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Young Children’s Spontaneous Manifestation of Self-Regulation and Metacognition During Constructional Play Tasks


by Ornit Spektor-Levy, Marisol Basilio, Antonia Zachariou & David Whitebread - 2017

The value of self-regulation for academic achievement is well established. Thus it is paramount to understand how these abilities are developed throughout childhood and to develop research methodologies appropriate to the abilities of young children. In light of this need, we analyzed performances of primary school children in two constructional play tasks: The Train Track Task (TTT) and the LEGO® Building Task (LBT). We asked: To what extent do young children manifest spontaneous self-regulatory abilities during constructional play tasks? To what extent is the manifestation of these abilities task dependent? The sample consisted of 106 children in Year 1 to 5 in the United Kingdom (i.e., aged 5 to 10 years). All participants were given the same tasks and were video-recorded. Clips were coded following the MetaSCoPE coding scheme. Results show that the different components of self-regulation do develop between Years 1 and 5 but not at a constant pace. Findings reveal inconsistency regarding the question of whether self-regulation abilities are task dependent. Our findings hold practical implications: Constructional play tasks are good opportunities to reveal young children’s self-regulation abilities in class. The development of teachers’ awareness may help to better understand children’s cognitive, affective, and social development and to adjust learning activities to the needs of young individuals.

Self-regulated learners are able to deploy a wide repertoire of cognitive and metacognitive strategies according to task demands: planning and setting goals, monitoring their performance, and changing strategies when necessary; being intrinsically motivated, they seek challenging tasks and persist in the face of difficulties (Pintrich & De Groot, 1990; Zimmerman, 2008). Given that the value of self-regulated learning for learning and academic achievement has been well established (Blair, Calkins, & Kopp, 2010), it is paramount to understand how and to what extent these abilities are developed throughout childhood, as well as which practices should be implemented to enhance these abilities. It is therefore key for researchers to develop research methodologies appropriate to the cognitive and verbal abilities of young children and to index behaviors that constitute self-regulatory skills. In light of this need, we present a study describing and comparing two observational tasks appropriate for children across the primary school years. The Train Track Task (TTT; reported in Bryce & Whitebread, 2012 for a narrower age range) and the new LEGO® Building Task (LBT). Both of these puzzlelike tasks use toy materials and involve challenging constructional play, allowing researchers to observe and reliably quantify spontaneous self-regulation and metacognitive abilities in an engaging context for children.


METACOGNITION AND SELF-REGULATION


Since the early work of Flavell (1979), who first coined the term metacognition, and Ann Brown (1987), who distinguished metacognitive knowledge from metacognitive awareness and control, the literature has become replete with studies investigating the development and the manifestations of metacognition. Metacognition refers to a high level of thinking that involves active control over the cognitive processes engaged in learning. It consists of two components: (a) knowledge of cognition, which includes declarative knowledge, procedural knowledge, and conditional knowledge, and (b) regulation of cognition, which includes three essential skills: planning; monitoring and control; and evaluating.


The term self-regulation was coined by Lev Vygotsky (1978, 1986) and has since become widely accepted and investigated (Iiskala, Vauras, & Lehtinen, 2004). It refers to the monitoring and control of individual performance, or intrapersonal regulation (as opposed to other and shared regulation). Self-regulated learning (SRL) allows learners to set their goals for learning and then to monitor, regulate, and control their cognition, motivation, and behavior, guided and constrained by their goals and the contextual features in the environment (Azevedo, 2009). Though there is not a universal definition, self-regulation is commonly understood to be a set of constructive, cognitive, affective, motivational, and social processes that allow individuals to continuously and flexibly adjust to changing situations and tasks (Best & Miller, 2010; Pintrich, 2002). It is accepted that self-regulation is more predictive of academic achievement than intelligence (IQ) or family background variables (Raver & Knitzer, 2002). It is also accepted that early childhood is a critical time period for the development of important educational skills, including self-regulation (Mitchell, Wylie, & Carr, 2008; Morrison, 2015; Sylva, Melhuish, Sammons, Siraj-Blatchford, & Taggart, 2004). Moreover, until two decades ago, most of the literature was based on the assumption that metacognitive knowledge is declarative and that metacognitive processes are available to conscious awareness (Nelson & Narens, 1994). However, extensive study has shown that metacognitive processes involve an element of nonconscious awareness too (Efklides, 2008; Veenman, Van Hout-Wolters, & Afflerbach, 2006; Whitebread & Basilio, 2012). Moreover, nonconscious self-regulation of cognition, emotion, and behavior can be detected even in very young children (Whitebread & Basilio, 2012) and is fundamental to later metacognitive and self-regulatory developments (Destan, Hembacher, Ghetti, & Roebers, 2014; Fitzsimons & Bargh, 2004; Wiebe et al., 2011).


There is extensive evidence of the impact of young (aged 3–5 years) children’s self-regulatory skills and executive control on their immediate and short-term educational achievements—including school readiness, classroom behavior, academic success, and social adaptation (Blair & Razza, 2007; McClelland et al., 2007). Specifically, self-regulatory skills in young children are predictive of academic competencies such as literacy and math skills (Birgisdóttir, Gestsdóttir, & Thorsdóttir, 2015; Hubert, Guimard, Florin, & Tracy, 2015; Ponitz, McClelland, Matthews, & Morrison, 2009). Conversely, poor self-regulation was found as a predictor of future problems in school (Blair & Razza, 2007; Ponitz et al., 2008; Sawyer, Miller-Lewis, Searle, Sawyer, & Lynch, 2015).


Whitebread et al. (2009b) noted that metacognitive skills were previously assumed to emerge at the ages of 8–10 years because of a reliance on inappropriate research methodologies and very few interventions specifically designed to encourage metacognitive or self-regulatory skills in young children. Nowadays, most researchers agree that emerging metacognitive skills, though relatively undifferentiated, can indeed be observed from an age of 3 years (Lyons & Ghetti, 2011; Whitebread & Basilio, 2012).


METHODOLOGICAL ASPECTS OF STUDIES WITH YOUNG CHILDREN


Detecting self-regulatory and metacognitive abilities in young children is challenging. In previous research, self-regulation and metacognition have been mainly detected through prospective and retrospective self-report measures (questionnaires and interviews), concurrent self-report measures (think aloud), and responses to hypothetical questions (Kvavilashvili & Ford, 2014; Veenman, 2005). These procedures are inadequate with young children, who lack the verbal proficiency necessary for succeeding in them. Moreover, even older individuals (let alone young children) can have difficulty reporting on their own mental processes (Flavell, Beach, & Chinsky, 1966; Nisbett & Wilson, 1977). Thus, it is increasingly recognized that research relying on self-report or verbally based experimental methodologies may significantly underestimate the metacognitive and self-regulated performance of young children (Van Hout-Wolters, 2000; Whitebread, Coltman, Anderson, Mehta, & Pino Pasternak, 2005; Winne & Perry, 2000). Studies relying less on the children’s verbal abilities have tended to show them to be more knowledgeable and skilled than originally suggested (Annevirta & Vauras, 2001).


One example of a study that explored the development of self-regulatory and metacognitive abilities in young children (aged 3–5 years) is the Cambridgeshire Independent Learning (C.Ind.Le) project (Whitebread et al., 2009b)—a 2-year study set in naturalistic educational settings in the United Kingdom (English Nursery and Reception classrooms). Whitebread et al. (2009b) showed that applying sensitive methodologies can reveal metacognitive and self-regulation abilities in young children. Using two observational tools they had developed, they found that given the opportunity, 3- to 5-year-old children are very capable of engaging in metacognitive activity and that although verbal indicators of such activity were more prevalent, nonverbal indicators accounted for over a third of all occurrences. Whitebread et al. (2009b) concluded that when exploring evidence of metacognitive processes and self-regulation skills among young children, simply relying on verbal behavior would underestimate and distort the picture concerning the prevalence and range of metacognitive behaviors. The following are factors and considerations that should be taken into account when studying self-regulation and metacognition amongst young children.


Contextual factors. Schneider and Lockl (2002) indicated that preschool children were much more accurate in recall tasks and prediction of future performance when the tasks were ecologically valid and meaningful to them. Other studies suggest that focusing on social interactions between children can be valuable for studies seeking to detect and analyze self-regulation and metacognition (Zimmerman & Schunk, 2001). To conclude, studies with young children should be ecologically situated in their natural environments, provide mediated instructions, involve social interactions, and employ various forms of expression and representation.

Playful tasks. Playful but structured activities offer children the opportunity to engage in flexible thinking (Salmon, 2016), goal-directed behavior, and negotiation and cooperation with peers, while focusing their attention on a particular task, all of which are fundamental to the development of self-regulation (Kemple, Oh, & Porter, 2015; Timmons, Pelletier, & Corter, 2016). Di Stefano, Gino, Pisano, and Staats (2014) showed evidence that children learn not only from an experience but also from reflecting on it. In other words, children learn by thinking about the thinking that takes place during play. Whitebread, Coltman, Jameson, and Lander  (2009a) found substantial evidence of metacognitive and self-regulatory events in contexts that were playful, consisted of pairs or small groups, and involved high levels of collaboration and talk. Robson (2010), using the C.Ind.Le coding scheme developed by Whitebread et al. (2009b), also found extensive evidence of metacognitive and self-regulatory behaviors in preschool children’s self-initiated play and reflective dialogues.


On-line observations. Observational tools are key to understanding and analyzing the emergence of young children’s metacognitive skills, given that the children’s limited language skills render most tools and techniques (e.g., questionnaires, self-report measures, concurrent think-aloud) as inadequate (Azevedo, 2009; Whitebread et al., 2009b). Veenman (2005) referred to observations as an “on-line” method because it gathers the information in “real time,” as the task is taking place (unlike “off-line” methods that gather data retroactively through means such as questionnaires and interviews) (Veenman, 20052011; Veenman et al., 2006). Thinking aloud, observation, eye-movement registration, and, more recently, logfile registrations of learner activities on the computer are examples of on-line methods used for the assessment of strategy use during tasks (Veenman, 2011).


Winne and Perry (2000) detailed three advantages of observation, noting that (a) it records what learners actually do rather than what they recall or believe they do, (b) it allows links to be established between learners’ behaviors and the context of the task, and (c) it does not depend on the verbal abilities of the participants. Employing such methods also provides the opportunity to record social processes involved in the development of metacognitive and self-regulatory abilities.


Systematic observation, particularly where it involves video-recording, affords the opportunity to record nonverbal as well as purely verbal behavior. Studies concerned with the role of gesture in conceptual learning and strategy development (Goldin-Meadow, 2002; Pine, Lufkin, & Messer, 2004) suggest that nonverbal is not only indicative of metacognitive processes in young children but might also be an important part of the processes by which they are acquired.


STUDY AIMS AND QUESTIONS


The study presented here sought to gain greater understanding of young children’s self-regulation and metacognitive abilities development by comparing the performance of children across the primary school ages in two constructional play tasks (the LBT vs. TTT). The following questions were addressed:


1.

To what extent do young children manifest spontaneous self-regulatory abilities during constructional play tasks?

2.

To what extent are children’s manifestations of self-regulatory abilities task dependent?


METHODOLOGY

RESEARCH SAMPLE


The sample consisted of 106 children (52 female): 34 children in Year 1 (aged 5–6 years), 36 children in Year 3 (aged 7–8 years), and 36 children in Year 5 (aged 9–10 years), recruited from three schools in Cambridge, United Kingdom. The sample was recruited as part of a larger intervention study (the Play, Learning and Narrative Skills Project1) and was selected for maximum variability in terms of writing skills (with low, average, and good writers per class/year).


RESEARCH TOOLS


All of the participants were given the same problem-solving tasks to complete, followed by a short structured interview (following the procedure described in Appendix A). The children were video-recorded, and the clips were coded in full (following the coding scheme described in Appendix B). Children’s behaviors during the task and responses to the interview questions were coded for evidence of self-regulation and metacognitive abilities. Notable indicators of absence of self-regulation and metacognitive abilities were also coded. This approach follows the previous study conducted by Bryce and Whitebread (2012) in that it looks for evidence of positive and negative indicators of both monitoring and control rather than focusing on just one component process.


The Train Track Task (TTT)


This is a task that has been widely used in the literature and in previous studies, and it is a reliable way of eliciting and coding children’s metacognitive skills during a problem-solving task. The task involves building a train track to match a predefined shape from a plan and was adapted from Karmiloff-Smith’s (1979) closed-circuit railway task. Children were instructed to match a plan as best they could (see Table 1), using as many pieces as required (see Figure 1). Importantly, the task materials were familiar to the children (giving the task inherent appeal), but the task demands were novel. The children attempted 2–4 shapes (one deemed “easy” and others gradually getting “harder” for each age group, based on pilot data). The task procedure is described in Appendix A.


Table 1. The Train Track Task Shapes, Starting Point, and Appropriate Timing

 

Shapes

Starting points

Appropriate Timing

1

[39_21931.htm_g/00001.jpg]

 

1:30

2

[39_21931.htm_g/00002.jpg]

Y1

2:00

3

[39_21931.htm_g/00003.jpg]

Y3

3:00

4

[39_21931.htm_g/00004.jpg]

Y5

3:30

5

[39_21931.htm_g/00005.jpg]

 

3:30


Figure 1. Train Track Pieces Available to the Children

[39_21931.htm_g/00007.jpg]


LEGO® Building Task (LBT)


This task involved building a LEGO model according to a plan—a finished diagram of a LEGO model was shown (see Table 2), and the child was asked to build it with LEGO pieces. They were provided only with the exact pieces needed to build the model. This task was inspired by Winsler (1998), but a range of new models was piloted with various levels of difficulty, varying in two of the four dimensions identified by Richardson, Jones, Croker, and Brown (2011) to predict children’s building time and accuracy. The children attempted 2–4 shapes with the starting point deemed “easy” and others gradually getting “harder” for each age group, based on pilot data. This was to elicit self-regulation in the context of cognitive challenge for each particular child. The full task procedure is described in Appendix A.


Table 2. Models Made From LEGO® Bricks—The LBT Models, Starting Point, and Appropriate Timing


 

LEGO® Model

Starting Points

Appropriate Timing

1

[39_21931.htm_g/00008.jpg]

Warm up for Y1

 

2

[39_21931.htm_g/00009.jpg]

Starting point Y1

00:35

3

[39_21931.htm_g/00010.jpg]


01:00

4

[39_21931.htm_g/00011.jpg]

Starting point Y3

2:30

5

[39_21931.htm_g/00012.jpg]

 

3:00

6

[39_21931.htm_g/00013.jpg]

Starting point Y5

03:30

7

[39_21931.htm_g/00014.jpg]

 

04:30

8

[39_21931.htm_g/00015.jpg]

 

05:30


THE METASCOPE CODING SCHEME


The MetaSCoPE coding scheme stands for Metacognitive Skills in Constructional Play Engagement. The coding scheme aimed to fairly represent, by numerical counts, verbal and nonverbal manifestations of on-line and off-line indicators of metacognition, indicators of self-regulation of emotions (motivation), and accuracy of the construction. It is based on the coding scheme developed by Bryce and Whitebread (2012) and previously employed in a pilot study.


The video analysis coded both verbal and nonverbal responses. Nonverbal responses could be manifested by “private gestures”—signs that children intentionally direct toward themselves or objects (Basilio & Rodríguez, 2011; Rodríguez & Palacios, 2007). Private gestures may reflect cognitive function (Garber & Goldin-Meadow, 2002; Pine et al., 2004) or in the spontaneous production of gestures when solving tasks that involve the use of spatial information (Chu & Kita, 2008). The analysis also looked for private speech. Private speech emerges during preschool years and becomes critical for the development of self-regulation. It is an intermediate step between self-regulatory external speech and internal speech (Savina, 2014), and although private speech is spoken out loud, it is used for self-guidance, planning, and problem-solving rather than for a communicative purpose (Lee, McDonough, & Bird, 2014; Vygotsky, 1997).


For coding, the researchers selected for each participant the model construction scenario that seemed to provide the most appropriate challenge for the child (i.e., in which the child produced the most accurate model within the appropriate time—as specified in Table 1 or 2). The coding was based on several indications, including building time, model accuracy, and evidences of monitoring, control, metacognitive knowledge, and motivation (Appendix B). The codes were then transformed into numeric value (on a scale of 0–3, or 1 point for each indication). In this report, we will focus on accuracy, on-line and off-line indicators of metacognition (Appendix B).


Three raters who were specialists in early childhood education research coded 10% of the video data gathered in this study. The three raters watched the videos carefully (each video at least twice) and analyzed each video according to the MetaSCoPE coding scheme (Appendix B). Interrater reliability between the three raters was calculated, producing a Cronbach’s alpha score of .97.

FINDINGS


First and foremost, the initial findings supported the validity of the TTT and LBT as an appropriate method for exploring children’s self-regulation and metacognitive abilities. The tasks are motivating for the children, they enable the observation of behaviors (verbal and nonverbal), and their difficulty can be adjusted according to the children’s abilities. The goal of the tasks is clear and observable, and the behaviors they elicit represent the child’s strategies (such as systematic checking or reminding him/herself of the goal by looking at the diagram).  


ARE MANIFESTATIONS OF SELF-REGULATION RELATED TO AGE?


To examine the first research question—To what extent do young children manifest spontaneous self-regulatory abilities during constructional play tasks?—we examined the performance of children from three age groups (Years 1, 3, and 5) in the LBT and TTT tasks. A one-way multivariate analysis of variance (MANOVA) was conducted for the LBT task measures and for the TTT task measures separately, with age group as the independent variable, and the scores on the five LBT task measures and the five TTT task measures as the dependent variables.


Differences Between the Three Age Groups on the LBT Task Measures


Age group was found to have a significant effect on the children’s LBT measures (accuracy, monitoring, control, lack of monitoring and control, total). The results of the one-way MANOVA showed simultaneous effect of age group on all five measures, F (12, 196) = 3.52, p < .001, Wilks’ λ = .68, η2 = .18. The F values of each measure are displayed in Table 3.


When examining each of the dependent variables separately, significant differences were found between the three age groups in all of the LBT task measures except the monitoring measure (awareness of task, checking, evaluation). As Table 3 shows, the Year 5 children outperformed the Year 1 and 3 children on the accuracy measure (accuracy of the LEGO model built). The Year 5 children also outperformed the Year 1 children on the control measure (planning, sorting, changing strategy, pausing to think). Calculation of the total performance of self-regulation throughout the LBT task shows that the Year 5 children performed significantly better than the Year 3 children but similar to the Year 1 children. The inverted measure, lack of monitoring and control, which presents the lack of regulation by means such as brute force, going off task, local error, and repetition of error, was significantly higher among the Year 3 than the Year 5 children.


Overall, in the context of this study, the findings in Table 3 show that although monitoring does not change significantly between Years 1 and 5, there were significant indications (control, lack of monitoring and control, total measure) that self-regulation and metacognitive abilities do develop between these age groups. The findings do not show consistent gradient change from Year 1 to Year 5, but rather a tendency toward change, as will be elaborated in the discussion section.


Table 3. Means (and SD) of the Five LBT Task Measures by Age Groups (Year 1, Year 3, Year 5)

 

Year 1 (N=34)

Year 3 (N=36)

Year 5 (N=36)

   
 

Mean

SD

Mean

SD

Mean

SD

F

η2

Scheffe

Accuracy

3.18

0.76

3.17

0.77

3.56

0.69

3.18*

.06

5>1,3,  1=3

Monitoring

2.53

1.05

2.33

0.79

2.19

0.75

1.30

.02

-------

Control

1.41

1.81

1.75

1.63

2.44

1.48

3.62*

.07

5>1, 1=3, 3=5

Lack of Mon./Con.

1.56

1.46

2.06

1.51

1.17

1.54

3.15*

.06

3>5, 1=3, 1=5

Total

4.32

2.65

4.03

2.65

5.47

2.46

3.13*

.06

5>3, 1=3, 1=5

*p < .05. ** p < .01.


Age group was found to have a significant effect on the children’s TTT measures (accuracy, monitoring, control, lack of monitoring and control, total). The results of the one-way MANOVA showed simultaneous effect of age group on all five measures, F (8, 200) = 3.66, p < .001, Wilks’ λ = .76, η2 = .13. The F values of each measure are displayed in Table 4.


When examining each of the dependent variables separately, significant differences between the three age groups were found in all the TTT task measures except the accuracy measure (whether the LEGO model building was a perfect model/a close model/different from model/off task). As can be seen in Table 4, the Year 5 and the Year 3 children outperformed the Year 1 children in all on-line indicators of metacognition. These findings strengthen the indications that self-regulation abilities develop between the ages of 5 and 10 years.   


Table 4. Means (and SD) of the Five TTT Task Measures by Age Groups (Year 1, Year 3, Year 5)

 

Year 1 (N=34)

Year 3 (N=36)

Year 5 (N=36)

   
 

Mean

SD

Mean

SD

Mean

SD

F

η2

Scheffe

Accuracy

2.62

.55

2.92

.60

2.86

.64

2.44

.04

-------

Monitoring

3.26

1.42

4.03

1.05

4.11

1.19

5.01**

.09

3,5>1, 3=5

Control

2.41

1.56

3.58

1.50

3.94

1.31

10.49***

.17

3,5>1, 3=5

Lack of Mon./Con.

3.00

1.74

2.00

1.60

1.72

1.28

6.53**

.11

1>3,5, 3=5

Total

4.68

3.22

7.61

2.97

8.33

2.86

14.32***

.22

3,5>1, 3=5

** p < .01. *** p < .001.


Correlations Between the Accuracy Measure and the Other Measures in the LBT and TTT Tasks  


To examine correlations between the LBT accuracy measure and the other LBT task measures and between the TTT accuracy measure and the other TTT task measures, Pearson correlations were conducted. Table 5 shows the Pearson correlation coefficient between the LBT accuracy measure and other LBT task measures, and Table 6 shows the Pearson correlation coefficient between the TTT accuracy measure and other TTT task measures.


Table 5. Correlations Between the LBT Accuracy Measure and Other LBT Task Measures (N = 106)

 

LBT accuracy

LBT monitoring

.20*

LBT control

.07

LBT lack of mon./con.

-.83***

LBT total

.58***

            *p < .05. *** p < .001.


Table 6. Correlations Between the TTT Accuracy Measure and Other TTT Task Measures (N = 106)

 

TTT accuracy

TTT monitoring

.50***

TTT control

.31***

TTT lack of mon./con.

-.47***

TTT total

.56***

             *** p < .001.


As Table 5 shows, positive correlations were found between the LBT accuracy measure and between the monitoring measure (awareness of task, checking, evaluation) and the total measure of self-regulation. These results indicate that a higher accuracy of LEGO model building from a plan was significantly related to higher performance of self-regulation abilities and motivational variables. The control measure (planning, sorting, changing strategy, pauses to think) was found to be not correlated with the accuracy measure. This might have been due to the fact that, as Table 3 shows, the development of the different measures of self-regulation between Year 1 and 5 did not occur at a constant pace. This point will be discussed further in the discussion section.


The data in Table 6 support the findings that the higher accuracy (of train track shapes) was significantly related to higher performance of self-regulation abilities. All on-line indicators of metacognition—monitoring, control, total measure, and the inverted indicator lack of monitoring and control—were highly correlated with the accuracy measure. The lack of monitoring and control measure was significantly correlated, but because it measures lack of abilities, it was negatively correlated to the accuracy of the model built.


ARE MANIFESTATIONS OF SELF-REGULATION ABILITIES TASK DEPENDENT?


To examine the second research question—To what extent are children’s manifestations of self-regulatory abilities task dependent?—Pearson correlations between the LBT task and TTT task measures were conducted. Table 7 shows the Pearson correlation coefficients between the LBT task and TTT task measures. Data show that there was a significant correlation between the LBT accuracy measure and the TTT accuracy measure. The LBT accuracy measure was also correlated with the TTT monitoring, control, and total measures. However, no correlation was found between the indicators of monitoring, control, and total measures of both LBT and TTT tasks. The inverted measure, lack of monitoring and control, which presents lack of regulation through behaviors such as using brute force, going off task, making a local error, and repetition of error, was significantly correlated between the LBT and TTT tasks. Thus, Table 7 reveals inconsistency regarding the question of whether self-regulation abilities were task dependent in the context of our study. We therefore conducted further analyses, using gender as an independent variable.


Table 7. Correlations Between the LBT Task and TTT Task Measures (N = 106)

 

TTT Accuracy

TTT Monitoring

TTT Control

TTT Lack of Mon./Con.

TTT Total

LBT Accuracy

.28**

.32***

.38***

-.19

.39***

LBT Monitoring

-.01

.10

.01

.06

.02

LBT Control

.19

.18

.02

.01

.07

LBT Lack of Mon./Con.

-.20*

-.19

-.16

.20*

-.25*

LBT total

.24*

.25**

.10

-.09

.19

*p < .05. ** p < .01. *** p < .001.


To examine whether correlations would be found between the LBT task and TTT task measures among boys and girls, Pearson correlations between the LBT task and TTT task measures were conducted for boys and for girls separately. No significant correlations were found between the LBT task and TTT task measures among girls. Pearson correlation coefficients among girls ranged from .03 to .22 (ps > .05). In contrast, significant correlations were found between some of the LBT task and TTT task measures among boys (Table 8). Thus, for boys, manifestations of self-regulation abilities were not task dependent for some measures: the accuracy of the model built, the relations between accuracy and the total measure of self-regulation, and lack of monitoring and control. For girls, however, all measures showed that manifestations of self-regulation abilities were task dependent, given that they performed differently in the two tasks, and therefore, no correlations were found between the various indicators.


Table 8. Correlations Between the LBT Task and TTT Task Measures Among Boys (N = 54)

 

TTT Accuracy

TTT Monitoring

TTT Control

TTT Lack of Mon./Con.

TTT Total

LBT Accuracy

.36**

.39**

.49***

-.35**

.54***

LBT Monitoring

.04

.22

.15

-.01

.15

LBT Control

.24

.19

-.02

.17

-.00

LBT Lack of Mon./Con.

-.23

-.21

-.22

.37**

-.34*

LBT total

.31*

.31*

.14

-.09

.23

*p < .05. ** p < .01. *** p < .001.


DISCUSSION


The study presented in this report aimed to investigate spontaneous self-regulation and metacognitive abilities in the context of constructional play tasks among young children of different ages. The study compared the children’s performances in problem solving using two constructional play tasks—the LBT and the TTT. In both tasks, age group was found to be a significant factor in the children’s self-regulation manifestation. In the context of the LBT, the findings show that, though monitoring does not change significantly between Year 1 and Year 5, there are significant other indications that self-regulation and metacognitive abilities do develop between Year 1 and Year 5, with the accuracy of the children’s model building from a plan manifesting as significantly improved at Year 5.


Self-regulatory abilities and metacognitive skills were also more frequently manifested by Year 5 children. This reflects the robust finding in the metacognitive literature that by the age of 5 or 6 years, children are capable of monitoring, but still have difficulty with control (Ponitz et al., 2009). In the context of the LBT, we found that monitoring (awareness of task, checking, evaluation) did not change significantly, but control (planning, sorting, changing a strategy, pausing to think) did develop between Year 1 and Year 5. This indicates that it is not simply the children’s awareness that is developing; it is their use of information and ability to select the appropriate way of doing the task.


The findings do not show constant gradient change from Year 1 to Year 5, but rather a tendency for change. However, this tendency is reinforced in the context of the TTT, where significant differences between the three age groups were found in all the TTT measures except accuracy. The Year 5 and Year 3 children outperformed the Year 1 children in all indicators of metacognition (including the inverted measure, lack of monitoring and control). These findings strengthen the evidence that self-regulation abilities develop between Years 1 and 5 (5–10 years old). Our findings are in line with other studies, which argue that age-related improvements in self-regulatory skills, such as allocation of study time (Schneider & Lockl, 2002), withholding uncertain responses (Roebers & Fernandez, 2002), or revising answers in an achievement test (Roebers, Schmid, & Roderer, 2009), are typically found during elementary school years.


Our results show that the development of the different measures of self-regulation does not occur at the same pace between Years 1 and 5. This, however, might be the consequence of individual differences. Posner and Rothbart (2000) claimed that children’s reactive tendencies to experience and expression of negative and positive emotions and their responsivity to events in the environment can be observed very early in life, but children’s self-regulatory executive attention develops relatively late and continues to develop throughout the early school years. Because executive attention is involved in the regulation of emotions, some children will be lacking in the control of emotions and actions that other children can demonstrate with ease. Thus, in the context of our study, individual differences between children may have caused the self-regulation development between Year 1 and 5 to have appeared inconsistent and to manifest as a general tendency of improvement rather than a constant rate of change.


Schneider, Vise, Lockl, and Nelson (2000) have suggested that the interplay between monitoring and control processes drives the development of self-regulation and metacognitive skills—that the development of one can reinforce and promote the development of the other. They also suggest, however, that among young children, the potential connection between the two is not yet fully realized and that information made available through monitoring processes is underutilized in controlling subsequent performance. Such discrepancies between monitoring and control processes may explain the differences in manifestations of monitoring and control we found in the context of the LBT and TTT tasks.  


The outcomes of the accuracy measure (i.e., whether the model/shape building was a perfect model/close to the model/different from model or off task) were different between the tasks. In the LBT, Year 5 children outperformed those in Years 1 and 3, whereas in the TTT, no differences were found between the age groups. These results may be due to the fact that the train track is more familiar to children, or to the fact that the LEGO models were more complicated—requiring the children to build a more sophisticated three-dimensional object rather than the mere flat surface required by the train track. These differences should be further investigated in future studies.


The results of the accuracy measure are interesting because they may indicate whether a higher accuracy model built from a plan is related to higher performance of self-regulation abilities and motivational variables. In the context of the LBT, all the measures except the control measure (planning, sorting, changing strategy, pausing to think) were found to be correlated with the accuracy measure. In the context of the TTT, all measures were highly correlated with the accuracy measure. Overall, these results indicate that high self-regulation and metacognition performance in Years 1–5 relate to higher quality and accuracy of task completion—model building from a plan. These findings are in line with other reports in the literature. There is considerable research documenting metacognition as an essential component of self-directed and self-regulated learning (Cao & Nietfeld, 2005). It enables the individual to control and plan his or her own mental activities and learning. Metacognition is involved in the selection and evaluation of cognitive tasks, in the detection of mistakes in the problem-solving or learning process, and in the choice of goals and adequate problem-solving/learning strategies (Bakracevic Vukman & Licardo, 2010), as was needed in the LBT and TTT tasks.


The above interpretation of findings led us to explore the second research question: To what extent are children’s manifestations of self-regulatory abilities task dependent? Our findings showed that there is a significant correlation between the LBT accuracy measure and the TTT accuracy measure. The LBT accuracy measure was also correlated with the TTT monitoring, control, and total measure. Thus, for most students, those who built a good or accurate LBT model manifested self-regulation and metacognitive abilities during the TTT. However, no correlation was found between the metacognitive indicators—monitoring, control, and total measures—of the LBT and TTT tasks. The inverted measure, lack of monitoring and control, which presents the lack of regulation through behaviors such as brute force, going off task, local error, and repetition of error, was significantly correlated between the LBT and TTT tasks. Thus, the findings reveal inconsistency regarding the question of whether self-regulation and metacognitive abilities are task dependent in the context of our study.


To further delve into this question, we decided to analyze the data with gender as an independent variable. No significant correlations were found between the LBT task and TTT task measures among girls. On the other hand, significant correlations were found between some of the LBT task and TTT task measures among boys, which resemble the general correlations found between both tasks. It can therefore be concluded that the overall correlations found between tasks were driven by the performances of the boys in this study. However, even among the boys, the results were inconsistent, given that correlations were not found between all measures.


For the girls, manifestations of self-regulation abilities and accuracy of model were task dependent. One explanation might be that girls performed differently in the two tasks, and therefore, no correlations between tasks were found for the girls. Maybe we could see different findings if the tasks did not rely on building models from a plan but on another kind of task that requires other skills, such as mixing materials to create chemical phenomena or sorting objects.


Overall, the empirical results from most countries reveal significant differences in self-regulation that tend to favor girls (Matthews, Ponitz, & Morrison, 2009). However, Hubert et al. (2015) found no gender differences in a sample of French children, and Wanless et al. (2013) also found no gender difference in samples of Asian children. Hubert et al. (2015) attributed the lack of difference to the more structured preschool experience children receive in France. Although Gunzenhauser and von Suchodoletz (2015) found initial differences favoring girls, boys made greater gains in self-regulation across preschool. They echoed Hubert et al.’s interpretation that the structured preschool system in Germany might help those boys who enter with relatively lower behavioral regulation skills. Consistent with this hypothesis, Matthews et al. (2009) found that in the United States, girls outperformed boys in self-regulation, but closer examination of the frequency distributions revealed that most of the gender difference was accounted for by about 10% of the boys at the very bottom of the scoring range. Clearly, further studies should explore this gender issue with large samples, in different contexts and in different cultures.


CONCLUSIONS


The observational method adopted in this study proved to be reliable thanks to the wealth of parameters that it addressed through the detailed observation and analysis of verbal and nonverbal responses of young children in the context of two problem-solving constructional play tasks. This methodological approach is in line with Azevedo (2009), who claimed that the convergence of multiple sources of data (such as observations, think-aloud, eye gazes) is a key to developing a comprehensive understanding of the underlying metacognitive and self-regulatory processes.


This article and the study it describes are subject to a number of limitations. The sample consisted of 106 children from three different age groups. Future studies should include larger samples to maximize reliability and significance. Another limitation is inherent in the challenge of exploring people’s thinking and mental dispositions. We applied the on-line observation approach, which, despite its advantages, also has limitations, given that only directly observable behaviors can be coded. Thus, if a theory predicts that all control behaviors must be preceded by internal monitoring, the internal monitoring is not visible and therefore difficult to be coded. Future studies should bypass this limitation by applying innovative technics such as eye tracking or log file registrations within computer-based tasks.


Despite these limitations, our findings hold practical implications. As Salmon (2016) contended, it is necessary for teachers to learn how to nurture children’s thinking skills and thinking dispositions. Our findings point to the visibility of active, constructional play tasks as good opportunities to reveal young children’s self-regulation and metacognitive skills. While attempting to solve the problem presented to them as part of a constructional play task or in spontaneous play, children respond in various ways: through thinking aloud, private speech, private gestures, facial expressions, gazes, vocal responses, and so on. The development of awareness on the part of teachers and caregivers may help both educators and researchers to better understand children’s cognitive, affective, and social development and to adjust learning activities to the needs of young individuals in ways that will foster self-regulation and metacognitive processes and better prepare these children for future learning and social communication.


Notes


1. David Whitebread (principal investigator), Marisol Basilio (research associate), Helen Bradford and Mary Anne Wolpert (coinvestigators). This research was funded by the LEGO® Foundation.


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APPENDIX A

PROCEDURES OF THE TWO PROBLEM-SOLVING CONSTRUCTIONAL PLAY TASKS: THE TRAIN TRACK TASK (TTT) AND THE LEGO® BUILDING FROM PLAN TASK (LBP)

 

TTT

LBT

Initial instruction


“I want you to have a go at making this model using these pieces of train track.”

 “You can use as many as you like, but you don’t have to use them all. The train track can be quite big. I want you to try to join up the track so that a train can go seamlessly around it.”

“You can take as long as you like and please tell me when you are finished.”

Place the selected model in front of the child.

“Did you understand what you have to do?”

Spread track pieces. Set timer and record children’s building times for each model.

“I want you to have a go at making some models using LEGO. I am going to give you a picture and the LEGO bits you need to build it.”


Give first model (Starting point).


“Look at the picture very carefully and try to build it as similar to the picture as you can. Let me know when you are finished.”



Spread LEGO pieces. Set timer and record children’s building times for each model.

During construction

Do not provide evaluative feedback about performance. You can only encourage the child to persist (“You are working really hard, keep on going”).

After construction

Let child play with the train on the track after finishing the first model. “You can play with the train while I take a photo of what you did” (only Y1).

“Very good, I will take a photo of what you did.”

Next model

Judgment of difficulty level: If the shape/model built is accurate and it took the child less than the timing specified, move to harder shape/model. If the child struggled with the shape/model, it is not accurate, or building time is over the time specified, move to easier shape/model.

“Ok, great! Now we are going to try and make the other shape/model.” “So let’s take this one apart...” (experimenter and child break apart previous shape/model).

“Now let’s try this one” (place second shape/model in front of the child).

Repeat “during and after construction” procedures as before.

If on both occasions the child has managed to produce a shape/model that clearly resembles the model under the specified time, give the child a third, more difficult shape/model: “Since you’ve done so well with the last two shapes/models, I’m going to let you do another one.”



APPENDIX B

THE METASCOPE CODING SCHEME*


The Self-Regulated Learner

Variables

Indicators

Examples

 

On-line Indicators of Metacognition

Has a wide repertoire of metacognitive and cognitive strategies

Monitoring

Awareness of task

Child shows evidence of being aware of previous knowledge or current understanding in relation to the task e.g., “I have LEGO at home”; “Here there are only two studs.”

Checking

Checking own model :pauses to look at the model built so far; glances at the plan he/she is working on; glances at the pieces available.

Evaluation; judgment of task

The child assesses, verbally or nonverbally, the difficulty of the task, his/her own competence or accuracy of the construction, e.g., “This is tricky,” “This is easy,” “It’s right,” “I’m not really good at this,” looking proud or frustrated with the task.

Control

Planning

The child makes reference to future actions pertinent to the task, e.g., “I will start with these pieces.”

Sorting

The child sorts, organizes, groups materials or arranges the space/own construction before or during the activity.

Changing a strategy

The child, having noticed an error, changes strategy, e.g., starts to build top down but then changes and builds bottom-up.

Pauses to  think

The child pauses to think about the construction when facing a difficulty, e.g., pauses for few seconds and looks at the model or/and the plan; points the shape and then the plan.

Lack of monitoring / control

Brute force  


The child persistently tries to force a piece that does not fit in the model; child tries to force two pieces that do not fit over and over again.

Off task

The child completely forgets the goal and tries to build a different shape. May engage in conversation unrelated to task e.g., “I’m going to make it as big as I can”; child stopped completely.

Local error

The child focuses on joining up some pieces locally, neglecting the overall construction; puts a different piece than the one needed or puts a piece in a wrong position.

Repetition of error

The child repeatedly produces the same mistake, with no change in strategy, e.g., placing the same bit unsuccessfully; child insists on using certain pieces that do not fit, and therefore, the model does not resemble the plan.

 

Off-line Indicators of Metacognition

Deploys these strategies appropriately and in agreement with specific task demands

Evidence of strategic awareness

How did the child work it out?

– Looking at the model - at the beginning.

 – Looking at the model - several times.

 – Sorting the pieces (by color, shape, size. . .).

 – Selecting/focusing on relevant pieces.

 – Comparing, placing the model built close to the picture.

 – Disassembling and rebuilding.

 – Other. . .

 

Plans, monitors the quality of performance, uses strategies and changes them when necessary

Metacognitive aspects

– Looking at the model.

– Thinking, planning.

– Changing strategy.

– Others. . .

 
 

Accuracy

 

Model/shape accuracy

Perfect model

 

Close to model

There are some pieces misplaced, over 80% correct.

Different from model or some parts do not join up.

The child tries to build the model, but the final model is quite different, the model does not join up, or there are many pieces misplaced.

Off task

Does not build or decides to build a different model.


*In this article, we focus on the on-line and off-line indicators of metacognition and accuracy. We do not relate to indicators of self-regulation of emotions (motivation).





Cite This Article as: Teachers College Record Volume 119 Number 13, 2017, p. 1-28
https://www.tcrecord.org ID Number: 21931, Date Accessed: 5/26/2020 12:33:47 AM

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About the Author
  • Ornit Spektor-Levy
    Bar Ilan University
    E-mail Author
    ORNIT SPEKTOR-LEVY is a faculty member at the Science Education Center, School of Education, Bar Ilan University. Her educational research focuses on development of scientific curiosity and literacy (Spektor-Levy, Baruch, & Mevarech, 2013); engineering thinking; information literacy; professional development of science teachers (Spektor-Levy & Abramovitch, 2016); and information and communication technologies. She is the director of Da-Gan Center–the Israeli National teacher center for STEM education in preschool.
  • Marisol Basilio
    University of Cambridge
    E-mail Author
    MARISOL BASILIO is a developmental and educational psychologist. She is a research fellow at the Faculty of Education of the University of Cambridge, working as part of the Research Centre in Play in Education, Development and Learning. Her research interests are concerned with the interplay between language, self-regulation, and play in children’s development.
  • Antonia Zachariou
    University of Roehampton
    E-mail Author
    ANTONIA ZACHARIOU is a lecturer in early childhood studies in the School of Education, University of Roehampton, London. Antonia initially studied for a BA (Honors) in Education-Primary School Education at the University of Cyprus. She went on to complete an MPhil and a PhD in education (psychology and education) at the Faculty of Education, University of Cambridge. Antonia’s PhD explored the relationship between children’s musical play and self-regulation.
  • David Whitebread
    University of Cambridge
    E-mail Author
    DAVID WHITEBREAD is the director of the Centre for Research on Play in Education, Development and Learning at the Faculty of Education, University of Cambridge, UK. He is a developmental psychologist whose research has focused on metacognition and self-regulation in young children, and the roles of play and oral language in its development. His publications include Teaching and Learning in the Early Years (4th Ed. 2015, Routledge) and Developmental Psychology & Early Childhood Education (2012, Sage).
 
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