Cognitive, Motivational, and Volitional Dimensions of Learning: An Empirical Test of a Hypothetical Model

Research in Higher Education, Vol. 44, No. 5, October 2003 ( 2003)

COGNITIVE, MOTIVATIONAL, AND VOLITIONALDIMENSIONS OF LEARNING:An Empirical Test of a Hypothetical Model

Antonio Valle,*,** Ramon G. Cabanach,* Jose C. Nunez,‡Julio Gonzalez-Pienda,‡ Susana Rodrıguez,* and Isabel Pineiro*

::: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : :The principal aim of this research is to contrast empirically a hypothetical model de-veloped on the basis of the fundamental assumptions of current self-regulated learn-ing models. In line with evaluation criteria of model fit, a high rate of congruencebetween the hypothesized theoretical model and the empirical data was observed.Analysis of the effects between the variables of the model revealed the followingrelevant aspects: students’ predisposition to feel responsible for the results of theiracademic behavior (internal attribution) is related to positive self-image (academicself-concept), both being important conditions for development of learning-orientedmotivation (learning goals). All of this involves selection and use of learning strate-gies for deep information processing (deep learning strategies), which leads studentsto assume responsibility with high levels of persistence, perseverance, and tenacityso as to achieve goals defined by the motivational orientation. This persistence andeffort to achieve the proposed goals has in turn a positive and significant effect onacademic achievement.

:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::KEY WORDS: motivation; deep learning strategies; self-regulated learning; academicachievement.

Eight years ago, Paul R. Pintrich (1994), one of the most relevant researchersin the field of educational psychology, published an interesting work on futureresearch trends in the education area. Pintrich invited readers to reflect on cur-rent information, to attempt to link it together, and to develop integrated theoret-ical models. He acknowledged that, till then, most of the research on the processof school learning had focused on the study of cognitive, motivational, andaffective components taken separately, and there were very few studies of the

*Department of Developmental and Educational Psychology, University of La Coruna.‡Department of Pychology, University of Oviedo.**Address correspondence to: Antonio Valle, Departamento de Psicologıa Evolutiva y de la Edu-

cacion, Universidad de La Coruna, Campus de Elvina s/n, 15071 La Coruna, Spain. E-mail: [email protected]

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0361-0365/03/1000-0557/0 2003 Human Sciences Press, Inc.

558 VALLE, CABANACH, NUNEZ, GONZALEZ-PIENDA, RODRıGUEZ, AND PINEIRO

interactions and interrelationships between these components. Consequently,Pintrich pointed out the need to develop integrated models that incorporate notonly general knowledge and cognitive strategies but also the motivational andwill components.

In this work, Pintrich (1994) predicted that in the near future, a large amountof empirical research would appear in which the relationships between the com-ponents would be studied explicitly. He was not mistaken. In the last 5 years, alarge number of theoretical contributions have been published that study indepth the integration of the cognitive, motivational, and self-regulation areas,and that make up the new “self-regulated learning” models. One of the bestexamples is the Handbook of Self-Regulation, edited by three currently veryimportant researchers (Boekaerts, Pintrich, and Zeidner, 2000), with the collabo-ration of an exceptional catalogue of experts in self-regulated learning.

According to these authors, self-regulated learning models attempt to inte-grate cognitive, affective-motivational, and behavioral aspects. The models pro-posed by Monique Boekaerts, John G. Borkowski, Barbara L. McCombs, PaulR. Pintrich, or Barry J. Zimmerman are examples of this integrative perspective.As Boekaerts (1999) pointed out, self-regulated learning models allow research-ers to (a) describe the various components involved in successful learning, (b)explain reciprocal and recurrent relationships established between these compo-nents, and (c) directly relate learning with the self, or, in other words, withgoals, motivation, will, and emotions.

In contrast to previous cognitive models, current self-regulated learning mod-els reflect the long neglected “human” (Zimmerman, 1995) or “warm” (Garcıaand Pintrich, 1994) side of learning. Student contribution to learning situationsis not exclusively identified with their intellectual instruments, but rather alsoinvolves motivational (Boekaerts and Niemivirta, 2000; Covington, 2000), af-fective, emotional (McCombs, 1998, McCombs and Marzano, in press), andvolitional aspects (Kuhl, 2000) that are related to personal equilibrium skills.Therefore, while students construct the meaning of curricular contents, they alsodevelop representations of their own didactic situation, which can be perceivedas stimulating and interesting or as overwhelming and unattainable. Studentsalso construct self-representations in which they see themselves as competent,interesting interlocutors both for teachers and classmates, able to solve prob-lems, or, contrariwise, as unskilled, incompetent people with few resources. AsSole (1993) stated, when students learn, they learn contents and they also learnthat they can learn. When they do not learn contents, they also learn something:they learn that they are incapable of learning.

In this study, we go beyond separate consideration of motivational, cognitive(learning strategies), and affective variables in the attempt to integrate themconjointly in a research model. Although unavoidably limited, our model showsthe specific and interrelated functioning of these variables and the way in whichthis accounts for learning and academic achievement in the university.

559COGNITIVE, MOTIVATIONAL, AND VOLITIONAL DIMENSIONS OF LEARNING

As displayed in Fig. 1, our research model is structured along three dimen-sions that account for achievement: (a) motivational-affective, (b) cognitive, and(c) volitional. In general, for meaningful learning to take place, students mustbe motivated and possess and mobilize the necessary cognitive strategies. Ahigh degree of willpower is also required to maintain the necessary effort, atten-tion, and concentration to attain the desired goal. The characteristics of thesedimensions and the causal links that connect them are discussed below in moredetail.

MOTIVATIONAL-AFFECTIVE DIMENSION

First, there are certain conditions or personal variables of a cognitive, motiva-tional (expectancies), and affective nature that are used by students as basiccriteria in the initial cognitive-motivational analysis of the learning task. Theresult of this analysis significantly affects development of a specific academicmotivation. According to McCombs (1998), these perceptions generate expec-tancies, both of results and of efficacy, which, on the one hand, become hopesof obtaining specific results or consequences and, on the other, a personal beliefin one’s ability to attain these results. If students perceive tasks as unattainable,this perception of low control will contribute to decreased success expectancies,and consequently, to little interest in complying with task requirements or inexpending effort to perform the task. Positive expectancy fosters intrinsic moti-

FIG. 1. Basic dimensions of the general model proposed in this investigation.


vation and interest in the task, so students can apply the cognitive processesrequired for task performance (Marzano, 1998).

Academic goals are some of the most important variables in current motiva-tional research. Goals are cognitive representations of the various aims thatstudents can adopt in different achievement situations (italics added; Gonzalez-Pienda et al., 2002, p. 279). Traditionally, the most representative authors ofthis research trend (Dweck, 1986; Nicholls, 1984) have differentiated two typesof goals: learning goals and performance goals. Learning-goal oriented studentsengage in learning to acquire knowledge and increase their competence, theyconsider effort the main cause of success or failure, they conceive of intelligenceas a variable and modifiable characteristic, they view difficult as tasks a chal-lenge, and they use deep processing strategies more frequently. Performance-goal oriented students are more interested in showing their ability, obtainingfavorable judgments of their competence levels and avoiding negative ones, theyregard learning as a means to prove their competence, they consider intelligencea fixed and stable trait, they perceive difficult tasks as possible failure situations,and they use low-complex level strategies more frequently.

Among the personal factors that determine motivational orientation (goals),the most relevant are causal attributions and academic self-concept, as well asthe increasable conception of intelligence, perceived ability, and past achieve-ment because of their direct or indirect influence.

One of the features of learning-goal oriented students is their belief that effortis the main cause of their academic results and that ability is a modifiable char-acteristic that depends on effort. For these individuals, more effort usually re-sults in improved learning, and, consequently, they become more competent inthat related knowledge area. In these circumstances, causal attributions to highlevels of effort lead to high perceived competence (Meece, 1994). Therefore,the effort-ability relations, in the aforementioned terms, and perceptions of con-trol and personal responsibility for academic results (all associated with an inter-nal attributional profile), contribute positively to development of learning-orien-tated motivation. In addition, we must consider the decisive role of affectivevariables and variables associated with the expectation component of motiva-tion, that is, self-perceptions and beliefs on the academic level (academic self-concept) and perceived competence. It is useless for people to be motivated tosolve a task if they are not convinced that they have the necessary ability andcompetence to do so. It is also evident that past academic results (past achieve-ment) is one of the most influential factors in academic self-concept and in highself-confidence. Students who experience success instead of failure will have amore positive self-concept, more trust in their ability, they will assume moreresponsibility for their results, and will display higher levels of engagement,effort, and persistence. All of this will lead to a more positive attitude towardlearning and, subsequently, to higher academic achievement.


In addition to these variables, there are those more situational and contextualin nature (for example, evaluation system, the teacher’s attitude, classroom orga-nization and structure, type of tasks, etc.; Ames, 1992), that also influence thetype of goals adopted by students. In this investigation, we have included someof these variables considered from viewpoint of the students’ perception of theacademic context, to see how the type of material, task characteristics, teachingstyle, and evaluation criteria influence students’ motivational-affective disposi-tion and the way they study and learn.

COGNITIVE DIMENSION

Second, the type and level of motivation thus acquired will significantly influ-ence employment of the cognitive resources needed for the task. The higher thesubject’s motivation, the more likely will be the use of cognitive and learningprocesses and strategies essential for optimal learning (Covington, 2000), espe-cially if motivation is intrinsic (Suarez, Cabanach, and Valle, 2001). In any case,depending on their strategy repertory, subjects will judge the appropriateness ofthe strategies according to task requirements.

One of the basic assumptions of this investigation is that the motives andintentions that guide students’ academic behavior determine, to a great extent,the type of cognitive resources that students will use in various learning situa-tions. This justifies the inclusion of learning strategies as an essentially cognitivevariable. Among the extensive diversity of definitions, they all coincide in indi-cating two essential elements of learning strategies: on the one hand, the strate-gies imply a sequence of mental activities or operations to facilitate learning and,on the other, they are conscious and intentional and involve decision-makingprocesses, depending on the goal or aim that the students hope to achieve.

In this specific case, we focus particularly on cognitive strategies, which aredirectly linked to information processing and to meaningful learning. Accordingto Mayer (1992), three of the most important strategies included in this cate-gory—selection strategies, organization strategies, and elaboration strategies—constitute the cognitive conditions of meaningful learning. Mayer defines mean-ingful learning as a process by which the learner engages in selecting relevantinformation, organizing that information into a coherent whole, and integratingthat information into the structure of preexisting knowledge.

A large number of investigations coincide in stating that adoption of learninggoals predisposes students to use cognitive strategies and self-regulation pro-cesses to learn the material. Learning-oriented individuals tend to use deep pro-cessing strategies that enhance their comprehension and require some effort,such as integrating information and controlling comprehension (Graham andGolan, 1991; Pintrich and Garcıa, 1991). Along the same lines, Middleton andMidgley (1997) found that learning-goal orientation predicts the use of self-


regulated learning strategies. Alternatively, performance-goal oriented studentsare less likely to behave this way because they are not so much engaged inlearning itself, and the use of learning strategies requires effort; and this couldlead to the awareness that they lack the ability, which is something these indi-viduals try to avoid by all means. Other studies (Nolen, 1988; Nolen and Hala-dyna, 1990) have reported that learning oriented students tend to evaluate posi-tively and to use cognitive strategies that enhance comprehension of the materialmore frequently than do performance oriented students. In contrast, Andermanand Young (1994) showed that performance-goal orientation correlates nega-tively with the use of deep processing strategies and positively with the use ofsuperficial processing strategies. In fact, many studies (Meece, 1994; Pintrichand De Groot, 1990; Pintrich and Garcıa, 1991; Seifert, 1995) state that perfor-mance-goal orientation is associated with the use of superficial and low-complexlearning strategies—for example, mechanical and repetitious memorizing of in-formation.

From the results of some of the aforementioned investigations, a very impor-tant fact about academic learning emerges: The use of strategies that enhancemeaningful learning is determined very directly by the person’s intrinsic motiva-tion. As stated by Schneider and Pressley (1989), although knowledge of differ-ent strategies may be necessary for their use, it is usually not enough; studentsmust be motivated to use that knowledge. According to this, knowledge of cog-nitive strategies may not be related to motivational beliefs, but the use of suchstrategies is related to students’ motivation (Garcıa and Pintrich, 1994). Teach-ers may come across students who, despite sufficient cognitive resources tosolve a certain task successfully, do not use them because they are not suffi-ciently motivated. In many cases, lack of motivation explains why students donot use a strategy even though they are cognitively prepared for it (Pintrich andSchrauben, 1992).

VOLITIONAL DIMENSION

Third, the activation of one or another type of learning strategy, and theirnumber, will significantly influence students’ persistence and effort (the willcomponent) in learning. These levels of persistence will affect both learning andacademic achievement.

In our model, in addition to students’ motivation and their use of learningstrategies that are congruent with their motivational state, all this must be associ-ated with willpower. This means that students must maintain the necessary con-trol and regulation of effort to enable them to behave with persistence, con-stancy, and perseverance when coping with problems or difficulties that comeup during the learning process. This mediating variable has also been includedamong the learning strategies and academic achievement in other cognitive-motivational models (see, for example, Pintrich and Schrauben, 1992). These


authors incorporate an intermediary variable between the motivational and cog-nitive components, called student’s engagement in learning, which is somewhatsimilar to our persistence variable. Many studies relate high persistence to learn-ing goals (Anderman and Young, 1994; Dweck, 1986; Midgley and Urdan, 1995;Pintrich and De Groot, 1990; Pintrich and Garcıa, 1991; Schunk, 1996) and lowpersistence to performance goals (Anderman and Young, 1994). Similarly, El-liot, McGregor, and Gable (1999) indicate that learning goals significantly pre-dict deep learning, effort, and persistence.

On the other hand, in the sense of Boekaerts (1999), in our research model,we contemplate the influence of the perception of contextual factors on theprocess mentioned in the three previous subheadings. Among these factors areunderlined the perception of evaluation criteria, the perception of teaching style,or the awareness of the type of material (psychology, chemical sciences, etc.).

Self-assessment of results achieved will significantly affect causality attribu-tions, which, in turn, will lead students to make various judgments and evalua-tions of personal control and self-efficacy regarding task requirements. Thesejudgments and evaluations will have reciprocal influence and will also affectmetacognitive, cognitive, and affective systems, as well as future perceptionsand expectancies in similar tasks. (It was not possible to study this aspect herebecause a transversal strategy was used.)

The aim of this study was to contrast empirically a hypothetical model thatwas developed on the basis of the most relevant assumptions of current self-regulated learning models. This aim is difficult to accomplish because of thelarge number of variables taken into account.

METHOD

Participants

Participants were 614 university students (26% males and 74% females), agedbetween 18 and 23 years. Of the total sample, 314 were 1st- or 2nd-year stu-dents, and 300 were 3rd-, 4th-, or 5th-year students. Regarding type of curricularcontent, 134 studied teaching, 111 nursing, 72 physiotherapy, 139 business man-agement, 90 psychopedagogy, and 68 chemistry. Conglomerate sampling wasemployed, with the group-class as the sampling unit.

Instruments and Procedure

Learning Strategies

The Learning and Study Strategies Inventory (LASSI; Weinstein, Schulte,and Palmer, 1987) was used to evaluate the construct deep learning strategies.The subscales of information processing, study aids, and self-testing from the


LASSI were used to evaluate the cognitive activities that are typical of deeplearning and which we shall designate with the term “deep learning strategies.”Previous studies coincide in the identification of a second-order three-factorstructure of the LASSI (Olejnik and Nist, 1992; Olivarez and Tallent-Runnels,1994): (a) affective/effort-related activities (time management, concentration,motivation, and attitude subscales); (b) anxiety-arousing activities or goal orien-tation (anxiety, selecting main ideas, and test strategies); and (c) cognitive activ-ities (information processing, study aids, and self-testing). More recently, Mur-phy and Alexander (1998) did not obtain an optimal fit of the three-factor modelof the LASSI, although this model best defines the factor structure of this instru-ment. As Murphy and Alexander stated that the lack of concordance of theirdata with those obtained in previous research could be due to variables of acultural nature, in our study, we wished to contrast this hypothesis by means ofan exploratory factor analysis using the 10 LASSI subscales. The results coin-cide entirely with those obtained by Olejnik and Nist and Olivarez and Tallent-Runnels. This justifies the use of the three subscales from the LASSI (informa-tion processing, study aids, and self-testing) to evaluate the cognitive activitiesthat are typical of a deep approach to learning. The reliability of this variable isfairly high (α = .83).

Academic Goals

Motivational orientation was assessed by means of the adaptation of the Ques-tionnaire to Measure Achievement Goal Tendencies, elaborated by Hayamizuand Weiner (1991). This instrument provides a precise evaluation of two generaltypes of academic goals: learning and performance goals (although performancegoals are subdivided into two types). Learning goals are defined in the sameterms as those used by Dweck (1986). On the other hand, one of the perfor-mance goals reflects students’ tendency to learn so as to obtain approval andavoid parents’ and teachers’ rejection (we shall call these goals social reinforce-ment goals), and the other goal reflects students’ tendency to learn to achievegood academic grades and advance in their studies (we shall call these goalsperformance goals). Hayamizu and Weiner obtained the following reliabilitycoefficients (Cronbach’s α coefficient) for each of the subscales: learning goals(.89), social reinforcement goals (.78), and performance goals (.71). In our sam-ple, we obtained coefficients (Cronbach’s α) of .82 for the whole scale, and .87for each individual subscale, which led us to consider the scale an instrumentwith fairly satisfactory reliability indexes. Similarly, the factor structure of thescale using our data coincides fully with the one obtained by Hayamizu andWeiner. The results regarding reliability and construct validity of the question-naire coincide entirely with previous studies (Nunez, Gonzalez-Pienda, Garcıa,


and Cabanach, 1994; Valle, Cabanach, Cuevas, and Nunez, 1996, 1997a; Valleet al., 1997b).

Evaluation of the Remaining Variables Included in the Model

To evaluate the remaining variables, we developed a questionnaire to gatherinformation about persistence in academic tasks (Item: “In general, if I have tosolve a problem and I cannot at first try, I try as often as necessary until Imanage to solve it correctly, or at least, I do not give up until I have tried manytimes and in very different ways”), the increasable concept of intelligence (Item:“Intelligence is a series of skills and knowledge that can be increased by one’sown behavior and by learning”), perceived ability (Item: “I think I have highability—skills, intelligence, etc.—for academic work”), the perception of evalu-ation criteria (Item: “The way I study and learn depends on the way I perceiveI will be examined later on about the knowledge acquired—type of exam”), theperception of the type of material (Item: “The way I study—the strategies Iuse—to learn the academic contents varies depending on the type of material Iam studying—mathematics, history, basic psychology, developmental psychol-ogy, etc.”), the perception of teaching style (Item: “The professor’s teachingstyle in the classroom—more or less formal, traditional, interactive, construc-tive, etc.—influences the type of study and learning strategy I use for the aca-demic tasks of that teacher’s subject matter”), and the analysis of the task char-acteristics (Item: “Before I start working on any academic task, I note the taskcharacteristics and then decide what kind of study and learning strategy I shoulduse to carry out the task correctly”). Responses were scored on a 5-point ratingscale (1 = completely disagree, 5 = completely agree), except for causal attribu-tions (where subjects completed the statement, “In general, I think my achieve-ment can be attributed to . . .” indicating the extent to which they attributedtheir results to their ability, effort, luck, and so forth, on a 5-point scale, rangingfrom not at all = 1 to very much = 5), and academic self-concept (measured ona 5-point scale ranging from very poor = 1 to very good = 5). In the case of pastacademic achievement, the students had to indicate their mean global grade inall the curricular subjects of the current course.

All assessment instruments were administered to the students by three of theauthors in one session, in a classroom during normal academic schedule, alwaysallowing the students enough time to fill in the instruments appropriately.

Statistical Analyses

Structural equation analysis was employed, as the main purpose of this re-search was to analyze the viability of a general cognitive-motivational model toexplain the principal cognitive, motivational, and volitional variables involved


in academic learning and academic achievement. This type of analysis is usedto verify the empirical plausibility of the causal relationships that make up themodel or initial theory. A transversal design with observed variables was em-ployed. For the analyses, we used the LISREL 7 (Joreskog and Sorbom, 1990)computer program. To contrast the research model, we followed one of the threestrategies described by Joreskog and Sorbom (1996): a strictly confirmatorystrategy, wherein a single a priori model is studied.

The variables used in this study are ordinal (as are the great majority ofvariables in psychoeducational research), and their distribution is normal or nearnormal. Table 1 shows the means, standard deviations, skewness, and kurtosisof all the variables. Methodologists consider that the CIs for this kind of vari-ables in large samples (such as the one used in this study), with regard to skew-ness and kurtosis, is between +1 and −1 (M skewness = −0.50; M kurtosis =0.23). Therefore, as seen in Table 1, except for the kurtosis of the variableinternal attribution (2.19), all the variables are within this interval.

Model to Be Contrasted

The basic assumptions of the model and the relationships between the corre-sponding variables are displayed in Fig. 2. In this causal diagram, which repre-sents the various types of variables and parameters designating the relationshipsbetween them, we have attempted to follow the basic criteria for type of vari-able, name, relation between variables, and so forth, according to language andthe various symbols used in causal models. However, in the model we present,some variables can be considered central to the model: These are the eightendogenous variables (dependent variables) that are represented in the modelwith uppercase letters (Y variables). Similarly, the main effects (β) betweenthese variables are represented by thick arrows. Exogenous variables—indepen-dent variables—(X variables) are also included in the model. These variablesreveal various effects (γ) on the endogenous or dependent variables. Possiblecorrelations (φ-) between the exogenous variables are also shown in the model,together with the disturbance terms (ζ), representing the effects of possible un-known variables or measurement errors of endogenous variables.

From a general perspective, in the research model (Fig. 2), it is hypothesizedthat:

1. The cause or causes to which students attribute their academic achievementsand academic self-concept are significantly determined by variables such astheir concept of intelligence (variable or stable), perceived competence (tocope successfully with specific academic tasks), and past academic achieve-ment.

2. The processes of causal attribution and academic self-concept significantly


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and directly influence the determination of students’ predominant motiva-tional orientation (academic goals). Through these two variables, perceivedcompetence, the concept of intelligence, and past academic achievement alsodetermine goal orientation, albeit indirectly. On the other hand, students’type of motivational profile will also be accounted for by the extent to whichthey consider the achievement evaluation criteria (what is evaluated andhow), their degree of awareness of specific task characteristics, their degreeof awareness of teaching style (mainly, with regard to the chance to be auton-omous at school tasks, flexibility in task assignments, the possibility to usedeep cognitive strategies, etc.), as well as the type of curricular contents (thatallow use of higher or lower level cognitive resources).

3. The type of motivational orientation thus established will significantly ac-count for the use of cognitive strategies (selection, organization, and elabora-tion) that lead to deep learning. The relation between both variables (motiva-tional orientation and strategies) should be positive; that is, the higher thelearning motivational orientation, the higher will be the use of the cognitivestrategies needed to carry out comprehensive learning, and vice versa. Con-versely, the higher the performance motivational orientation, the lower willbe the use of complex cognitive strategies (except when achievement corre-sponds with learning; that is, what is evaluated is the process and not only theend product or final result). Similarly, in addition to students’ predominantmotivational orientation, the use of cognitive strategies will also be ac-counted for by the consideration of the contextual variables mentioned initem 2.

4. When students possess and use a broad range of cognitive strategies (what,how, and when to use strategies) that lead to comprehensive and meaningfullearning, and the observed contextual criteria enhance this kind of learning,students tend to expend a great deal of effort, attention, concentration, andpersistence in coping with difficulties or problems that may come up (whichhas to do with the construct of willpower).

5. Finally, the described scenario should lead to corresponding academicachievement; that is, the more the effort and persistence, the higher the aca-demic achievement and vice versa.

The model was contrasted using two procedures. On the one hand, analysis ofglobal model fit to verify the extent to which the hypothesized model correctlyreproduces the relationships of the correlation matrix of the empirical data. Thesecond procedure was the estimation and analysis of the relationships betweenthe variables posited in the model. A series of statistical indicators from LISREL7 was used to ascertain the model fit, among them, goodness-of-fit indexes (GFIand AGFI), level of statistical significance (p) of chi-square (χ2), and root meansquare residual (RMSR). According to experts in this kind of methodology (Byrne,


2001; Marsh and Balla, 1994; Marsh, Balla, and Hau, 1996), when the sampleis large and the distribution of the variables is not normal, goodness-of-fit in-dexes should be used as preferential criteria to evaluate the fit of the models (indetriment of chi-square).

RESULTS

The means, standard deviations, skewness, and kurtosis of each of the vari-ables integrated in the model can be seen in Table 1.

Model Goodness of Fit

To begin with possibly the most demanding criterion to contrast models (levelof significance p), the results of our research revealed no statistically significantdifferences between the proposed model and the empirical data, χ2 (48, N = 614) =42.73, p = .688, which indicates an excellent fit between the hypothesized theo-retical model and the data. This fit criterion is quite clearly met.

In addition to this evaluation coefficient, various indexes, although less re-strictive and sensitive to variable deviations from normality than chi-square, arenonetheless important criteria to ascertain the model fit. We refer to the GFIand AGFI, with values ranging from 0 to 1, where 1 is the perfect fit. As acoefficient equal to or larger than .90 is an indication of the fit of the model,

TABLE 1. Means, Standard Deviations, Skewness, and Kurtosis of the VariablesIncluded in the Model

Variables M Skewness SD Kurtosis

1. Concept of increasable intelligence (X1) 3.91 −0.68 1,04 0.552. Perceived ability (X2) 3.96 −0.85 0.84 0.173. Past academic achievement (X3) 3.78 −0.50 0.83 0.484. Perception of evaluation criteria (X4) 3.62 −0.40 0.99 −0.345. Analysis of task characteristics (X5) 3.65 −0.45 1.02 −0.316. Perception of teaching style (X6) 3.71 −0.60 1.06 −0.197. Perception of curricular content (X7) 3.87 −0.86 1.07 0.148. Internal attribution (Y1) 4.07 −0.95 0.72 2.199. External attribution (Y2) 2.72 0.16 1.04 −0.47

10. Academic self-concept (Y3) 3.35 0.33 0.60 −0.0111. Learning goals (Y4) 27.58 −0.09 5.05 0.0812. Performance goals (Y5) 24.55 −0.91 4.34 0.7413. Deep learning strategies (Y6) 54.08 −0.37 9.08 0.3114. Persistence in academic tasks (Y7) 4.12 −0.79 0.93 −0.2115. Academic achievement (Y8) 3.46 −0.55 0.78 0.27


the proposed model achieves excellent fit (AGFI = .97; GFI = .989), one moreplausibility criterion. It is also noteworthy that the RMSR value is less than .05(RMSR = .023), indicating that the residuals after comparing the theoretical andempirical matrixes are not significantly different, which in turn means that themodel obtained is similar to the hypothesized one. Readers are reminded thatthe RMSR value approaches zero as the model fit improves.

Taking into account all the above-mentioned evaluation criteria, the proposedmodel is plausible. In other words, there is a high degree of correspondencebetween the hypothesized model, its theoretical relationships between variables,and the empirical data.

The determination coefficient (DC), more than an index of the model fit, is acoefficient offering information about the amount of variance accounted for bythe communal relationships specified in the model. According to the value ob-tained for the determination coefficient (DC = .52), approximately 52% of theconjoint endogenous variables is accounted for by the relationships posited inthe model (in the model, coefficients with dashed arrows represent the amountof unexplained variance in each endogenous variable). The following valueswere obtained for these coefficients (see Fig. 2): internal attributions: ζ = .841;external attributions: ζ = 1.000; academic self-concept: ζ = .738; learning goals:ζ = .779; performance goals: ζ = .913; deep learning strategies: ζ = .842; persis-tence in academic tasks: ζ = .834; academic achievement: ζ = .579. Therefore,in the model, 15.9% of internal attributions is accounted for (in percentageterms, this difference is the result of subtracting .841 from 1), the model doesnot explain external attributions, and 26.2% of academic self-concept, 22.1% oflearning goals, 8.7% of performance goals, 15.8% of deep learning strategies,16.6% of persistence, and 42.1% of academic achievement are accounted for.

Evaluation of Individual Parameters

Following the strategy of structuring the hypothetical model along three di-mensions (motivational-affective, cognitive, and volitional), the most relevantresults obtained with regard to specific relations in the model to be contrastedare presented in Fig. 3.

Motivational-Affective Dimension

In this study, students’ (motivational) goal orientation is differentiated de-pending on whether they are learning or performance oriented. As hypothesized,learning orientation is significantly and internally determined by internal causalattribution (β = .113), increasable concept of intelligence (γ = .144), perceivedability (γ = .153), and academic self-concept (β = .147). As all these relationsare positive, which indicates that motivational learning orientation will be higher


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as students accept responsibility for their academic achievements, believe thatlearning involves increasing competence, trust their ability to learn, and, in gen-eral, have a positive concept of themselves as students. At the external level,motivational learning orientation is conditioned by the perception of evaluationcriteria (γ = −.130) and the analysis of task characteristics (γ = .237). The nega-tive sign in the relation between evaluation criteria and motivational learningorientation indicates that the more students take evaluation criteria into account,the lower will be their motivational learning orientation. This seems a contradic-tion, but it makes sense in our teaching system, because, in practice, teachers’,parents’, and society’s evaluations generally place more emphasis on achieve-ment. Nobody asks students “how much you have learned?” but they do askthem “what was your achievement?” Achievement, and not learning, is reinforced.When the study method depends on the way students perceive they will beevaluated, then their hopes of high grades condition their academic performancepatterns, so that they focus on the final result of learning (in terms of grades)rather than on the learning process itself. These behaviors are similar to whatBiggs (1987) and Entwistle (1987) called, respectively, achievement approachand strategic approach. This is corroborated by the fact that the relation betweenthis variable (perception of evaluation criteria) and motivational performanceorientation is positive (γ = .216). Motivational performance orientation is alsoaccounted for by an external attributional pattern (β = .097: the more achieve-ments are attributed to external causes, the higher the performance orientation),as well as by what students do when they learn (that is, the less learning strate-gies are used, the more a motivational orientation toward results is developed).

All this indicates that, if we want students to feel motivated to learn (learninggoals) in our classrooms, we must undertake teaching processes in which: (a)learning activities are designed so that, on the one hand, students can learn ifthey make an effort and work hard (so they attribute their results to internalcauses) and, on the other hand, activities are within students’ potential ability(so they feel capable of coping with the activities and, consequently, they makethe effort); (b) evaluation criteria are made explicit and are focused on the learn-ing activities; that is, more on the process than on the result (this would allowsome control by students over the result obtained); and (c) cognitive processesand their use are increased.

However, this is not sufficient to develop a motivational learning orientation.The results of our analysis indicate that all the aforementioned variables, takenconjointly, are only capable of accounting for 22% of motivational learningorientation. Consequently, although the variables seem relevant to account forthe students’ type of motivation, there are other variables, such as the family,and more or less strategic teaching processes, for example (Gonzalez-Pienda etal., 2002), not contemplated in this research and that should be taken into ac-count at schools.


Cognitive Dimension

The use of deep cognitive strategies is directly accounted for by a motiva-tional orientation toward mastery, learning, or the task (β = .270), and indirectlyby internal causal attributional processes (that generate responsibility) via theirincidence on the definition of motivational learning orientation. On the otherhand, the use of these strategies is also directly affected (γ = .151) by the percep-tion of the specific task characteristics to which these strategies are applied. Acharacteristic trait of good information processors is their ability to choose theappropriate strategies according to task requirements (Borkowski and Muthuk-rishna, 1992), which means being capable of planning the execution of certainstrategies on the basis of prior analysis of task requirements and nature. Theseissues are related to what Paris, Lipson, and Wixson (1983) called “conditionalknowledge,” or knowing why, where, and when to apply strategies (Weinsteinand Hume, 1998). Symons, Snyder, Cariaglia-Bull, and Pressley (1989) ex-pressed these ideas clearly when they stated

a competent thinker analyzes task situations to determine the strategies that wouldbe appropriate. A plan is the formed for executing the strategies, and progress duringstrategy execution is monitored. In the face of difficulty, ineffective strategies areabandoned in favor of more appropriate ones. These processes are supported by appro-priate motivational beliefs and a general tendency to think strategically. (p. 8)

So, analysis of task characteristics influences directly and significantly stu-dents’ decisions about which strategies to put to use to achieve their goals mostefficiently. Nevertheless, contrary to the postulation, we observed that percep-tion of teaching style, perception of evaluation criteria, or perception of the typeof curricular material did not significantly influence the use of deep learningstrategies.

Nonetheless, despite the relevance of the type of variables commented on, itshould be noted that, in this model, only 16% of the use of learning strategiesis accounted for. This means that there are many variables involved in the deter-mination of the psychological processes of learning. Taking this fact into ac-count will enhance the scientific study of all these variables. Even so, despitethe large amount of variance still unexplained with regard to why students usemore or less strategies in the university, one should not forget the importanceof contemplating the variables we do know are relevant—such as those consid-ered here and in other studies—when designing teaching activities.

Volitional Dimension

As initially hypothesized, the use of learning strategies within a comprehen-sive learning situation is positively and significantly associated with persistencein academic tasks (β = .121). This indicates that the more strategies are used,


the higher will be the degree of effort and persistence in learning. On the otherhand, learning oriented motivation, in addition to its indirect affect via the use oflearning strategies, also directly influences effort and persistence in learning tasks(β = .188). This indicates that, when learning tasks do not require much use oflearning strategies (i.e., organization, elaboration, etc.), then effort and persistenceare supported by the intrinsic motivational level. Persistence is also directly ac-counted for by the belief that effort is the cause of achievement (β = .187). At anexternal level, it is also conditioned by the result of the analysis of task characteris-tics (more complex tasks require more persistence than simpler tasks).

However, only 17% of persistence is accounted for, which indicates that thisvariable also depends on other variables not reflected in the present study. Inany case, we believe that the relation between the use of strategies and persis-tence would highly increase as, via instructional processes, learning processescome to depend on the explicit use of learning strategies. In this case, persis-tence in the classroom would, to a great extent, be accounted for by this fact,because persistence would also be much more related to the final achievement.In this study, as in others, the relation was only .128. This indicates that therelation between effort or persistence and achievement is not as clear as itshould be (“if I make more effort, I achieve more; if I make less effort, I achieveless”). This is due to many factors, among which are the type of achievementevaluated (based more on memory than on understanding) and the low relationbetween customary teaching processes and the use of learning strategies (in fact,they are not usually used in our classrooms).

All this is supported by the fact that students’ grades are directly associatedwith variables whose effect should be indirect via the described process. Specifi-cally, the students who obtained more credit were those who believed morestrongly that their achievement was a consequence of their ability and effort (β =.237) and also partly, although to a lesser extent, to external causes (β = .106).Individuals who believed they were good students (β = .275), those who ob-tained good grades in the past (γ = .259), and, logically, those whose motivationwas “to get a good grade” (β = .075), rather than to learn, obtained more creditthan another one.

From our viewpoint, this is the result of the kind of teaching carried out inthe classroom and of the type of achievement evaluated. Thus, as teaching doesnot focus on the use of strategies (which could provide control, self-confidence,perceived competence associated with the use of these instruments, persistencewhen faced with failure, etc.) but rather on content; and achievement is mea-sured by how many things students know rather than by how students masterknowledge and apply it to new situations; it is logical for high-achieving stu-dents to believe that this is due to their higher intelligence (as there are otherstudents who, in the same conditions, are unsuccessful), and to feel they aregood students (and the others are poor students), so they maintain high interestin continuing, and so forth.


We should change teaching processes now. We have sufficient data to knowwhat to do and what to avoid, and we hope that the results of this research willcontribute more information along these lines.

DISCUSSION

As pointed out in the analysis of the results, the series of causal relationshipsspecified in the model only accounted for approximately 52% of the total vari-ance. Although this in no way invalidates the satisfactory model fit, it is animportant limitation in the capacity of the model to explain the causal effectsbetween its variables. To a certain extent, the results suggest that the empiricalcorrespondence of the originally posited theoretical relationships between vari-ables is quite acceptable. However, at the same time, potential changes or varia-tions in the relationships seem to be determined not only by the variables in-cluded in the model we analyzed but also by other variables that are absent.Moreover, not all the endogenous variables considered in the model are ac-counted for at the same level. Whereas some variables present a relatively highpercentage of accounted-for variance, other variables are only minimally ac-counted for by the relationships established in the model (for instance, the vari-able “academic achievement,” for which 58% of the variance is not accountedfor by the causal relationships of the model). According to our research, themodel appears to account for deep learning rather than for academic achieve-ment.

What is the basis for the above statements? The variable “academic achieve-ment” is a synonym of academic grades obtained, in other words, the productof learning that is institutionally evaluated by means of grades (Biggs, 1989).This should not be confused with the quality and depth of the contents. Beingsuccessful and obtaining good results do not necessarily imply learning, becausemany students are successful at the university, but they do not acquire meaning-ful and permanent knowledge (Romainville, 1994). Quality learning may beassociated with high grades, but frequently, mechanical and repetitious learningalso lead to high grades. Thus, the deep learning-academic achievement differ-entiation may partially explain why a relatively high percentage of the varianceof academic achievement is not accounted for by the proposed causal relation-ships.

Even so, in accordance with the fundamental hypotheses of the self-regulationmodels reviewed in the posits under study, our model appears to lend supportto the concept that predisposition to accept responsibility for the results of one’sacademic behavior is related to positive self-image as a student. These condi-tions are important for the development of a significant learning-motivated ori-entation and the development of personal competence. Of course, this impliesthe selection and use of learning strategies aimed at deep information-process-ing, which in turn leads students to assume responsibility, display high levels


of persistence, perseverance, and the tenacity to achieve the aim of this motiva-tional orientation. Lastly, this persistence and effort to reach goals has a signifi-cant and positive effect on academic achievement. However, the effect of persis-tence would probably be greater if it were related to learning and not to academicachievement, although this is probably only possible to verify if an experimentaldesign with a specific learning task (on-line) is employed.

Taking into account our above comments, and reviewing some of the morerelevant results of the analysis of the research model, the present research hastheoretical implications, as well as implications for educational practice. In theeducational practice, we refer especially to cognitive-motivational and volitionaldeterminants in university students. In line with many studies, perceived ability,past academic achievement, and students’ concept of intelligence as modifiableby effort and new learning all contribute to a positive academic self-conceptand a high degree of responsibility for academic results, attributing results tointernal causal factors. All of this has a high influence on the development oflearning-goal-orientated motivation, which in turn leads students to use a seriesof strategies adapted to these goals and intentions to achieve highly comprehen-sive and meaningful learning. At the same time, in the academic context, analy-sis of task characteristics and requirements (level of difficulty, steps to followto solve the problem, cognitive resources, effort, and abilities to be displayed,etc.) enhance positive results when setting learning-goals, using deep learningstrategies, or increasing levels of persistence and effort during task performance.

On the other hand, some university students’ attributional patterns are charac-terized by avoiding all responsibility for academic results, and their only motiva-tion is to achieve good academic results—with no concern for quality of curricu-lar content. Perceived competence, concept of modifiable intelligence, academicself-concept, the desire to learn and to improve one’s knowledge, or use of deeplearning strategies do not foster this type of behavior in an academic context.However, neither self-concept, intrinsic motivation, nor learning strategies aredetermined by many of the abovementioned, essentially extrinsic, variables. Infact, sometimes, some variables hamper or have negative effects on the others.

The aforementioned results are unavoidably associated with the characteristicsof this study (instruments, design, sample, etc.). In our case, a possible researchlimitation was the type of design employed. We only have one measurement,taken at one point in time, of the series of variables taken into account. How-ever, we attempted to contrast the relationships of these variables in a causalmodel. In other words, we used a transversal design to study supposed causaleffects among variables. Strictly speaking, in causal analysis, a temporal se-quence between two variables is required to establish a cause–effect relation-ship; that is, the cause-variable should precede the effect-variable in time. Thisrequirement can only be met when a longitudinal design is employed (MacCal-lum and Austin, 2000). Thus, for example, it is reasonable to believe that students’


current academic achievement may determine future academic self-concept; thatthe current level of perceived ability may influence future self-concept; thatcurrent self-concept may affect future academic achievement, and so forth. It ishard to imagine that these variables would undergo substantial changes in shortperiods of time. Therefore, this should be taken into consideration in futureresearch, and the model should be contrasted using a longitudinal design, forexample, establishing more than two evaluation periods at various time inter-vals, with different instruments and with a latent variable model. Only then arewe likely to encounter reciprocal relationships (in time) between many of thevariables considered in the model (for example, self-concept and attributions,self-concept and academic achievement, etc.).

Nevertheless, although some variables in this cognitive-motivational modelare directly related to the instructional context (perception of teaching style, ofthe evaluation criteria, etc.), further research of these relations is required tostudy them more in depth and more concretely. For example, it should be speci-fied which teaching styles or which evaluation criteria contribute to the develop-ment of different motivational orientations, and to the use of certain learningstrategies. Therefore, assuming that neither the cognitive nor the motivationalcomponents are independent of the context, a challenge to future research willbe the specific analysis of the influence of the teaching/learning-process vari-ables, which are essentially contextual and interpersonal. In fact, it is essentialto study in depth the way students process the instructional situation (for in-stance, how they perceive the teacher’s expectancies, task characteristics andrequirements, etc.). This is the most important determinant of what they willlearn, even more important than the teacher or other instructional agents (Shuell,1993).

We refer to research of models of self-regulated “situated” learning. Criticismfrom the viewpoint of school learning—specifically, the situated cognition ap-proach—has been expressed about the excessively decontextualized nature ofcognitive research of learning and has emphasized that the learning processshould take into account the social and cultural context in which learning takesplace (Lave and Wenger, 1991; Pintrich, 1994; Rogoff, 1990).

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Received March 8, 2002.

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Cognitive, Motivational, and Volitional Dimensions of Learning: An Empirical Test of a Hypothetical Model