Effects of Concurrent Performance Monitoring on Cognitive Load as a Function of Task Complexity Tamara van Gog (tamara.vangog@ou.nl) Centre for Learning Sciences and Technologies & Netherlands Laboratory for Lifelong Learning, Open University of The Netherlands P.O. Box 2960, 6401 DL Heerlen, The Netherlands Fred Paas (fred.paas@ou.nl) Centre for Learning Sciences and Technologies & Netherlands Laboratory for Lifelong Learning, Open University of The Netherlands P.O. Box 2960, 6401 DL Heerlen, The Netherlands Psychology Department, Erasmus University Rotterdam, The Netherlands Abstract For self-regulated learning to be effective, students need to be able to accurately monitor their performance while they are working on a task, use this as input for self-assessment of that performance after the task, and select an appropriate new learning task in response to that assessment. From a cognitive load perspective, monitoring can be seen as a secondary task that may become hard to maintain and may hamper performance on the primary task (i.e., learning) under high load conditions. Therefore, this study investigated the effects of concurrent performance monitoring on cognitive load and performance as a function of task complexity. Task complexity was varied as between-subjects factor and monitoring as within-subjects factor. It was hypothesized that monitoring would significantly increase cognitive load and decrease performance on complex, but not on simple tasks. Results from a pilot study based on data from 31 participants seem to confirm this hypothesis. Keywords: education; cognitive load; monitoring; task complexity; self-regulated learning. Cognitive Demands of Self-regulated Learning A major aim of many contemporary educational programs is to foster students self-regulation skills. It is often assumed that this aim can be achieved in a ‘learning by doing’ manner (i.e., by providing learners with a high amount of control over their learning process). Unfortunately, however, studies that compared the effects of learner controlled vs. system controlled instruction, often show detrimental effects on learning outcomes of providing learners with control over what tasks they work on, in what order, and for how long (e.g., Niemic, Sikorski, & Walberg, 1996). So even if learners would acquire self-regulation skills this way (which can also be questioned, considering the findings on learning by doing in acquiring problem solving skills; cf. Kirschner, Sweller, & Clark, 2006; Sweller, Van Merrienboer, & Paas, 1998), giving learners a high degree of control may have unwanted effects when it comes to learning outcomes. These effects, however, are not entirely surprising if we look at the cognitive demands imposed by self-regulated learning. For self-regulated learning to be effective, students need to be able to accurately monitor their performance while they are working on a task, use this as input for self- assessment of that performance after the task, and select an appropriate new learning task (one that allows them to train the task aspects they do not yet master sufficiently) in response to that assessment (cf., Ertmer & Newby, 1996; Zimmerman, 1990). Research has shown, however, that accurate self-assessment is very difficult for learners. Not only are humans prone to several biases that make accurate self-assessment difficult (see Bjork, 1999), but accuracy of self-assessment also seems to be related to the amount of experience in a domain (Dunning, Johnson, Erlinger, & Kruger, 2003). Presumably, advanced learners are more accurate self-assessors because their experience not only provides them with more task knowledge, but also with more knowledge of the criteria and standards that good performance should meet (Dunning et al., 2003). Interestingly, it is also the case that when positive effects on learning outcomes are reported in studies on learner control, this tends to be for high prior knowledge learners (Lawless & Brown, 1997; Scheiter & Gerjets, 2007; Steinberg, 1989). This suggests that the accuracy of self-assessment indeed plays a very crucial role in the effectiveness of self- regulated learning. However, as mentioned before, self-assessment of performance after task completion, also relies on accurate performance monitoring while working on the task. If learners do not have a good recollection of their performance, for example, of what actions they took and what the results of those actions were, they cannot accurately assess it. So, another possible explanation (which is not mutually exclusive with the other ones mentioned here) for why learners, and especially novice learners, are not accurate self-assessors, is that difficulties may already arise in the performance monitoring stage. Monitoring and Cognitive Load Many learning tasks are complex, that is, they impose a high intrinsic cognitive load (Sweller, 1988; Sweller et al., 1998). Intrinsic cognitive load depends on task complexity,
[1]
P. Ackerman,et al.
Motivation and cognitive abilities: an integrative/aptitude-treatment interaction approach to skill acquisition
,
1989
.
[2]
T. Gog,et al.
Uncovering Expertise-Related Differences in Troubleshooting Performance: Combining Eye Movement and Concurrent Verbal Protocol Data
,
2005
.
[3]
KIMBERLY A. LAWLESS,et al.
Multimedia learning environments: Issues of learner control and navigation
,
1997
.
[4]
R. Keen.
Why People Fail to Recognize Their Own Incompetence
,
2010
.
[5]
D. Leutner,et al.
Direct Measurement of Cognitive Load in Multimedia Learning
,
2003
.
[6]
Robert A. Bjork,et al.
Assessing our own competence: Heuristics and illusions.
,
1999
.
[7]
F. Paas,et al.
Cognitive Architecture and Instructional Design
,
1998
.
[8]
Gillian B. Yeo,et al.
A multilevel analysis of effort, practice, and performance: effects; of ability, conscientiousness, and goal orientation.
,
2004,
The Journal of applied psychology.
[9]
Katharina Scheiter,et al.
Making your own order: Order effects in system- and user-controlled settings for learning and problem solving.
,
2007
.
[10]
Peggy A. Ertmer,et al.
The expert learner: Strategic, self-regulated, and reflective
,
1996
.
[11]
F. Paas,et al.
Cognitive Load Measurement as a Means to Advance Cognitive Load Theory
,
2003
.
[12]
B. Zimmerman.
Self-Regulated Learning and Academic Achievement: An Overview
,
1990
.
[13]
Herbert J. Walberg,et al.
Learner-Control Effects: A Review of Reviews and a Meta-Analysis
,
1996
.
[14]
John Sweller,et al.
Cognitive Load During Problem Solving: Effects on Learning
,
1988,
Cogn. Sci..
[15]
F. Paas,et al.
Instructional Efficiency: Revisiting the Original Construct in Educational Research
,
2008
.
[16]
Lev Vygotsky.
Mind in society
,
1978
.
[17]
Frank E. Ritter,et al.
In Order to Learn: How Order Effects in Machine Learning Illuminate Human Learning
,
2005
.
[18]
E. R. Steinberg.
Cognition and learner control: a literature review, 1977–1988
,
1989
.
[19]
F. Paas.
Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach.
,
1992
.
[20]
Richard E. Clark,et al.
Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching
,
2006
.