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Learning and Individual Differences 35 (2014) 122–129

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Learning and Individual Differences journal homepage: www.elsevier.com/locate/lindif

Personal variables, motivation and avoidance learning strategies in undergraduate students Fernando Doménech-Betoret a,⁎, Amparo Gómez-Artiga b,1, Susana Lloret-Segura c,2 a b c

Dept. of Educational and Developmental Psychology, Universitat Jaume I, Castellón, Spain Developmental and Educational Psychology, Universitat de València (Estudi General), València, Spain Methodology of the Behavioral Sciences, Universitat de València, Valencia, Spain

a r t i c l e

i n f o

Article history: Received 25 June 2013 Received in revised form 27 May 2014 Accepted 28 June 2014 Keywords: Instructional model Theoretical framework Research in the classroom Higher education Educational setting

a b s t r a c t This study examines the relationships among students' personal variables, their initial motivation and the avoidance learning strategies they used during the teaching/learning process followed in the Educational Psychology subject matter. The sample comprised 195 Spanish undergraduate students who studied Educational Psychology. A questionnaire was administered at the beginning of the academic year to measure students' personal variables and their initial motivation, while another was administered at the end of the academic year to measure students' involvement in their learning process through the avoidance strategies they used. The data analysis was done by using structural equation modeling. The results reveal important associations among students' personal variables, their motivation at the beginning of the academic year and the avoidance strategies used during the learning process followed in the Educational Psychology subject matter. The implications of these ﬁndings for teaching and learning a speciﬁc subject matter in formal classroom contexts are discussed. © 2014 Elsevier Inc. All rights reserved.

1. Introduction The main objective of this research is to better understand why certain students, even those in the same educational setting, get involved and make an effort to learn using all the resources and strategies they have available, while others make the minimum effort and work to learn. This phenomenon, known by the terms avoidance strategies or work avoidance, reﬂects passivity or inaction and, when applied to education, refers to those strategies that students should use to learn, but they do not use them. Previous research has suggested that “work avoidance may be an academic goal in which students seek to minimize the amount of work they do at school” (Seifert & O'Keefe, 2001, p. 81). In order to study this phenomenon, we ﬁrstly revised the literature to identify, select, and later evaluate, the most characteristic behaviors that describe students who use avoidance strategies. Secondly, we designed an explicative causal model of such behavior by taking the Educational Situation Quality Model (MCSE ‘Modelo de Calidad de Situación Educativa’), developed by Doménech (2006, 2011a,b, 2012,

⁎ Corresponding author at: Departamento de Psicología Evolutiva, Educativa, Social i Metodología, Universitat Jaume I, Campus Riu Sec, 12071 Castellón, Spain. Tel.: +34 964 72 95 50; fax: +34 964 72 92 62. E-mail addresses: [email protected] (F. Doménech-Betoret), [email protected] (A. Gómez-Artiga), [email protected] (S. Lloret-Segura). 1 Tel.: +34 96 386 44 20x4709; fax: +34 96 386 46 71. 2 Tel.: +34 96 3864585; fax: +34 963864697.

http://dx.doi.org/10.1016/j.lindif.2014.06.007 1041-6080/© 2014 Elsevier Inc. All rights reserved.

2013), as a reference, whose characteristics are brieﬂy commented on below. The main contribution of this study is to help explain why students use avoidance strategies, which undermine their performance and limit their capacity to learn. As we become capable of understanding why students use avoidance strategies that make their learning difﬁcult by identifying the variables responsible for this behavior, we will be able to design preventive actions in the classroom to improve students' implication and efﬁcacy in their learning. Despite the importance of this issue due to the practical implications derived, studies centered on avoidance strategies are relatively scarce, especially in higher education. 1.1. Educational Situation Quality Model (MCSE ‘Modelo de Calidad de Situación Educativa’) The MCSE could be deﬁned as an instructional model which simultaneously considers the teaching and learning process where motivation plays a central role. It also provides a methodological way to research in the educational setting. As seen in Fig. 1, the model is made up of ﬁve blocks of variables arranged into three major sequential phases: input, process and product. The components and variables of the model considered in this study are explained below. 1.1.1. The input phase: personal variables (Block 1) Below, we brieﬂy comment on students' personal variables from Block 1.

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Fig. 1. Educational Setting Quality Model (MCSE): Organization and functional relationship of input, process and product (a revised version of Doménech, 2006).

1.1.1.1. Prior knowledge about the subject. According to the constructivist learning approach, prior knowledge facilitates new learning and students' comprehension. Research has veriﬁed that individuals with greater prior knowledge of a topic understand and remember more than those with more limited prior knowledge (Schneider & Pressley, 1997). Furthermore, prior knowledge in speciﬁc domains has a positive inﬂuence on students' learning and achievement (Alexander & Judy, 1988; Dochy, Segers, & Buehl, 1999; Thompson & Zamboanga, 2004). 1.1.1.2. Interest in the subject. The beneﬁcial effects of interest in learning are well-documented (Renninger & Hidi, 2002). The Person–Object Conception of Interest (POI) was developed in the Educational Psychology ﬁeld. In accordance with this conception, researchers usually adopt the distinction between situational and individual. In the past, many studies have focused on examining the inﬂuence of individual interest on learning process and achievement, and the obtained results have veriﬁed that level of interest accounts for about 10% of the variance observed in achievement (Schiefele, Krapp, & Winteler, 1992). 1.1.1.3. Beliefs about the formative scope of the subject. The value that a particular task or subject has for students is an important stimulus to generate motivation (Pintrich, 1989; Pintrich & De Groot, 1990). Yet the value that students attribute to a particular subject depends, to a certain extent, on the utility that they perceive it has for them in terms of developing their skills both professionally and personally. 1.1.1.4. General academic self-efﬁcacy. Self-efﬁcacy is a component of Bandura's Social Cognitive Theory (SCT) (1986) and is deﬁned as “an individual's belief in his or her own ability to organize and implement action to produce the desired achievements and results” (Bandura, 1997, p. 3). Perceived self-efﬁcacy that a person has of one's own capabilities to perform or undertake a task increases the likelihood of the task being successfully performed (Bandura, 1986). Prior research has veriﬁed that students' self-efﬁcacy beliefs are associated with other motivation constructs and with students' academic performances and achievement (Pajares, 1996, 1997). 1.1.1.5. General self-esteem. Self-esteem is the evaluative component of Self-concept, which can be deﬁned as “the positivity of a person's evaluation of self” (Baumeister, 1998, p. 694). Self-esteem has been associated with learning strategies (Núñez et al., 1998), with academic achievement (Mestre, García, Frías, & Llorca, 1992) and with goal

progress and intrinsic motivation at school (Vasalampi, Salmela-Aro, & Nurmi, 2010). 1.1.2. The initial phase of the process: motivational positioning variables (Block 3) The motivational positioning variables (MPV), generated by students at the beginning of the educational process, refer to their initial expectations or ideas about how the teaching/learning process, with a speciﬁc subject matter and a speciﬁc teacher, will be implemented. This idea may have been generated before classes began, caused by previous experiences with a similar content, or be based on the information students already have about the teacher, etc. It may also arise on the ﬁrst days of class when they meet the teacher and ﬁnd out about the study syllabus, evaluation requirements, how the teacher is going to conduct the class, etc. Students now have sufﬁcient information to enable them to answer four important implicit questions deriving from the motivational theory proposed by Pintrich (1989), Pintrich and De Groot (1990), and from the Expectancy-Value Theory proposed by Feather (1982) and Vroom (1964): a) Will I be successful in this subject? b) What value has this subject for me? c) How will I feel studying this subject, and d) How much time and effort will I devote to studying this subject according to the value it has for me? This same process similarly occurs with the teacher, but is not considered in this study. 1.1.3. The interactive involvement phase: avoidance learning strategies (Block 4) The interactive involvement phase (Block 4) refers to the way that the teacher and students interact though a speciﬁc curricular content; that is to say, everything the teacher does to teach and everything students do to learn that particular content. The degree of teacher and student involvement in this phase and the degree of their interrelationship will depend on the expectations generated by them in the previous phase (the initial positioning phase). The present study centered on what students do to avoid learning, speciﬁcally on the avoidance learning strategies they used during the teaching/learning process undertaken in Educational Psychology. Previous motivational research has suggested that work avoidance in the school context is an academic goal in which students seem to make little effort to understand or complete academic tasks (Jarvis & Seifert, 2002; Seifert & O'Keefe, 2001). Seifert and O'Keefe (2001) indicated that students tend to avoid effort or minimize the amount of work they do at school for three major reasons: feeling of competence, boredom and lack of control. Students

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who perceive little control or competence seek to avoid effort because they believe they cannot do the work, or because they want to feel protected from the humiliation and shame associated with failure (Covington, 1984). Moreover, the students who perceive themselves capable of doing a task, but do not see any reason for doing it (for instance, because they ﬁnd that the task is meaningless), also try to avoid making efforts in their learning process (Seifert & O'Keefe, 2001). Sloan (2007) found that motivational traits are one of the major reasons that teachers used to explain why students avoid work. Finally, attendance in classroom sessions of the Educational Psychology subject matter was also taken into account since it can be understood as a kind of avoidance strategy. 1.2. Objectives and hypothesis The main aim of this study was to examine the process which explains why undergraduate students employ avoidance learning strategies. Based on the aforementioned rationale, the hypothesized connections have been addressed and tested simultaneously using the Structural Equation Modeling (SEM) procedure. The causal model presented (see Fig. 2) has been designed in accordance with the conﬁguration of the MCSE displayed in Fig. 1. As seen in the hypothesized model, the following predictions have been addressed. First, personal variables (from Block 1, the input phase) such as Prior knowledge, Interest in the subject, General Academic Self-Efﬁcacy, Formative scope of the subject, General Self-Esteem, were expected to be good predictors of the students' initial motivation or intention to learn at the beginning of the T/L process (from Block 3, the preprocess phase) in Educational Psychology. Second, the students' initial motivation (from Block 3, the preprocess phase) was expected to be a good predictor of avoidance strategies and classroom attendance (from Block 4, the process phase).

2. Method 2.1. Participants and procedure The sample consisted of 195 psychology students, of whom 159 were female (81.5%) and 36 male (18.5%), who were aged between 20 and 47 years old (M: 22.78, SD: 3.83). The participants studied Educational Psychology during the 2010–11 and 2011–12 academic years at the Universitat Jaume I, Castellón (East Spain). Educational Psychology is an annual core subject and is taught in the third year of the Psychology degree. The study was carried out during two consecutive academic years in the Educational Psychology subject matter (an annual core subject taught in the third year of the Psychology degree). Questionnaires were administered twice, at the start (time 1) and at the end (time 2) of the teaching/learning process conducted in this subject matter. The teacher, the methodology and the evaluation system were the same in the studied educational settings. As the effect of these variables was neutralized, the classes studied can be treated as a unique educational setting. 2.2. Measures I would like to underline that all the scales used have been reviewed and reﬁned in previous studies (Doménech, 2006, 2011a,b, 2012, 2013) based on the MCSE model. Nevertheless, we tested the adequacy of these measures in a two-stage process. First, an explorative factor analysis, the principal component method with varimax rotation, was conducted in all the scales to check their construct validity. An observed measure was obtained by averaging the items included in each factor or subscale. Second, we included these subscales as the observed variables in the measurement model part of the structural model tested.

Fig. 2. Hypothesized Model (HM). Relationship among students' personal variables (block 1), students' positioning variables (block 3) and avoidance strategies (block 4). All the effects among the ﬁve personal variables and the four students' positioning variables are hypothesized as positive; all the effects among the four students' positioning variables on classroom attendance are hypothesized as positive, while the effect of these variables on avoidance strategies are hypothesized as negative.

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This procedure avoids including every single item in the measurement model, thus reducing the number of estimated parameters. Scales used are brieﬂy described on below, for more information see Table 1. 2.2.1. Input phase (time 1): students' personal variables scales (SPVS) 2.2.1.1. Prior knowledge scale (3 items). This scale was used to assess the prior knowledge about the subject matter taught that students had at the beginning of the academic year; that is, Educational psychology. Students indicated their level of agreement on a Likert scale ranging from 1 (I am not convinced) to 4 (I am absolutely convinced). A single component was extracted from the exploratory factor analysis. 2.2.1.2. Student interest scale (5 items). The interest scale was used to assess the extent of student interest in the content of the subject matter. Students indicated their level of agreement, ranging from 1 (completely false) to 6 (completely true). A single component was extracted from the exploratory factor analysis. 2.2.1.3. Scale of beliefs about the formative scope of the subject matter (8 items). The Subject formative scope scale was used to assess the extent that students believed at the beginning of the academic year that this subject would provide them with effective formative scope to succeed in their future job. Students indicated their level of agreement within a range from 1 (minimum formative scope) to 4 (much formative scope). The results obtained from the exploratory factor analysis conducted indicated the bidimensional structure of the scale. 2.2.1.4. Students' general academic self-efﬁcacy scale (20 items). This scale was adapted by Doménech (2011a) for Spanish university students based

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on the original scales created by Bandura (1990) and Pastorelli et al. (2001). This subscale was used to assess students' self-perception of how competent they were in the academic ﬁeld. Students indicated their level of agreement within a range from 1 (Very bad) to 4 (Very good). Five factors corresponding to the ﬁve academic skills were extracted from a second exploratory factor analysis conducted on the remaining 19 items. 2.2.1.5. General self-esteem scale (10 items). One of the most extensively used instruments to assess self-esteem is the Rosenberg Self-Esteem Scale (RSES; Rosenberg, 1965, 1989). This instrument was translated into Spanish and validated in the university context by Martín-Albo, Núñez, Navarro, and Grijalvo (2007). The Spanish version was used in this study. Students indicated their level of agreement within a range from 1 (completely disagree) to 6 (completely agree). The exploratory factor analysis conducted supported the two-factor structure of the scale (positively orientated and negatively orientated) in line with previous research in Spanish university students (see Martín-Albo et al., 2007). 2.2.2. The initial phase of the process (time 1): the motivational positioning variables scale (MPVS) We designed this scale composed of 17 items to measure students' initial motivation (through MPV), with the subject being taught, which was generated at the beginning of the teaching/learning process. It was structured and designed based on the Motivational Theory proposed by Pintrich (1989) and Pintrich and De Groot (1990). Students indicated their level of agreement on a 5-point Likert scale within a range from 1 (I am absolutely not convinced) to 5 (I am absolutely convinced). The exploratory factor analysis conducted supported the 4-factor structure of the scale.

Table 1 Summary of the factor analysis, internal consistency and item example of the scales. Scales

Factors

Input phase: Students' personal variables Prior Knowledge

1

M

S.D.

Variance

Cronbach's α

Item exemple

3

2.89

0.52

65.79

.72

5 8 5 3 19 5 4 4 3 3 10 5 5

3.64

1.25

.88

2.16 2.67

.54 .65

.87 .83

“It will help me to live and live alongside others” “It will help me become a good professional”

2.93 3.17 3.24 2.83 2.82

.61 .53 .43 .55 .56

.85 .81 .76 .83 .71

“How good are you at making summaries to help you study?” “How well do you memorize what you study for an exam?” “How well do you cope with teamwork with colleagues?” “How well do you express what you want to say in writing?” “How do you cope in exam situations?”

3.26 2.85

.44 .28

67.95 65.34 38.98 26.36 67.13 17.27 13.94 12.91 12.51 10.48 58.43 29.34 29.09

“The knowledge acquired in previous academic years will help you learn this subject matter?” “I would not like to work as a psychologist in a school”

1 2

.83 .77

“I think that I have a good number of qualities” “I sometimes feel really useless”

17 5 4 4 4

2.95 2.60 3.02 2.49

.64 .56 .46 .59

76.30 22.83 21.52 17.53 14.89

.93 .91 .88 .86

“The time and effort you must devote to pass this subject will be too much ?” “How useful is this subject for you?” “Do you think you will be able to obtain good marks for this subject?” “Do you think you will ﬁnd this subject enjoyable?”

26 6 6

3.04 1.56

.68 .46

61.77 13.98 13.78

.85 .82

F3: Effort and challenges

6

2.10

.58

12.95

.85

F4: Novelty

4

2.18

.59

11.75

.81

“I played a passive role during classes” “I have actively collaborated with my colleagues in teamwork tasks” (reverse-coded) “I have decided to do those tasks which are easier for me so I can avoid difﬁcult challenges” “I preferred those activities which I found familiar than those which introduced new

procedures and concepts” F5: Help seeking

4

2.08

.66

9.29

.76

Interest Beliefs about the Formative Scope of the subject F1: Personal development F2: Professional development General Academic Self-Efﬁcacy F1: Verbal skills F2: Study techniques F3: Team work skills F4: Memorization capacity F5: Coping with exam situations General Self-Esteem F1: Positively orientated items F2: Negatively orientated items Initial process phase: Initial motivation Motivational variables of positioning F1: Time and effort expected to be done F2: Subject value F3: Achievement expectations F4: Enjoyable learning expectations Process phase: Students' learning strategies Students' avoidance strategies scale F1: Participation during the classroom F2: Team work collaboration

5

2

4

5

Items (n)

“When I did not know how to do a task, I tried to do it my way instead of asking for help”

−.053 −.319⁎⁎ −.215⁎⁎ −.203⁎ .174⁎ .082 −.241⁎⁎ −.077 −.105 .169⁎ −.070 −.089 −.111 −.031 −.074

−.089 .008 −.056 .014 −.004

.032 .042 .105 .000 .134 .059

.097 −.100 .017 −.128 −.104 −.076 .069 −.046 −.134 −.039 −.060 .101 −.065 −.092 −.025 −.067 .094

Gender: 1 Male; 2 Female. ⁎ p b .05. ⁎⁎ p b .01.

−.129 .010 −.040 −.031 −.049 −.021 .052 −.086 −.027 .158⁎ .018 −.042 .110 .119 .121 .012 .093 .096 .138 −.288⁎⁎

−.078 −.070 −.199⁎ −.215⁎⁎ .128 −.144

.029 −.168⁎ .044 −.100 .257⁎⁎

.057 −.014 −.095 −.025 −.026 .092 −.026

−.076 −.160⁎

.066 .360⁎⁎ .224⁎⁎

−.047 .486⁎⁎ −.219⁎⁎

−.037 .228⁎⁎

−.025 −.285⁎⁎ −.071 −.145 .287⁎⁎

−.105 .021 −.020 −.012 .046 .032

−.082 −.051 −.065 −.028 −.097 −.024 .032

−.033 .443⁎⁎ −.330⁎⁎ −.006 .506⁎⁎ −.240⁎⁎

.116 .506⁎⁎

.088 −.160⁎ .304⁎⁎ −.189⁎

.091 −.030 −.108 .075 −.138 −.262⁎⁎ .150 .102 .063 .324⁎⁎

−.069 −.143 .159⁎ .725⁎⁎

.160⁎ −.006 .083 .256⁎⁎ .035 .326⁎⁎ .268⁎⁎ −.149⁎ .046 −.116 −.154⁎

.054 .076 −.043 .107 .066 .166⁎

−.027 .014 .118 .117 .134 .146 .094 .234⁎⁎

−.065 −.033 .053 −.155⁎ −.160⁎ .461⁎⁎ .484⁎⁎

3 2 Gend.

1 1 −.145⁎

A bivariate correlational analysis was carried out as an approach to explore the relationships between input (students' personal variables), students' initial motivation (MPV) and the learning process (avoidance strategies) followed by students. In general, positive and signiﬁcant correlations were obtained between personal variables and students' initial motivation, the most remarkable was the one found between interest and value of the subject (r = .725, p b 0.01). Negative and signiﬁcant correlations were also obtained between students' initial motivation (MPV) and the avoidance strategies used by students, the most

Age

3.2. Correlation between variables

Age Gender 1. Prior Know. 2. Interest 3. Study tech. 4. Memorizat. 5. Team work 6. Verbal skills 7. Cop. exams 8. Prof. Devel. 9. Pers. Devel. 10. S-E posit. 11. S-E negat. 12. Ex. effort 13. Value 14. Achiev. Ex. 15. Enjoy L. Ex. 16. Av. Partic. 17. Av. Collab 18. Av. Effort 19 Av. Novel. 20. Av. Help 21. Attendance

The mean, standard deviation, reliability, structure of the scales and item examples are shown in Table 1. In general, the factorial analyses performed conﬁrmed the scales' original structure and conﬁguration. Cronbach's alpha coefﬁcients indicated good internal consistency for all the scales with a 0.71 to 0.94 range. See Table 1 for more details.

Table 2 Pearson's bivariate correlations between the constructs considered.

3.1. Descriptive statistics and internal consistency of scales

−.021 .000 .218⁎⁎ .193⁎ .202⁎⁎

4

3. Results

.043 .043 .170⁎

.128 .205⁎⁎ .219⁎⁎

5

.235⁎⁎ .160⁎ −.016 −.034 .135 .114 −.040 .065 .093 .022 −.197⁎

6

.368⁎⁎ −.263⁎⁎ −.099 .214⁎⁎

7

−.230⁎⁎ −.264⁎⁎ .451⁎⁎ .280⁎⁎

8

.596⁎⁎ −.218⁎⁎ −.166⁎

9

−.116 −.128 .089 .606⁎⁎

10

The Maximum Likelihood (ML) Robust method of estimation, developed by Satorra and Bentler (1988, 1994), appears to be a good approach when the multivariate normality assumption is violated and the sample size is small (Curran, West, & Finch, 1996; Hu, Bentler, & Kano, 1992). This study suffers from both problems, as is frequently the case in social sciences. Sample size is slightly under the widely recommended value of 200 subjects (Hoe, 2008). Regarding the normality assumption, all except one of the variables in the study did not conform to it. “Interest” was the only variable that did it (for this variable, the K-S test was nonsigniﬁcant, p = .398). Therefore, the ML robust method of estimation was used to test the hypothesized connections by means of the EQS program (Bentler, 1995, 2006). Since the chi-square test is sensitive to sample size, the use of relative ﬁt indices, such as the CFI, the NNFI and the RMSEA, is strongly recommended (Bentler, 1990a,b). Values smaller than .05 for RMSEA indicate a good ﬁt, whereas values of up to .08 indicate a poor ﬁt (Browne & Cudeck, 1993). For NNFI and CFI, values greater than .90 (Hoyle, 1995) or even .95 (Hu & Bentler, 1999) have been ﬁxed as the cutting-off point.

.540⁎⁎ .162⁎

11

.093 −.175⁎ .309⁎⁎

2.3. Statistics analyses

−.093 −.060 .063 .152 −.040 .053 −.049

12

.153 .190⁎ .291⁎⁎

13

−.009 .582⁎⁎ −.321⁎⁎

14

.162⁎ −.003 −.028 −.204⁎ −.141 .088 −.074

15

−.256⁎⁎ .085 −.201⁎ −.057 −.145 .255⁎⁎

16

.069 .186⁎ .155 .289⁎⁎ −.335⁎⁎

17

.132 .029 .043 .006

18

.612⁎⁎ .282⁎⁎ −.051

19

.206⁎ −.013

20

2.2.3. The interactive involvement phase (time 2): the student avoidance strategies scale (SASS) The second questionnaire was made up of ﬁve scales: avoiding novelty (6 items); avoiding help seeking (6 items); avoiding effort and challenges (6 items); avoiding participation in the classroom (6 items); avoiding peer collaboration (6 items). The ﬁrst two avoidance strategies scales (avoiding novelty and avoiding help seeking) are a version (Doménech, 2011a) for the Spanish university context that was adapted from those used by Turner et al. (2002) for sixth-grade elementary school students. The remaining scales (avoiding effort and challenges, avoiding participation, and avoiding peer collaboration) were created for the present study. Students indicated their level of agreement on a 5-point Likert scale ranging from 1 (Totally disagree) to 5 (Totally agree). Five factors were extracted from a second exploratory factor analysis conducted on the remaining 26 items, corresponding to the ﬁve avoidances strategies considered. Finally, attendance in the classroom sessions of the Educational Psychology course was also taken into account since it can be understood as a kind of avoidance strategy. Therefore, students were required to inform about how frequently they attended these classroom sessions during the course on a 4-point Likert scale ranging from 1 (never/hardly ever) to 4 (always/almost always).

-.088

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−.064 .362⁎⁎

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Fig. 3. Optimized Model (OM). Relationship among students' personal variables (block 1), students' positioning variables (block 3) and avoidance strategies (block 4). Structural conﬁguration and standardized coefﬁcients of the optimized model are displayed.

remarkable was the one found between value of the subject and avoiding participation (r = .321, p b 0.01). See Table 2 for more details. 3.3. Structural equation modeling Structural equation analysis was carried out to explore the predicted connections in the hypothesized model (HM) among students' personal variables from Block 1, the motivational positioning variables (students' intention to learn) from Block 2 and avoidance strategies from Block 3. The ﬁt indices values obtained using the ML Robust method (Satorra– Bentler scaled χ2 = 250.013; p = .000, d.f. = 166; χ2/d.f. = 1.506; NNFI = .77; CFI = .82; IFI = .84; RMSEA = .061) indicated that the model did not completely ﬁt the data. Following the recommendations of Wald and Lagrange's tests for dropping and adding parameters (provided by the EQS program), some adjustments were then introduced and an optimized model (OM) was proposed and tested. We dropped the General Self-esteem variable (Block 1) and some structural effects that were not signiﬁcant across variables (see the paths that have been deleted in the optimized model presented in Fig. 3 in comparison to the hypothesized model displayed in Fig. 2). The most remarkable modiﬁcation relates to deleting General Self-esteem. This adjustment has not only been based on statistical information, but has also been supported by the fact that General Academic Self-efﬁcacy and General Self-esteem are general personal variables and are both highly related. Self-esteem is the affective side of Self-concept (the cognitive side). Self-concept and Self-efﬁcacy are overlapping and analogous constructs since they share many similarities (Bong & Skaalvik, 2003). In operationalized terms, the Self-esteem items refer to a subject's overall evaluation of his/her own worth, whereas the General Academic Selfefﬁcacy items refer to a subject's belief in his/her ability to accomplish academic tasks. It seems that students may have found it difﬁcult to discriminate between the level of academic competence that they report (General Academic Self-efﬁcacy) and the perception of his/her own worth when completing the scales. Fig. 3 shows the

structural conﬁguration and standardized coefﬁcients for the optimized model (OM) obtained. The ﬁt indices values obtained using the ML Robust method of estimation (Satorra–Bentler scaled χ 2 = 185.228; p =.0155, d.f. = 146; χ2/d.f. = 1.268; NNFI = .89; CFI = .91; IFI = .91; RMSEA = .044) indicate that the model ﬁtted the data. Regarding the relationship between constructs, the results of the optimized model (OM) can be summarized as follows. As can be seen in Fig. 3, the regression equations revealed that a relevant amount of variability in students' initial motivation (intention to learn) is explained by the personal variables included in the model: 40% of variance of Achievement expectation is explained by General Academic Selfefﬁcacy; 72% of variance of Value of the subject perceived by students at the beginning of the educational process is explained by Interest in the subject, together with Beliefs in the Formative Scope of the Subject; 53% of variance of Enjoyable Learning is explained by Beliefs in the Formative Scope of the Subject and General Academic Self-efﬁcacy; and ﬁnally, 11% of the variance of Dedication expectation (in terms of time and effort), students reported to work the subject is explained by General Academic Self-efﬁcacy. The regression equations obtained for avoidance student strategies revealed that, as expected, the motivational positioning variables (intention to learn) have an effect on them, although this effect is not as strong as in the previous case. The avoidance strategies that students used have been partially explained (14% of variance) by Achievement expectations and by Value of the subject, and students' attendance has been partially explained (16% of variance) by Value of the subject, Enjoyable learning expectations, and Prior Knowledge about the subject. 4. Discussion and conclusions The hypothesized connections shown in the Model presented (Fig. 2) have been examined simultaneously to explore the system of relationships. It was expected that, ﬁrst, personal variables would be good predictors of students' initial motivation or intention to learn

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(measured through MPV at the beginning of the T/L process); and second, students' initial motivation would be good predictors of Avoidance strategies and Classroom attendance in Educational Psychology. The results are discussed below. An optimized model, presented in Fig. 3, was obtained after introducing some adjusting parameters. The results are discussed below. General Academic Self-efﬁcacy (from Block 1) has a signiﬁcant and positive inﬂuence on Achievement Expectations (from Block 3) which, in turn, has a signiﬁcant and negative effect on Avoidance Strategies. This means that Avoidance Strategies are inﬂuenced directly and negatively by Achievement Expectations and that Achievement Expectations is explained well by General Academic Self-efﬁcacy. Self-efﬁcacy is a component of Bandura's Social Cognitive Theory (1986). Bandura's theory postulated that the feeling of competence determines the quality of task engagement. Bandura (1986) differentiated between “efﬁcacy expectations”, deﬁned as an individual's belief in his/her own capability in accomplishing a given task, and “outcome expectations”, deﬁned as one's belief that the effort that one exerts would lead to a desired outcome. The literature usually encompasses both constructs of selfefﬁcacy under the label “expectation for success” (Liem, Lau, & Nie, 2008). General academic self-efﬁcacy is similar to “efﬁcacy expectation” and, according to Boekaerts (1999), it is considered a general construct and categorized as a superordinate/middle level, whereas Achievement expectations is similar to “outcome expectations” and, according to Boekaerts (1999), it is categorized as a domain-speciﬁc construct. The results indicate, ﬁrstly, that students' beliefs in a behavior resulting in successful outcomes determine the degree of avoidance strategies used and, secondly, that students' beliefs of competence in accomplishing academic tasks determine their beliefs in outcomes. As a whole, this result suggests that “expectation for success”, formed at the beginning of the teaching/learning process conducted with Educational Psychology, will affect the amount of work students are willing to do to learn this subject. The students who feel they are not capable of doing the work avoid making effort because they are trying to protect self-worth or because they believe they cannot succeed despite such effort (Covington, 1984). Beliefs in the Formative Scope of the subject and Interest in the subject (from Block 1) have a signiﬁcant, positive inﬂuence on Value of the subject matter (from Block 3) which, in turn, has a signiﬁcant and negative effect on Avoidance Strategies and a signiﬁcant and positive effect on Classroom attendance. It means that Avoidance Strategies and Classroom attendance are inﬂuenced directly by Value of the subject matter and that Value of the subject matter is well explained by Beliefs in the Formative Scope of the subject and, to a lesser extent, by Interest in the subject. The Expectancy-value theory posits that the goals students hold in learning (mastery, performance and work avoidance) are inﬂuenced by their self-efﬁcacy and task value (Wigﬁeld & Eccles, 2000). Students make their best efforts and spend a signiﬁcant amount of their time to engage in and master the academic task if they perceive it as important and useful for them (Miller & Brickman, 2004). In line with this, teachers must inculcate among undergraduate students the belief that the subject is important and instrumental since the contents selected in this subject are useful for practicing their profession or for professional performance. In this way, the Value of the subject matter will increase since students' interest and their belief in the formative value of the subject will improve. Beliefs in the Formative Scope of the subject (from Block 1) has a signiﬁcant, positive inﬂuence on students' enjoyable learning expectations (from Block 3) which, in turn, has a signiﬁcant and positive effect on Classroom attendance. It means that Classroom attendance is inﬂuenced directly and positively by Enjoyable learning expectations and that enjoyable learning expectations are well explained by Beliefs in the Formative Scope of the subject. The results indicate that, ﬁrstly, students' expectations of enjoyable learning formed on the ﬁrst days of the T/L process to be conducted with Educational Psychology predict the degree of avoidance strategies used by students to learn; secondly,

students' perception of the formative value of Educational Psychology has a positive effect on their Enjoyable learning expectations. Other than learning, students must also enjoy learning. This occurs when students feel positive emotions during their learning process. For instance, work avoidance has been related to lack of perceived meaning and boredom (Dowson & McInerney, 2001; Seifert & O'Keefe, 2001). In the same vein, Pekrun, Goetz, Daniels, Stupnisky, and Perry (2010) have claimed that “boredom” is positively related to attention problems, and negatively to intrinsic motivation, effort, use of elaboration strategies, selfregulation and subsequent academic performance. According to the Control–Value Theory of Achievement emotions (Pekrun, Frenzel, Goetz & Perry, 2007), control appraisals and value appraisals of achievement activities and outcomes are the proximal determinants of these emotions. Therefore, one way of improving enjoyable expectations will, on the one hand, consist in explaining and emphasizing the formative value that the subject matter has and, on the other hand, make students feel they control their learning. Finally, and contrary to our expectations, the results revealed that Prior knowledge has a direct, positive and signiﬁcant effect on Classroom attendance, indicating that Classroom attendance also depends, although moderately, on the Prior knowledge students have of the subject. It suggests that the students who do not have the appropriate knowledge or prerequisites to follow the progress made in class tend to avoid Classroom attendance. It also suggests that the evaluation of undergraduate students' Prior knowledge at the beginning of the educative process is recommended to detect any deﬁciencies that may prevent them from following the progress of the course, which may lead to not attending classes. In summary, and in line with previous studies with the MCSE (Doménech, 2006, 2011b), the Motivational Positioning Variables perceived by students on the ﬁrst days of the teaching/learning process play an important role in explaining the degree of students' engagement and the amount of work they are willing to do in Educational Psychology. Speciﬁcally, according to the MCSE, the answers that students give to some important implicit questions raised on the ﬁrst days of the educative process should be taken into account to help improve student learning and success: a) Will I be successful in this subject?; b) What value has this subject for me?; c) How will I feel studying this subject matter? This suggests that the messages (implicit or explicit, verbal or non verbal) that the teacher transmits to students on the ﬁrst days of the educative process, for instance, through their attitudes or through the way they present the course syllabus (assessment, methodology, assignments, etc.) and the subject matter, are very important as they inﬂuence students' engagement and the quality of learning right from the beginning.” 4.1. Practical implications Previous research done in the ﬁeld of MCSE (Doménech, 2006, 2011b) and the results obtained herein seem to indicate that motivational positioning variables (MPV) are capable of predicting students' involvement in their learning process. In this sense, we wish to underline the importance of taking into account such variables on the ﬁrst days of the educative process. Therefore, it is recommended that teachers evaluate these variables at the beginning of the teaching/learning process since it will provide them with valuable information about the extent to which students will be engaged in studying and working on a speciﬁc subject. Furthermore, evaluating certain personal variables (Prior knowledge, Interest in the subject, General Academic Self-Efﬁcacy, Formative scope of the subject) is also recommendable. Thus, if necessary, preventive and corrective instructional measures may be applied in time when motivation deﬁciencies are detected. Such early intervention can help reduce the avoidance strategies that students adopt throughout the educative process, improving their learning and academic performance. This work provides teachers of the Psychology subject matter with useful and easy tools to apply to help diagnose and evaluate the motivational level of

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