A Motivational Perspective on the Relation Between Mental Effort and Performance: Optimizing Learner Involvement in Instruction

TL;DR: In this article, an alternative motivational perspective of the relation between mental effort and performance is presented, and a procedure to compute and visualize the differential effects of instructional conditions on learner motivation is proposed.

Abstract: Motivation can be identified as a dimension that determines learning success and causes the high dropout rate among online learners, especially in complex e-learning environments. It is argued that these learning environments represensent a new challenge to cognitive load researchers to investigate the motivational effects of instructional conditions and help instructional designers to predict which instructional configurations will maximize learning and transfer. Consistent with the efficiency perspective introduced by Paas and Van Merrienboer (1993), an alternative motivational perspective of the relation between mental effort and performance is presented. We propose a procedure to compute and visualize the differential effects of instructional conditions on learner motivation, and illustrate this procedure on the basis of an existing data set. Theoretical and practical implications of the motivational perspective are discussed.

Summary

A Motivational Perspective on the Relation between Mental Effort and Performance: Optimizing Learners' Involvement in Instructional Conditions

• An increasing number of instructional theories stress the importance of rich learning environments based on real-life tasks as the driving force for learning.
• Such tasks are expected to help learners to integrate knowledge, skills and attitudes and to improve transfer of what is learned to work settings or daily life (Merrill, 2002) .
• In particular, the authors will show that the constructs of mental effort and performance, which play a central role in cognitive load theory in defining the efficiency of instructional conditions, have both cognitive and motivational components.
• Second, the role of motivation in learning is discussed and related to mental effort and performance.
• In the discussion, theoretical and practical implications of the proposed method are discussed.

Cognitive Load Theory

• Cognitive load theory (CLT; Paas, Renkl, & Sweller, 2003 , 2004; Sweller, 1988 Sweller, , 1999) ) offers a versatile framework for understanding the instructional implications of the interaction between information structures and cognitive architecture.
• CLT is concerned with the instructional control of the high cognitive load typically associated with the learning of complex cognitive tasks.
• An important result of these activities was the recognition by Paas and Van Merriënboer (1993) that measures of cognitive load can reveal important information about the efficiency of instructional conditions that is not necessarily reflected by traditional performance-based measures.
• The efficiency of an instructional condition is considered high if high performance can be attained with little mental effort, and considered low if high mental effort is associated with low performance.

The Role of Motivation

• The efficiency perspective has enriched their knowledge of the cognitive effects of instructional conditions.
• Keller´s (1983) ARCS model made a key contribution to motivational theory and instructional design.
• Generally, the results of these studies indicated that high variability of practice can be used as an instructional strategy to encourage learners to invest mental effort in learning.
• Goal orientation, defined as the broad goals held by an individual as he or she faces a learning task, has been identified as a motivational variable that affects how individuals approach learning tasks (Dweck & Leggett, 1988) .
• This definition suggests that the amount of mental effort invested in a certain learning task can be considered a reliable estimate of the learner's motivation or involvement in that task.

A Motivational Perspective 9

• The Calculation of Task Involvement Consistent with the efficiency perspective introduced by Paas and Van Merriënboer (1993; see also Tuovinen & Paas, 2004) the authors present an alternative motivational perspective on the relation between mental effort and task performance, which can be used to calculate and visualize the relative involvement in instructional conditions.
• Particular points in this coordinate system may refer to mental effort z-scores and related performance z-scores of experimental conditions or groups of participants.
• The motivational perspective assumes that the complex relation between mental effort and performance can be used to compare the motivational effects of instructional conditions.
• The students with no prior content knowledge experienced much less efficient learning conditions when involved in the exploration practice than in the worked examples practice.
• The two exploration practice groups show the highest task involvement, which is consistent with the common belief that discovery and exploratory environments are motivating for learners.

A Motivational Perspective 12

• Cognitive load theorists have focused on the alignment of the instruction with cognitive architecture without recognizing the need for training experiences to be coupled with the motivation to achieve well.
• This research has predominantly used mental efficiency algorithms to select training tasks dynamically (Camp, Paas, Rikers, & van Merriënboer, 2001; Kalyuga & Sweller, this issue; Salden, Paas, Broers, & van Merriënboer, 2004) .
• In most cases the results of these studies did not reveal differences between dynamic task selection methods.
• So, to take advantage of the motivational approach to the relation between mental effort and performance it is important to use rating scales with verbal labels related to 'invested mental effort'.
• Despite these shortcomings the authors believe that the presented motivational perspective can broaden the horizon of cognitive load researchers and contribute to the optimization of learners´ involvement in instructional conditions.

Figures

Figure 2. Motivational effects of instructional conditions in Tuovinen and Sweller (1999).

Figure 1. Mental Effort – Performance coordinate system to visualize motivational effects of instructional conditions.

Full text

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A Motivational Perspective on the Relation between
Mental Effort and Performance:
Citation for published version (APA):
Paas, F., Tuovinen, J., van Merrienboer, J. J. G., & Darabi, D. (2005). A Motivational Perspective on the Relation
between Mental Effort and Performance: Optimizing Learner Involvement in Instruction. Etr&D-Educational
Technology Research and Development, 53(3), 25-34. https://doi.org/10.1007/BF02504795
DOI:
10.1007/BF02504795
Document status and date:
Published: 01/09/2005
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A Motivational Perspective 1
1Running head: A MOTIVATIONAL PERSPECTIVE
This is a pre-print of:
Paas, F., Tuovinen, J., van Merriënboer, J. J. G., & Darabi, A. (2005). A motivational
perspective on the relation between mental effort and performance: Optimizing learner
involvement in instruction. Educational Technology, Research and Development, 53, 25-34.
Copyright Springer, The original publication is available at www.springerlink.com
A Motivational Perspective on the Relation between Mental Effort and Performance:
Optimizing Learner Involvement in Instruction
Fred Paas
Educational Technology Expertise Center,
Open University of the Netherlands
Juhani E. Tuovinen
School of Education,
Charles Darwin University, Australia
Jeroen J. G. van Merriënboer
Educational Technology Expertise Center,
Open University of the Netherlands
Abbas Darabi
Learning Systems Institute
Florida State University

A Motivational Perspective 2
Abstract
Motivation can be identified as a dimension that determines learning success and causes the
high dropout rate among online learners, especially in complex e-learning environments. It is
argued that these learning environments represent a new challenge to cognitive load
researchers to investigate the motivational effects of instructional conditions and help
instructional designers to predict which instructional configurations will maximize learning
and transfer. Consistent with the efficiency perspective introduced by Paas and Van
Merriënboer (1993), an alternative motivational perspective of the relation between mental
effort and performance is presented. We propose a procedure to compute and visualize the
differential effects of instructional conditions on learner motivation and illustrate this
procedure on the basis of an existing data set. Theoretical and practical implications of the
motivational perspective are discussed.

A Motivational Perspective 3
A Motivational Perspective on the Relation between Mental Effort and Performance:
Optimizing Learners’ Involvement in Instructional Conditions
An increasing number of instructional theories stress the importance of rich learning
environments based on real-life tasks as the driving force for learning. Such tasks are
expected to help learners to integrate knowledge, skills and attitudes and to improve transfer
of what is learned to work settings or daily life (Merrill, 2002). However, a severe risk of
such learning tasks is that learners may not be sufficiently motivated to deal with their
complexity (Van Merriënboer, Kirschner, & Kester, 2003). Moreover, learning tasks are often
presented in electronic, on-line learning environments, which also pose high demands on
learners’ motivation and persistence (Frankola, 2001). Until now, cognitive load theory has
focused on the alignment of instruction with cognitive processes without recognizing the role
of motivation in training. The goal of this article is to introduce a new, motivational
perspective. In particular, we will show that the constructs of mental effort and performance,
which play a central role in cognitive load theory in defining the efficiency of instructional
conditions, have both cognitive and motivational components.
The structure of this article is as follows. First, cognitive load theory and, especially,
the constructs of mental effort, performance and instructional efficiency are briefly reviewed.
Second, the role of motivation in learning is discussed and related to mental effort and
performance. We argue that the relationship between mental effort and performance can be
used not only to test assumptions concerning instructional efficiency, but also to compare
learner motivation under different instructional conditions. Third, a computational method to
compare the learners´ involvement in instructional conditions is presented and illustrated on
the basis of an existing data set. A major benefit of the presented method is that it enables
cognitive load theorists and instructional designers to compare instructional formats not only
in terms of their efficiency but also in terms of their effects on learners’ motivation. In the
discussion, theoretical and practical implications of the proposed method are discussed.

A Motivational Perspective 4
Cognitive Load Theory
Cognitive load theory (CLT; Paas, Renkl, & Sweller, 2003, 2004; Sweller, 1988,
1999) offers a versatile framework for understanding the instructional implications of the
interaction between information structures and cognitive architecture. CLT is concerned with
the instructional control of the high cognitive load typically associated with the learning of
complex cognitive tasks. The theory suggests that learning happens best under conditions that
are aligned with the cognitive architecture. The theory’s focus on the interaction between
information structures and cognitive architecture has resulted in the development of many
effective and efficient instructional methods, requiring less training time and less mental
effort to attain better learning and transfer performance than conventional instructional
methods (for an overview see Paas, Renkl, & Sweller, 2003, 2004; Sweller, Van Merriënboer,
& Paas, 1998).
CLT incorporates specific claims concerning the role of cognitive load within an
instructional context and its relation to learning. Cognitive load is not simply considered as a
by-product of the learning process, but as the major factor determining the success of an
instructional intervention in attaining transfer of knowledge and skills. The instructional
control of cognitive load by creating an optimal balance between the intrinsic load of the task
and the ineffective/effective load ratio of the instruction is considered the essence of the
theory (Paas, Renkl, & Sweller, 2003). Therefore, it is obvious that activities to define the
construct of cognitive load and to measure cognitive load have played and continue to play an
important role in cognitive load research and in the advancement of the theory (for an
overview see, Paas, Tuovinen, Tabbers, & van Gerven, 2003).
An important result of these activities was the recognition by Paas and Van
Merriënboer (1993) that measures of cognitive load can reveal important information about
the efficiency of instructional conditions that is not necessarily reflected by traditional
performance-based measures. In particular, they claimed that a combined measure of

Frequently asked questions

Q1. What have the authors contributed in "A motivational perspective on the relation between mental effort and performance:" ?

Consistent with the efficiency perspective introduced by Paas and Van Merriënboer ( 1993 ), an alternative motivational perspective of the relation between mental effort and performance is presented. The authors propose a procedure to compute and visualize the differential effects of instructional conditions on learner motivation and illustrate this procedure on the basis of an existing data set. Theoretical and practical implications of the motivational perspective are discussed. The goal of this article is to introduce a new, motivational perspective. In particular, the authors will show that the constructs of mental effort and performance, which play a central role in cognitive load theory in defining the efficiency of instructional conditions, have both cognitive and motivational components. The structure of this article is as follows. The authors argue that the relationship between mental effort and performance can be used not only to test assumptions concerning instructional efficiency, but also to compare learner motivation under different instructional conditions. Third, a computational method to compare the learners ́ involvement in instructional conditions is presented and illustrated on the basis of an existing data set. In the discussion, theoretical and practical implications of the proposed method are discussed. The authors argue that this shift A Motivational Perspective 6 of focus from non-authentic laboratory experiments to authentic e-learning environments represents a new challenge. The ARCS model provides a typology that can help instructional designers organize their knowledge about learner A Motivational Perspective 7 motivation and motivational strategies. Regarding the cognitive effects, it has been well documented that variability of practice may result in beneficial effects on schema construction and transfer of training provided the total task cognitive load is kept within the bounds of the working memory capacity ( e. g., Paas & van Merriënboer, 1994a ; Quilici & Mayer, 1996 ; van Merriënboer, Schuurman, de Croock, & Paas, 2002 ). Indeed, the amount of invested mental effort is considered a more accurate measure of motivational behavior than self-report methods, which require learners to indicate their perceived motivation level ( Song & Keller, 2001 ). Therefore, the authors believe that an instrument to capture learner motivation should not only take the invested mental effort into account but also the associated performance data. The Calculation of Task Involvement Consistent with the efficiency perspective introduced by Paas and Van Merriënboer ( 1993 ; see also Tuovinen & Paas, 2004 ) the authors present an alternative motivational perspective on the relation between mental effort and task performance, which can be used to calculate and visualize the relative involvement in instructional conditions. Consistent with this line of reasoning, the combined mental-effort and performance scores can provide information on the relative involvement of students in instructional conditions and can be used to compare the effects of instructional conditions on the learners ́ motivation. Thus this graph provides a visual display of the motivation effort and performance relationships. Where shifts to the upper right of the coordinate system that is presented in Figure 1 indicate an increase in involvement, and shifts to the lower left, indicate a decrease in involvement. This finding is consistent with the ‘ expertise reversal effect ’ ( Kalyuga, Ayres, Chandler, & Sweller, 2003 ), where learning strategies found to facilitate learning for novices, such as the superiority of worked examples over exploration learning, become less effective or even dysfunctional as a function of increasing expertise. The standardized values for the performance, mental effort and the involvement results of the two treatments with the two prior knowledge levels are shown in Figure 2, using the conventional presentation introduced by Paas and van Merriënboer ( 1993 ). In this case the trend appears to be that the exploration practice provided greater involvement than the worked examples practice, and that this effect is strongest for the higher prior knowledge students. The theory suggests that learning happens best under conditions that are aligned with the cognitive architecture. However, the authors argue that this perspective does not recognize that meaningful learning can only commence if training experience is coupled with the motivation to achieve well. This definition suggests that the amount of mental effort invested in a certain learning task can be considered a reliable estimate of the learner ’ s motivation or involvement in that task. Motivational Perspective 11 INSERT FIGURE 2 ABOUT HERE -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -We can now compare the involvement of the worked examples and exploration groups by computing the involvement measures using the above formula.