditions: a traditional design based on the
direct instruction approach to learning and its extension with a collaborative activity based upon the
community of inquiry approach to learning. This activity was built upon a set of textual trigger questions to
elicit cognitive abilities and support knowledge formation. A total of 115 students participated in the
experiments and a number of third-level computer science classes where divided in two groups. A control
group of learners received the former instructional design while an experimental group also received the latter
design. Subsequently, learners of each group individually answered a multiple-choice questionnaire, from
which a performance measure was extracted for the evaluation of the acquired factual, conceptual and
procedural knowledge. Two measures of mental workload were acquired through self-reporting
questionnaires: one unidimensional and one multidimensional. These, in conjunction with the performance
measure, contributed to the definition of a measure of efficiency. Evidence showed the positive impact of the
added collaborative activity on efficiency.
Cognitive Load Theory (CLT), relevant in
educational psychology, is based on the assumption
that the layout of explicit and direct instructions
affects working memory resources influencing the
achievement of knowledge in novice learners.
Kirschner and colleagues (2006) pointed out that
experiments, based on unguided collaborative
methodologies, generally ignore the human mental
architecture. As a consequence, these types of
methodologies cannot lead to instructional designs
aligned to the way humans learn, so they are believed
to have little chance of success (Kirschner, Sweller
and Clark, 2006). Under the assumptions of CLT,
learning is not possible without explicit instructions
because working memory cannot receive and process
information related to an underlying learning task.
This study focuses on a comparison of the efficiency
of a traditional direct instruction teaching method
against an extension with a collaborative activity
informed by the Community of Inquiry paradigm
(Garrison, 2007). The assumption is that the addition
of a highly guided collaborative and inquiring
activity, to a more traditional direct instruction
methodology, has a higher efficiency when compared
to the application of the latter methodology alone. In
detail, the collaborative activity based upon the
Community of Inquiry is designed as a collaborative
task based on explicit social instructions and trigger
cognitive questions (Orru et al., 2018).
The research question investigated is: to what
extent can a guided community of inquiry activity,
based upon cognitive trigger questions, when added
to a direct instruction teaching method, impact and
improve its efficiency?
The remainder of this paper is structured as
follows. Section 2 informs the reader on the
assumptions behind Cognitive Load Theory, its
working memory effect and the Community of
Inquiry paradigm that inspired the design of the
collaborative inquiry activity. Section 3 describes the
design of an empirical experiment and the methods
employed. Section 4 present and critically discuss the
results while section 5 summarise the paper
highlighting future work.
Orru, G. and Longo, L.
Direct Instruction and Its Extension with a Community of Inquiry: A Comparison of Mental Workload, Performance and Efficiency.
DOI: 10.5220/0007757204360444
In Proceedings of the 11th International Conference on Computer Supported Education (CSEDU 2019), pages 436-444
ISBN: 978-989-758-367-4
2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
2.1 The Cognitive Load Theory
Cognitive Load Theory (CLT) provides instructional
techniques aimed at optimising the learning phase.
These are aligned with the limitations of the human
cognitive architecture (Atkinson and Shiffrin, 1971)
(Baddeley, 1998) (Miller, 1956). An instructional
technique that considers the limitations of working
memory aims at reducing the cognitive load of
learners which is the cognitive cost required to carry
out a learning task. Reducing cognitive load means
increasing working memory spare capacity, thus
making more resources available. In turn this
facilitates learning that consists in transferring
knowledge from working memory to long-term
memory (Sweller, Van Merrienboer and Paas, 1998).
Instructional designs should not overcome the
working memory limits, otherwise transfer of
knowledge is hampered and learning affected.
Explicit instructional techniques are the necessary
premises for informing working memory on how to
process information and consequently building
schemata of knowledge. The research conducted in
the last 3 decades by Sweller and his colleagues
brought to the definition of three types of load:
intrinsic, extraneous and germane loads (Sweller, van
Merriënboer and Paas, 2019). In a nutshell, intrinsic
load depends on the number of items a learning task
consists of (its difficulty), while extraneous load
depends on the characteristic of the instructional
material, of the instructional design and on the prior
knowledge of learners. Germane load is the cognitive
load on working memory elicited from an
instructional design and the learning task difficulty. It
depends upon the resources of working memory
allocated to deal with the intrinsic load. In order to
optimise these working memory resources, research
on CLT has generated a number of approaches to
inform instructional design. One of this is the
Collective Working Memory effect whereby sharing
the load of processing complex material among
several learners and their working memories lead to
more effective processing and learning. The level of
complexity of the task remains constant but the
working resources expand their limits because of
collaboration (Sweller, Ayres and Kalyuga, 2011). It
is assumed that collaborative learning foster
understanding just for high load imposed by the task
when individual learners do not have sufficient
processing capacity to process the information (Paas
and Sweller, 2012). Results in empirical studies
comparing collaborative learning with individual
learning are mixed. Positive effects are found in
highly structured and highly scripted learning
environments where learners knew what they had to
do, how to do it, with whom and what they had to
communicate about (Dillenbourg, 2002) (Fischer et
al., 2002) (Kollar, Fischer and Hesse, 2006)
(Kirschner, Paas and Kirschner, 2009). In these
environments, student working collaboratively
become more actively engaged in the learning process
and retain the information for a longer period of time
(Morgan, Whorton and Gunsalus, 2000). The main
negative effect is the cognitive cost of information
transfer: the transactive interaction could generate
high cognitive load hampering the learning phase
instead of facilitating it. This depends upon the
complexity of the task, and in tasks with high level of
complexity, the cognitive cost of transfer is
compensated by the advantage of using several
working memories resources. In contrast, in tasks
with low level of complexity, the individual working
memory resources are supposed to be enough and the
transfer costs of communication might hamper the
learning phase. It is hard to find unequivocal
empirical support for the premise that learning is best
achieved interactively rather than individually but the
assumption of the Collective Working Memory effect
is clear: joining human mental resources in a
collaborative task correspond to expanding the
human mental load capacity (Sweller, Ayres and
Kalyuga, 2011).
2.2 The Community of Inquiry
John Dewey reconceptualised the dualistic
metaphysic of Plato who split the reality in two: ideal
on one side and material on the other. In this context,
Dewey suggests to rethink the semantic distinction
between Technique as practice and Knowledgeas
pure theory. Practice, in fact, is not foundationalist in
its epistemology anymore. In other words, it does not
require a first principle as its theoretical foundation.
Technique, in the philosophy of Dewey, means an
active procedure aimed at developing new skills
starting from the redefinition of the old ones (Dewey,
1925). Therefore, the configuration of epistemic
theoretical knowledge is a specific case of technical
production and Knowledge as a theory is the result of
Technique as practice. Both are deeply interconnected
and they share the resolution of practical problems as
starting point for expanding knowledge (Dewey,
1925). The research conducted by Dewey inspired the
work of Garrison (2007) who further develop the
Community of Inquiry. This may be defined as a
teaching and learning technique. It is an instructional
Direct Instruction and Its Extension with a Community of Inquiry: A Comparison of Mental Workload, Performance and Efficiency
technique thought for a group of learners who,
through the use of dialogue, examine the conceptual
boundary of a problematic concept, processing all its
components in order to solve it. Garrison provides a
clear exemplification of the cognitive structure of a
community of inquiry. Firstly, exploring a problem
means exchanging information on its constituent
parts. Secondly, this information needs to be
integrated by connecting related ideas. Thirdly, the
problem has to be solved by a resolution phase ending
up with new ideas (Garrison, 2007). The core ability
in solving a problem consists in connecting the right
tool to reach a specific aim. According to Lipman
(2003), who extended the Community of Inquiry with
a philosophical model of reasoning, the meaning of
inquiry should be connected with the meaning of
community. In this context, individual and the
community can only exist in relation to one another
along a continuous process of adaptation that ends up
with their reciprocal, critical and creative
improvement (Lipman, 2003).
2.3 Instructional Efficiency and the
Likelihood Model
Efficiency of instructional designs in education is a
measurable concept. A high efficiency occurs when
learning outcomes, such as test results, are produced
at the lowest level of financial, cognitive or temporal
resources (Johnes, Portela and Thanassoulis, 2017) .
One of the measures of efficiency developed within
Education is based upon a likelihood model (Hoffman
and Schraw, 2010). Efficiency here is the ratio of
work output to work input. Output can be identified
with learning, whereas input with work, time or
effort. These two variables can be replaced
respectively with a raw score of performance of a
learner or a learning outcome denoted as P, and a raw
score for time, effort or cognitive load denoted as R:
R can be gathered with any self-report scale of
effort or cognitive resources employed or an objective
measure of time (Hoffman and Schraw, 2010). An
estimation of the rate of change of performance is
calculated by dividing P by R and the result represents
the individual efficiency based on individual scores
(Hoffman and Schraw, 2010). Previously, Kalyuga
and Sweller (2005) employed the same formula
extended with a reference value: the critical level
under or above which the efficiency can be
considered negative or positive (Kalyuga and
Sweller, 2005). As shown in figure 1, the authors
suggest to divide the maximum performance score
and the maximum effort exerted by learners in order
to establish whether a learner is competent or not.
Ecr = Pmax / Rmax
Figure 1: Critical level of efficiency (Kalyuga and Sweller,
A given learning task is considered efficient if E
is greater than Ecr, relatively inefficient if E is less
than or equal to the critical level Ecr. The ratio of the
critical level is based on the assumption that an
instructional design is not efficient if a learner invests
maximum mental effort in a task, without reaching
the maximum level of performance (Kalyuga and
Sweller, 2005). Instead, an instructional design is
efficient if a learner reaches the maximum level of
task performance with less than the maximum level
of mental effort. Intermediate values are supposed to
be evaluated in relation to the critical level (Kalyuga
and Sweller, 2005). This measure of efficiency has
been adopted in this research and measures of
performance, effort and mental workload have been
selected as its inputs, as described below.
2.4 The Bloom’s Taxonomy and
Multiple Choice Questionnaires
One way of designing instructional material is
through the consideration of the educational
objectives conceived in the Bloom’s Taxonomy
(Bloom, 1956). In educational research, this has been
modified in different ways and one of the most
accepted revision is proposed by Anderson et al.
(2001). In connection to the layout of multiple choice
questions (MCQ), this adapted taxonomy assumes
great importance because it explains how a test
performance can be linked to lower or higher
cognitive process depending on the way it is designed
(Scully, 2017). In other words, the capacity of a test
performance to evaluate ‘higher or lower cognitive
CSEDU 2019 - 11th International Conference on Computer Supported Education
process’ may depend on the degree of alignment to
the Bloom’s Taxonomy. This revised version has
been adopted in this research to design a MCQ
performance test.
2.5 Measures of Mental Workload
Mental workload can be intuitively thought as the
mental cost of performing a task (Longo, 2014)
(Longo, 2015). A number of measures have been
employed in Education, both unidimensional and
multidimensional (Longo, 2018). The modified
Rating Scale of Mental Effort (RSME) (Zijlstra and
Doorn, 1985) is a unidimensional mental workload
assessment procedure that is built upon the notion of
effort exerted by a human over a task. A subjective
rating is required by an individual through an
indication on a continuous line, within the interval 0
to 150 with ticks each 10 units (Zijlstra, 1993).
Example of labels are ‘absolutely no effort’,
‘considerable effort’ and ‘extreme effort’. The overall
mental workload of an individual coincides to the
experienced exerted effort indicated on the line. The
Nasa Task Load Index (NASA-TLX) is a mental
workload measurement technique, that consists of six
sub-scales. These represent independent clusters of
variables: mental, physical, and temporal demands,
frustration, effort, and performance (Hart and
Staveland, 1988). In general, the NASA-TLX has
been used to predict critical levels of mental workload
that can significantly influence the execution of an
underlying task. Although widely employed in
Ergonomics, this has been rarely adopted in
Education. A few studies have confirmed its validity
and sensitivity when applied to educational context.
(Gerjets, Scheiter and Catrambone, 2006) (Gerjets,
Scheiter and Catrambone, 2004) (Kester et al., 2006).
2.6 Summary of Literature
Explicit instructional design is an inherent
assumption of Cognitive Load Theory. According to
this, information and instructions have to be made
explicit to learners to enhance learning (Kirschner,
Sweller and Clark, 2006). This is in contrast to the
features of the Community of Inquiry approach that,
instead, do not focus only on explicit instructions to
construct information, but on the learning connection
between cognitive abilities and knowledge
construction. The achievement of factual, conceptual
and procedural knowledge, in connection to the
cognitive load experienced by learners, is supposed to
be the shared learning outcome under evaluation in
the current experiment. Kirshner and colleagues
(Kirschner et al., 2006) affirmed that unguided
inquiring methodologies are set to fail because of
their lack of direct instructions. Joanassen (2009), in
a reply to Kirchner et al. (2006), stated that, in the
field of educational psychology, a comparison
between the effectiveness of constructivist inquiry
methods and direct instruction methods does not exist
(Jonassen, 2009). This is because the two approaches
come from different theories and assumptions,
employing different research methods. Moreover,
they do not have any shared learning outcome to be
compared. We argue that both the approaches have
own advantages and disadvantages for learning. This
research study tries to fill this gap and it aims at
joining the direct instruction approach to learning
with the collaborative inquiry approach, taking
maximum advantage from them.
A primary research experiment has been designed
following the approach by Sweller et al. (2010) and
taking into consideration the cognitive load effects.
Two instructional design conditions were designed:
one merely following the direct instruction approach
to learning (A), and one that extends this with a
collaborative activity inspired by the community of
inquiry approach to learning (B). In detail, the former
involved a theoretical explanation of an underlying
topic, whereby an instructor presented information
through direct instructions. The latter involved the
extension of the former with a guided collaborative
activity based upon cognitive trigger questions. These
questions, aligned to the Bloom’s Taxonomy, are
supposed to develop cognitive skills in
conceptualising and reasoning, that stimulate
knowledge construction in working memory (Popov,
van Leeuwen and Buis, 2017).
An experiment has been conducted in third-level
classes at the Technological University Dublin and at
the University of Dublin, Trinity College involving a
total of 115 students. Details as below:
Semantic Web [S.W]: 42 students;
Advanced Database [A.D]: 26 students;
Research Methods [R.M]: 26 students;
Amazon Cloud Watch Autoscaling [AWS]: 21
Each class was divided into two groups: the
control group received design condition A while the
experimental group received A followed by B. Each
student voluntarily took part in the experiment after
Direct Instruction and Its Extension with a Community of Inquiry: A Comparison of Mental Workload, Performance and Efficiency
being provided with a study information sheet and
signed a consent form approved by the Ethics
Committee of the Technological University Dublin.
The participation was based on a criterion of
voluntary acceptance. Consequently, it can be
deducted that who accepted to participate in the
experiment had a reasonable level of motivation,
contrary to a number of students who denied to
participate. At the beginning of each class, lecturers
asked whether there was someone familiar with the
topic, but no evidence of prior knowledge was
observed. The Rating Scale Mental Effort (RSME)
and the Nasa Task Load index (NASA-TLX)
questionnaire were provided to students in each group
after the delivery of the two design conditions. After
these, students received a multiple-choice
questionnaire. The collaborative activity B was made
textually explicit and distributed to each student in the
experimental group which in turn, was sub-divided
into smaller groups of 3 or 5 students. Table 1 list the
instructions for executing the collaborative activity.
Table 1: Instructions for the collaborative activity.
SECTION 1: Take part in a group dialog considering the
following democratic habits: free-risk expression,
encouragement, collaboration and gentle manners.
SECTION 2: Focus on the questions below and follow
these instructions:
Exchange information related to the underlying topic
Connect ideas in relation to this information
FIRST find an agreement about each answer
collaboratively, THEN write the answer by each group
member individually
Followed by trigger questions
The first section explains the social nature of the
inquiry technique while the second section outlines
the cognitive process involved in answering the
trigger questions. Examples of the questions are
showed in tables 2 and 3, and are adapted from the
work of Satiro (2006). They are aimed at developing
cognitive skills of conceptualisation by comparing
and contrasting, defining, classifying, and reasoning
by relating cause and effect, tools and aims, parts and
whole and by establishing criteria (Sátiro, 2006).
Table 2: Examples of trigger questions employed during the
collaborative activity in the ‘Semantic Web’ class.
What does a Triple define? (Conceptualising)
How a Triple is composed of? (Reasoning)
Table 3: Examples of trigger questions employed during the
collaborative activity in the ‘Advanced Database’ class.
What is a Data-warehouse? (Conceptualising)
How is a date dimension defined in a dimensional
model? (Reasoning)
With a measure of performance (the multiple-
choice score, percentage) and a mental workload
score (RSME or NASA-TLX), a measure of
efficiency was calculated using the likelihood models
described in section 2.3 (Hoffman and Shraw, (2010).
Figure 2 summarise the layout of the experiment. The
research hypothesis is that the efficiency of the design
condition B is higher than the efficiency of the design
condition A. Formally: Efficiency B > Efficiency A.
Figure 2: Layout of the experiment.
Table 4 and 5 respectively list the descriptive
statistics of the Rating Scale Mental Effort and the
Nasa-Task Load indexes associated to each group.
Table 4: Means and standard deviations of the Rating Scale
Mental Effort responses.
RSME mean (STD)
Control Group
RSME mean (STD)
Experimental Group
36.00 (12.83)
47.91 (13.72)
56.00 (25.64)
68.57 (32.07)
47.08 (8.38)
67.85 (23.67)
61.92 (29.19)
66.31 (32.08)
Table 5: Means and standard deviations of the Nasa Task
Load indexes.
NASA mean (STD)
Control Group
NASA mean (STD)
Experimental Group
43.61 (15.39)
47.80 (9.91)
50.00 (8.19)
54.45 (16.12)
49.38 (9.37)
49.85 (8.96)
47.74 (10.98)
50.62 (9.43)
As noticeable from tables 4 and 5, the
experimental group experienced, on average more
effort (RSME) and more cognitive load (NASA-
CSEDU 2019 - 11th International Conference on Computer Supported Education
TLX) than the control group. Intuitively this can be
attributed to the extra mental cost required by the
collaborative activity. Table 6 shows the performance
scores of the two groups. Also in this case, the
collaborative activity increased the level of
performance of the learners belonging to the
experimental group.
Table 6: Mean and standard deviation of MCQ.
MCQ mean (STD)
Control Group
MCQ mean (STD)
Experimental Group
42.92 (21.26)
54.91 (14.27)
61.33 (15.52)
66.42 (13.36)
68.41 (15.72)
69.57 (18.88)
34.42 (18.10)
47.12 (18.77)
Despite of this consistent increment across topics,
the results of a non-parametric analysis of variance
(depicted in table 7) shows how the scores associated
the control and experimental groups are, most of the
times, not statistically significantly different. Given
the dynamics of third-level classes and the
heterogeneity of students having different
characteristics such as prior knowledge and learning
strategy, this was not a surprising outcome.
Table 7: P-values of the non-parametric analysis of variance
of the performance scores (MCQ), the perceived effort
scores (RSME) and the workload scores (NASA-TLX).
In order to test the hypothesis, an efficiency score
for each participant was computed according to the
likelihood models described in section 2.3 (Hoffman
and Schraw, 2010). Table 8 lists the efficiency scores
across groups and topics. Under the assumptions of
the likelihood model, the evidence of the positive
impact of the collaborative inquiry activity (design
condition B) is limited to the Semantic Web class
where there is a significant difference in the
efficiency of design conditions when the RSME is
used. The control group is on average below the
critical level, while the experimental group above it.
In detail, as depicted in figure 3 and 4, the distribution
of the efficiency scores, with the RSME measure,
reveals that 54% of the students in the control group
experienced an efficiency below the critical level
whereas 46% above it. Contrarily, in the experimental
group, a higher 66.6% of students experienced an
efficiency above the critical level and 33,3% below it.
Table 8: Mean of efficiency computed with the RSME (+ is
positive, - is negative). Critical Level 100/150=0.666.
Mean efficiency (with
Control group
Mean efficiency (with
Experimental Group
1.304 > 0.666 (+)
1.239 > 0.666 (+)
1.440 > 0.666 (+)
1.336 > 0.666 (+)
1.505 > 0.666 (+)
1.152 > 0.666 (+)
0.656 < 0.666 (-)
0.922 > 0.666 (+)
Figure 3: Distribution of the efficiency scores of learners in
the control group for the topic ‘Semantic Web’.
Figure 4: Distribution of the efficiency scores of learners in
the experimental group for the topic ‘Semantic Web’.
A similar analysis was conducted by using the
NASA-TLX as a measure of mental workload for the
likelihood model (Table 9) where a more coherent
picture emerges. In fact, the efficiency scores are on
average always higher in the experimental group.
Table 9: Mean of Efficiency computed with the NASA-
TLX (+ is positive, - is negative). Critical Level 100/100=1.
Mean efficiency (with
Control group
Mean efficiency
(with NASA)
Experimental Group
1.153 > 1 (+)
1.244 > 1 (+)
1.241 > 1 (+)
1.340 > 1 (+)
1.425 > 1 (+)
1.440 > 1 (+)
0.750 < 1 (-)
0.964 < 1 (-)
Despite of the general increment of the efficiency
scores, these are not statistically significantly
different across design conditions. In fact, all the p-
values of Table 10 are greater than the significance
level (alpha=0.05).
Direct Instruction and Its Extension with a Community of Inquiry: A Comparison of Mental Workload, Performance and Efficiency
Table 10: P-values of the Kruskal-Wallis test on the
efficiency scores (with the NASA-TLX) and the Wilcoxon
test of the efficiency scores (with the RSME).
Kruskal-Wallis (NASA)
Wilcoxon RSME
The Likelihood model behaves differently when
used with different measures of mental workload. In
fact, on one hand, with the unidimensional Rating
Scale Mental Effort, the design condition B
(experimental group) on average had a lower
efficiency than the design condition A (control group)
across topics. On the other hand, with the
multidimensional NASA Task Load Index, the
efficiency of the design condition B (experimental
group) was always better than the design condition A
(control group) across topics. This raises the question
of the completeness of the unidimensional measure of
mental workload (RSME). In line with other
researches in the literature of CLT, the criticism is
whether effort is the main indicator of cognitive
load (Paas and Van Merriënboer, 1993) or others
mental dimensions influence it during problem
solving (De Jong, 2010). In this research, we believe
that a multidimensional model of cognitive load
seems to be more suitable than a unidimensional
model when used in the computation of the efficiency
of various instructional designs. We argue that a
multidimensional model, such as the NASA-TLX,
better grasps the characteristics of the learner and the
features of an underlying learning task. Results shows
that the average performance (MCQ) is higher in the
experimental group than the control group. This can
be attributed to the layout of the collaborative activity
designed to boost the learning phase and enhance the
learning outcomes, namely the achievement of
factual, conceptual and procedural knowledge.
According to the collaborative cognitive load theory
(Kirschner et al., 2018), nine principles can be used
to define complexity. Among these, task
guidance/support, domain expertise, team size and
task complexity were the principles held under
control in the current experiments. In particular, three
factors were observed that can be used to infer task
complexity: 1) amount of content delivered; 2) time
employed for its delivery; 3) level of prior knowledge
of learners. In relation to 1 and 2, A.D. had 28 slides
(50 mins), A.W.S. 25 slides (25 mins), R.M. 20 slides
(35 mins), S.W. 55 slides (75 mins). In relation to
point 3, prior knowledge can be inferred from the year
the topic was delivered: S.W. first year BSc in
Computer Science; AWS (third year), A.D. (fourth
year), and R.M. (post-graduate). As it is possible to
note, S.W. was the learning task with the higher level
of complexity in terms of slides, delivery time and
prior-knowledge. Results are in line with the
assumption of the collaborative cognitive load theory:
collaborative learning is more effective when the
level of the complexity of an instructional design is
high (Kirschner et al., 2018). In fact, on one hand
A.D., A.W.S. and R.M. are of lower complexity to
justify the utility of a collaborative activity that
involves sharing of working memory resources from
different learners. On the other, the higher complexity
of S.W. justifies the utility of the collaborative
activity and the exploitation of extra memory
resources from different learners in processing
information and enhance the learning outcomes.
A literature review showed a lack of studies aimed at
comparing the efficiency of instructional design
based on direct instruction and those based on
collaborative inquiries techniques. Motivated by the
statement provided by Kirshner and colleagues
(2006) whereby inquiries techniques are believed to
be ineffective in the absence of explicit direct
instructions, an empirical experimental study has
been designed. In detail, a comparison of the
efficiency between a traditional instructional design,
purely based upon explicit direct instructions, and its
extension with a guided inquiry technique has been
proposed. The likelihood model of efficiency,
proposed by Kalyuga and Sweller (2005), was
employed. This is based upon the ratio of
performance and cognitive load. The former was
quantified with a multiple-choice questionnaire
(percentage) and the latter with a unidimensional
measure of effort first (the Rating Scale Mental
Effort) and a multidimensional measure of mental
workload secondly (the NASA Task Load Index).
Results demonstrated that extending the traditional
direct instruction approach, with an inquiry
collaborative activity, employing direct instructions,
in the form of trigger questions, is potentially more
efficient. This is in line with the beliefs of Popov, van
Leeuwen and Buis (2017) whereby the development
of cognitive abilities, through the implementation of
cognitive activities (here collaboratively answering
trigger questions and following direct instructions),
facilitates the construction and the achievement of
knowledge. Future empirical experiments are
necessary to demonstrate this point held statistically.
CSEDU 2019 - 11th International Conference on Computer Supported Education
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