Abstract
Problem statement: Selfdirected learning is a factor essential for learners. But, factors both differ and overlap. The structures of various factors are not consistent. Research Questions: The model of selfdirected learning characteristics of technical college students was developed valid or not? Purpose of the Study: This research aims to develop a model of selfdirected learning characteristics of technical college students by factor analysis and multidimensional item response analysis, and to validate the selfdirected learning model. Research Methods: Samples were two groups. The first sample was used to analysis the model factors (230 students) and the second sample, used to validate the model (1,563 students). The instruments were 44 items (four choices). Data were analyzed through the methods of means, standard deviation, confirmatory factor analysis, and multidimensional item response analysis. Findings: First, results of analyzing item of the model are as follows. Factor analysis: items were suitable for the selfdirected learning model (93.18%). Multidimensional item response analysis: all questions were suitable for the model (100.00%). The selected model had eight factors (32 items). Second, the model had validity. Conclusions: The model developed from the Guglielmino’s theory and the selfdirected learning model was consistent with empirical data. The standard of the selfdirected learning model was interpreted on four levels: awareness, interest, attempts at learning, and actual practice.
Keywords: Selfdirected learningvalidation
Introduction
At present, advanced sciences and technologies, and the easy access to information, are inevitably
affecting how people live their lives and make decisions, causing social, economic and cultural
changes. It is, therefore, important to stay abreast of information. One of the key factors of human
development is education. The National Education Plan, Revised Edition (20092016) mentioned
educational development with the following qualitative aims: 1) making Thai people moral, efficient,
happy, knowledgeable, professional, ethical, eager to learn and for knowledge; and 2) turning Thai society into a society of integrity, wisdom and education by creating knowledge, innovation and
technologies leading to a sustainable learning society (Office of the Education Council, 2010).
Education is key to human resource development and the foundation of the country’s growth.
However, the results of Thailand’s education reform did not turn out as expected, as seen in a report
by the International Institute of Management Development (IMD) that ranks competitiveness of
different countries on the global scale. Thailand’s education in 2006 was ranked no. 46 out of 55
countries while, in 2008, it was ranked no. 43 out of 55 countries (Office of the Higher Education
Commission, 2008). Results from the evaluation of education quality of 17,562 schools nationwide
(49.1% of all schools), six years after the education reform and enactment of the National Education
Act, B.E. 2542, showed that only 26.5% of the learners had selfdirected learning skills, loved learning
and continuous personal development and were of quality (Ratmanee, 2011). These results showed that
education quality had not improved as anticipated and learners were not actively seeking knowledge
and developing themselves.
Education has to adjust to changes in the scientific, technological, social, economic and cultural
domains. Selfdirected learning is a characteristic essential for learners in the modern world where
everything changes so rapidly. This is consistent with the National Education Act, Section
Objectives and Principles, Article 7), stating that the learning process must encourage learners to
continuously rely on themselves, be creative, eager for knowledge and learn independently (Office of
the Education Council, 2002). This is also consistent with the concept of Isarawat (1999) who said that
selfdirected learning could be created and developed. Whenever a learner learns by themselves, and
comes to love searching for knowledge, that learner will keep learning until he/she becomes a lifelong
learner. This leads to the social practice of learning (Hung et al, 2009), which is the goal of modern
academics. Selfdirected learning is a trait important for education and in everyday life.
Theorists disagree on the number of key traits involved in selfdirected learning. According to
Knowles (1975) there are nine traits, according to Guglielmino (1977) there are eight traits, Skager
(1978) claims there are seven traits, Isarawat (2000) suggests there are only three, and the Educational
Research Division (2000) suggests eight traits. Accordingly, these traits both differ and overlap. The
structures of the various factors are not consistent. Translation criteria are also not always clear or
suitable for the Thai context. This is similar to the theory of Sapyaprapa (1997), who states that a good
measuring model has to be based on culture, values and characteristics of each particular country, with
validity measurement, population and standard criteria created for people in each country.
Considering the structure of factors, there are many concepts of selfdirected learning. They are not
consistent. Scores are not interpreted based on standard criteria suitable for Thai society. Previous
studies inside and outside Thailand have focused on factors affecting selfdirected learning by students,
followed by selfdirected learning characteristics. Most of these studies addressed basic education.
Therefore, a selfdirected learning model must be developed.
This research was conducted with students from technical colleges affiliated with the Office of
Vocational Education Commission of Thailand. They were selected for study because they were
becoming adults and could play their roles in society appropriately, adjust themselves, accept changes,
have mental and emotional freedom, prepare to select their careers, and demonstrate intelligence and an
understanding of sophisticated concepts (Kotrakun, 2005). Moreover, they were eager to learn and
enter into the higher education system. They were competitive and ready to deal with problems and
enter into the labour market. The Office of the Education Council (2008) found that there were not
enough researches and vocational advances. Therefore, this study will focus on development of learners
in an effective and efficient way. This research also helped learners develop a selfdirected learning
system using selfassessment.
Purpose of the study
This research aims to develop a valid and reliable model to measure and determine selfdirected
learning by technical college students. Specific objectives are as follows: To analysis the selfdirected
learning model of technical college students by factor analysis and multidimensional item response
analysis, and to validate the selfdirected learning model for technical college students.
Research methods
Population and sample
The population sampled was technical college students, affiliated with the Office of Vocational
Education Commission of Thailand.
There were two sample groups. The first sample group was used to analysis the model factors of
technical college students affiliated with the Office of Vocational Education Commission of Thailand
acquired from twostage random sampling. The first step was simple random sampling, by which
students were classified by region. The provinces with technical colleges were random units. The
second step was simple random sampling of students in technical colleges acquired from the first step.
Then 230 students were sampling units. The number of sample groups were enough according to the
principles of analysis of Hair et al. (2006), who suggested determining the size of the sample groups
using the ratio of the number of variables to the number of samples, 1:20. This research had eight
variables. Therefore, there should be 160 sample units according to the principles of analysis set out in
the multidimensional Rasch model. There were 200 students in the sample group (Wright, & Stone,
1979 cited in Yen, & Fitzpatrick, 2006).
The second sample group, used to validate the model and standards setting, included technical
college students from the academic year 20112012. The sample was acquired from a simple random
sampling. First, the researcher determined the size of the sample group based on the sampling principle
of Hair et al. (2006), who use 20 sample units per variable. In this research, the items on the evaluation
forms were used as observed variables. There were 40 items on each form. Thus, the sample group
should have at least 800 students. The sample groups were classified by region, determined by the
National Research Council of Thailand as the Central Region (including the East), the North, the
Northeast and the South.
Second, the sample groups were acquired from a threestage random sampling. The first step was a
simple random sampling with sampling units. The second step was a simple random sampling with
technical colleges as the sampling units. The third step was a simple random sampling with technical
college students as the sampling units. In total, there were 1,563 students in the sample group.
Instrument
The instruments were 44 items with four choices developed from two principles: the factor principle
and the selfdirected learning principle. The latter was developed from the work of Guglielmino (1977),
which was adapted to suit the Thai context. This principle includes eight factors: 1) openness to
learning opportunities, 2) selfconception as an effective learner, 3) initiative and independence in
learning, 4) informed acceptance of responsibility for one’s own learning, 5) love of learning, 6)
creativity, 7) a positive orientation to the future, and 8) the ability to use basic study skills and problem
solving skills. The selfdirected learning principle was synthesized from Rogers’s concept of new
knowledge introduction (Rogers, 1983) and Cross’s concept of selfdirected learning (cross, 1981):
Awareness (1 score), interest (2 scores), attempts at learning (3 scores) and practice (4 scores).
The evidence of reliability and validity of the multimethod used in this model was tested for
internal consistency and stability, while the construct validity was tested by correlation analysis,
confirmatory factor analysis, and comparative analysis. The sample group for this study was 119
technical college students. This first retest found a reliability, in terms of internal consistency, of .67,
the second retest scored .74 and stability was calculated at .60. The construct validity of the test was
related to a mediumtohigh level. The selfdirected learning model was consistent with experimental
data, and did not change with time.
Procedure
The research procedure was as follows. First, relevant documents were collected. Additional studies
on selfdirected learning were conducted. Secondly, research instruments were developed according to
factors and the learning sequence. Third, the selfdirected learning factors were developed and
confirmatory factor analysis based on the Classical Test Theory (CTT), and multidimensional item
response analysis based on the Item Response Theory (IRT), were performed. Fourth, validate of the
selfdirected learning model. Confirmatory analysis was conducted using the LISREL.
Findings
Model analysisto model development
General information of respondents
Respondents were technical college students: 63 from the North (27.39%), 58 from the Northeast,
(25.22%), 55 from the Central Region (including the East) (23.91%) and 54 from the South (23.48%).
The proportion of males and females was slightly different: 129 females (56.09%), 98 males (42.61%)
and three not indicating their gender (1.30%). Regarding education, 183 students were pursuing high
vocational certificates (79.57%), 45 students were pursuing vocational diplomas (19.57%), while two
students didn’t indicate their education status (0.86%); their collective GPA was 2.89 (s=.47).
Analysis of the basic statistics concerning selfdirected learning showed the means to be between
2.31 and 3.16. Standard deviation was .90 to 1.21. Most responses were negative skewness and
platykurtic. The correlation coefficients of 118 pairs of items were from .243 to .323, which were
related with a statistical significance of .05. The measure of sampling adequacy (MSA) was between
.34 and .71. More than half of the items yielded predictions without discrepancy from the other 26
items (59.00%), considered from the value of .50 and up (Hair et al, 2006). The result from Bartlett’s
Test of Sphericity was 1365.590 (df=946, p=.000). This meant that the independent variables in the
data analysis were related enough for an analysis of factors and multidimensions. The Kaiser–Meyer–
Olkin (KMO) used to evaluate appropriateness of the data was .526, which was more than that of Hair
et al (2006), who suggested upwards of .50. It can therefore be said that the data were appropriate for
analysis of factors and multidimensions.
Analysis of the selfdirected learning model using confirmatory factor analysis
In the preadjusted model structure, factor loading was found to be between .20 and .58. Factor
loading of 28 of the items had statistical significance of .05. This showed that most items were
significant or related to the factors when other variables were removed. The squared multiple
correlations (R2) of the items were between .00 and .33. This showed that variation of the items in each
factors that could be explained by factors were at the low to high level: 0.0033.00%. When the self
directed learning structure and experimental data were examined, the chisquare test statistic was found
to be 1037.30 (df=874, p=.00011) with statistical significance. The goodnessoffit index (GFI) was
.83, while the adjusted goodnessoffit index (AGFI) was .81. The root mean squared residual (RMR)
was .065, and the root mean squared error of approximation (RMSEA) was .029. The Index of the
model showed congruence of the selfdirected learning structure and empirical data.
In the postadjusted model structure, factor loading was found to be between .14 and .61. Factor
loading of 28 of the items had statistical significance of .05. This showed that most items were
significant or related to the factors when other variables were removed. The squared multiple
correlations (R2) of the items were between .00 and .37. This showed that variation of the items in each
factors that could be explained by factors were at the low to high level: 0.0037.00%. When the self
directed learning structure and experimental data were examined, the chisquare test statistics was
found to be 933.94 (df=865, p=.051) without statistical significance. The goodnessoffit index (GFI)
was .84, and the adjusted goodnessoffit index (AGFI) was .82. The root mean squared residual
(RMR) was .062, while the root mean squared error of approximation (RMSEA) was .019. The Index
of the model showed congruence between the selfdirected learning structure and empirical data.
Statistical significance and practical significance showed each factor with the following details.
First, openness to learning opportunities (OLO) passed the statistical significance criteria: one item
passed the practical significance criteria and one did not. Second, selfconception as an effective learner
(SEC) passed the statistical significance criteria: four items passed the practical significance criteria and
one did not. Third, initiative and independence in learning (IIL) passed the statistical significance
criteria: three items passed the practical significance criteria and two did not. Fourth, informed
acceptance of responsibility for one’s own learning (REL) passed the statistical and practical
significance criteria: six items passed the statistical significance and three items passed the practical
significance. Fifth, love of learning (LOL) passed the statistical significance criteria: four items passed
the statistical significance criteria and one did not. Sixth, creativity (CRE) passed the statistical and
practical significance criteria: three items passed the statistical significance criteria and two items
passed the practical significance criteria. Seventh, positive orientation to the future (POO) passed the
statistical and practical significance criteria: four items passed the statistical significance criteria and
one item passed the practical significance criteria. Eighth, ability to use basic study skills and problem
solving skills (ABS) passed the statistical and practical significance criteria: four items passed the
statistical significance criteria and one item passed the practical significance criteria.
Analysis of the selfdirected learning model using multidimensional item response
analysis
Analysis of the multidimensional items, according to the multidimensional item response theory
(MIRT) and the multidimensional form of the partial credit model using ConQuest 2.0, showed
congruence between the selfdirected learning model structure with item fit. The OUTFIT MNSQ for
all the items was between .93 and 1.11, while the INFIT MNSQ statistics for all the items were
between .94 and 1.10. All of the 44 items had OUTFIT MNSQ and INFIT MNSQ at the rating scale:
.60 to 1.40 (Wright et al., 1994). This showed that the selfdirected learning measuring structure was
suitable for the items. According to the responses to the multidimensional tests, the structure had
deviance statistics (G2)=26655.297, parameters=168, Akaike information criterion (AIC)=26991.297.
Each factor was found to have appropriate items for all items.
Analysis of items leading to model development
The principles were as follows. First, the items were deemed to be consistent/passing the criteria in
all analytical dimensions, on the basis of the Classical Test Theory (CTT), which was a confirmatory
factor analysis, and the Item Response Theory (IRT), which was a multidimensional item response
analysis. Secondly, four items in each factor were selected in order to explain the significance of each
factor with the same weight complied with the model development principle Hair et al. (2006) suggest
that each factor structure should have at least three indicators. Thirdly, if any factor had more than four
items, items with better qualification should be identified using analysis of factors, with both statistical
and practical significance being considered. If their qualifications were still the same, better factor
weight should be considered. Analysis of multidimensional items should be based on values at an
appropriate range. Fourth, if any factor had fewer than four items, items with acceptable qualification in practice should be identified using analysis of factors. An analysis of multidimensional items as a result
of analysis found 44 items, analysis based on eight factors with 32 items as seen in Table
Validation of the model
The researchers used the model acquired from development to conduct validation of the self
directed learning model of technical college students, using confirmatory factor analysis and the
LISREL.
General data of respondents
The respondents were technical college students: 428 from the North (27.45%), 380 from the Central
Region (including the East) (24.31%), 379 from the Northeast, (24.25%) and 375 from the South
(23.99%). The proportion of males and females was 1,157 males (74.02%) to 386 females (24.70%),
with 20 not indicating their gender (1.28%). Regarding education, more than half of the students were
pursuing vocational diplomas  879 students (56.24%) and 684 students (43.76%) were pursuing high
vocational certificates. Their collective GPA was 2.84 (s=.48).
Examining the structural of selfdirected Learning from confirmatory factor analysis
The chisquare test statistic was 418.96 (df=376, p=.062) with no statistical significance. Other
statistics were considered as follows: the goodnessoffit index (GFI) was .98, the adjusted goodness
offit index (AGFI) was .98, the root mean squared residual (RMR) was .023, and the root mean
squared error of approximation (RMSEA) was .008. The Index of the model showed congruence
between the selfdirected learning structure and empirical data, i.e. the selfdirected learning model of
technical college students had construct validity as developed and seen in Table 2.
Conclusions
The model developed from the theory of Guglielmino and the selfdirected learning model that the
researcher developed (44 items) had items suitable for 41 items of the selfdirected learning model
(93.18%). The model was consistent with empirical data (Chisquare = 933.94 (df = 865, p = .051), GFI
= .84, AGFI = .82, RMR = .062, RMSEA = .019). When responses were analyzed, all multidimensional
items were found suitable for the selfdirected learning model (100.00%). The model had G2 =
26655.297, np = 168, AIC = 26991.297 (The two analyzing methods were highly congruent, with a
value of .93.) The selfdirected learning model that was developed had eight factors: 1) openness to
learning opportunities, 2) selfconception as an effective learner, 3) initiative and independence in
learning, 4) informed acceptance of responsibility for one’s own learning, 5) love of learning, 6)
creativity, 7) a positive orientation to the future, and 8) the ability to use basic study skills and problem
solving skills (32 items).
The selfdirected learning model of technical college students had validity that was developed. The
chisquare test statistic was 418.96 (df=376, p=.062) with no statistical significance. The goodnessof
fit index (GFI) was .98. The adjusted goodnessoffit index (AGFI) was .98. The root mean squared
residual (RMR) was .023, and the root mean squared error of approximation (RMSEA) was .008.
Discussion
The first topic for discussion is the selfdirected learning model. This study found that, in the self
directed learning model analyzed by analysis factors, when considered itembyitem, three items
(6.82%) did not pass the statistical significance and/or the practical significance. It is considered that
this model was perfectly appropriate (93.18%) and consistent with overall statistics of the model, which
can be considered from statistics of congruence with the experimental data. All statistics were
acceptable and reflected congruence between the model and experimental data.
The selfdirected learning model analyzed by the multidimensional factors found appropriateness of
every item and item fit. Wright et al. (1994) suggest statistics of congruence based on estimation with
values from .60 to 1.40. This means that all items were congruent with the selfdirected learning model
under the theory of responses to multidimensional tests (Chianchana, 2009), i.e. students with high self
directed learning capabilities had a higher chance of answering these items than students with low self
directed learning capabilities.
It can be seen that there were congruence between factor analysis and multidimensional analysis with the value of .93. This showed that factor analysis and multidimensional analysis were congruent at a very high level, which was consistent with the reliability consideration of Nunnally & Bernstein (1994), who suggested considering an evaluation with high impact at .90. This was with Chianchana (2012), who suggested that the confidence was .60.79. The result was quite good and .90 up meaning very good.
The selfdirected learning model of technical college students was developed from eight factors,
namely openness to learning opportunities, selfconception as an effective learner, initiative and
independence in learning, informed acceptance of responsibility for one’s own learning, love of
learning, creativity, a positive orientation to the future, and the ability to use basic study skills and
problemsolving skills via factor analysis. Analysis of responses to multidimensional tests
(multidimensional item response theory) showed validity according the developed structure with chi
square test statistics of 418.96 (df=376, p=.062) with no statistical significance. Goodnessoffit index
(GFI) was .98. Adjusted goodnessoffit index (AGFI) was .98. Root mean squared residual (RMR)
was .023. Root mean squared error of approximation (RMSEA) was .008. All indexes complied with a
consideration of Bollen in 1989 Kelloway in 1998 and Diamantopoulos, & Siguaw in 2000, who
showed the model developed, with congruence with empirical data (Bollen, 1989; Kelloway, 1998;
Diamantopoulos, & Siguaw, 2000).
Regarding implementation, the selfdirected learning model should be implemented among technical
college students, in terms of examination and interpretation, in order to develop a reliable selfdirected
learning model suitable for their age groups and schools. The selfdirected learning model was
consistent with experimental data. Future studies should aim to develop a selfdirected learning system
by way of selfassessment.
Acknowledgments
This research work is funded by the Office of the Higher Education Commission, and King
Mongkut's University of Technology, North Bangkok, Thailand.
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Cite this article as:
Chianchana, C. (2016). Developing of the Measurement Model of SelfDirected Learning Characteristics. In Z. Bekirogullari, M. Y. Minas, & R. X. Thambusamy (Eds.), Cognitive  Social, and Behavioural Sciences  icCSBs 2016, May, vol 8. European Proceedings of Social and Behavioural Sciences (pp. 717). Future Academy. https://doi.org/10.15405/epsbs.2016.05.2