he primary purpose of the present study is to:

1. identify relations between salient motivational and cognitive correlates of students’ academic achievement in different curriculum domains.

A secondary purpose of the study was to:

Most educators agree that effective learning involves the ability to self-regulate a variety of thoughts, feelings, and actions associated with learning processes (eg. Meece, 1994; Schunk, 1991; Zimmerman, 1990). In particular, the ability to activate, and appropriately apply, a variety of cognitive and metacognitive strategies in order to acquire specific content has been heavily implicated in the quality of students’ academic performance and the extent of their achievement (Meece, 1994; Derry, 1990). In response to this, recent research has focused on the nature and function of the cognitive and metacognitive strategies students use (or do not use) to acquire, integrate, and retrieve information (Hong, 1995; Zimmerman & Martinez-Pons, 1988).

Theoretical models using cognitive and metacognitive strategies to explain students’ achievement have, however, not always adequately explained (a) why students may or may not (particularly in ‘real life’ classroom situations) activate strategies during given learning tasks, and (b) why students fail to transfer relevant strategies from one task or situation to another (Pintrich & Schrauben, 1992). In other words, these cognitive models have not always adequately explained why students may, or may not, expend effort to activate and/or transfer strategies. This is particularly important because successful activation and transfer of strategies requires effort. If students do not expend appropriate effort their strategic knowledge will be rendered ineffective (or, at least, be of reduced effectiveness) in contributing to their academic performance.

The selective activation and transfer of strategies may be attributed to purely cognitive factors such as routinisation, effective encoding, and the productive use of self-regulatory processes (Schneider & Pressley, 1989). However, recent research indicates that strategy activation and transfer is also dependent upon a variety of motivational variables (Graham & Golan, 1991; Meece, Blumenfeld, & Hoyle, 1988). Hence, students’ level of cognitive engagement (the extent to which students activate and transfer prior knowledge and strategies) is a function of both motivational and cognitive factors working together (Pintrich & Schrauben, 1992). In particular, students’ motivational goals (the purposes they have for wanting to achieve in academic situations) have been implicated in the quality of students’ cognition and their subsequent academic achievement (Meece, 1994; Pintrich & Schrauben, 1992).

Despite this, the interaction of motivational and cognitive variables (such as students’ goals and strategies) in explaining students’ cognitive engagement and subsequent academic achievement, has been largely avoided or ignored. With some exceptions it has, until recently, been more common to explain students’ performance and achievement in either motivational or cognitive terms rather than through a combination of both. Examining, the interaction of motivational and cognitive variables, however, as the present study does, should help explain more fully the functioning of students’ cognitive processes and the effect(s) these have on students’ achievement. (Borkowski, Carr, Rellinger, & Pressley, 1990; Pintrich & Schrauben, 1992). The present study involves such an examination.

In order to facilitate this examination, the present study proposes a research model linking students’ goal orientations (their purposes for academic achievement), strategy use, and academic achievement. Consistent with the literature reviewed above, the model proposes that students’ goal orientations influence their strategy use which, in turn, influences their academic achievement. The research model is presented diagrammatically below.

The data in the study represent responses from six-hundred and two (602) middle school students attending four (4) high schools in the Sydney metropolitan region. The schools were selected from a range of geographic and educational regions within the Sydney metropolitan area. Approximately equal numbers of male and female students from a wide cross-section of cultural, socio-economic, and academic backgrounds are represented in the sample. Descriptive statistics for the participants are presented in Table One below.

The study surveyed the participants to determine their multiple motivational goal orientations and use of cognitive and metacognitive strategies. The instrument used to collect these data was the Goal Orientation and Learning Strategies Survey (GOALS-S), the psychometric properties of which have been established in a previous study (Dowson and McInerney, 1997a). Specifically, the GOALS-S was designed to measure a selection of academic goals (n=3), social goals (n=5), cognitive strategies (n=3) and metacognitive strategies (n=3). Table Two below provides brief definitions of the academic and social goals measured by the GOALS-S

Table Three, below, provides brief definitions of the cognitive and metacognitive strategies measured by the GOALS-S.

Item scales were devised to measure each of these constructs. The factorial validity of the scales was assessed using Confirmatory Factor Analyses (CFAs) in Linear Structural Relations (LISREL), Version 7 (Joreskog & Sorbom, 1989). Some variations to the composition of the scales were made during the CFAs. Only scales which demonstrated substantial validity were included in the present research. The reliability of each of the scales was also confirmed. Chronbach’s Alpha for the scales ranged between 0.77 and 0.91. For the present study, the means of each of the scales were used in the path analyses described below.

In addition to the above scales, the study collected data for students’ academic performance in two curriculum areas: Mathematics and English. Students’ academic performance in these areas was represented by their end-of-year examination results which were standardised between curriculum areas and schools.

After listwise deletion of cases, five-hundred and sixty-one cases were available for further analysis. Relations between students’ motivational orientations, cognitive and metacognitive strategy use, and academic achievement in the two curricula domains were assessed using path analyses within in LISREL V.7. All paths were assessed simultaneously. This meant that the co-efficient associated with each path represents the unique association of the two variables linked by that path without interference from other paths (relationships).

For the purposes of clarity, the path analyses are reported in three separate, but related, sections below. Analysis One describe relations between students’ goals and strategies (the first part of the research model). Analysis Two describes relations between students’ strategies and their academic achievement (the second part of the model). Analysis Three selects the most important motivational and cognitive variables associated with students’ achievement (as identified in Analyses One and Two) and combines them in a single, simplified, path model.

As indicated above, Analysis One investigates relations between students’ goal orientations and their strategy use. This set of relationships is represented diagrammatically below.

The table of path coefficients between students’ goal orientations and their strategy use is presented below. Significant results at the 0.05 level are bold-faced. Significant results at the 0.001 level are bold-faced and asterixed.

Table Four indicates that students’ mastery goal orientations are clearly most strongly associated with their use of a variety of cognitive and metacognitive strategies (four highly statistically significant paths out of four possible paths). The next most influential goals are students’ work avoidance, social approval, and social conformity orientations (each with two highly significant paths out of four possible paths). Not surprisingly, students’ work avoidance goals are negatively associated with their use of cognitive and metacognitive strategies. All other goals in the study, with the exception of students’ performance goals, were positively associated with their strategy use.

As indicated above, students’ mastery goals are most strongly associated with their strategy use. This result is consistent with the literature which has found that, using a variety of methodologies and samples, students who are motivated to achieve academically in order to understand (or master) academic work are much more likely to employ strategic approaches to learning even if these require greater effort than less strategic approaches (Ainley, 1993; Graham and Golan, 1991).

Also as indicated, two of students’ social goals, their desire to achieve academically in order to win the approval of others (social approval) and their desire to achieve academically in order to conform to social expectations (social conformity) are quite strongly associated with their strategy use. This finding is interesting for two reasons. First, it confirms not only that students’ academic reasons for achievement are associated with their strategy use, but also that students’ social reasons for achievement are associated with their strategy use. Second, this finding confirms that externally referenced motivations for learning and achievement (for example, in the present case, being approval or conformity oriented) are not necessarily detrimental to strategic approaches to learning. Much has been said in the literature about the, potentially, maladaptive effects of externally referenced, versus internally referenced, motivations for learning and achievement (Dweck, 1992; Deci & Ryan, 1985). Consistent with other studies, however (eg. Dowson & McInerney, 1997b; Pintrich, Marx, & Boyle, 1993), the present study has found that externally referenced motivations may not, necessarily, be detrimental to adaptive (strategic) approaches to learning. This may be especially true when externally referenced motivations are held in combination with other, internally referenced, motivations.

Analysis Two examined relations between students’ strategy use and their academic achievement. This set of relationships is represented diagrammatically below.

The table of path coefficients between students’ strategies and academic achievement is presented below (Table Five). As above, significant results are bold-faced, or bold-faced and asterixed, to indicate their significance.

Table Five indicates that students’ regulatory and monitoring strategies are associated most strongly with their academic achievement (two highly statistically significant paths out of two possible paths). Students’ general cognitive strategies are significantly associated with their mathematics achievement but not with their English achievement. Students’ planning strategies are associated with neither their mathematics or English achievement.

The results of Analysis Two indicate the following. First, a variety of students’ cognitive and metacognitive strategies are associated with enhanced academic achievement. This reinforces the importance of students’ not only using strategies but also of students’ having available a range of appropriate strategies from which to choose in given academic situations. Cantwell (1992) calls this a flexible approach strategy use.

Second, some strategies appear portable across curriculum areas. In the present example, students’ regulatory and monitoring strategies are associated with both their mathematics and English achievement. Thus, the strategic practices used in one academic context (eg. mathematics) appear useful in other contexts (eg. English) as well. This said, it should not be assumed that every strategy will be equally useful in every academic context. In the present study, students’ general cognitive strategies appeared to enhance their mathematics achievement but not their English achievement. This result may an artefact of content, instructional, or assessment practices associated with particular curricula areas. Whatever the case, however, these results again highlight the importance of having available a variety of strategies from which to choose. In the present case, for example, it may be hypothesised that students’ who only had general cognitive strategies on which to call may experience more difficulty in English than those with other strategies on which to call (such as regulatory or monitoring strategies) These later strategies may compensate for the apparent lack of effectiveness of general cognitive strategies in this particular subject area.

Third, some strategies may not be effective in more than one curriculum area. The interesting result that students’ planning strategies are neither associated with their Mathematics or English results reinforces the point, made above, that particular strategies may not necessarily be associated with students’ academic achievement. In the present case, it could be hypothesised that middle school students may not need planning strategies in order to be academically successful. Approaches to middle school curricula might, for example, be expected to be more teacher-directed than student-directed. If so, then students’ planning strategies may be largely redundant in these contexts. Whatever the case, in the present study, middle school students’ planning strategies were clearly not as strongly associated with students’ academic achievement as other strategies investigated.

Analysis Three sought to combine the results of Analyses One and Two in order to identify a parsimonious model linking students’ motivational goals, strategy use, and achievement.

Combining the results of Table Four (Analysis One) and Five (Analysis Two) yields some interesting combined results. First, it is possible to eliminate students’ planning strategies from the research model for reasons indicated immediately above. Second, it is, therefore possible to disregard paths linking students’ motivational goals to their use of planning strategies. If this is done then, clearly, students’ mastery goals are most strongly associated with their strategy use of all the goals in the study. That is, even with the elimination of paths linking students’ goals to their planning strategies, students’ mastery goals are still linked to the three remaining strategies by three highly statistically significant (p < 0.001) paths. This compares to a maximum of one highly statistically significant path for any other goal. Thus, of the ten original motivational goals examined in the study, one goal, students’ mastery goals, appear to be the most salient indicator of their strategy use.

This said, it should still be recognised that, with the elimination of paths linking students’ goals to their planning strategies, six goals, other than students’ mastery goals, remain linked by one highly significant path to students’ strategy use. Moreover, two of these six goals: students’ social status (present) and social welfare goals; are linked to students’ strategy use by an additional significant (p < 0.05), although not highly significant path. However, in searching for a parsimonious model linking students’ goals, strategies and achievement it seems reasonable to select, initially at least, only highly significant paths as most strongly representing relations between students’ motivation, strategy use, and achievement.

Diagram four, below, represents a model of relations between students’ goals, strategies, and achievement which includes only highly significant paths linking these variables.

Insert Diagram 4 about here.

Diagram four simplifies the original research model considerably. The original model had fourty-eight (48) hypothesised paths. The simplified model represents the fourteen (14) most salient paths implicated in students’ achievement. This said, Diagram Four still indicates that students’ mathematics and English achievement may be linked to their use of a variety of cognitive and metacognitive strategies which, in turn, are related to various social and academic goal orientations.

Taken as a whole, the analyses above confirm that students’ achievement is associated with a complex, but reasonably well defined, set of relations between students’ motivational orientations and cognitive processes. In other words, students’ academic achievement should not be seen as the product, purely, of cognitive variables. Rather students’ achievement should be conceptualised as being underpinned by a web of interacting motivational orientations and cognitive processes (Wentzel, 1991). This implicates both the validity, and desirability, of including multiple motivational and cognitive variables in studies attempting to account for students’ academic achievement.

Conversely, the study mitigates against approaches which might use only motivational or cognitive variables to account for students’ achievement. Such studies may minimise both the complexity and diversity of variables associated with students’ academic achievement. As indicated in the theoretical orientation to this paper, cognitive models of learning have, not atypically, conceptualised cognitive processes (of which the application of strategies is one) as ‘cold’ ie. not impacted upon by motivational, social, or other variables. The present research, in contrast, suggest that students’ cognition is a ‘hot’ process ie. influenced, or at least associated with, motivational variables. Thus, students’ cognitive approaches to their learning (in the present case the strategies they choose to use or not to use) are not implemented without regard for the purposes students’ have with respect to their learning. Put more simply, how students learn is associated with the reasons why students want to learn.

This said, the present study also confirms evidence gathered over the previous fifteen years, in particular, which has implicated the role of students’ strategy use in their academic achievement. This trend continues with very recent studies also suggesting that systematic training in, and application of, a variety of strategies improves academic performance (Mifsud, Evans, & Dowson, 1997). The present study also confirms, however, that not every strategy will be maximally useful in every situation. This said, the present study supports the differential usefulness of various strategies, across different curricula domains, despite extensive relations between students’ strategy use and achievement overall.

On the basis of the above, it seems reasonable to recommend that future studies should continue to use combinations of motivational and cognitive variables when assessing students’ academic performance and achievement. Whilst the pattern of relations between these variables might be expected to vary from study to study, the overall strategy of conceptualising and operationalising academic achievement as the product of a ‘hot’ process involving motivational and cognitive variables seems to be both applicable and desirable on the basis of this and related studies.

Practitioners should also recognise that both motivational and cognitive processes are associated with students’ achievement. Specifically, both the academic and the social reasons students’ hold for wanting to achieve may be associated with their engagement in learning and their subsequent academic achievement. This said, it is particularly clear that mastery motivated students may be expected to adopt a broad range of strategic approaches to learning which result in enhanced academic achievement. Both researchers and practitioners should, therefore, be particularly concerned to facilitate mastery motivation amongst their students as a means of promoting strategic approaches to learning.