Development and Initial Validation of the Self-Directed Learning ...

557728 research-article2014

JPAXXX10.1177/0734282914557728Journal of Psychoeducational AssessmentSuh et al.

Article

Development and Initial Validation of the Self-Directed Learning Inventory With Korean College Students

Journal of Psychoeducational Assessment 1–11 © 2014 SAGE Publications Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0734282914557728 jpa.sagepub.com

Han Na Suh1, Kenneth T. Wang2, and Brooke J. Arterberry1

Abstract This article describes the development and psychometric evaluation of the Self-Directed Learning Inventory (SDLI) tailored to Korean college students, based on study evidences of differences in learning behavior across culture and educational level. With a sample of 605 female college students in Korea, exploratory factor analysis (EFA) results suggested a 28item, eight-factor solution: (a) Learning Needs, (b) Utilizing Skills, (c) Enduring Challenges, (d) Self-efficacy in Learning, (e) Planning Skills, (f) Evaluating Skills, (g) Completing Tasks, and (h) Internal Attribution; confirmatory factor analysis cross-validated the EFA solutions. The SDLI demonstrated adequate internal consistency, as well as evidences that support construct validity. Keywords self-directed learning, Korean college students, scale development and validation

Self-directed learning (SDL) can be defined as a learning process in which individuals take the initiative, with or without the help of others, in diagnosing their learning needs, formulating learning goals, identifying human and material resources for learning, choosing and implementing appropriate learning strategies, and evaluating learning outcomes (Knowles, 1975). The concept has become increasingly critical today, as the skills to independently process and absorb information are even more essential. For instance, today’s learners are exposed to a vast amount of information, and there is a stronger need for each individual to self-learn, independently of teachers or classroom setting, compared with the past. In addition, some universities have started to gradually increase the web-based education, alternating traditional courses. Researchers (Hartley & Bendixen, 2001) have argued that SDL is one of the critical characteristics a learner should possess for better adjustment and success in the online learning setting. Although SDL is important within any learning environment, it has been especially pertinent in web-based setting, as it increases the learner’s control of the instruction (Garrison, 2003). Furthermore, SDL can be

1University 2Fuller

of Missouri, Columbia, MO, USA Theological Seminary, Pasadena, CA, USA

Corresponding Author: Han Na Suh, Department of Educational, School, and Counseling Psychology, University of Missouri, 16 Hill Hall, Columbia, MO 65211, USA. Email: [email protected]

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used as an indicator of success for both the learner and instructor in guiding students’ educational path (e.g., online vs. face-to-face). The promoted movement of lifelong learning, which was initiated from the early 1970’s progressive educators, also directed the focus on SDL. Some researchers (e.g., Bolhuis, 2003) contend that SDL is tied to the modern society’s demand on “World Initiative on Lifelong Learning,” a shared initiative of national and multinational business, educationalists and international organizations such as United Nations Educational, Scientific and Cultural Organization (UNESCO) and the Organization for Economic Co-Operation and Development (OECD; Longworth & Davies, 1996). Lifelong learning is thought to be propagated for a variety of reasons, which in sum is to promote individuals’ continuing educational participation throughout life, so that a broad range of people with knowledge productivity can contribute as the economic motor (Bolhuis, 2003). And SDL is the tool to facilitate such lifelong learning. Therefore, SDL has become a critical component in the learning realm to obtain knowledge and skills, and facilitate environments for learning to enhance individual and organizational performance (Ellinger, Ellinger, Yang, & Howton, 2002). Along with the change in social climate and increased importance of adults’ learning, the globalization movement that promotes the standardization of educational methods and practices across cultures has brought attention to SDL in Korea (e.g., Hodges, Maniate, Martimianakis, Alsuwaidan, & Segouin, 2009). In general, educational methods reflect cultural and ideological values, and SDL has been criticized that it relies heavily on Western cultural values, such as democracy, individualism, and egalitarianism (Wong, 2011). More specifically, researchers (e.g., Gwee, 2008; Khoo, 2003) have indicated that student-centered education (i.e., expectation that students speak up in class, ask questions, and challenge opinions) has caused tensions due to its inconsistent format with other learning approaches (e.g., teacher-centered, lecture-based educational approaches) in non-Western settings. Also, Frambach, Driessen, Chan, and Vleuten (2012) concluded that although SDL can be applied in different cultural contexts, globalizing the concept should not be postulated as uniform processes and outcomes; and instead, culturally sensitive alternatives should be developed. Although SDL has been criticized for being seemingly incompatible with Asian cultural attitudes, strong self-discipline and collaborative learning among students actually align with this framework (Khoo, 2003) and imply the need to incorporate SDL in the Korean learning culture. However, existing Korean SDL scales were developed mainly for elementary to middle school students (e.g., Jung, Lim, Jung, Kim, & Yoon, 2012). Many stage theories, including those focusing on cognitive mechanism as Piaget (1983), moral maturity (Kohlberg, 1976), and Erik Erikson’s (1968) psycho-social mechanism, stress differences at each stage. The learning process which involves cognitive, moral, and psycho-social components would also differ across developmental stages. Therefore, the scales developed from elementary- to junior-level students may not be simply generalized to college-level students. Furthermore, Stockdale and Brockett (2011) suggested that given the difficulties of making a general instrument relevant to diverse sociocultural contexts, an important direction for future research in SDL is the need to develop instruments designed for specific settings. Thus, developing a SDL measure tailored to Korean college students seems imperative.

Dimensions of SDL and Association With Other Concepts SDL’s broadest definition can be described as people taking the initiative, with or without the help of others, to diagnose learning needs, formulate goals, decide and implement appropriate learning strategies, and assess the outcomes, which are all based on a sense of self-confidence in learning (Williams, 2004). Various forms of SDL have been identified across multiple studies and populations, such as learning needs, utilizing skills, enduring challenges, self-efficacy in learning, planning the process, evaluating the process, completing tasks, and internal attribution

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(Guglielmino, 1978; Knowles, 1975). For example, in one study that targeted Korean students’ enhancement in SDL, Hyun (1999) found SDL to consist of openness to learning, self-confidence in learning, internal attribution, autonomy in learning, creativity, utilizing the knowledge and skill, and evaluating the learning process. Other concepts such as optimal experience (i.e., flow experience: complete absorption in present activity resulting in high interest and high comprehension; Schiefele & Csikszentmihalyi, 1995) and self-regulation (i.e., individual choice to act in a situation; Deci & Ryan, 1985) have also been suggested to associate with SDL. Past studies have shown that optimal experience in learning directly relates to SDL and has been associated with academic success (Lee, 2010; Yoo, Choi, & Choi, 2010). Research has also indicated a direct relationship between self-control and optimal experience (Csikszentmihalyi, 1990; Schiefele & Csikszentmihalyi, 1995), where studies have suggested that self-regulation in academic settings strengthens an individual’s control of the learning process (Han, 2004). Moreover, self-regulation has been found to mediate the relationship between optimal experience and SDL (Lee, 2010; Yoo et al., 2010). Considering the current notion of validation studies, which is geared toward the approach to justify the usage of test for a particular purpose (Kane, 2013), the SDL should show higher degree of contribution on optimal learning experience above and beyond self-regulation. Although past studies (Park, Lee, & Hong, 2005; Ryan, 2004) provided evidences of the self-regulation measurement (i.e., Academic Self-Regulation Questionnaire [SRQ-A]) to have relation to academic achievements, it only focuses on how to regulate oneself in the situation. As SDL comprises factors more than mere regulation of the self that are critical in learning experiences (e.g., self-efficacy, evaluation), which the scale should show better prediction on the learning experiences of the students than self-regulation for its justified usage. Considering the importance of SDL and the development of culturally relevant assessments as it relates to academic success, the purpose of this study was twofold: (a) develop a SDL scale tailored to Korean college students and (b) provide evidence in support of the construct validity of the scale. We first examined the Self-Directed Learning Inventory (SDLI) factor structure and reliability. Including confirmatory factor analysis (CFA), we examined evidences that support the validity of SDLI by examining convergence of SDLI with other learning-related constructs, and also the difference of SDL from self-regulation, supporting the usage of SDLI to help students’ optimal learning experiences.

Method Participants Participants included 605 female students from a 4-year public university located in an urban setting of Korea. Participants were randomly divided into two samples, one for exploratory factor analysis (EFA; n = 285) and the other for CFA (n = 320). The first sample’s mean age was 20.07 (SD = 1.48) years, where 97 (34.1%) were freshmen, 85 (29.9%) sophomores, 91 (32.0%) juniors, and 12 (4.2%) seniors. In the second sample, mean age was 20.27 (SD = 1.69) years, with 102 (31.9%) freshmen, 100 (31.3%) sophomores, 89 (27.8%) were juniors, and 29 (9.1%) seniors.

Item Development The items were generated through a review of the theoretical and empirical literature, in-depth review with experts in the fields of psychology and psychometrics, and a pilot study with 336 university students. A team of three professionals in education identified eight categories and generated 108 items for the initial item pool. The initial SDLI was then piloted with a group of university students for refinement. The items were rated on a 5-point Likert-type scale (1 =

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strongly disagree, 2 = disagree, 3 = undecided, 4 = agree, 5 = strongly agree). One of the major criteria for refinement prior to empirical analysis in the review process with experts included removal of cultural specific items (e.g., If I have something that I want to learn, I make a group to study together) that were deemed assertive and more characteristic of Western orientation. After a thorough content review by the experts and item-level analysis (e.g., mean, standard deviation, item-total correlation), a total of 45 items were retained for the final item pool.

Other Measures Optimal Experience in Learning Scale (OELS). The OELS (Seok, 2008) was developed to measure optimal experience in learning and consists of 35 items using a 5-point Likert-type scale. The OELS was normed on university students, with adequate internal consistency (α = .87) and construct validity was supported by positive association with academic achievement (Seok, 2008). In the current study, Cronbach’s alpha was .93. Self-Regulation Questionnaire-Academic. The Korean version of the SRQ-A (Park et al., 2005; Ryan, 2004) was used to examine self-regulation; the questionnaire consists of 12 items rated on a 5-point Likert-type scale. The Cronbach’s alpha of SRQ-A scores was .84 (Park et al., 2005). The scale’s construct validity was supported by its positive association with academic achievement (Park et al., 2005). In the current study, the Cronbach’s alpha was .83.

Procedure Participants were recruited by asking faculty members at a 4-year public women’s university to administer the questionnaire. The purpose of the study was explained and consent was obtained from the participants. The study was administered in the classroom setting using a paper–pencil format questionnaire that was presented in Korean. It took approximately 15 min to complete the study.

Data Screening The data were first screened at the item level. Following guidelines from Roth and Switzer (1999), 10 cases that had more than 5% of missing values were removed. As the remaining cases had less than two missing items, and the standard error of individual items were not vital (ranged from .03 to .04), expectation–maximization (EM) method with single imputation was applied for missing value replacement.

Statistical Methods EFA was performed with the first sample to determine the SDLI factors and select items for each factor. Specifically, a minimum average partial (MAP; Velicer, 1976) test was performed twice to determine the number of factors. After the initial MAP test, items that (a) had cross-loading(s) over .32 (Costello & Osborne, 2005), and (b) loaded on the factor that contained conceptually different items were eliminated. After an additional MAP test, principal-axis factor analysis with promax rotation was performed to confirm the factor solution. In addition, it was determined by consideration of (a) factor loadings above .40 (Stevens, 1992), (b) no cross-loading(s) above .32, and (c) meaningful membership of each item in each factor. Furthermore, CFA was conducted with AMOS 7.0 to cross-validate the factor structure. Several rival models were tested to confirm the most appropriate model. In addition, second-order factor structure was performed to examine whether the hypothesized factors can be determined to reflect one common construct of SDL

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(Chen, Sousa, & West, 2005). The multivariate normality was tested. Then Maximum Likelihood estimation was applied, which global fit (e.g., comparative fit index [CFI], root mean square error of approximation [RMSEA]) of .95 has been suggested as a cutoff (Hu & Bentler, 1999). The standardized regression weights in hypothesized paths between the item and factor were also examined to test for local fit, where the criterion is suggested to be over .45 (Smith & McMillan, 2001). To examine the convergence of SDLI with other variables through correlation analysis of SDLI with other study variables that were considered conceptually similar. Finally, the contribution of SDLI on optimal learning experience was examined through hierarchical regression.

Results Evidence Supporting Construct Validity EFA and reliability analysis. We first conducted an EFA for item selection with sample 1 (n = 285). The Kaiser–Meyer–Olkin measure of sampling adequacy for the initial EFA (.87) and Bartlett’s test of sphericity, χ2(990) = 5983.87, p < .001, indicated that the data were appropriate for factor analysis. MAP test on the initial item pool suggested a possible 11-factor solution. After running an initial EFA, several items were eliminated so that each factor could be defined as interrelated but distinct variables (Yong & Pearce, 2013). After eliminating items based on the factor loading and cross-loading criteria above, the SDLI resulted in a total of 28 items. An additional MAP test suggested an eight-factor solution; thus, to confirm the factor, a principal-axis factor analysis using a promax rotation was conducted on 28 items with seven- to nine-factor solutions. The seven-factor solution accounted for less than 50% of the total variance whereas a nine-factor solution resulted with several cross-loading items. The eight-factor solution accounted for 66.0% of the total variance with eigenvalues ranging from 1.27 to 7.02. All the items had a factor loading above .40 on one factor with no cross-loadings over .30 (see Table 1). Thus, the most theoretically relevant and parsimonious solution included eight factors: (a) learning needs, (b) utilizing skills, (c) enduring challenges, (d) self-efficacy in learning, (e) planning skills, (f) completing tasks, (g) evaluation skills, and (h) internal attribution. Item analysis (mean and standard deviation of each items) and item-total correlation analysis were conducted to determine item performance. The item means ranged from 2.90 to 4.14 and standard deviations ranged from .67 to 1.04 indicating little concern for floor or ceiling effects with adequate variation in the responses. Internal consistency estimates ranged from .62 to .82 with seven of the eight subscales exceeding .70. Thus, subsequent analyses were conducted using the 28-item, eight-factor solution. CFA. Items were constrained to load onto their corresponding factors, while allowing the eight factors to correlate with each other in the CFA model. The eight-factor structure of the model was suggested as most appropriate, as the rival models (i.e., one-, seven-, and nine-factor structures) showed comparably poor results (see Table 2). All the univariate skewnesses and kurtoses were below the threshold (e.g., skewness = 2, kurtosis = 8; Institute of Transportation Studies, 2008). The standardized factor loadings suggested good local fit, ranging from .48 to .82 for Learning Needs, .64 to .81 for Utilizing Skills, .60 to .68 for Enduring Challenges, .70 to .82 for Selfefficacy in Learning, .53 to .82 for Planning Process, .57 to .92 for Completing Tasks, .65 to .76 for Evaluating Process, and .48 to .74 for Internal Attribution. The fit statistics for the measurement model supported a modestly good fit to the data, χ2(322) = 568.26, p < .001; Tucker–Lewis index (TLI) = .91; CFI = .92; standardized root mean square residual (SRMR) = .06; RMSEA = .05. The second-order model (i.e., all eight factors loading onto an overall SDLI factor) was also examined to determine the utility of a unidimensional scale and indicated a moderate fit, χ2(342) = 660.07, p < .001; TLI = .89; CFI = .90; SRMR = .07; RMSEA = .05.

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Table 1. Exploratory Factor Analysis Result. Factor loadings Items

F1

F2

F3

F4

F5

F6

2. I have an inquiring mind for knowledge. .77 .02 .22 .21 −.03 .04 4. I have a great desire for learning things. .73 .24 .12 .01 .09 .11 3. I like to learn new things. .73 .05 .11 .17 −.11 −.09 1. I always try to learn things. .66 .15 .11 −.08 .25 .21 5. I am aware of my urge to learn. .63 .18 .02 .11 .18 .18 16. I can understand even the most difficult .14 .79 .09 .13 .04 .06 things that are being discussed in the class. 15. If I were to answer the questions right, I .16 .76 .12 .10 −.02 .11 am sure that I can get them right. 17. I am good at tests. .13 .72 .08 .15 .14 .10 18. I am able to learn knowledge and skills .13 .64 .23 .24 .17 .06 perfectly. 20. I try to learn things no matter how busy .17 .04 .82 .10 .14 .02 I am. 19. Even for the challenging subject, I figure .12 .19 .73 .08 .21 .02 out some way to learn it eventually. 22. I can stay up all night to finish learning .00 .14 .68 .05 −.05 .28 what I am interested in. 21. Though there is the possibility of failure, I .25 .11 .54 .03 .06 .06 try to solve the difficult questions. 13. I am satisfied with my performance in .12 .10 .14 .86 .10 .06 utilizing resources to learn things. 12. I am satisfied with my performance in .09 .21 .05 .81 .01 .13 reading and comprehending things. 14. I am satisfied with my performance in .16 .21 .06 .74 .20 .18 answering the questions. 11. It is not difficult for me to make study .10 .11 .17 .14 .81 .10 plans. 10. I make study plans before I start studying. .07 .02 .11 .04 .79 .19 9. If needed, I fix the due dates and time for .05 .12 .04 .12 .69 .14 my studies or assignments. 8. I always finish the assignments. .03 .08 .05 .14 .16 .84 6. I always turn in my assignments on time. .12 .04 .05 .19 .13 .76 7. I always complete the tasks I started. .19 .19 .24 .03 .15 .71 23. It is important to evaluate my .08 .06 −.03 .01 .12 .05 performance in learning. 24. It is interesting for me to evaluate my .01 .10 .21 .16 −.01 .04 performance in learning. 25. It is important to evaluate the .21 .16 .09 −.10 .16 −.02 effectiveness of the study plans. 26. If my performance turns out good, I think −.03 .00 .22 −.03 .07 .20 it is the result of my efforts in it. 27. The reason of getting good results is .17 .13 .04 .15 .25 .08 because I managed well with the process. 28. I consider that the bad results of my .13 .15 −.02 .05 −.14 −.12 performance are due to the lack of efforts. Cronbach’s .81 .82 .73 .79 .74 .74 % of variance 25.08 7.58 7.13 6.35 5.51 5.07

F7

F8

h2

M

SD

.12 .01 .09 .01 .07 .15 .20 .05 −.11 .12 .11 −.05

.67 .78 .66 .73 .79 .69

4.14 4.12 3.61 3.16 3.09 3.42

.84 .80 .87 .99 .86 .69

−.01

.12

.76 3.51

.88

.21 .08

.12 .23

.75 3.04 .60 3.53

.92 .83

−.01 −.01

.51 3.92

.83

−.03

.18

.59 3.77

.70

.17

.07

.72 4.14

.69

.19

.04

.66 3.28

.87

.00

.05

.69 2.90

.82

.10

.07

.66 3.28

.78

−.03

.05

.63 3.20

.74

.15 −.05

.67 3.20

.81

.16 −.04 −.05 .31

.73 3.05 .90 .60 3.17 1.04

.12 .01 −.05 .17 .00 −.05 .85 .12

.42 .71 .75 .62

3.13 3.91 3.67 3.93

.82 .89 .90 .67

.82

.05

.53 3.76

.71

.69

.13

.63 3.47

.80

.11

.78

.70 3.28

.90

.09

.67

.62 3.74

.77

.12

.64

.63 3.31

.84

.76 .62 4.76 4.53

Note. Final 28 Self-Directed Learning Inventory items (n = 285). F1 = learning needs; F2 = utilizing skills; F3 = enduring challenges; F4 = self-efficacy in learning; F5 = planning the process; F6 = evaluating the process; F7 = completing tasks; F8 = internal attribution.

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Suh et al. Table 2. Model Fit of the Rival Models.

One-factor structure Seven-factor structure Eight-factor structure Nine-factor structure

Chi-square (df)

CFI

TLI

RMSEA

SRMR

1,715.407 (350)*** 742.369 (329)*** 568.257 (322)*** 554.597 (314)***

.552 .864 .919 .921

.516 .844 .905 .905

.111 .063 .049 .049

.099 .066 .060 .060

Note. CFI = Comparative Fit Index; TLI = Tucker–Lewis Index; RMSEA = Root Mean Square Error of Approximation; SRMR = Standardized Root Mean Square Residual. ***p < .001.

Table 3. Intercorrelations Between Study Variables and Descriptive Statistics (n = 320). F1 F1 F2 F3 F4 F5 F6 F7 F8 SDLI SRQ-A OELS

.46** .50** .28** .33** .40** .31** .17** .75** .62** .66**

F2

.44** .50** .30** .25** .26** .26** .72** .51** .65**

F3

F4

.37** .20** .28** .35** .18** .70** .49** .57**

.24** .13* .30** .13* .59** .34** .46**

F5

.28** .37** .23** .57** .32** .36**

F6

.19** .26** .55** .44** .38**

F7

.23** .58** .31** .45**

F8

.45** .32** .29**

SDLI

Range

.70** .79**

6-25 17.58 4-20 12.95 7-20 12.78 3-15 10.06 5-15 11.29 3-15 10.19 6-15 11.68 3-15 11.78 72-140 98.30 19-60 41.68 72-175 116.00

M

SD 3.17 2.67 2.64 2.18 2.03 2.15 2.03 1.80 11.76 6.14 16.64

Note. F1 = learning needs; F2 = utilizing skills; F3 = enduring challenges; F4 = self-efficacy in learning; F5 = planning the process; F6 = evaluating the process; F7 = completing tasks; F8 = internal attribution; SDLI = Self-Directed Learning Inventory; SRQ-A = Academic Self-Regulation Questionnaire; OELS = Optimal Experience in Learning Scale. *p < .05. **p < .01.

The intercorrelation of the subscales, along with mean and standard deviation of each subscale are provided in Table 3. Intercorrelations among subscales were significant and ranged from .13 (small) to .50 (strong). Learning Needs and Enduring Challenge as well as Utilizing Skills and Self-efficacy in Learning had the strongest intercorrelations. However, the Internal Attribution scores had the lowest intercorrelations with other SDLI subscale scores. Convergence with other variables. Convergent validity was examined through correlation analyses of SDLI subscales related to the OELS. As shown in Table 4, the SDLI subscale and total scores were significantly correlated with OELS scores, ranging from .29 (moderate) to .79 (strong). Contribution of SDLI on optimal learning experiences. To examine the contribution of SDLI being used for students’ optimal learning experience, OELS score was used as the dependent variable, SRQ-A score was entered in Step 1, and SDLI score was entered in Step 2 of the hierarchical regression. As shown in Table 3, the SDLI total score predicted 15% of the variance of the OELS score above and beyond SRQ-A score. The model had a variance inflation factor (VIF) of 1.97 and a Tolerance of .52 suggesting multicollinearity was not an issue; therefore, indicating the SDLI and SRQ-A measured different constructs.

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Table 4. Hierarchical Regressions for Incremental Validity in Predicting Optimal Experience in Learning Above and Beyond Academic Self-Regulation (n = 320).

Step 1 Constant SRQ-A Step 2 Constant SRQ-A SDLI

B

SE

31.93 2.02

4.28 0.10

−0.42 1.01 0.76

4.36 0.12 0.06

∆R2

∆F

dfs

.74***

.55***

394.11

1, 318

.37*** .53***

.15***

155.88

1, 317

Note. SRQ-A = Academic Self-Regulation Questionnaire; SDLI = Self-Directed Learning Inventory. *p < .05. **p < .01. ***p < .001.

Discussion The purpose of this study was to develop and validate a scale targeting Korean college students’ SDL. Both EFA and CFA results supported an eight-factor structure: Learning Needs, Utilizing Skills, Enduring Challenges, Self-Efficacy in Learning, Planning Skills, Evaluating Skills, Completing Tasks, and Internal Attribution. Also, the goodness of fit indices for both the eightfactor and second-order models indicated the subscale scores could be used as well as a total SDLI score. The eight subscales and total SDLI scores showed adequate internal consistency reliability coefficients. Finally, both correlation and regression analyses provided some evidence that could support construct validity of SDLI. Developing a culturally sensitive measure of SDL was a focus of this study. For example, SDL aspects such as assertive behaviors were removed as this was considered a Western ideal (Wong, 2011), whereas strong self-discipline was added to represent Asian values (Gwee, 2008; Khoo, 2003). The emphasis on self-discipline in learning was shown through the eight factors and their corresponding items (e.g., “I can stay up all night to finish learning what I am interested in” and “I make study plans before I start studying”). Thus, the SDLI has extended the theoretical framework of SDL through including an Asian perspective (Khoo, 2003). Construct validity of the SDLI was supported through the moderate to strong correlations among the SDLI total and subscale scores with academic self-regulation and optimal experience in learning. Among the eight SDLI factors, Internal Attribution had relatively moderate correlations with other SDLI factors, indicating a slight conceptual difference. This finding could mean that Internal Attribution measures a different aspect of SDL and is related to ruminating about the result of learning (e.g., “If my performance turns out good, I think it is the result of my efforts in it.”). Thus, including items related to Internal Attribution has expanded the theoretical framework of SDL and highlights the importance of tailoring the SDL construct to the Korean College student population. Furthermore, Learning Needs, Utilizing Skills, and Enduring Challenges had the strongest correlations with flow experience. Thus, interventions designed to focus on these specific factors may enhance students’ learning experience. In addition, the SDLI showed significant variance in predicting optimal experience above and beyond self-regulation, implying its usage in helping students’ better optimal learning experience. The SRQ-A was developed to focus on self-regulation (e.g., “I do my homework because it’s fun.”), whereas the SDLI included more facets, such as awareness of one’s learning and confidence. Previous research has suggested self-regulation as a major factor leading to academic success (Han, 2004); however, the current study indicated that a more comprehensive focus may provide a better understanding of academic success. In essence, enhancing students’

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various skills related to the eight SDLI factors would allow for more optimal experience in learning, which in turn would lead to better academic performance. Although the results of this study provided initial support for the SDLI, several limitations exist. First, the study relied on self-report data. Second, it is unclear whether SDL actually translates to performance. Using additional indicators, such as grade point average (GPA), in future studies would provide further validation of the scale. Third, the majority of participants were women living in a certain region of Korea, thus limits the generalizability of our results. Future studies could further examine the SDLI in other geographical areas and settings that include male students. Also, to generalize and utilize the scale internationally, future research could examine students from other countries. In conclusion, this study was a response to Stockdale and Brockett’s (2011) call that an important direction for research in SDL is to develop instruments designed for specific populations and settings. The SDLI was developed and initial evidence of construct validity was provided with Korean college students to measure students’ learning style and help educators provide an adequate learning environment. This promising measure of SDL could also provide educators with a better understanding of the learning process and ways to help students with difficulties in learning. Utilizing assessment tools with a culturally relevant design, researchers and educators can develop targeted interventions that would facilitate students’ independent learning process and lifelong learning. Authors’ Note The content is based on the authors’ perspectives and does not represent official views of Seoul Women’s University or any other funding institutions.

Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the co-funding on Seoul Women’s University, Admissions Office system from Korean Ministry of Education and Korean Council for University Education.

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