Executive Functions and Oonscious Self-Regulation as Predictors of Native Language Learning Success in Russian Middle School Children

Participants. The study was performed on a group of state secondary school students from Moscow and the Moscow Region aged 13-15 (N=286): seventh graders (N=147, mean age 13±0,5 years) and ninth graders (N=139, mean age 15 ± 0.5 years). Gender was distributed almost evenly within the sample group (50.3 % female).

Self-regulation Measure. To assess conscious self-regulation development, the «Self- Regulation Profile of Learning Activity Questionnaire - SRPLAQ» was used (Morosanova & Bondarenko, 2015). It consists of 67 statements describing typical situations of achieving educational goals which generate 10 scales: planning (e. g. «I often try to set a certain amount of time needed to complete a learning task»); modelling (e. g. «Unexpected changes in the timetable throw me off my stride»); programming (e. g. «When preparing for a test (exam), I usually think over the order of studying the material»); results evaluation (e. g. «Even when I'm tired, I tend to study until I'm satisfied with the result»); flexibility (e. g. «If I need to get prepared for a class, I can work even in an uncomfortable and unfamiliar situation»), initiative (e. g. «I use every opportunity to make reports in class»); reliability (e. g. «I do not postpone studying even if I'm tired or sick»), responsibility (e. g. «I do not give up preparing for classes even if I have to choose between studying and spending time with my friends»), social desirability (e. g. «I always admit my mistakes») and the general level of selfregulation. SRPLAQ was previously validated in a sample group of 14-18-year-old students (N=702). The validation study showed that coefficients of internal consistency of items for each scale ranged from 0.58 to 0.76, indicating an overall reasonable homogeneity of the items in each scale. The subscales were significantly correlated with each other (r = 0.22-0.66, p<0.001). The responses to each statement are given on a 4 point scale (Yes - Probably Yes - Probably No - No). The responses are then reduced to only «Yes» and «No» by counting «probably yes/probably no» as «yes/no», respectively. The «yes» responses are then added up (items are reversed if necessary) so that the high scores (maximum 9 for each scale) denote high self-regulation.

EF measures. We used three standard tasks for the assessment of basic EFs (Miyake et al., 2000). To assess inhibition, we used the Eriksen Flanker task. The stimuli are five horizontally arranged black arrows presented against a white background in two conditions: a congruent condition (>>>>>, <<<<<) and an incongruent condition (>><>>, <<><<). The respondent's task is to attend to the arrow in the middle and to indicate its direction by pressing the corresponding key («z» for left and «/» for right). A training session comprised of 36 trials is included. The main series contains four blocks of 36 unique trials in each. The maximum response time is 1,500 ms. The response to stimulus interval is fixed at 1,000 ms. Four response parameters are registered: average reaction time, percentage of correct answers, the difference in reaction time and accuracy between the congruent and noncongruent trials (the interference effect).

To assess switching, we used the LetterNumber task with predictable task changes. The task shows a white screen divided into four quadrants. A pair of symbols, a digit and a letter, is presented clockwise in each quadrant, starting from the upper left. The respondent's task is to determine whether the digit is even or odd if the characters are located in one of the upper quadrants and whether the letter is a consonant or a vowel if the symbols appear in one of the lower quadrants. The answer is given by pressing a key («z» for odd digits and vowel letters and «/» for even digits and consonant letters). The training series consists of 24 letter-digit pairs. The main series consists of 128 trials. The stimuli remain on the screen until the response is given. The response to stimulus interval is 500 ms. Six response parameters are registered: average reaction time and accuracy, repetition trials' reaction times and accuracy, switching trials' reaction times and accuracy, and two switch costs indicating switching efficiency (the differences in reaction time and accuracy between switching and repetition trials).

To evaluate working memory updating, we used the N-Back task. The digits from 1 to 8 are presented in a pseudo-random order. The respondent's task is to answer quickly and correctly whether the currently presented digit coincides with the digit presented two positions before (2-back). The training series contains 32 trials, and the two main series each contain 48 trials. Each figure appears six times in each series (four times in the training series), once as a target. Stimulus presentation time is 500 ms. The inter-stimulus interval is 2,000 ms. The answer is given by pressing a key («/» for yes or «z» for no). Average reaction time, accuracy, reaction times and counts for different response types (hits, correct rejections, false alarms, and misses) are recorded.

To assess error correction, we computed the post-error slowing (PES) effect. PES (Du- tilh et al., 2012) is the effect of trials after an incorrect trial exhibiting longer reaction times. The PES effect is associated with the activity of the conscious error monitoring and correction system within the anterior cingulate cortex (Botvinick, Braver, Barch, Carter & Cohen, 2001). We computed PES by subtracting the average reaction time from the average reaction time in the post-error trials in each EF task.

LC measures. We used two tasks for LC diagnostics (Bozhovich, 2016). The first task consists of 20 sentences printed on a sheet of paper. The sentences include eight types of errors: spelling (misspelt words), punctuation (a sign at the end of a sentence), morphological (incorrect word forms), lexical (incorrect collocations), syntactical (improper connection between words), semantic-syntactical (use of a structure that does not correspond to the content of sentence), semantic (the content of the sentence does not correspond to the non- linguistic reality), and stylistic errors (wrong choice of words, sentence structure, word order, word combinations, etc.). Each sentence contains one error. Each type of error is contained in two sentences. There is no more than one error in a sentence, so there are 16 sentences with errors and four distracter sentences without errors. The respondent's task is to find errors in the sentences and mark them on the sheet.

The second task requires active transformation of language elements. It also consists of 20 sentences; 16 sentences contain the eight types of errors from the first task. Similarly to Task 1, four sentences do not contain any errors. The respondent's task is to copy the sentence to the answer sheet if there are no errors; if there is an error, the respondent is expected to rewrite the sentence, correcting it. No time limits were imposed in Tasks 1 and 2. The collected data are compared with an answer key, and error omissions are evaluated according to the «cost» of an error: spelling and punctuation errors add three points to the LC score; morphological, syntactical, syntactic-semantic, semantic, lexical, and stylistic errors add two points; and «false alarms» (wrongly recognized errors) add one point. According to the type of error, errors made in the rewritten sentences are indicated as an additional measure. Overall LC scores (computed separately for Task 1 and 2) range from 0 to 60. A separate score is computed for each competence (given by error type). A higher LC score is indicative of less competent language use.

Procedure. The students performed tasks for the assessment of LC, SR, and intelligence in the classroom. Computerized tasks for EF assessment were performed at a computer lab on another day. The study was conducted in accordance with the Helsinki Declaration. Ethical agreement and consent for access to school were provided by the XXX and approved by the local ethic committee (protocol no. 2018/218, research project «Conscious self-regulation in the system of cognitive and non-cognitive mechanisms of success in learning Russian at school»).

Data Analytic Plan or Analytic Strategy

Data reduction. Due to a large number of EF indicators, a data reduction procedure was applied. First, standardized data on EF were submitted to a factor analysis with VARI- MAX-rotation. Then, factors were extracted based on Kaiser's rule (eigenvalue over one). In total, ten factors for 74 % of the variance were extracted (Table 1). The factors reflected the basic EFs (inhibition, switching, updating, and error correction) quite well. However, for most basic EFs, an RT and an accuracy factor were extracted.

Factor 1 is dominated by the accuracy inhibition indicators with some influence of working memory updating. This is not surprising as working memory function and inhibitory control are closely related. Therefore, we call this the Inhibition factor. It is related to the ability to suppress unwanted representations and stimuli, that is, to the control of internal and external attention. Factor 2 is composed of switching accuracy indicators and is therefore called Switching Accuracy. It is related to cognitive flexibility, i. e. the ability to change between tasks and representations quickly. In the same vein, Factor 3 is called Updating Accuracy. It is related to the ability to hold and processes cognitive representations in working memory. Factor 4 is also clearly composed of working memory updating indicators (but they are related to the processing speed of representations in working memory). We call this factor Updating Efficiency. Factor 5 is composed of switching RT indicators and is therefore called Switching Efficiency. Factors 6, 9, and 10 all include various indicators of conflict and error monitoring and resolution (conflict adaptation and post-error slowing measures). Based on its composition, Factor 6 is called Conflict Adaptation and Factors 9 and 10 are called Error

Fig. 1. The conceptual model of the relationship between EFs, SR, and LC

Table 1. Factor Analysis for EF Measures

Notes. U = Updating. S = Switching. I = Inhibition. RT = Reaction time. ACC = Accuracy. PES = Post-error slowing. CR = Correct rejections. Cost(Errors) = Error-related switch cost.

Table 2. Factor Analysis for LC Measures

Resolution 1 and 2, respectively. They are all related to the ability to monitor for conflicts and errors and to recover from them. All these factors presumably assess functions ascribed to the anterior cingulate cortex. Factor 7 is clearly an Interference Control factor comprised of accuracy indicators of how god interference is controlled in the flanker task. This factor is related to the effectiveness of attention control.

Factor 8 is complex in its interpretation as it comprises a switching efficiency indicator and a conflict monitoring indicator. As switching involves a lot of conflicts, we call it the Cognitive Flexibility factor.

We also applied a data reduction procedure to language competences (LC) data. LC data from both LC tests were submitted to an alpha factor analysis with Equimax rotation and Kaiser's normalization. As a result, a factor solution (eigenvalues over 1) with four factors was obtained (see Table 2 for factor loadings, only indicators over 0.3 are presented), explaining 67 % of the variance.

Factor 1 is dominated by semantic-lexical aspects of language use and is therefore called Language Command. Factor 2 comprises spelling and morphology competences that relate to the word level of the grammatical structure.

It is therefore called Word Structure. Factor 3 is comprised of spelling, punctuation, but also syntax indicators, so we call it Sentence Structure. Last, Factor 4 is dominated by the semantics and pragmatics of the sentences («meaning/sense»), so we call it the Sense factor.

Structural equation modelling. The conceptual model of the relationship between EFs, SR, and LCs is presented in Figure 1.

Various structural models were fitted using the AMOS software. The best-fitting model is presented in Figure 2. The fit indices for this model were good to excellent: x²/df = 1.19; p= 0.04; GFI=0.92; CFI= 0.95; RMSEA =0.029; Pclose=0.99.

Discussion

SR, EF, and Native Language Learning Success. The resulting structural model revealed the specific contribution of regulatory predictors - conscious SR and EF to academic performance in the native language. Both indicators make an indirect contribution to the annual grade in the Russian language. In the first case, fluid intelligence (P = .17) acts as a mediating variable. EF influence the annual grade (P = .96) through the Language Competences, whose contribution, in turn, is quite significant (P = .59). The model assesses EFs' contribution to the conscious SR (P = .35), confirming results of the previous studies on the relationship between SR and EF (e. g., Welsh & Peterson, 2014 Kaplan, Lichtinger & Gorodetsky, 2009; Skibbe, Montroy, Bowles & Morrison, 2019). Researchers agree that the metacognitive levels of SR and EF are closely related developmentally (Roebers & Feurer, 2016). It is worth noting that our result was obtained from a sample of middle school students. Such studies are very rare for the native language subject.

Conscious self-regulation in this model is represented by the regulatory processes of Goal Planning, Condition Modelling, and Results Evaluation, as well as the regulatory personality traits of Flexibility and Initiative. Including these indicators into the model allowed for confirming results of the previous studies (Bondarenko, Fomina & Morosanova, 2020) and reveals the mechanism of the regulatory predictors' influence on the annual grade in the native language. Successful learners plan their own goals, take advantage of conditions relevant for achieving the goals, are sensitive to feedback from their teachers, flexibly cope with obstacles and participate in additional learning activities. These results obtained are in good agreement with the few foreign data on the influence of the Self-Regulated Learning on native language proficiency (Zimmerman, Schunk, 2001; Kaplan, Lichtinger & Gorodetsky, 2009; Rutherford, Buschkuehl, Jaeggi & Farkas et al., 2018). Longitudinal studies also revealed that children who developed the ability to self-regulate early demonstrated higher literacy and language skills, better reading comprehension, were more successful in phonetics, and had a broader vocabulary (Skibbe & Foster, 2019).

A theoretical basis for the work of Lim- po, Alves, and Fidalgo in analyzing high-level writing skills was the model proposed by Hayes and Flower in 1980 (Limpo et al., 2014; Hayes, Flower, 1998). They assumed that there are three different processes governing writing: planning, translation and proofreading, which is close to our theoretical model of conscious self-regulation. Moreover, there are intrigu

ing results obtained by Limpo and colleagues, which indicate a change in the size of the contribution of planning and evaluation processes in grades 4-9 (Limpo et al., 2014). Allen, Snow, and McNamara reported on the positive contribution of Flexibility to academic performance: better writing is associated with higher flexibility, which, in turn, is a function of individual differences associated with writing skills such as vocabulary and general knowledge (Allen et al., 2015).

EF and LC. Research on the relationship between EFs and various LCs has shown that phonological awareness is associated with retaining speech sounds in the working memory (Lonigan, 2009). At the same time, it has been demonstrated that academic skills requiring more complex coordination (e. g., writing comprehension) are more associated with conscious regulation (Bondarenko, Fomina & Morosa- nova, 2020). LCs in our model are represented by all four indicators, the most significant of which is Language Proficiency. Since it is made up of the high-level semantic indicators of the written language proficiency associated with stylistically and lexically correct sentence organization, we believe that Language Proficiency characterizes the so-called Sense of Language (language proficiency and ability to apply it depending on the situation, largely intuitively without relying on the formal rules knowledge (Bozhovich, 2016). It is important to note that EFs, contrary to LCs, do not directly contribute to the annual grade in the Russian Language in the middle and high school periods. Recently, Rutherford and colleagues came to similar conclusions (Rutherford et al., 2018). However, establishing EFs mediator contribution through LCs has theoretical and practical implications.

SR, Intelligence and Native Language Learning Success. Another contribution to determining performance in the native language is made by intelligence. It was found that, unlike mathematics, where intelligence plays a leading role in ensuring high grades, academic performance in the Russian language depends on intelligence to a much lesser extent. Bondarenko, Potanina, Morosanova (2020) have revealed that with an average and high level of the regulatory process of Conditions Modelling, an increase in the intelligence level leads to a decrease in writing mistakes. However, high intelligence with a low level of Modelling does not guarantee a decrease in errors. It may be the reason why we can observe similar effects in gifted students (Morosanova, Fomina & Bondarenko, 2015).

Limitations and Future Directions

Our findings concerning the regulatory feature of flexibility differ from those found by Allen, Snow, and McNamara: better writing is associated with higher flexibility, which, in its turn, is a function of individual differences associated with writing skills such as vocabulary and erudition (Allen et al., 2015). The results obtained in our study demonstrated that when it comes to regulatory flexibility, such an indicator of the cognitive level of conscious self-regulation as switching is of particular importance. The more efficiently a student switches from one task to another, the more syntax errors he makes. The study revealed the phenomenon of «excessive flexibility» leading to errors and reducing them - a deliberate slowdown in the pace of doing a task (requires further research).

Conclusions

The results of structural modelling gave us grounds for confirming the hypothesis on the regulatory predictors of academic performance in native language learning. Research results indicate a significant contribution of the conscious SR to academic achievement in the mother tongue. The study revealed that the basic neurocognitive level of self-regulation, represented by EFs, acts in two ways. On the one hand, EFs are primarily responsible for acquiring LCs in certain areas of native language learning. On the other hand, EFs are interconnected with the conscious SR of learning activity through which they indirectly influence academic performance as well. The results obtained can be used to create psychological interventions and teaching technologies aimed both at developing students' LCs and improving their conscious SR of the learning activity.

References

1. Allen, L. K., Snow, E. L. & McNamara, D. S. (2016). The narrative waltz: The role of flexibility in writing proficiency, In: Journal of Educational Psychology, 108(7), 911-924. http://dx.doi.org/10.1037/ edu0000109

2. Alvarez, J. A. & Emory, E. (2006). Executive function and the frontal lobes: a meta-analytic review, In: Neuropsychology Review, 16(1), 17-42. https://doi.org/10.1007/s11065-006-9002-x

3. Baumeister, R.F., Leith, K.P., Muraven, M., Bratslavsky, E. (2002). Self-Regulation as a Key to Success in Life. In: Pushkar D., Bukowski W. M., Schwartzman A. E., Stack D. M., White D. R. (eds) Improving Competence Across the Lifespan (pp. 117-132). Boston, MA.: Springer, https://doi.org/10.1007/0-306- 47149-3_9

4. Bondarenko, I., Fomina, T., Morosanova, V. (2020). Regulatory predictors of linguistic competences and academic achievement in native language among secondary school students, In: European Proceedings of Social and Behavioural Sciences EpSBS. 91. 148-155. https://doi.org/10.15405/epsbs.2020.10.04.19

5. Bondarenko, I.N., Potanina, A.M. & Morosanova, V.I. (2020). Conscious self-regulation as a resource for success in the Russian language in students with different levels of intelligence, In: Experimental Psychology (Russia), 13(1), 63-78. doi:10.17759/exppsy.2020130105

6. Botvinick, M.M., Braver, T.S., Barch, D.M., Carter, C.S. & Cohen, J.D. (2001). Conflict monitoring and cognitive control, In: Psychological Review, 108(3), 624-652. https://psycnet.apa.org/doi/10.1037/0033- 295X.108.3.624

7. Bozhovich, E.D. (2016). Razvitie iazykovoy kompetentsii kakpsikhologicheskoy sistemy [The development of language competence as a psychological system]. Extended abstract of doctor's thesis. Moscow. (In Russian).

8. Diamond, A. (2013). Executive functions, In: Annual review of psychology, 64, 135-168. https://doi. org/10.1146/annurev-psych-113011-143750

9. Dutilh, G., Vandekerckhove, J., Forstmann, B.U., Keuleers, U., Brysbaert, M. & Wagenmakers, E.-J. (2012). Testing theories of post-error slowing, In: Attention, Perception, and Psychophysics, 74(2), 454465. https://doi.org/10.3758/s13414-011-0243-2

10. Gervais, J. (2016). The operational definition of competency-based education, In: The Journal of Competency-Based Education. 1(2), 98-106. https://doi.org/10.1002/cbe2.1011.

11. Glaesser, J. (2019). Competence in educational theory and practice: a critical discussion, In: Oxford Review of Education, 45(1), 70-85. https://doi.org/10.1080/03054985.2018.1493987

12. Gooch, D., Thompson, P., Nash, H.M., Snowling, M.J. & Hulme, C. (2016). The development of executive function and language skills in the early school years, In: Journal of Child Psychology and Psychiatry, 57(2), 180-187. https://doi.org/10.1111/jcpp.12458

13. Gorgoz, S. & Tican, C. (2020). Investigation of middle school students' self-regulation skills and vocabulary learning strategies in foreign language, In: International Journal of Educational Methodology, 6(1), 25-42. https://doi.org/10.12973/ijem.6.1.25

14. Hofmann, W., Schmeichel, B.J. & Baddeley, A.D. (2012). Executive functions and self-regulation, In Trends of Cognitive Science, 16(3), 174-180. http://dx.doi.org/10.1016Zj.tics.2012.01.006

15. Kaplan, A., Lichtinger, E. & Gorodetsky, M. (2009). Achievement of goal orientations and selfregulation in writing: An integrative perspective, In: Journal of Educational Psychology, 101(1), 51-69. https://psycnet.apa.org/doi/10.1037/a0013200

16. Kecskes, I., Sanders, R. E. & Pomerantz, A. (2018). The basic interactional competence of language learners, In: Journal of Pragmatics, 124, 88-105. https://doi.org/10.1016/j.pragma.2017.10.019

17. Lieven, E. (2008). Building language competence in first language acquisition, In: European Review, 16(4), 445-456 doi: 10.1017/S1062798708000380

18. Limpo, T., Alves, R. A. & Fidalgo, R. (2014). Children's high-level writing skills: Development of planning and revising and their contribution to writing quality, In British Journal of Educational Psychology, 84(2), 177-193. https://doi.org/10.1111/bjep.12020

19. Lonigan, C.J., Anthony, J.L., Phillips, B.M., Purpura, D.J., Wilson, S.B. & McQueen, J.D. (2009). The nature of preschool phonological processing abilities and their relations to vocabulary, general cognitive abilities, and print knowledge, In: Journal of educational psychology, 101(2), 345-358. https://psycnet.apa. org/doi/10.1037/a0013837

20. Marslen-Wilson, W.D. & Tyler, L.K. (2007). Morphology, language and the brain: The decomposition- al substrate for language comprehension, In: Philosophical Transactions of the Royal Society: Biological Sciences, 362, 823-836. https://doi.org/10.1098/rsth.2007.2091

21. Miyake, A., Friedman, N.P., Emerson, M.J., Witzki, A.H., Howerter, A. & Wager, T.D. (2000). The unity and diversity of executive functions and their contributions to complex «frontal lobe» tasks: A latent variable analysis, In: Cognitive psychology, 41(1), 49-100. https://doi.org/10.1006/cogp.1999.0734

22. Morosanova, V.I. & Bondarenko, I.N. (2015). Diagnostika samoreguliatsii cheloveka [Diagnostics of self-regulation in individuals]. Moscow: Kogito-Tsentr.

23. Morosanova, V.I., Fomina, T.G., Kovas, Y. & Bogdanova, O.Y. (2016). Cognitive and regulatory characteristics and mathematical performance in high school students, In: Personality and Individual Differences, 90, 177-186. https://doi.org/10.1016Zj.paid.2015.10.034

24. Morosanova, V.I., Fomina, T. & Bondarenko, I.N. (2015). Academic achievement: Intelligence, regulatory, and cognitive predictors, In: Psychology in Russia, 8(3), 136. https://doi.org/10.11621/ pir.2015.0311

25. Morosanova, V.I (2014). Osoznannaia samoreguliatsiia cheloveka kakpsikhologicheskii resurs dos- tizheniia uchebnykh i professional'nykh tseley [Conscious self-regulation of personal activity as a psychological resource for achieving goals], In: Theoretical and experimental psychology [Teoreticheskaia i eksperimental'naiapsikhologiia], 7(4). 16-38 http://www.tepjournal.ru/images/pdf/2014/4/07.pdf

26. Morosanova. V.I., Bondarenko. I.N. & Fomina T. G. (2019). Vklad ispolnitel'nykh funktsiy i osoznan- noy samoreguliatsii v uspeshnost ' po russkomu iazyku v sredney shkole [Contribution of executive functions and conscious self-regulation to success in the Russian language in secondary school], In: Theoretical and experimental psychology [Teoreticheskaia i eksperimental'naia psikhologiia], 12(4). 54-66. http://www. tepjournal. ru/image s/pdf/2019/04/06.pdf

27. Nota, L., Soresi, S. & Zimmerman, B.J. (2004). Self-regulation and academic achievement and resilience: A longitudinal study, In: International journal of educational research, 41(3), 198-215. https://doi. org/10.1016/j.ijer.2005.07.001

28. Roebers, C.M. & Feurer, E. (2016). Linking executive functions and procedural metacognition, In: Child Development Perspectives, 10(1), 39-44. https://doi.org/10.1016/j.dr.2017.04.001

29. Rutherford, T., Buschkuehl, M., Jaeggi, S.M. & Farkas, G. (2018). Links between achievement, executive functions, and self-regulated learning, In: Applied Cognitive Psychology, 32(6), 763-774. https://doi. org/10.1002/acp. 3462

30. Skibbe, L.E. & Foster, T.D. (2019). Participation in the Imagination Library book distribution program and its relations to children's language and literacy outcomes in kindergarten, In: Reading Psychology, 40(4), 350-370. https://doi.org/10.1080/02702711.2019.1614124

31. Skibbe, L. E., Montroy, J. J., Bowles, R. P. & Morrison, F. J. (2019). Self-regulation and the development of literacy and language achievement from preschool through second grade, In: Early childhood research quarterly, 46, 240-251. https://doi.org/10.1016/j.ecresq.2018.02.005

32. Tsuda, A. & Nakata, Y. (2013). Exploring self-regulation in language learning: A study of Japanese high school EFL students, In: Innovation in Language Learning and Teaching, 7(1), 72-88. https://doi.org/ 10.1080/17501229.2012.686500

33. Velichkovsky, B.B., Bondarenko, I.N. & Morosanova, V.I. (2019). The relationship between executive functions and language competences in middle school children, In: Psychology in Russia: State of the art, 12 (1). 104-117. https://doi.org/10.11621/pir.2019.0108

34. Veraksa, A.N., Bukhalenkova, D.A. & Koviazina, M.S. (2018). Language proficiency in preschool children with different levels of executive function, In: Psychology in Russia: State of the art, 11(4). DOI: 10.11621/pir.2018.0408

35. Verbitskaia, L.A., Malykh, S.B., Zinchenko, Iu.P. & Tikhomirova, T.N. (2015). Cognitive predictors of success in learning Russian, In: Psychology in Russia: State of the Art, 8(4), 91-100. http://doi.org/10.11621/ pir.2015.0408

36. Welsh, M. & Peterson, E. (2014). Issues in the conceptualization and assessment of hot executive functions in childhood, In: Journal of the International Neuropsychological Society, 20(2), 152-156. https://doi. org/10.1017/S1355617713001379

37. Zimmerman, B.J. & Schunk, D.H. (Eds.). (2001). Self-regulated learning and academic achievement: Theoretical perspectives. London, Routledge, 484 p.

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