Developmental dynamics of metacognitive knowledge and text comprehension skill in the first primary...

Developmental dynamics of metacognitive knowledgeand text comprehension skill in the first primary school years

Tiina Annevirta & Eero Laakkonen & Riitta Kinnunen &

Marja Vauras

Received: 13 June 2006 /Accepted: 1 June 2007 /Published online: 21 July 2007# Springer Science + Business Media, LLC 2007

Abstract The aim of this study was, firstly, to explore the development of primary schoolchildren’s metacognitive knowledge from preschool to the 2nd grade and the developmentof their text comprehension skill from the 1st grade to the 3rd grade. Secondly, thedevelopmental dynamics between metacognitive knowledge (MCK) and text comprehen-sion skill (CS) across the first three school years were investigated. The longitudinal sampleincluded 181 children who were tested three times from preschool spring to the spring termof the 3rd grade using an identical set of measurements: the Metacognitive Knowledge Testand Listening and Reading Comprehension Tests. In studying developmental change usingLatent Growth Curve modelling (LGC), no uni-construct effect for the development ofMCK or for text CS was found. However, a slight multi-construct cumulative developmentbetween the children’s MCK and reading CS was identified. Thus, it could be cautiouslyinterpreted that the more the children’s MCK developed from preschool to the end of the2nd grade, the better reading CS they showed during the first three school years.

Keywords Metacognitive knowledge . Text comprehension skill .

Latent growth curve analyses

Introduction

The most recent 30 years of research on metacognition have convincingly shown thatmetacognitive knowledge and control of cognitive processes play important roles incomprehension, reading and learning (e.g., Hacker 1998; Guthrie and Wigfield 1999; Parisand Winograd 1990; Pressley and Afflerbach 1995). Effective comprehension and learningfrom texts—both read and heard—call for active thinking and strategic actions (e.g., Brown etal. 1981; Pressley et al. 1989; Vauras et al. 1994). Strategic actions in turn are regulated by

Metacognition Learning (2007) 2:21–39DOI 10.1007/s11409-007-9005-x

T. Annevirta (*) : R. KinnunenCentre for Learning Research, University of Turku, Turku, Finlande-mail: [email protected]

E. Laakkonen :M. VaurasDepartment of Teacher Education, University of Turku, Turku, Finland

resources representing three different aspects of metacognition: (1) metacognitive knowledgeabout cognitive processes and strategies (Flavell 1976; Paris and Oka 1989), (2) skills to carryout the strategies in practice (Cross and Paris 1988; Garner 1987; Rubman and Waters 2000),and (3) control skills to monitor and regulate strategic performance (Baker 2002; Pressley andGhatala 1990).

Specialized metacognitive knowledge, a large repertoire of strategies, and high-levelregulation have been proven to be among the factors which characterise the superiorperformance of high achieving students as compared to low-achievers (for a review, seee.g., Paris and Newman 1990; Pressley et al. 1989; Wong and Wong 1986). There is alsoevidence that metacognitive knowledge and skills can be learnt by deliberate instructionand whenever improvement is found in them, improvement is also found in achievement(e.g., Case et al. 1992; Collins et al. 1989; Gaskins 1996; Vauras et al. 1999). A frequentresult has been that older students are more knowledgeable and skilful than youngerstudents in metacognitive knowledge and skills (e.g., Miller 1994; Pressley and Afflerbach1995; Vauras et al. 1994). However, there is also evidence that the development ofmetacognitive knowledge and skills begins quite early in children’s thinking and may havereached a relatively high level by the school age if the circumstances have been favourable(e.g., Annevirta and Vauras 2006; Perry 1998; Perry et al. 2002; Whitebread 1999). Suchfindings have shown the importance of metacognition in learning, but they have also raisedchallenging questions concerning how, when and under which conditions metacognitiveknowledge and strategies are developed or acquired by children.

In the present study, we examined the developmental dynamics between primary schoolchildren’s general metacognitive knowledge and their skills to learn from heard and readtexts. The main question is how the development of children’s metacognitive knowledge,i.e. their knowledge about different strategies, and the development of their comprehensionskills are mutually related in the first three years of schooling. Although it is well knownthat good resources of metacognitive knowledge are associated with high-qualitycomprehension skills and, vice versa, that poor comprehension skills are associated withlack of metacognitive knowledge (Afflerbach 2002; Borkowski et al. 1989; Brown 1987;Pressley and Gaskins 2006; Swanson and Alexander 1997), the developmental interactionbetween these skills is nonetheless little studied, especially at the early stage of theirformation in intentional learning during the first school years.

Children’s understanding of cognitive processes

Normally, knowledge and understanding about cognition increase during the first 6, 7 years(Flavell et al. 1995; Wellman 1988). This knowledge forms the basis for a model of how thehuman mind works as an active part of one’s person (Astington 1993; Baker 1996;Wellmanand Hickling 1994). Gradually, the significance of one’s own cognitive activities as thegenerator of the outcome becomes clear, as well as the observation that one can change theoutcome by changing the ways of doing things. This realisation is the basis ofunderstanding cognitive strategies as intentional means and consequently, the basis of theability to identify, test and revise strategies.

When children begin school the opportunities of acquiring metacognitive knowledge andskills increase markedly. Learning becomes intentional and numerous learning situations—bothat school and at home—provide opportunities for the children to increase their awareness andknowledge about strategies based on their own experiences (e.g., Perry 1998; Perry et al. 2002).In addition, opportunities for guiding, feedback and discussions concerning metacognitiveaspects of learning are increased. During the first school years children’s knowledge about

22 T.S. Annevirta, et al.

cognitive processing is significantly increased, although it is not true of all children. In somelongitudinal studies children were found who hardly developed at all during the first schoolyears in metacognitive knowledge (e.g., Annevirta and Vauras 2001; Flavell 1988; Schneiderand Sodian 1991). On the other hand, the different categories of metacognitive knowledge mayhave different developmental paths. Certain cognitive processes may be more difficult toconceptualise than others, or they are typically less frequently attended to in different phases ofchildren’s lives. For instance, in their longitudinal study, Annevirta and Vauras (2001) foundthat children rarely began to understand cognitive processing in connection with comprehen-sion before the 2nd grade (at the age of 8 or 9), whereas knowledge about remembering andlearning seemed to develop earlier.

Intentional learning and comprehension skills

Intentional learning in school sets new demands on children’s comprehension skills. Learningpresupposes comprehension, which is why children strive to comprehend and teachers attemptto facilitate comprehension (Bransford et al. 2004; Brown et al. 1983). Because the means ofteaching and learning is language, both spoken and written, it is no wonder that the ability tounderstand and use language have been found to be an important basis for comprehensionskills (e.g., Cain and Oakhill 1999; Lerkkanen et al. 2004). The result of a comprehensionprocess is always a personal interpretation based on the information attended to, meaningsactivated, schemata and knowledge used, inferences and connections made, mental imagesseen, strategies chosen, emotions aroused, metacognitions experienced, to mention some ofthe processes (see e.g., Baker and Brown 1984; Graesser et al. 1996; Pressley and Gaskins2006; Van Dijk and Kintsch 1983). Orchestrating such a multiplicity of processes, knowledgeand sub skills, is a high-level comprehension skill, and developing it with all of its compositesand making it serve intentional learning is a demanding task.

When children’s learning becomes intentional they start to develop (or are taught to use)learning strategies that serve their learning goals. They learn to select, structure, and storeinformation from lessons and texts keeping in mind their later use. This has an effect on theways they comprehend the learning contents. Children learn to use comprehensionstrategies, which serve both the intentional enhancement of comprehension and theformation of knowledge structures in accordance with the requirements of their learninggoals. Some examples of such strategies are: activating prior knowledge and schemata,looking for the gist and the main ideas, making connections and inferences, monitoringone’s level of comprehension, and making questions or corrective actions whenevercomprehension fails (for review, see Mayer 2004). The experiences of intentional learningprovide children with opportunities to adopt new strategies, but they also offer themopportunities to acquire metacognitive knowledge related to the strategies (cf. Pressley andGaskins 2006). Paris et al. (1983; cf. Brown et al. 1981) introduced a distinction betweendeclarative, procedural and conditional knowledge in connection with strategies.Declarative knowledge contains facts and rules, such as knowing that planning beforereading is often useful. Procedural knowledge refers to the knowledge of how to executestrategies, and conditional knowledge refers to the awareness of when and why particularstrategies are needed. The repertoire of strategies and the wealth of declarative, proceduraland conditional knowledge are often thought to represent the level of a person’smetacognitive knowledge. For instance, young primary school children’s skill to refer tomental processes in explaining verbally why their earlier chosen strategies were the mostuseful ones in certain cognitive activities has been used to describe the children’s level ofmetacognitive knowledge (Annevirta and Vauras 2001).

Metacognitive knowledge and text comprehension... 23

Development of comprehension skills and metacognitive knowledge

Grade by grade a growing proportion of school learning takes place through reading, andchildren’s reading comprehension skills improve with the increasing amount of practise.However, researchers have shown that the developmental paths of reading comprehensionmay be different for different children. In some studies, a smooth increase in skill level isfound (e.g., De Jong and Van der Leij 2002; cf. also Juel 1988; Lerkkanen et al. 2004),whereas others show evidence on accelerated phases. For instance, first graders whoinitially progressed slowly in reading were later found to catch up with the others (e.g.,Aunola et al. 2002; Bast and Reitsma 1998; Leppänen et al. 2004; Poskiparta et al. 1998).

As stated earlier, the comprehension process in all of its complexity is an essential partof intentional learning whether resulting from reading or listening. Therefore, one factorthat is a possible source of differences in the course of development in readingcomprehension is the level of comprehension skills. Before children start to read theylearn by listening and apply their comprehension skills on the information that they haveacquired by listening. Reading researchers often refer to these skills as listeningcomprehension skills, which form the basis for reading comprehension skills. Researchevidence shows that these skills are highly correlated, and that listening comprehensionskills are important antecedents of reading comprehension skills during the first schoolyears (e.g., Dufva et al. 2001; Juel 1988; Leppänen et al. 2004). Good listeningcomprehension skills may even help children with poor decoding skills to achieve goodreading comprehension (Kinnunen et al. 1998; cf. Juel 1988). The results of De Jong andVan der Leij (2002) indicated that listening comprehension, more importantly thandecoding and vocabulary, exerted influence on the further development of readingcomprehension after the 1st grade. They also suggested that reciprocal effects of linguisticcomprehension and reading comprehension are likely to emerge and individual differencesin these skills become more associated. On the other hand, Sticht and James (1984) foundthat after the 4th grade, there are an increasing number of students whose readingcomprehension is equal to or even better than their listening comprehension. Due to rapidautomatization of decoding skills (cf. Lie and Linnakylä 2004; Linnakylä et al. 2004), wecan assume that this effect takes place even earlier among Finnish children.

Another factor that may produce developmental differences is the amount and quality ofmetacognitive knowledge. Knowledge about cognitive phenomena, concepts and actionsmakes it easier for a child to become aware of (perceive, identify and attend to) and elaboratefurther on the on-going cognitive processes. On the other hand, the more one is able to elaborateon these processes, the more experience and new knowledge are gained and the better thechances of awareness and elaboration. Such a positive “cycle” (cf. Aunola et al. 2002) mayexplain why good metacognitive skills and good learning achievement are related.Consequently, differences in children’s metacognitive competencies when they enter schoolmay produce different courses of development in their skills to comprehend, read and learnintentionally (e.g., Baker and Brown 1984; Cross and Paris 1988; cf. Vauras et al. 1999).

Research questions and hypotheses

In this study we examined the cumulative developmental interaction between metacognitiveknowledge and text comprehension skills during the first years of schooling. In otherwords, we wanted to examine if the earlier levels of the children’s MCK or their text CSwould influence the rate of increase of the same skills later on (cf. Aunola et al. 2002; Bastand Reitsma 1998). In addition, there might be a certain developmental trend or growth rate


of metacognitive knowledge that would be in association with the development of the textcomprehension skill. Two main research questions were addressed.

First, what kind of developmental trends of metacognitive knowledge (MCK) and textcomprehension (LC & RC) skill emerge during the first two school years? There is no clearevidence for cumulative development either in MCK or in RC skill. Firstly, this means thatthere are noteworthy individual differences in the development of metacognitive thinking(Annevirta and Vauras 2001; Flavell 1988; Schneider and Sodian 1991) as well as in RC skill(Aunola et al. 2002; Bast and Reitsma 1998; Poskiparta et al. 1998; Vauras et al. 1994).Secondly, these individual differences seem to be rather stable or even increasing over time inMCK (e.g., Alexander et al. 1995; Hannah and Shore 1995; Schwanenflugel et al. 1997) andrather stable in RC (e.g., De Jong and Van der Leij 2002; Lerkkanen et al. 2004; Vauras et al.1994). However, some recent studies, looking more carefully at the individual transitionsacross time, have revealed different individual developmental trends, both progressive andregressive, thus changing the relative position of a number of children (in metacognition, seee.g., Annevirta and Vauras 2001; in RC, see e.g., Aunola et al. 2002). Therefore, we cannotexpect to find straightforward cumulative developmental trends in either skill domain. It isworthwhile to note that there is relatively little research into the skill of listeningcomprehension (LC) itself, as the significant role of LC as an antecedent to RC and mathperformance has been shown (Aunola et al. 2004; Dufva et al. 2001; Leppänen et al. 2004).

Secondly, what kind of cumulative developmental interaction is there between metacog-nitive knowledge (MCK) and text comprehension (LC & RC) skills? Intervention studies withpoor readers provide strong evidence of a causal relationship between students’ MCK, e.g.,their knowledge of reading strategies, and reading achievement (e.g., Brown and Pressley1994; De Corte et al. 2001; Paris and Oka 1986; Wong and Wong 1986). Furthermore, earlymetacognitive proficiency has been found to predict later RC skill and to be associated withresponsiveness to training efforts in RC (Vauras et al. 1999; cf. Melot 1998). On the basis ofthe previous research we expect that there might be some kind of a developmental associationbetween the children’s MCK and RC skill. In other words, the more one begins to understandoneself as a learner during the first school years the better possibilities one has in realizing thevalue and use of self-regulation processes and strategies in enhancing cognitive achievement,such as comprehension (e.g., Annevirta and Vauras 2006; Brown 1997; Pressley et al. 1996;Schraw 1994; Van Kraayenoord and Schneider 1999).

One way to analyse the cumulative development in the field of psychology is the LatentGrowth Curve (LGC) analysis, in which the measurement of a particular construct (of onevariable) across time is divided into several components, i.e., the level and trend or growth(linear, quadratic, etc.). If such models demonstrate a positive association between level andgrowth, this would provide direct evidence for the cumulative development of the certainvariable. In contrast, if there is no such association, or the path is negative, this will be evidencecontrary to the notion of a cumulative development. On the other hand, if no evidence for acumulative development can be found at the above described uni-construct level it is stillpossible that such cumulative processes are going on at the multi-construct level, i.e., an earliermeasurement of a construct will predict the subsequent measurement of another construct, aftercontrolling for the earlier level of the dependent variable. In this case, the measurements ofeach construct across time are again separately divided into several components, such as leveland trend. The cumulative development can now be examined by investigating the associationbetween the level and trend components of these two constructs. A positive associationbetween the level of one construct and the growth component of another one could beinterpreted to indicate that individuals’ high values on one construct are associated with anincreasing growth on the other construct across time (cf. Aunola et al. 2002; 2004).


Method

Participants

The longitudinal sample used in the analyses of the development of metacognitive knowledge(MCK) consisted of 196 preschoolers (M=6.76, SD=0.30 years) who were to enter the 1stgrade in four urban Finnish schools the following autumn. The data collection of MCK wasfirst done in the spring term of the preschool year and repeated annually in the 1st and 2ndgrades. The data collection of listening and reading comprehension (LC & RC) skills was firstdone in the spring term of the 1st grade and repeated annually in the 2nd and 3rd grades, themean age of the children being 7.76 (SD=0.30) years, 8.85 (SD=0.30) years, and 9.75 (SD=0.30) years, respectively. The complete data for all measurement time-points were ultimatelyavailable from a total of 181 children due to the fact that a number of the families had movedto other districts after the 2nd grade, and the children were not able to participate in the entirestudy. All the tests were presented and performed individually.

Measures and procedures

In this study, our goal is to examine the developmental dynamics between children’sdeveloping MCK and text CS across four measurement points, using Latent Growth Curve(LGC) modelling. This procedure sets at least two requirements for the measurement ofboth skills: first, the measurements should be identical across the measurement points, andsecond, there should be no floor or ceiling effects at any of the measurement points asvalues of skewness and kurtosis in Table 1 show (Aunola et al. 2004; Williamson et al.1991). These requirements ought to be met in the case of slowly developing academicskills, such as MCK and text comprehension skill.

Metacognitive knowledge (MCK) test The children’s knowledge of three cognitiveprocesses—remembering, understanding and learning—was assessed by a series ofverbally and pictorially presented tasks (Annevirta and Vauras 2001; Vauras et al. 1994).The same test was used in the measurements, administered annually in the spring terms ofpreschool, the first and the second grades. In each task, children were presented withpictures (line drawings) depicting familiar situations in which a young boy or a girl wasrequired to remember, to understand or to learn something. Two or three drawings were

Table 1 Sample correlations (above the diagonal) and covariance (below the diagonal) matrixes forobserved metacognition (MCK) and text comprehension (LC & RC Skills) variables and their means (M) andstandard deviations (SD) (N=181)

1 2 3 4 5 6 7 8 9 M SD Skewness Kurtosis

MCK (Pres.) 38.29 0.59 0.51 0.47 0.51 0.43 0.40 0.43 0.42 42.02 6.19 −0.410 0.050

MCK (1st Gr) 27.48 57.33 0.58 0.53 0.51 0.47 0.45 0.50 0.50 45.75 7.57 0.352 0.467

MCK (2nd Gr) 23.27 32.35 55.08 0.36 0.49 0.40 0.39 0.49 0.45 53.01 7.42 0.392 1.189

LC (1st Gr) 50.94 70.31 47.36 313.04 0.54 0.51 0.44 0.48 0.41 35.86 17.69 0.047 −0.688LC (2nd Gr) 49.41 59.88 57.14 149.77 242.53 0.60 0.41 0.58 0.53 47.22 15.57 −0.361 −0.346LC (3rd Gr) 37.54 49.16 41.71 125.28 129.71 195.49 0.52 0.59 0.63 46.41 13.98 −0.246 −0.033RC (1st Gr) 49.34 69.25 56.64 156.03 127.25 145.70 407.06 0.49 0.53 36.10 20.18 0.052 −0.596RC (2nd Gr) 52.58 75.22 72.04 166.78 177.60 162.52 196.56 391.46 0.56 47.48 19.79 0.168 −0.378RC (3rd Gr) 45.41 66.10 57.93 127.22 142.62 154.63 185.59 194.81 304.40 57.46 17.45 −0.588 0.262


prepared to describe the situations. A more detailed description of the contents of the 24tasks, in the MCK Test, has been provided earlier (Annevirta and Vauras 2001). During thetest, the experimenter described verbally each alternative situation in the task while thechild was just looking at the drawings. Firstly, the child was instructed to choose the bestalternative, i.e., she or he was asked to point out the picture that indicated the best way toremember, learn or understand in a particular cognitive task. Secondly, after each choice,the child was further asked to verbally explain the reasons for selecting this alternative.

All the alternatives for the different situations had been scored beforehand either from 1to 2 or from 1 to 3. Among the given alternatives, score 1 was given to “the least effectiveway”, and the highest possible score (2 or 3) was given to “the most effective way” to carryout the cognitive activity in question. The explanations were assessed on a four-point scale,ranging from 1 (the lowest score) to 4 (the highest score). Thus, the points were given onthe basis of children’s explanations, including references to mental, cognitive processing.One (1) point was given, if the explanations were trivial or naive, and two (2) points weregiven for less explicit, indirect references to the person’s mental cognitive processing.Explanations with three (3) points described fairly adequate mental, cognitive processing,and four (4) points were given to more explicit explanations referring to relevant cognitiveprocessing (for more details and examples, see Annevirta and Vauras 2001).

Moreover, at each grade level, two separate summed scores were computed for each set oftasks (memory, comprehension, and learning), reflecting (a) the quality of selected choices, and(b) the quality of given explanations. Because of the children’s overall success to choose “thebest” alternative in almost all tasks in the first, preschool test, only the quality of the givenexplanations was included in subsequent analyses. Thus, children’s metacognitive knowledgewas manifested by their ability to verbally refer to cognitive mental processing in theirexplanations, which was also in accordance with Flavell’s 1976 theoretical idea aboutmetacognitive knowledge that reflects understanding of cognitive processes. The threesummed scores of explanations for remembering, understanding and learning (min=8, max=32) at each grade level were combined, and the resulting metacognitive knowledge (MCK)score (min=24, max=96) was used in the statistical analyses. The coefficient alpha values foritems loading on each of the 24 tasks were 0.78–0.80 (preschool), 0.80–0.82 (1st grade), and0.77–0.78 (2nd grade) (Annevirta and Vauras 2001), indicating a high degree of internalconsistency of the test for each school year. Three independent judges rated the children’sverbal explanations twice. In the second rating, the inter-rater reliabilities were reasonablyhigh: all the three judges were unanimous in 75–90% of the answers, depending on the item.As a rule, the difference was one point, and any disagreements were resolved by discussion.

Listening comprehension (LC) test The children’s LC skill was assessed individually at theend of the 1st, 2nd and the 3rd grades by using one expository and one narrative (135-word) texts, each describing the behaviour of an animal (original tasks by Vauras andFriedrich 1994, 2003; see also, e.g., Dufva et al. 2001; Dufva and Voeten 1999; Kinnunenet al. 1998). The texts were read aloud twice to the child in different sessions, and sixquestions concerning the main ideas of the text were presented. The order of thepresentation was counterbalanced. The orally given answers (tape-recorded and latertranscribed) to these questions were scored on a 0 to 2 scale for questions demanding one ortwo words for a proper answer and on a 0 to 3 scale for questions demanding a morecomplex utterance for an exhaustive answer. The score indicated the accuracy ofunderstanding the specific main idea. The child was not awarded more for a detailedverbatim response than for a condensed answer providing the main message (for moredetails, see Dufva and Voeten 1999). Two independent judges rated the answers twice. Both


intrarater and interrater reliabilities were high—ranging from 0.80 to 0.98, depending on thequestion. Discrepancies were resolved by discussion. The composite score (i.e., the sum ofthe points) for each text was computed and transformed to percentile scores. The mean of twotexts at each grade represented the level of listening comprehension. The means of percentilescores of each individual text are shown in the Appendix.

Reading comprehension (RC) test Comparable tasks and procedures were used to assess thechildren’s RC skills. The child read the text, but the questions were asked orally and thechild also answered orally like in the listening comprehension condition. The onlydifference was that the narrative and the expository texts were easier (95 words) than thetexts in the LC test. The texts were given in each grade, and thus, the composite score oftwo texts at each grade level represented the level of reading comprehension. Since easiertasks were used in the assessment of reading comprehension, the means of listening andreading comprehension cannot be directly compared, when we interpret the results. Themeans of percentile scores of each single text are also shown in the Appendix.

Statistical analysis

The examination of the extent to which the development of children’s metacognitiveknowledge (MCK) and text comprehension skills (CS) follow a cumulative cycle requiresthe investigation of developmental change. In the present study, we used Latent GrowthCurve Analysis (LGC) in a SEM framework by using LISREL 8.30 (Jöreskog et al. 1999),the maximum likelihood method for parameter estimation. In case of non-normality, themodels in this statistical method were estimated with robust maximum likelihoodestimation. Furthermore, the measurements of a particular variable over time (in our casefrom time 1 to time 3) are divided into several latent components, such as the initial level ofthe variable, and different growth components over time (e.g. linear or quadratic trend). Inaddition to the chi-square test, various goodness-of-fit statistics were used to evaluate the fitof the models: the Root Mean Square Error of Approximation (RMSEA), the StandardizedRoot Mean Square Residual (SRMR), the goodness of fit index, (GFI), and theComparative Fit Index (CFI). The chi-square measures the discrepancy between the

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Fig. 1 The developmental trajectories of metacognitive knowledge (MCK) and listening and readingcomprehension (LC & RC) skills


observed covariance matrix and the covariance matrix implied by the model (Jöreskog et al.1999). The RMSEA refers to the lack of fit, per degree of freedom, of the model to thepopulation covariance matrix. According to Hu and Bentler (1999), a cut-off value close to0.06 for RMSEA indicates a good fit. The SRMR is the average of the standardized residualof the predicted covariance matrix from the observed covariance matrix; a cut-off valueclose to 0.08 indicates a good fit (Hu and Bentler 1999). The GFI is an index of a relativeamount of observed variance and covariance accounted for by the model, and the CFIassesses how much better the model fits as compared to the independence model.According to Hoyle and Panter (1995), 0.90 is the agreed-upon critical value for theseoverall fit indices (GFI, CFI).

Results

The statistical analyses were performed in two steps. First, in the uni-construct analysis theassociations between the initial level of the chosen skill (MCK and text CS) and its growthover time were analysed by LGC models with structured means. Second, the uni-constructmodels of metacognition and comprehension variables were collected together usingmultivariate LGC analyses. The sample correlations and covariance matrixes, and relatedmeans (M) and standard deviations (SD), for the observed variables are presented inTable 1. The individual trajectories of observed MCK and LC and RC skills are shown inFig. 11.

1 The means and standard deviations of each measure in each measurement point and sample sizes of thepresent and the original samples are shown in the Appendix.

Table 2 Estimated latent growth curve model for metacognitive knowledge and listening and readingcomprehension skill (standard errors in parentheses) unstandardised solutions

Parameters Metacognitive knowledge model 1 Listening comprehension model 2 Reading comprehension model 3

α1 42.02(0.46) 35.86(1.31) 36.42(1.42)

α2 1.95(0.90) 17.45(2.07) 10.61(0.66)

α3 1.77(0.43) −6.09(0.93)y11 24.88(3.58) 133.36(18.02) 191(26.39)

y22 4.50(1.41) 0a 0a

y12 0a 0a 0a

y33 0a 0a

y13 0a 0a

y23 0a 0a

θ1 13.77(2.74)b 173.45(21.95) 205(19.22)b

θ2 25.56(3.49) 88.99(8.96)b 205(19.22)b

θ3 13.77(2.74)b 88.99(8.96)b 116.18(19.99)

χ2 (2)=2.80, p=0.25 χ2 (3)=3.65, p=0.30 χ2 (4)=1.13, p=0.89

RMSEA=0.00, p=0.39 RMSEA=0.00, p=0.45 RMSEA=0.00, p=0.95

SRMR=0.033 SRMR=0.069 SRMR=0.015

GFI=0.99 GFI=0.99 GFI=1.00CFI=1.00 CFI=1.00 CFI=1.00

a Fixedb Estimated as equal


Latent growth curve models

In order to investigate the extent to which the level of MCK and the level of text CS wouldbe associated with the developmental trends of the same variables at the uni-construct level,separate analyses according to LGC models were carried out for these three constructs.First, a model of two growth factor components that is the intercept growth factor (Level)and the linear growth rate (Linear Trend) was estimated separately for metacognition andtwo comprehension variables. If the model of linear growth did not fit the data very well,also the quadratic growth rate (Quadratic Trend) was estimated. The model was constructedby fixing the loading of the observed variables across Time 1 (preschool or 1st grade) toTime 3 (2nd or 3rd grade). All final LGC models (Models 1, 2 & 3) for metacognition andcomprehension variables fit the data very well and all estimated parameters werestatistically significant (Table 2).

Univariate model for MCK For the metacognition variables (Model 1) the level and thelinear trend components were stochastic y11(SE)=24.88(3.58) and y22(SE)=4.50(1.41).The term stochastic means that the variance of the component is significant, suggesting herethat there is individual variability both in the initial levels and in the rates of change overtime. Furthermore, the variance of the quadratic term was statistically non-significant,suggesting that this component was not stochastic and it was further fixed to 0. Thecovariance between the latent level and linear term was statistically non-significant (t=1.10). Thus, no association between the level and growth components of MCK was found.The positive mean of the level and the stochastic trend and the deterministic quadratic termwere α1(SE)=42.02(0.46), α2(SE)=1.95(0.90) and α3(SE)=1.77(0.43), respectively,indicating that the improvement of MCK from the 1st grade to the 2nd grade was greaterthan that observed from preschool to the 1st grade. In sum, the developmental growth ofMCK across the three measurements showed a quadratic slope, but the development wasnot cumulative.

Univariate model for LC skill For the LS skill variables (Model 2) the growth model wasquadratic. The level component was stochastic, y11(SE)=var(level)=133.36(18.02), and the

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LC sk illRC ski ll

Fig. 2 The mean vectors of metcognitive knowledge (MCK) and listening and reading comprehension (LC& RC) skills


variances of linear and quadratic terms were zero, suggesting that both trend componentswere not stochastic and they were further fixed to 0. The mean parameters of these threelatent components were statistically significant (α1(SE)=35.86(1.31), α2(SE)=17.45(2.07),and α3(SE)=−6.09(0.93)). The mean of the deterministic linear trend was positive,suggesting that the level of the LC skill increased each year, while the negative mean of thedeterministic quadratic component indicated that the improvement of the LC skill fromTime 2 (2nd grade) to Time 3 (3rd grade) was not as great as that observed from the 1st tothe 2nd grade. In sum, the developmental growth of the LC skill across the threemeasurements also showed a quadratic slope, but the development was not cumulative.

Univariate model for RC skill For the RC skill development a linear growth model wasconsequently constructed. The linear growth model fit the data fairly well and all estimatedparameters were statistically significant (Model 3). Only the latent level component wasstochastic y11(SE)=191(26.39), and the variance of the trend component was zero. Due tothe deterministic trend component, the covariance between the latent level and trend termscould not be estimated. The mean parameters of these two components were statisticallysignificant (α1(SE)=36.42(1.42) and α2(SE)=10.61(0.66)), suggesting that the growth ofthe children’s RC skill was linear throughout the measurement from Time 1 (1st grade) to

Time 1 Time 2 Time 3

Metacognitive Metacognitive Metacognitive knowledge knowledge knowledge (Preschool) (1st Grade) (2nd Grade)

1 1 1 2 1 4 1 0 0

Level Linear trend Quadratic trend

.37 .73

Level Linear trend 1 0

1 1 1 2

Reading Reading Reading comprehension comprehension comprehension (1st Grade) (2nd Grade) (3rd Grade)

Fig. 3 Estimated correlation between the level and trend components of the multivariate latent growth curve(LGC) model for the metacognitive knowledge (MCK) and reading comprehension (RC) skill variables


Time 3 (3rd grade). However, the development of RC skill across the three measurementsdid not show a cumulative pattern.

Multivariate latent growth models

As we found no evidence of a cumulative development cycle at the uni-construct level(described above), we were interested in examining whether there would be multi-constructassociations between the level and trend components of MCK and text CS. To investigatethis, the final LGC models for the MCK, LC and RC variables (Models 1, 2 and 3, Table 2)were estimated together, first without any relationships between the latent components, andafter that the models exhibiting mutual relationships were estimated. In each multivariatemodel, the residual variances of the observed MCK, LC and RC skill variables were firstestimated freely and after that, if possible, they were estimated further as equal. The meanvectors of MCK, RC, and LC skill variables from Time 1 to Time 3 are shown in Fig. 2.

Multivariate model for RC and LC skills For the RC and LC skills the level component ofboth variables was stochastic y11(SE)=191.52(26.36) and y33(SE)=131.91(17.64). Also thecovariance between the level components of RC and LC skill was stochastic y13(SE)=151.92(18.82). The linear trend components and the quadratic term for the LC skill were notstochastic (t=0.41, t=0.04 and t=0.45, respectively), and their variances were fixed to 0. Themodel fit the data reasonably well and all estimated parameters were statistically significant(χ2(13, N=181)=15.47, p=0.28, RMSEA=0.032, p=0.65, SRMR=.042, GFI=0.97, CFI=0.99). The correlation between the latent levels of the RC skill and the LC skill was positive(r=0.96), suggesting that the children who showed good RC skills were also good at the LCskill across the three measurements. There were no other statistically significant correlationsbetween the multivariate model components. Thus, the results could not be interpreted asevidence for a multi-construct cumulative cycle between RC and LC skills.

Multivariate model for MCK and LC skill For the MCK and LC skills the level componentsof both variables were stochastic y11(SE)=26.65(3.61) and y44(SE)=132.23 (17.90). Also

Table 3 Estimated multivariate latent growth curve model for reading comprehension and metacognitiveknowledge (standard errors in parentheses), unstandardised solutions (estimated correlations [italics] areabove diagonal)

Model 4

Reading comprehension Metacognitive knowledge

Level Linear trend Level Linear trend Quadratic trend

Reading comprehension

Level 191.33 (26.32) 0.74 0.37 0a

Linear trend 0a 0a 0a 0a 0a

Metacognitive knowledge

Level 51.61 (8.19) 0a 25.74 (3.61) 0a 0a

Linear trend 7.91 (3.86) 0a 0a 2.43 (1.24) 0a

Quadratic trend 0a 0a 0a 0a 0a

χ2 (12)=8.41, p=0.75, RMSEA=0.00, p=0.94

AIC=38.41, SRMR=0.054, GFI=0.99, CFI=1.00

a Fixed


the covariance between the level of MCK and the level of the LC skill was stochasticy14(SE)=49.66(6.72). The variance of the trend component and quadratic term was zeroand there was no covariance between the other components. The model fit the datareasonably well and all estimated parameters were statistically significant (χ2(12, N=181)=19.59, p=0.075, RMSEA=0.059, p=0.33, SRMR=0.079, GFI=0.96, CFI=0.98). Thecorrelation between the latent levels of MCK and LC skill variables was also positive (r=0.84), suggesting that the children who had good MCK across the three measurements(from preschool to the 2nd grade) were also good at the LC skill from the 1st to the 3rdgrade. There were no other statistically significant correlations between the multivariatemodel components. Thus, the results cannot be interpreted as evidence for a multi-constructcumulative cycle between MCK and LC skills.

Multivariate model for RC skill and MCK For the RC skill and MCK the model 4described in Fig. 3 fit the data reasonably well and all estimated parameters werestatistically significant. The estimated parameters and the goodness of fit statistics areshown in Table 3. The level components of both variables and the linear trend componentof the MCK variables were stochastic y11(SE)=191.33(26.32), y33(SE)=25.74(3.61), andy44(SE)=2.43(1.24). The variances of the linear trend components and the quadratic termwere zero. The covariance between the level of the RC skill and the level of MCK and thecovariance between the level of the RC skill and the linear trend of MCK were stochasticy13(SE)=51.61(8.19) and y14(SE)=7.91(3.86). There was no covariance between the othercomponents. Furthermore, the correlation between the latent levels of the RC skill andMCK was positive (r=0.74), suggesting that the children who showed good RC skillsacross the three measurements (from 1st to the 3rd grade) also had good MCK frompreschool to the 2nd grade. Further, the correlation between the level of RC skill and thetrend of MCK was positive (r=0.37). These results could be cautiously interpreted asevidence for a multi-construct cumulative cycle: the more positive the change in thechildren’s metacognitive knowledge from preschool to the 2nd was the better RC skill theyshowed during the first three school years. There were no other statistically significantcorrelations between the multivariate components.

Discussion

There is a great amount of research into metacognition and comprehension as importantsubcomponents of children’s cognitive development (Borkowski and Kurtz 1987; Brownand Pressley 1994; Paris and Oka 1986; Vauras et al. 1999; Wong and Wong 1986). In themain part of this research, metacognition has been studied together with competentlearning, or learning disabilities, or as an antecedent of a certain cognitive skill, whilechildren’s RC and LC skills have been estimated mostly together with the basic readingskill (decoding) as a subcomponent of overall reading achievement (Aunola et al. 2002;2004; De Jong and Van der Leij 2002; Dufva et al. 2001; Leppänen et al. 2004; Lerkkanenet al. 2004). The present study is an attempt towards better understanding of thedevelopmental relationship of children’s general metacognitive knowledge (MCK) and LCand RC skills during the first school years. The starting point for the study was theassumption that children’s general knowledge of strategies and their text comprehensionskills would be associated with each other, particularly when skilful text comprehensionrequires the use of different strategies (cf. Borkowski and Kurtz 1987; Pressley 2002;


Vauras et al. 1994). Our first aim was to investigate the growth trend of each singlecognitive component and our second aim was to investigate the developmental relationshipbetween children’s MCK and LC and RC skills.

Development of metacognitive knowledge (MCK) and text comprehension(LC and RC) skill

The results of the present study show that the development of the children’s MCK did notfollow a cumulative pattern; the earlier level of the children’s MCK did not affect thedevelopmental trend of the children’s MCK later on. Thus, contrary to the previous findings(Alexander et al. 1995; Hannah and Shore 1995; Schwanenflugel et al. 1997) the childrenwith initially better metacognitive readiness did not acquire any more knowledge than thechildren with initially lower MCK. Similarly the children with initially low MCK did notdeveloped more in their MCK than children with initially high MCK. In general, the growthof MCK from preschool to the 2nd grade showed an upward slope (Fig. 2), indicating that thechildren’s MCK increased more from the spring term of the 1st grade to the spring term of the2nd grade than from preschool to the 1st grade. One explanation of this result might be that itis not until after the first school year, after mastering basic “school skills” and becoming “apupil”, that children are able to better concentrate and think about their own cognition: ‘Whatdoes it mean to learn?’, ‘How do I learn?’, ‘Why am I better/worse in some subjects thanothers?’, ‘What can I do to become a better student?’/‘How can I improve?’ etc.

The development of the children’s LC and RC skills did not follow a cumulative patterneither. The earlier level of both LC and RC skills did not affect the developmental trend of thesame skill later on. Thus, in contrast with Aunola et al. (2002) and Bast and Reitsma (1998),the children with initially poor RC skills in the spring term of the 1st grade did not improveany more in the RC skill than those who showed initially better RC skills. And the other wayaround, the children with initially better RC skills did not develop more than those withinitially poor RC skills. In other words, the hypothesis about unchangeable individualdifferences in the children’s developing RC skill during the first school years was supported(cf. De Jong and Van der Leij 2002; Lerkkanen et al. 2004). Although, it is also notable thatin both univariate models (RC and LC) the non-significant finding about the cumulativedevelopment may be related to large residual variances in these models (see Table 2). Ingeneral, the children’s RC skills improved smoothly in each year (Fig. 2), whereas their LCskills increased seemingly more from the 1st to the 2nd grade than from the 2nd to the 3rdgrade. Thus, Sticht’s and James’s (1984) finding about the 4th graders’ equal or even superiorperformance in RC skill compared to LC skill after the 4th grade was partly supported andseemed to take effect even earlier among the Finnish children. On the other hand, because RCtexts were easier than LC texts, the means of the LC and RC skill texts cannot be directlycompared.

Developmental dynamics between metacognitive knowledge (MCK) and textcomprehension (LC and RC) skill

The main idea of the present study was to examine the developmental relationship betweenthe children’s general knowledge of strategies and their skill to understand texts. The resultsshow that there was a slight cumulative developmental interaction between the children’sMCK and their RC skill, but there were some other interesting findings, too.

First, the investigation of the multi-construct relationship between the children’s LC andRC skills showed that the levels of both components were strongly associated with each


other: the children with better LC skills were also better in RC skills across the threemeasurements and vice versa. However, the level of the LC skill did not contribute towardsthe growth of the RC skill and, the level of the RC skill did not predict subsequentdevelopment of the LC skill either. Thus, the developmental relationship between LCand RC was not cumulative, and the result did not reveal more than the earlier findingsabout the role of the LC skill as an important antecedent of the RC skill in the firstschool years (e.g., Dufva et al. 2001; Kinnunen et al. 1998; Leppänen et al. 2004). Theinfluence of children’s earlier developing LC skill should be more thoroughly estimatedbefore further conclusions about the role of the LC skill in the development of the RC skillcan be made. On the other hand, as already mentioned in connection with univariatemodels, the non-significant finding in this multi-construct model may also be related tolarge residual variances.

The next finding was that the level of the children’s MCK was strongly associated withthe level of the children’s LC and RC skills across the first school years. Thus, consistentlywith Van Kraayernoord and Schneider (1999) and Schraw (1994) and our hypothesis, themore the children expressed general understanding of mental, cognitive processing frompreschool to the 2nd grade, the better LC and RC abilities they had. In other words, thechildren’s ability to skilfully explain the use of certain strategies revealed that they had theirown experiences about the use of these strategies, which again appear as a good ability intheir cognitive achievement (cf. Annevirta and Vauras 2001, 2006). The third interestingresult was the association between the growth of the children’s MCK from preschool to the2nd grade and the level of their RC skill across the first school years (from 1st to the 3rdgrade). However, it should be noted that the t value (=1.96) for the linear trend of the MCKwas only little under the significant value of 2. Thus, this positive association between thegrowth of MCK and the level of the RC skill might be cautiously interpreted as indicatingthat the growth of the children’s MCK from preschool to the 2nd grade is associated withhigh values of the RC skill during the first three school years.

To conclude, the results of the present study proved that the development of anindividual’s MCK in understanding the idea of oneself as an active agent of one’s academicperformance contributed to the ability to comprehend texts better, i.e. to be strategic inreading (cf. Alexander and Schwanenflugel 1994; Annevirta and Vauras 2006; Meloth1990; Paris and Oka 1989; Pressley and Gaskins 2006). However, the individual differencesin the growth of MCK and the RC skill should be more thoroughly measured by using amore person-oriented approach simultaneously with the variable-oriented methods used inthis study (cf. Aunola et al. 2002). On the other hand, the results would have been differentif the children had been divided into developmentally different groups of metacognitiveknowledge and the uni- and multivariate growth curve models would have been fitted toeach group, which was not possible to do in the present study because of the small samplesizes. Furthermore, the MCK measured in this study consisted of more general knowledgeof strategies (cf. Borkowski and Kurtz 1987), and the results would have been different ifthe children’s domain-specific knowledge about the comprehension skill would have beenobserved together with the developing LC and RC skills. Thus, more thorough longitudinalinvestigations are needed on domain-specific strategic knowledge and individuals’ use ofstrategies during text comprehension.

Acknowledgements The present study is based on a longitudinal study (Decoding, Comprehension, andMotivation) conducted at the Centre for Learning Research, and supported by grant no. 1071265 and grantno. 4131 from the Council for Social Sciences Research, the Academy of Finland, to Marja Vauras andPekka Niemi. We would like to thank the heads, teachers and students of 14 kindergartens and four primaryschools in Turku, who unfortunately must remain anonymous.


Appendix

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Table 4 Means (M), standard deviations (SD) and sample sizes (N) of the metacognitive knowledge and textcomprehension skill variables

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