3. Research findings

and the learning outcomes, mutually interact, forming a dynamic system, and all of them affects

In our research, we focused on identification of relation between two intrapsychic factors on a student's side (academic self-efficacy and learning approach) and their academic achievement (heteronomous evaluation of academic achievement and the autonomous evaluation of academic achievement). We assume that the heteronomous evaluation (expressed by GPA and given by a teacher/teachers) significantly affects the autonomous evaluation of academic achievement (expressed in own believes of a student about the knowledge, skills or competence he or she acquired during a school year including own values, priorities and objectives related to learning and education). We assume that academic self-efficacy and approach to learning are significant predictors of heteronomous and autonomous evaluation of academic achievement of adolescents.

The sample consisted of 457 adolescents studying at secondary schools in the Slovak Republic (268 girls, 189 boys) aged from 15 to 18 years (mean age 16.24). We provide data collected at the end of the 2nd grade in public high schools throughout the nation (end of school year 2015/ 2016). Students' participation in the research was voluntary (the research battery was administrated personally in 18 high schools). Before administration of research methods, every group

Academic self-efficacy was measured by The Morgan-Jinks Student Efficacy Scale [17], which was designed to acquire information about student efficacy beliefs that is related to school success. The scale consists of 30 items with 3 subscales: talent (15 items), context (9 items) and effort (6 items). All the items are designed using a four-interval scale (from 1 = really agree, 2 = kind of agree, 3 = kind of disagree and 4 = really disagree.). The score for academic selfefficacy was calculated by summing up the scores for the 30 items, after reversing the scores for 9 items (4, 6, 15, 17, 19, 21, 23, 25 and 28). Scores for general academic self-efficacy range from minimum 30 to maximum 120, scores for the talent subscale ranges from minimum 15 to maximum 60, scores for the context subscale ranges from minimum 9 to maximum 36 and scores for the effort subscale range from minimum 6 to maximum 24. The reliability of the scale was Cronbach's alpha = 0.687. The reliability of subscales was: talent: Cronbach's alpha = 0.720,

Heteronomous evaluation of academic achievement (HAA) we measured by GPA in whole study subjects in the end of school year 2015/2016. In the Slovak Republic a student's achievements in individual subjects are measured using the following grades: 1 = excellent, 2 = very good, 3 = good, 4 = sufficient and 5 = poor/unsatisfactory, while the final evaluation in a given subject included in the school report at the end of each school year results from an average of many grades given regularly in relation with achieved results (a teacher can take/takes into account also attributes other than only marks given during the school year). The resulting GPA reflects an average academic achievement calculated on the basis of all end-year grades at a

of students were given a complete explanation of the research study.

context: Cronbach's alpha = 0.601 and effort: Cronbach's alpha = 0.402.

school report (minimum 1, maximum 5).

learning of student [47].

182 Health and Academic Achievement

2. Methods and procedures

#### 3.1. Academic self-efficacy, approach to learning, heteronomous and autonomous evaluation of academic achievement: descriptive statistics and correlation analysis

The basic descriptive indicators of all variables considered in our study are presented in Table 1.

On the basis of the identified mean of the ASE, we have found out that adolescents in our study achieved a higher academic self-efficacy than the median of this variable is (also talent and effort). In the research variable deep approach to learning is the mean value below the median (also deep motive and deep strategy); for the surface approach to learning the mean value is almost equal to median (also surface motive and surface strategy). Academic achievements evaluated by teachers (HAA) and by adolescents (AAA) are better than the median for the both variables is.

From the perspective of academic self-efficacy (ASE) and its factors (talent, context and effort), we have identified significant relations between this variable and (Table 2):


Table 1. Academic self-efficacy, approach to learning strategy, heteronomous and autonomous academic achievement: descriptive statistics.


When it comes to mutual relations of the heteronomous evaluation of academic achievement (GPA), we have found out that it significantly correlates, in addition to academic self-efficacy (and its three factors: talent, context and effort), also with other variables (the only exception is the surface strategy subscale), while the better is the assessment of a student at the end of a school year (as expressed in GPA), the more a student prefers the deep approach to learning (including both factors: deep motive and deep strategy) and the less he or she prefers the surface approach to learning (with factors only at the level of surface motive).

N = 457

ASE

 Pearson

Sig.

Talent

 Pearson

Sig.

Context

 Pearson

Sig.

Effort

 Pearson

Sig.

> DA

Pearson

Sig.

DM

 Pearson

Sig.

> DS

Pearson

Sig.

> SA

Pearson

Sig.

> SM

Pearson

Sig.

> SS

Pearson

Sig.

HAA

 Pearson

Sig.

AAA

 Pearson

Sig.

> \*p < 0.05.

\*\*p < 0.01.

\*\*\*p < 0.001.

Table 2.

Academic self-efficacy,

 learning strategy and academic

achievement:

 correlation

 analysis. 185

0.000

 0.000

 0.020

 0.000

 0.000

 0.000

 0.027

 0.025

 0.037

 0.073

 0.000

—

 0.426\*\*\*

0.000

 0.000 0.451\*\*\*

0.109\*

0.230\*\*\*

0.171\*\*\*

0.201\*\*\*

0.103\*

0.105\*

0.097\*

0.084

 0.671\*\*\*

—

http://dx.doi.org/10.5772/intechopen.70948

 0.005

 0.000

 0.001

 0.000

 0.016

 0.002

 0.000

 0.106

—

 0.406\*\*\*

0.746

 0.928 0.402\*\*\*

0.132\*\*

0.267\*\*\*

0.161\*\*

0.176\*\*\*

0.113\*

0.143\*\*

0.171\*\*\*

0.076

—

 0.691

 0.238

 0.000

 0.000

 0.000

 0.000

 0.000

—

0.015

 0.004

0.368

 0.612

 0.805 0.019

0.055

0.184\*\*\*

0.163\*\*\*

0.170\*\*\*

0.863\*\*\*

0.489\*\*\*

—

Academic Self-Efficacy, Approach to Learning and Academic Achievement

 0.026

 0.000

 0.000

 0.000

 0.000

—

 0.042

 0.024

 0.012

 0.104\*

0.739

 0.729

 0.930

 0.548

 0.000 0.346\*\*\*

0.277\*\*\*

0.350\*\*\*

0.862\*\*\*

—

 0.000

 0.000

—

 0.016

 0.016

0.005

 0.042

 0.256 0.004

 0.028

 0.000

 0.000 0.307\*\*\*

0.255\*\*\*

0.301\*\*\*

—

 0.000

—

0.130\*\*

0.095\*

0.053

0.196\*\*\*

0.894\*\*\*

0.629\*\*\*

—

0.000

 0.000

 0.067

 0.000

 0.000

—

0.000

0.375\*\*\*

0.368\*\*\*

0.086

0.340\*\*\*

0.910\*\*\*

—

 0.000

 0.097

 0.000

—

0.285\*\*\*

0.263\*\*\*

0.078

0.300\*\*\*

—

0.000

 0.000

 0.019

—

 0.568\*\*\*

0.000

 0.005 0.448\*\*\*

0.110\*

—

—

 0.547\*\*\*

0.000

—

0.131\*\*

—

 0.885\*\*\*

—

—

—

ASE

Talent

 Context

 Effort

 DA

DM

DS

SA

 SM

 SS

 HAA

 AAA



• Deep approach to learning (p < 0.001), and from the perspective of individual factors combined in the deep approach to learning among adolescents (deep motive and deep strategy), both these factors significantly correlate with ASE, talent and effort. We have recorded no significant correlation with the context factor. We have identified that the higher academic self-efficacy an adolescent has, the more he or she applies the deep approach to learning. • Heteronomous evaluation of academic achievement (p < 0.001), and all three factors combined in the academic self-efficacy (talent, context, effort) strongly correlate with HAA. We have observed that the higher academic self-efficacy an adolescent has, the better the results of his or her education expressed in GPA at the end of a school year are.

Table 1. Academic self-efficacy, approach to learning strategy, heteronomous and autonomous academic achievement:

\*

descriptive statistics.

184 Health and Academic Achievement

Median as quantile value of variable in research method.

N = 457 Min. Max. Mean Std. Dev. Med.

Academic self-efficacy (ASE) 36 106 61.23 7.69 75 Talent (ASE-T) 15 60 29.50 5.73 37.50 Context (ASE-C) 15 36 23.99 3.29 22.50 Effort (ASE-E) 6 12 7.73 1.44 13 Deep approach to learning (DA) 10 45 27.33 5.78 30 Deep motive (DM) 5 24 14.13 3.33 15 Deep strategy (DS) 5 22 13.20 3.08 15 Surface approach to learning (SA) 13 47 30.91 5.49 30 Surface motive (SM) 5 23 15.29 3.18 15 Surface strategy (SS) 7 25 15.61 3.18 15 Heteronomous academic achievement (HAA) 1 2.75 1.57 0.43 3 Autonomous evaluation of academic achievement (AAA) 1 6 2.36 0.86 3.5

\*

• Autonomous evaluation of academic achievement (p < 0.001), and all three factors combined in the academic self-efficacy (talent, context and effort) strongly correlate with AAA. We thus conclude that the higher the academic self-efficacy of an adolescent is, the better the subjective evaluation of achieved academic results, based on assessment of acquired information, skills and competences in individual subject, is as well (they perceive a significantly greater set of information, skills and competences in a given school year). When it comes to mutual relations of the heteronomous evaluation of academic achievement (GPA), we have found out that it significantly correlates, in addition to academic self-efficacy (and its three factors: talent, context and effort), also with other variables (the only exception is the surface strategy subscale), while the better is the assessment of a student at the end of a school year (as expressed in GPA), the more a student prefers the deep approach to learning (including both factors: deep motive and deep strategy) and the less he or she prefers the

surface approach to learning (with factors only at the level of surface motive).

Within the analysis of mutual relations between heteronomous evaluation of academic achievement and autonomous evaluation of academic achievement we have identified a significant correlation (p < 0.001).

approach to learning explains only 2.0% of the variability of heteronomous evaluation of

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187

3.3. Academic self-efficacy, learning strategy and heteronomous evaluation of academic

In the second step of our research we determined participants' autonomous evaluation of academic achievement by the academic self-efficacy (and factors of ASE), approach to learning (deep and surface and factors of DA and SA) and heteronomous evaluation of academic of

Findings related to academic self-efficacy and factors of ASE as predictors of autonomous evaluation of academic achievement are shown in Table 6. We found out that a student's academic self-efficacy significantly predicts the autonomous evaluation of academic achievement. Academic self-efficacy explains 18.2% of the variability of autonomous evaluation of academic achievement. Variance in factors of ASE shows that only one factor: talent is a

On the base of findings represented in Table 7, we note that preferred deep approach to learning significantly predicts the autonomous evaluation of academic achievement (variance in factors of DA shows that only one factor: deep motive is a meaningful predictor). The deep approach to learning explains only 2.9% of the variability of autonomous evaluation of aca-

Variable R R2 F B Beta t

Table 4. Deep approach to learning (and factors of DA) as predictor of heteronomous evaluation of academic

Variable R R2 F B Beta t SA 0.143 0.020 9.511\*\* 0.011 0.143 3.084\*\* SM 0.172 0.029 6.892\*\*\* 0.024 0.177 3.333\*\*\* SS 0.001 0.011 0.202

Table 5. Surface approach to learning (and factors of SA) as predictor of heteronomous evaluation of academic

DA 0.161 0.026 12.139\*\*\* 0.012 0.003 3.484\*\*\* DM 0.176 0.031 7.256\*\*\* 0.022 0.174 2.928\*\* DS 0.000 0.003 0.052

achievement as predictors of autonomous academic achievement

academic achievement.

achievement.

meaningful predictor.

demic achievement.

achievement (HAA).

achievement (HAA) regression analysis.

\* p < 0.05. \*\*p < 0.01. \*\*\*p < 0.001.

\* p < 0.05. \*\*p < 0.01. \*\*\*p < 0.001.

#### 3.2. Academic self-efficacy and learning strategy as predictors of heteronomous academic achievement

In the first step of our research we determined participants' heteronomous evaluation of academic achievement by the academic self-efficacy (and factors of ASE), deep approach to learning (and factors of DA) and surface approach to learning (and factors of SA) as our independent variables. All independent variables are included into the presage factors in students '3P' model of teaching and learning [47]. We note that these independent variables are not all "internal" factors on the side of a student which influence learning and education process, but our intention is to identify their predictive effect to heteronomous (in second step autonomous) evaluation of academic achievement of adolescents.

Findings focused on the academic self-efficacy (ASE) and factors of ASE as predictors of heteronomous evaluation of academic achievement are shown in Table 3. We found out that a student's academic self-efficacy significantly predicts the heteronomous evaluation of academic achievement, variance in factors of ASE shows that only two factors: talent and effort are meaningful predictors. Academic self-efficacy explains 16.5% of the variability of heteronomous evaluation of academic achievement.

Findings related to preferred deep approach to learning (and factors of DA: deep motive and deep strategy) as a predictor of heteronomous evaluation of academic achievement are shown in Table 4. We note that preferred deep approach to learning significantly predicts the heteronomous evaluation of academic achievement (variance in factors of DA shows that only one factor: deep motive is a meaningful predictor). But the deep approach to learning explains only 2.6% of the variability of heteronomous evaluation of academic achievement.

According to Table 5, we identified that the preferred surface approach to learning significantly predicts the heteronomous evaluation of academic achievement (variance in factors of SA shows that only one factor: surface motive is a meaningful predictor). But the surface


p < 0.05. \*\*p < 0.01.

\*\*\*p < 0.001.

Table 3. Academic self-efficacy (and factors of ASE) as predictor of heteronomous evaluation of academic achievement (HAA).

approach to learning explains only 2.0% of the variability of heteronomous evaluation of academic achievement.

#### 3.3. Academic self-efficacy, learning strategy and heteronomous evaluation of academic achievement as predictors of autonomous academic achievement

In the second step of our research we determined participants' autonomous evaluation of academic achievement by the academic self-efficacy (and factors of ASE), approach to learning (deep and surface and factors of DA and SA) and heteronomous evaluation of academic of achievement.

Findings related to academic self-efficacy and factors of ASE as predictors of autonomous evaluation of academic achievement are shown in Table 6. We found out that a student's academic self-efficacy significantly predicts the autonomous evaluation of academic achievement. Academic self-efficacy explains 18.2% of the variability of autonomous evaluation of academic achievement. Variance in factors of ASE shows that only one factor: talent is a meaningful predictor.

On the base of findings represented in Table 7, we note that preferred deep approach to learning significantly predicts the autonomous evaluation of academic achievement (variance in factors of DA shows that only one factor: deep motive is a meaningful predictor). The deep approach to learning explains only 2.9% of the variability of autonomous evaluation of academic achievement.


p < 0.05. \*\*p < 0.01.

Within the analysis of mutual relations between heteronomous evaluation of academic achievement and autonomous evaluation of academic achievement we have identified a sig-

3.2. Academic self-efficacy and learning strategy as predictors of heteronomous academic

In the first step of our research we determined participants' heteronomous evaluation of academic achievement by the academic self-efficacy (and factors of ASE), deep approach to learning (and factors of DA) and surface approach to learning (and factors of SA) as our independent variables. All independent variables are included into the presage factors in students '3P' model of teaching and learning [47]. We note that these independent variables are not all "internal" factors on the side of a student which influence learning and education process, but our intention is to identify their predictive effect to heteronomous (in second step

Findings focused on the academic self-efficacy (ASE) and factors of ASE as predictors of heteronomous evaluation of academic achievement are shown in Table 3. We found out that a student's academic self-efficacy significantly predicts the heteronomous evaluation of academic achievement, variance in factors of ASE shows that only two factors: talent and effort are meaningful predictors. Academic self-efficacy explains 16.5% of the variability of heteron-

Findings related to preferred deep approach to learning (and factors of DA: deep motive and deep strategy) as a predictor of heteronomous evaluation of academic achievement are shown in Table 4. We note that preferred deep approach to learning significantly predicts the heteronomous evaluation of academic achievement (variance in factors of DA shows that only one factor: deep motive is a meaningful predictor). But the deep approach to learning explains only

According to Table 5, we identified that the preferred surface approach to learning significantly predicts the heteronomous evaluation of academic achievement (variance in factors of SA shows that only one factor: surface motive is a meaningful predictor). But the surface

Variable R R2 F B Beta t ASE 0.406 0.165 89.770\*\*\* 0.023 0.406 9.475\*\*\* ASE: Talent 0.420 0.177 32.395\*\*\* 0.026 0.346 7.219\*\*\* ASE: Context 0.010 0.075 1.738 ASE: Effort 0.031 0.104 2.180\*

Table 3. Academic self-efficacy (and factors of ASE) as predictor of heteronomous evaluation of academic achievement

2.6% of the variability of heteronomous evaluation of academic achievement.

autonomous) evaluation of academic achievement of adolescents.

omous evaluation of academic achievement.

nificant correlation (p < 0.001).

186 Health and Academic Achievement

achievement

\* p < 0.05. \*\*p < 0.01. \*\*\*p < 0.001.

(HAA).

\*\*\*p < 0.001.

Table 4. Deep approach to learning (and factors of DA) as predictor of heteronomous evaluation of academic achievement (HAA).


\*\*\*p < 0.001.

Table 5. Surface approach to learning (and factors of SA) as predictor of heteronomous evaluation of academic achievement (HAA) regression analysis.

According to research findings in Table 8, we identified that preferred surface approach to learning significantly predicts the heteronomous evaluation of academic achievement, but the surface approach to learning explains only 1.1% of the variability of autonomous evaluation of academic achievement.

of academic achievement is presented in Table 10. The academic self-efficacy of adolescents and the heteronomous evaluation of academic achievement by teachers were shown as best/

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189

In the last step of our research, we determined participants' academic self-efficacy (and factors of ASE) by autonomous academic achievement of the academic as our independent variable. The autonomous evaluation of academic achievement is a strong predictor of the academic self-efficacy of students in adolescence (and all factors of ASE, too, Table 11). The autonomous

Variable R R2 F B Beta t

AAA 0.671 0.450 371.694\*\*\* 1.352 0.671 19.279\*\*\*

Table 9. Heteronomous evaluation of academic achievement (HAA) as predictor of autonomous academic achievement

Step 1 ASE 0.426 0.182 100.919\*\*\* 0.048 0.426 10.046\*\*\* Step 2 ASE 0.438 0.192 35.812\*\*\* 0.047 0.418 9.453\*\*\*

Step 3 ASE 0.692 .479 103.732\*\*\* 0.020 0.179 4.642\*\*\*

Variable R R2 F B Beta t

ASE 0.426 0.182 100.919\*\*\* 3.782 0.426 10.046\*\*\* ASE: talent 0.451 0.204 116.423\*\*\* 2.985 0.451 10.790\*\*\* ASE: context 0.109 0.012 5.486\* 0.416 0.109 2.342\* ASE: effort 0.230 0.053 25.427\*\*\* 0.382 0.230 5.042\*\*\*

Table 11. Autonomous academic achievement (AAA) as predictor of academic self-efficacy (ASE, factors of ASE).

Table 10. Autonomous academic achievement (dependent variable): final model.

Variable R R2 F B Beta t

DA 0.004 0.024 0.512 SA 0.014 0.091 2.050\*

DA 0.003 0.021 0.558 SA 0.002 0.011 0.307 HAA 1.196 0.593 15.771\*\*\*

strongest predictors of autonomous evaluation of academic achievement.

\* p < 0.05. \*\*p < 0.01. \*\*\*p < 0.001.

\* p < 0.05. \*\*p < 0.01. \*\*\*p < 0.001.

\* p < 0.05. \*\*p < 0.01. \*\*\*p < 0.001.

(AAA).

Findings related to heteronomous evaluation of academic achievement as a predictor of autonomous evaluation of academic achievement are shown in Table 9. The heteronomous evaluation of academic achievement (the average of the marks, rating by teachers, in all subjects in the end-year report: as GPA) is a strong predictor of the autonomous evaluation of academic achievement. The heteronomous evaluation of academic achievement explains 45.00% of the variability of autonomous evaluation of academic achievement. The final model tested through individual steps of impact of independent variables of our research on the autonomous evaluation


Table 6. Academic self-efficacy (and factors of ASE) as predictor of autonomous academic achievement (AAA).


Table 7. Deep approach to learning (and factors of DA) as predictor of autonomous academic achievement (AAA).


Table 8. Surface approach to learning (and factors of SA) as predictor of autonomous academic achievement (AAA).

of academic achievement is presented in Table 10. The academic self-efficacy of adolescents and the heteronomous evaluation of academic achievement by teachers were shown as best/ strongest predictors of autonomous evaluation of academic achievement.

In the last step of our research, we determined participants' academic self-efficacy (and factors of ASE) by autonomous academic achievement of the academic as our independent variable.

The autonomous evaluation of academic achievement is a strong predictor of the academic self-efficacy of students in adolescence (and all factors of ASE, too, Table 11). The autonomous


Table 9. Heteronomous evaluation of academic achievement (HAA) as predictor of autonomous academic achievement (AAA).


\* p < 0.05.

According to research findings in Table 8, we identified that preferred surface approach to learning significantly predicts the heteronomous evaluation of academic achievement, but the surface approach to learning explains only 1.1% of the variability of autonomous evaluation of

Findings related to heteronomous evaluation of academic achievement as a predictor of autonomous evaluation of academic achievement are shown in Table 9. The heteronomous evaluation of academic achievement (the average of the marks, rating by teachers, in all subjects in the end-year report: as GPA) is a strong predictor of the autonomous evaluation of academic achievement. The heteronomous evaluation of academic achievement explains 45.00% of the variability of autonomous evaluation of academic achievement. The final model tested through individual steps of impact of independent variables of our research on the autonomous evaluation

Variable R R2 F B Beta t

Table 6. Academic self-efficacy (and factors of ASE) as predictor of autonomous academic achievement (AAA).

Variable R R2 F B Beta t

DA 0.171 0.029 13.688\*\*\* 0.026 0.171 3.700\*\*\* DM 0.203 0.041 9.793\*\*\* 0.059 0.225 3.810\*\*\* DS 0.011 0.038 0.646

Table 7. Deep approach to learning (and factors of DA) as predictor of autonomous academic achievement (AAA).

Variable R R2 F B Beta t SA 0.105 0.011 5.082\* 0.017 0.105 2.254\* SM 0.106 0.011 2.577 0.020 0.074 1.384 SS 0.013 0.048 0.893

Table 8. Surface approach to learning (and factors of SA) as predictor of autonomous academic achievement (AAA).

ASE 0.426 0.182 100.919\*\*\* .048 0.426 10.046\*\*\* ASE: Talent 0.455 0.207 39.437\*\*\* 0.065 0.431 9.165\*\*\* ASE: Context 0.013 0.049 1.162 ASE: Effort 0.019 0.032 0.681

academic achievement.

188 Health and Academic Achievement

\* p < 0.05. \*\*p < 0.01. \*\*\*p < 0.001.

\* p < 0.05. \*\*p < 0.01. \*\*\*p < 0.001.

\* p < 0.05. \*\*p < 0.01. \*\*\*p < 0.001. \*\*p < 0.01.

\*\*\*p < 0.001.

Table 10. Autonomous academic achievement (dependent variable): final model.


Table 11. Autonomous academic achievement (AAA) as predictor of academic self-efficacy (ASE, factors of ASE).

evaluation of academic achievement explains 18.2% of the variability of academic self-efficacy of adolescents.

subscales related to motivation and strategy: except for the surface strategy subscale) with academic achievement (heteronomous and autonomous) we have reported significant correlations. We are convinced that verified relations between academic self-efficacy, approach to learning and academic achievement are important for health cognitive functions of adolescents

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In relation to the variables that constitute a group of the so-called presage students factors we have identified that both academic self-efficacy and preferred approach to learning (deep or surface) are significant predictors of heteronomous evaluation of academic achievement, as evaluated from the outside: that is the output evaluation of academic achievements in learning subjects with a grade provided by teachers on a school certificate at the end of each school year. The conclusion that academic self-efficacy influences academic achievement was con-

We have recorded a significant predicative correlation of heteronomous evaluation of academic achievement (measured by GPA) on autonomous evaluation of academic achievement that serves as an expression of students' perception of acquired knowledge, skills and competences within school subjects (their educational progress). Furthermore, we have found out that autonomous evaluation of academic achievement is a significant predictor of academic self-efficacy: and the more positively students evaluate the level of acquired knowledge, skills and competences, the higher their academic self-efficacy is. Similarly, Stankov [54] also concludes that there is a real difference between our actual ability to solve problems in a study (study subjects) and what we think our ability level in a particular domain is. People also think that they are good (self-concept) in some study subject (e.g., in English) and they are bad in other (e.g., in mathematics) and may be afraid (anxious) when solving problems in the study subject in which are bad. When shown bad study subject problems our self-efficacy is also affected to some extent by what we believe our strengths and weaknesses are and by our previous experiences with similar problems. Obviously, what we believe about ourselves does matter when we are engaged in academic pursuits and it may play an important role in, for example, the selection of the academic discipline for study and in career choice. Self-confidence

The implication is that researchers and teachers should be looking for students beliefs about their educational capabilities, because they are important components of motivation and of academic achievement. Based on our research findings as well as on a previously published study [10], we conclude that when students believe in their success in a given school subject (e.g., Slovak language, English language, Mathematics or any other subject) or generally believe in their good academic achievements, they demonstrate high levels of academic selfefficacy. The self-efficacy beliefs are important as through them the learning processes, motivations, passion and selectiveness regulates the individual's use in different areas [55]. An important factor, however, in this process is the heteronomous evaluation by a teacher that affects autonomous evaluation of academic achievement on a student's side as a part of his or

A teacher plays an important role in providing opportunities for students to be successful. Our study implies that the impact of educational factors depending on a student, particularly the

is related to our self-beliefs and it is also related to cognitive performance.

her metacognition process.

and promote health behaviour (similarly [2, 16, 19, 29]).

firmed also by numerous other researchers [16, 20, 21].
