


































Frontiers of Contemporary Education 
ISSN 2690-3520 (Print) ISSN 2690-3539 (Online) 

Vol. 4, No. 4, 2023 

www.scholink.org/ojs/index.php/fce 

13 
 

Original Paper 

Variables of Self-Regulated Learning as Predictors of Academic 

Achievement 

Clara R. P. Ajisuksmo
1,2

 

1
 Faculty of Psychology, Atma Jaya Catholic University of Indonesia, Jakarta, Indonesia  

2
 Centre for Societal Development Studies, Atma Jaya Catholic University of Indonesia, Jakarta, 

Indonesia 

 

Received: November 22, 2023  Accepted: November 30, 2023  Online Published: December 8, 2023 

doi:10.22158/fce.v4n4p13               URL: http://dx.doi.org/10.22158/fce.v4n4p13 

 

Abstract 

This study examined the four variables of self-regulated learning, namely processing strategies, 

regulation strategies, mental models of learning, and learning orientations in predicting students’ 

academic achievement. The study constituents included 578 first year students [(Female n=479; 

82.9%); Male n=99; 17.1%)] enrolled at Faculty of Psychology, in three universities in three big cities 

in Indonesia, Medan North Sumatra (n=258; 44.6%), Jakarta the capital city (n=209; 36.2%) and 

Surabaya East Java (n=111; 19.2%). The range of the participants’ age was from 17 to 23 years, and 

the mean age was 18.99 years. The Indonesia version of Vermunt’s Inventory of Learning Styles (ILS) 

was implemented to measure the four variables of self-regulated learning. The result of this study 

revealed that the four variables of self-regulated learning were significant predictors of students’ 

academic achievement. The study also showed the differences in the four variables of self-regulated 

learning in terms of gender and the university.   

Keywords 

academic achievement, higher education, learning process, metacognition, self-regulation 

 

 

 

 

 

 

 

 



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1. Introduction 

Learning quality is used to determine the degree of implementation of learning activities and processes 

carried out by students and is always linked to achievement (Ghufron & Hardiyanto, 2017; İlçin, 

Tomruk, Yeşilyaprak, et al., 2018). Ghufron and Hardiyanto (2017) also stated that one of the 

important indicators that characterizes the quality of learning is the quality of students. According to 

Lawson and Kirby (in Ali & Masih, 2021) and İlçin, Tomruk, Yeşilyaprak, et al., (2018) one of the 

factors that influences the quality of learning is the learning process and approaches carried out by the 

learner along with the resulting knowledge structure which is stored in memory. 

Various studies have reported that self-regulation plays important role in learning, for example learning 

the second language (Khodarahmi & Zarrinabadi, 2016; Alzubaidi, Aldridge & Khine, 2016; Huang, 

2022; Wijaya & Setiawan, 2021), mathematical problems (Perels, Dignath & Schmitz, 2009; Labuhn, 

Zimmerman & Hasselhorn, 2010), science (Li, Zheng, Liang, Zhang & Tsai, 2016), and medical 

education (Siddaiah-Subramanya, Nyandowe & Zubair, 2017). Thus, self-regulation in learning will 

influence academic achievement (Schapiro & Livingston, 2000; de Acedo Lizarraga, Ugarte, et al., 

2003; Vermunt, 2005; Labuhn, Zimmerman & Hasselhorn, 2010), and professional career (Endedijk, 

Brekelmans, Sleegers, et al., 2016).  

According to Schunk (in Schraw, Crippen & Hartley, 2006) and Valle, Núñez, Cabanach, et al. (2009), 

self-regulation refers to the individuals’ ability to understand and monitor their learning by setting 

goals and strategies to achieve the goals, implementing the strategies in learning, and monitoring the 

progress in achieving the goals. De Corte emphasized the importance of self-regulation in learning in 

higher education students by stated that successful learners and problem solvers can simultaneously 

perform two functions: executing a task, and organizing/evaluating (=self-regulating) the task-related 

activities by orienting oneself to the task, planning one’s approach to the task or problem, monitoring 

and evaluating the activities, and reflecting after a task has been performed (p. 266). Self-regulation, 

therefore, is crucial competencies for success in learning (De Corte, 2016; Baez-Estradas & 

Alonso-Tapia, 2017).  

The study of Baez-Estradas and Tapia (2017), reported that students’ effort to learning will also depend 

on the way they regulate their learning when confronting the learning tasks. The more difficult the 

learning tasks are, the more negative emotions will arise that will affect the thinking process. Vermunt 

and Vermetten (2004) mentioned the interplay of cognitive processing strategies, metacognitive 

regulation strategies, conceptions of learning, and learning orientations as four important components 

of student learning. Based on when and how many times the students took the test, Mello (2016) 

analyzed students’ self-monitoring of their learning process. The study of Mello (2016) indicated that 

more students used tests as self-assessment rather than as a way of monitoring their learning process. 

The study of Mello (2016) also revealed that students of higher achievers used more self-directed 

learning than the low achievers.  

 



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Referring to the previous explanations, then the issue to be raised in this study was what variables of 

self-regulated learning that support students’ academic achievement. Thus, the aim of this study was to 

examine the correlations of four variables of self-regulated learning such as processing strategies, 

regulation strategies, study orientations and conceptions of learning. This study also examines the 

differences of four variables of self-regulated learning in terms of gender and the university. 

 

2. Method 

2.1 Sampling Technique and Sample Size  

A non-random sampling technique applied in this study. In total 578 first year students [(Female n=479; 

82.9%); Male n=99; 17.1%)] enrolled at Faculty of Psychology, in three universities in three big cities 

in Indonesia, Medan North Sumatra (n=258; 44.6%), Jakarta the capital city (n=209; 36.2%) and 

Surabaya East Java (n=111; 19.2%) participated in this study. The participants’ age was from 17 to 23 

years, and the mean age was 18.99 years. The participation was voluntarily, and informed consent was 

signed. The Indonesian version of Vermunt’s Inventory of Learning Style (ILS) was administered in a 

group in a classroom.  

2.2 Research Instrument 

The Indonesian version of Vermunt’s Inventory of Learning Style (ILS) consisted of 130 items and 4 

subscales was used to measure four variables of learning, processing of learning (27 items), regulations 

of learning (28 items), orientation to study (35 items), and mental model of learning or conceptions of 

learning (40 items). Processing of learning subscale has three components, deep processing, stepwise 

processing, and concrete processing. Regulation of learning subscale has three components, 

self-regulated, externally regulated, and lack of regulation. Study orientation subscale has five 

components, certificate directed, vocation directed, self-test directed, personally interested, and 

ambivalent. Conception of learning subscale has five components, intake of knowledge, construction of 

knowledge, use of knowledge, stimulating education, and cooperation. A five-point Likert scale is used 

to rate the items. In the processing of learning and regulation of learning subscales, from “never do” to 

“always do”. In the study orientations and conceptions of learning subscales, from “mostly disagree”, 

to “mostly agree”. The range internal consistency or the Cronbach α of the subscales was 0.53-0.81, in 

which the lowest is the external regulation result and the highest is deep processing. Students’ GPA 

scores were used as the measure of academic achievement.  

2.3 Data Collection Procedures and Analysis 

Appropriate heads of department in each university provided permission for students’ participation, and 

each student voluntarily participated in the research. Students signed the informed consent. Completion 

of the ILS was anonymous, and confidentiality was assured. The ILS was administered to the students 

in groups in the classrooms. Prior to the administration of ILS, the aim of the research was informed to 

the students. Pearson Correlation and Multiple Regression are used to determine the correlation 

between each scale, and the contribution of each scale to academic achievement.  



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3. Result 

3.1 Correlations among Variables of Self-Regulated Learning 

Table 1 showed that deep processing has no significant correlations with lack of regulation, certificate 

directed, intake of knowledge, stimulating of education and cooperation. Deep processing showed 

significant correlation with self-regulation in learning (r=.60; p<.01). Deep processing has significant 

correlations with the other two components of cognitive processing, stepwise processing (r=.60; p<.01) 

and concrete processing (r=.59; p<01), and two components of regulation strategies, self-regulation 

(r=66; p<01) and external regulation (r=.42; p<.01). Deep processing has also significant correlations 

with three component of study orientation, vocational directed (r=.23; p<.01), self-test (r=.24; p<.01), 

personally interested (r=.28; p<.01). Deep processing has significant correlations with two components 

of conceptions of learning, construction of knowledge (r=.44; p<.01) and use of knowledge (r=.22; 

p<.01). Deep processing has significant negative correlation with ambivalent (r=-.17; p<.01).   

Self-regulation showed significant correlations with vocational directed (r=.28; p,.01), self-test (r=.31; 

p<.01), personally interested (r=.29; p<.01), and components of conceptions of learning, intake of 

knowledge (r=.15; p<.01), construction of knowledge (r=.53; p <.01), use of knowledge (r=.23; p<.01), 

stimulating education (r=.11; p<.01) and cooperation (r=.13; p<.01). Self-regulation showed negative 

correlation with ambivalent (r=.12; p<.01). 

 

Table 1. Correlations among Variables of Self-Regulated Learning 

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 

Deep  1                

Stepwise  .60** 1               

Concrete  .59** .45** 1              

Self-Reg .66** .64** .53** 1             

Ext-Reg  .42** .63** .44** .53** 1            

Lack-Reg -.05 -.03 .02 -.03 .03 1           

Certificate  -.01 .10* .02 .07 .28** -.19** 1          

Vocational .23** .29** .31** .28** .39** -.01 .42** 1         

Self-test  .24** .28** .24** .31** .38** 0.9* .43** .58** 1        

Personally  .28** .20** .25** .29** .15** .05 .22** .45** .36** 1       

Ambivalent -.17** -.05 -.17** -.12** -.05 .50** .23** -.09* .07 .01 1      

Intake  .02 .25** .14** .15** .43** .22** .46** .44** .44** .25** .24** 1     

Construction  .44** .35** .38** .53** .27** -.11** .10* .35** .43** .45** -.05 .28** 1    

Use   .22** .23** .42** .23** .31** .01 .19** .51** .41** .38** .01 .50** .47** 1   

Stimuli of  .03 .15** .06 .11** .27** .23** .28** .29** .28** .28** .25** .69** .21** .30** 1  

Cooperation .03 .09* .10** .13** .20** .16** .19** .17** .25** .08** .21** .41** .20** .22** .33** 1 

* p<.05. ** p<.01. 

 



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3.2 Four Variables of Self-Regulated Learning as Predictors of Academic Achievement 

Three components of cognitive processing strategies (deep processing r=.16, p<.01; stepwise 

processing (r=.24, p<.01; concrete processing (r=.17, p<.01), were significantly correlated to students’ 

academic achievement. Multiple regression analysis was used to test if the three components of 

cognitive processing strategies significantly predicted students’ academic achievement. The results of 

the regression indicated that three components of cognitive processing strategies explained 6.1% of 

students’ academic achievement (R
2
=.061, F3,574=12.38, p<.01). Of the three components of cognitive 

processing strategies, stepwise processing was significantly predicted academic achievement (β=.012, 

p<.01). 

The components of regulation strategies (self-regulation r=.14, p<.01; external regulation r=.19, p <.01; 

lack of regulation r=-.20, p<.01) were significantly correlated to students’ academic achievement. 

Multiple regression analysis was used to test if the three components of regulation strategies 

significantly predicted students’ academic achievement. The results of the regression indicated that 

three components of regulations strategies explained 8.2% of academic achievement (R
2
=.082, 

F3,574=16.99, p<.01). Of the three components of regulations strategies, external regulation was 

significantly predicted academic achievement (β=.018, p<.01), and lack of regulation was significantly 

negative in predicting academic achievement (β=-.207, p<.01), 

Not all components of study orientation have correlations with students’ academic achievement. Only 

certificate directed (r=-.069, p<.05) and ambivalent (r=-.169, p<.01) that were significantly correlated 

negatively to students’ academic achievement. Thus, multiple regression was processed only for 

certificate directed and ambivalent. The results of the regression indicated that the certificate directed 

and ambivalent explained 3% of academic achievement (R
2
=.030, F3,574=8.752, p<.01). Of the 

components of study orientation, ambivalent was negative predictor for academic achievement 

(β=-.162, p<.01), 

Three components of conceptions of learning were significantly correlated to academic achievement 

(use of knowledge processing (r=.10, p<.01; stimulating education (r=-.11, p<.01; cooperation (r=-.21, 

p<.01). Multiple regression analysis was used to test if the three components of conceptions of learning 

significantly predicted students’ academic achievement. The results of the regression indicated that 

three components of conceptions of learning explained 8.9% of academic achievement (R
2
=.089, 

F3,574=11.18, p<.01). Of the three components of conceptions of learning, use of knowledge was 

significantly predicted academic achievement (β=.18, p<.05). Meanwhile, stimulating education 

(β=-.100, p<.01), and cooperation (β=-.213, p<.01), were negatively in predicting academic 

achievement.  

 

 

 

 



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3.3 Gender Differences on Variables of Self-Regulated Learning 

The scores of self-regulated learning variables measured in this study were not normally distributed, 

thus the Mann-Whitney U test was used to analyze gender differences on variables of self-regulated 

learning. A Mann-Whitney test indicated that female was greater than male in stepwise processing 

(U=19686.5, p=.008, r=.11), self-regulation (U=18730.5, p=.001, r=.14), external regulation 

(U=16609.5, p=.00, r=.20), certificate directed (U=18550.5, p=.001, r=.14), vocational directed 

(U=19322.5, p=.004, r=.12), self-test (U=19161.0, p=003, r=.13), intake of knowledge (U=18008.5, 

p=.00, r=.16), use of knowledge (U=19722.5, p=.008, r=.11), and stimulating education (U=20318.5, 

p=.025, r=.09).  

3.4 University Differences on Variables of Self-Regulated Learning 

The result of this study indicated that there were statistical differences on the variables of self-regulated 

learning among three universities. Stepwise processing (χ
2
=15.44; p=.00) with a mean rank of 331.60 

for Jakarta, 314.29 for Surabaya, and 269.83 for Medan. Concrete processing (χ
2
=7.72; p.021) with a 

mean rank of 332.44 for Surabaya, 308.28 for Jakarta, and 280.26 for Medan. External regulation 

(χ
2
=18.66; p=.00) with a mean rank of 337.71 for Jakarta, 307.47 for Surabaya, and 268.08 for Medan. 

Certificate directed (χ
2
=10.97; p=.004) with a mean rank of 321.65 for Jakarta, 303.36 for Medan, and 

260.37 for Surabaya. Vocational directed (χ
2
=10.28; p=.006) with a mean rank of 314.55 for Medan, 

309.06 for Jakarta and 256.35 for Surabaya. Self-test (χ
2
=11.62; p=.003) with a mean rank of 326.92 

for Medan, 288.95 for Jakarta, and 266.83 for Surabaya. Ambivalent (χ
2
=13.12; p=.001) with a mean 

rank of 317.95 for Jakarta, 308.70 for Medan, and 255.41 for Surabaya. Intake of knowledge (χ
2
=6.88; 

p=.032) with a mean rank of 320.90 for Jakarta, 294.88 for Medan, and 279.64 for Surabaya. 

Construction of knowledge (χ
2
=9.84; p=.00) with a mean rank of 324.32 for Medan, 292.06 for Jakarta, 

and 266.98 for Surabaya.  

 

4. Conclusion and Discussion 

The results of this study revealed that the interplay of four variables of self-regulated learning, namely 

cognitive processing strategies, regulations strategies, study orientations and conceptions of learning 

significantly correlated. The study also indicated that for cognitive processing strategies, students of the 

three universities applied more stepwise processing rather than deep processing and concrete 

processing. Students at the university in Jakarta used more stepwise processing than the other two 

universities. For the components of regulations strategies, the study indicated that external regulation 

was significant predictors of students’ academic achievement, while lack of regulation was a negative 

predictor of academic achievement. Previous cross-cultural studies on the patterns or styles of learning 

reported that the perceptions of course requirements influenced students’ approach to learning and that 

the learning patterns are related to the local cultures. Learning patterns of Asian students different from 

the Western students (Volet & Renshaw, 1996; Wong, 2004; Marambe, Vermunt & Boshuizen, 2012), 

and that also the case of Indonesian students who showed as a “passive recipient in their cognitive 



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strategies and regulations strategies of learning” (Ajisuksmo, 1996; Ajisuksmo & Vermunt, 1999). The 

learning patterns of the Indonesian are also related to the assessment systems that require students to 

remember and to reproduce their learning. Analytical and critical thinking as a deep approach in 

learning are rarely emphasized in learning, though students’ centered learning is implemented. A 

further study on what factors influenced the cognitive strategies, regulations strategies, study 

orientation and conceptions of learning in higher education should be carried out, as well as method of 

delivering knowledge and skills which is appropriate in fostering deep and active processing of 

learning and self- regulations of learning. 

 

References 

Ajisuksmo, C. R. P. (1996). Self-regulated learning in Indonesian higher education (Doctoral thesis). 

Tilburg University, The Netherlands & Atma Jaya Catholic University, Indonesia. 

Ajisuksmo, C. R. P., & Vermunt, J. D. (1999). Learning styles and self-regulation of learning at 

university: An Indonesian study. Asia Pacific Journal of Education, 19(2), 45-59. 

https://doi.org/10.1080/0218879990190205 

Ali, M. A., & Masih, A. (2021). (Literature Review) Enhancing the quality of learning through changes 

in students’ approach to learning. International Journal of Asian Education, 2(3), 455-461. 

https://doi.org/10.46966/ijae.v2i3.242 

Alzubaidi, E., Aldridgw, J. M., & Khine, M. S. (2016). Learning English as a second language at the 

university level in Jordan: Motivation, self-regulation and learning environment perceptions. 

Learning Environment Research, 19, 133-152. https://doi.org/10.1007/s10984-014-9169-7 

Baez-Estradas, M., & Alonso-Tapia, J. (2017). Training strategies for self-regulating motivation and 

volition: Effect on learning. anales de psicología, 33(2), 292-300. 

https://doi.org/10.6018/analesps.33.2.229771 

de Acedo Lizarraga, M. L. S., Ugarte, M. D., Iriarte, M. D., & de Acedo Baquedano, M. T. S. (2003). 

Immediate and long-term effects of cognitive intervention on intelligence, self-regulation, and 

academic achievement. European Journal of Psychology of Education, 18, 1, 59-74. 

https://doi.org/10.1007/BF03173604 

De Corte. (2016). Improving higher education students’ learning proficiency by fostering their 

self-regulation skills. European Review, 24(2), 264-276. 

https://doi.org/10.1017/S1062798715000617 

Endedijk, M. D., Brekelmans, M., Sleegers, P., & Vermunt, J. D. (2016). Measuring students’ 

self-regulated learning in professional education: Bridging the gap between event and aptitude 

measurements. Quality and Quantity, 50, 2141-2164. https://doi.org/10.1007/s11135-015-0255-4 

 

 

 

https://doi.org/10.1080/0218879990190205
https://doi.org/10.46966/ijae.v2i3.242
https://doi.org/10.1007/s10984-014-9169-7
https://doi.org/10.6018/analesps.33.2.229771
https://doi.org/10.1007/BF03173604
https://doi.org/10.1017/S1062798715000617
https://doi.org/10.1007/s11135-015-0255-4


www.scholink.org/ojs/index.php/fce           Frontiers of Contemporary Education             Vol. 4, No. 4, 2023 

20 
Published by SCHOLINK INC. 

Ghufron, A., & Hardiyanto, D. (2017). The quality of learning in the perspective of learning as a 

system. Advances in Social Science, Education and Humanities Research (ASSEHR), 66. 1st 

Yogyakarta International Conference on Educational Management/Administration and Pedagogy 

(YICEMAP 2017). https://doi.org/10.2991/yicemap-17.2017.43 

Huang, C. (2022). Self-regulation of learning and EFL Learners’ hope and joy: A review of literature. 

Frontiers in Psychology, 13, 833279. https://doi.org/10.3389/fpsyg.2022.833279 

İlçin, N., Tomruk, M., Yeşilyaprak, S. S. et al. (2018). The relationship between learning styles and 

academic performance in TURKISH physiotherapy students. BMC Med Educ., 18, 291. 

https://doi.org/10.1186/s12909-018-1400-2 

Khodarahmi, E., & Zarrinabadi, N. (2016). Self-regulation and academic optimism in a sample of Iranian 

language learners: Variations across achievement group and gender. Current Psychology, 35, 

700-710. https://doi.org/10.1007/s12144-015-9340-z  

Labuhns, A. S., Zimmerman, B. J., & Hasselhorn, M. (2010). Enhancing students’ self-regulation and 

mathematics performance: the influence of feedback and self-evaluative standards. Metacognition 

Learning, 5, 173-194. https://doi.org/10.1007/s11409-010-9056-2 

Li, M., Zheng, C., Liang, J. C., Zhang, Y., & Tsai, C. C. (2016). Conceptions, self-regulation, and 

strategies of learning science among Chinese high school students. International Journal of 

Science and Mathematics Education, 1, 69-87. https://doi.org/10.1007/s10763-016-9766-2 

Marambe, K. N., Vermunt, J. D., & Boshuizen, H. P. A. (2012). A cross-cultural comparison of student 

learning patterns in higher education. Higher Education, 64, 299-316. 

https://doi.org/10.1007/s10734-011-9494-z 

Mello, L. V. (2016). Fostering postgraduate student engagement: Online resources supporting 

self-directed learning in a diverse cohort. Research in Learning Technology, 24, 1-17. 

https://doi.org/10.3402/rlt.v24.29366 

Perels, F., Dignath, C., & Schmitz, B. (2009). Is it possible to improve mathematical achievement by 

means of self-regulation strategies? Evaluation of an intervention in regular math classes. 

European Journal of Psychology of Education, XXIV(1), 17-31. 

https://doi.org/10.1007/BF03173472 

Schapiro, S. R., & Livingston, J. A. (2000). Dynamic self-regulation: The driving force behind 

academic achievement. Innovative Higher Education, 25(1), 23-35. 

https://doi.org/10.1023/A:1007532302043 

Schraw, G., Crippen, K. J., & Hartley, K. (2006). Promoting self-regulation in science education: 

Metacognition as part of a broader perspective on learning. Research in Science Education, 36, 

111-139. https://doi.org/10.1007/s11165-005-3917-8 

Siddaiah-Subramanya, M., Nyandowe, M., & Zubair, O. (2017). Self-regulated learning: Why is it 

important compared to traditional learning in medical education? Advances in Medical Education 

and Practice, 8, 243-246. https://doi.org/10.2147/AMEP.S131780 

https://doi.org/10.2991/yicemap-17.2017.43
https://doi.org/10.3389/fpsyg.2022.833279
https://doi.org/10.1186/s12909-018-1400-2
https://doi.org/10.1007/s12144-015-9340-z
https://doi.org/10.1007/s11409-010-9056-2
https://doi.org/10.1007/s10763-016-9766-2
https://doi.org/10.1007/s10734-011-9494-z
https://doi.org/10.3402/rlt.v24.29366
https://doi.org/10.1007/BF03173472
https://doi.org/10.1023/A:1007532302043
https://doi.org/10.1007/s11165-005-3917-8
https://www.ncbi.nlm.nih.gov/pubmed/?term=Siddaiah-Subramanya%20M%5BAuthor%5D&cauthor=true&cauthor_uid=28360542
https://doi.org/10.2147/AMEP.S131780


www.scholink.org/ojs/index.php/fce           Frontiers of Contemporary Education             Vol. 4, No. 4, 2023 

21 
Published by SCHOLINK INC. 

Valle, A., Núñez, J. C., Cabanach, R. G., González-Pienda, J. A., Rodríguez, S., Rosário, P., 

Muñoz-Cadavid, M. A., & Cerezo, R. (2009). Academic goals and learning quality in higher 

education students. The Spanish Journal of Psychology, 12(1), 96-105. 

https://doi.org/10.1017/S1138741600001517 

Vermunt, J. D. (2005). Relations between student learning and personal and contextual factors and 

academic performance. Higher Education, 49, 205-234. 

https://doi.org/10.1007/s10734-004-6664-2 

Vermunt, J. D., & Vermetten, Y. J. (2004). Patterns in student learning: Relationships between learning 

strategies, conceptions of learning, and learning orientations. Educational Psychology Review, 

16(4). https://doi.org/10.1007/s10648-004-0005-y  

Wijaya, K. F., & Setiawan, N. A. (2021). Graduate students’ motivation regulation strategies in facing 

academic writing amid covid-19 pandemic. LLT Journal: A Journal on Language and Language 

Teaching, 24(2), 597-613. https://doi.org/10.24071/llt.v24i2.3142 

Wong, J. K. K. (2004). Are the learning styles of Asian international students culturally or contextually 

based? International Education Journal, 4(4).  

 

  

https://doi.org/10.1017/S1138741600001517
https://doi.org/10.1007/s10734-004-6664-2
https://doi.org/10.1007/s10648-004-0005-y
https://doi.org/10.24071/llt.v24i2.3142

