Development of a Model to Explain Motivation of Students to Undertake Skill based Courses
Anita Gupta1*, Ajeya Jha2*, Neeta D. Sharma3, Saibal Kumar Saha4, Samrat Kumar Mukherjee5
1 Research Scholar1, SMIT, Sikkim Manipal University
2 Professor, SMIT, Sikkim Manipal University
3,4,5 SMIT, Sikkim Manipal University
Email: 1[email protected], 2[email protected] ABSTRACT
Skill development has been identified as the major factor to improve employability across the world. Despite this understanding selling skill courses to the future employees has been an uphill task primarily because of the lack of motivation the students have shown in this respect. Consequently, the major challenges to make skill development acceptable for the students have invited attention of many researchers. Low motivation for skill courses have also been reported from the Himalayan state of Sikkim in India. This study has been undertaken primarily to understand the variables that influence the demotivation of stake-holders for the skill-based courses. From an exploratory study the major factors identified included lack of government-jobs through skilling, resistance to move out of Sikkim, lack of industry base in the state, stigma of school-drop out students associated with skill courses, poor social standing of jobs based on skilling and lack of awareness of skill-based job opportunities. Data has been collected from various stake holders including students, parents, teachers, policy-makers and industry managers. The sample size is 670 and multiple sampling methods has been used to decide the respondents. The analysis of the data is based on correlation followed by regression. Regression model has been developed for the entire sample as well as separately for the stake-holders. The results provide interesting insight of the beliefs held in this respect in general as well as by distinct stake-holder groups. It is found that models for differing stake-holder groups differ considerably in terms of the relative influence of the selected factors on the overall motivation of students for undertaking skill-based courses. The conclusion of the study provides valuable insight for the policy-makers to make skill courses more acceptable to the stake- holders.
Keywords
Sikkim, Resistance to relocate, lack of industrial culture, low awareness and stigma against labor oriented jobs
INTRODUCTION
Skill development has been recognized as the major factor to improve employability across the world. Despite this understanding selling skill courses to the future employees has been an uphill task primarily because of the lack of motivation the students have shown in this respect.
Consequently, the major challenges to make skill development acceptable for the students have invited attention of many a researchers. Low motivation for skill courses have also been reported from the Himalayan state of Sikkim in India. This study has been undertaken primarily to understand the variables that influence the demotivation of stake-holders for the skill-based courses. The ministry of national Skill development and entrepreneurship is responsible for coordinating and evolving skill development frameworks, mapping of existing skills and certification and industry-institute linkage [1]. This has attracted the interest of many researchers in the recent years [2-12].
LITERATURE REVIEW
Though a focus group interview revealed causes for the lack of motivation for skill-based courses amongst students but a literature review is important to understand if other researchers also hold similar views.
Unwillingness to migrate has been recognized as one noteworthy explanation for the joblessness of gifted individuals (13-14). Women in particular are discouraged to migrate by their respective families [15). Difference in this respect is intergenerational also [16]. Some recent studies in the adjoining regions, however, bring the opposite view and suggest that more and more youngsters are migrating for better job prospects [17-19]. This could be because of stark economic difficulties because many of these youth have shown inclination to return back to their homes if options are made available [19]. Skill-based jobs may necessitate the youth to migrate outside Sikkim for jobs. Their unwillingness to do so may be adversely affecting their motivation for such jobs.
Stigma towards skill-based job has also been widely documented [20-23]. It is suggested that job at the end of vocational education may neutralize the stigma widely faced by vocational graduates. [22])). It is reported that in Germany vocational training is respected as highly as academic training, and hence vocational education is not stigmatized [23]. Because of such a stigma students may be demotivated to join skill-based courses.
Preference for Government jobs amongst youth also has drawn attention of many researchers.
[24-28]. As skill-based jobs are not a part of Governance, naturally such individuals lose out on Government jobs. This in turn may be demotivates youth of the state for skill-based training.
Association of skill-jobs with low academic performance has also been reported widely [29- 32]. Students and their parents shun skill-based jobs because it automatically implies low academic aptitude and which leads to a stigma. This naturally demotivates them.
Lack of industrial base in the region has also invited attention of researchers. [33-36]. This implies lack of quality jobs within the region and which results again in fostering demotivation for the jobs. Lack of Industrial culture has been a topic of considerable interest for researchers [37-39]. Lack of such a culture implies that only a cultural change can orient the youth towards skill-based training and employment. In the absence of such a transformation the youth in Sikkim find little reason to undertake this path.
The review of literature provides existence of multiple factors leading to a demotivating effect on the students for skill-based courses. Even as factors have been identified, relationship amongst these factors has not been explored. This paper therefore focuses on identifying association between demotivation for skill-based courses and carious causal factors. To explore the issue in the entirety the researchers have built regression models on the basis of belief expressed by various stake-holders.
METHODOLOGY
The research-design for the research work is conclusive. Sikkim, the study area is a small Himalayan state in India. It has had a underdeveloped economy till recently. New age era has made it mandatory for its youth to seek employment other than those provided by the government. Even as educational standards have gone up, the unemployment remains high. One reason is the reluctance of youth to join skill based courses. The objective of the study is to build a model for explaining the lack of motivation amongst Sikkimese youth for skill-based electrical and electronics courses.
Study is based on primary data collected through a survey in which data has been collected from students (400), teachers (100), parents 100), industry managers (50) and policy makers (20).
Random and judgmental sampling methods have been utilized for this study.
Information accumulation was carried out in three stages. For the exploratory examination partners engaged with in aptitude improvement were drawn nearer with open inquiries
approaching them to distinguish the difficulties for this undertaking with regards to province of Sikkim. In the second stage the contributions from exploratory examination were utilized to think of an organized meeting plan. These timetables have been conveyed among the example partners and their reactions gathered. For the understudies and their folks, translators were utilized who could clarify the inquiries in local (Nepalese) dialect. The reluctance to react was amazingly low at around 1.8%, for the most part from understudies and guardians. The announcements were surrounded on a five-point Likert scale. The filled polls were gathered that day and on a couple of events following a couple of days.
The variables included in the study have been shown in table-1 along with their respective codes.
Table 1: Coding of variables
SN Code Variable
1 PGJ preference for Govt. jobs
2 RTMO Resistance to move out of Sikkim for work 3 LlO Lack of Industrial opportunities in Sikkim 4 LIC Lack of Industrial Culture
5 LACS
D
Lack of awareness and career counseling about Skill Development programmes
6 LAP skill courses are for drop outs and low academic performers 7 SLOJ Stigma against labour oriented jobs
Reliability of the data is an important measure for any study of this kind which is based on the beliefs of people. The reliability of the data collected for this study has been measured by Cronbach's alpha. In statistics, Cronbach's alpha (Cronbach LJ (1951) is a coefficient of internal consistency. For our study it was found to be 0.87. A value of Cronbach alpha above 0.7 is considered good for further analysis of data. 0.87 may be viewed as an excellent reliability.
For analysis the data has been subjected to correlation to find if lack of motivation is found to have significant correlation with one or more variables. If strong variables are found the data is further subjected to linear regression. To capture the varying view of stake holders regression model has been developed for combined data, students, teachers, industry managers, parents and finally for the policy-makers.
RESULT AND DISCUSSION:
Overall: In order to understand the impact independent variables have on the dependent variable (Lack of motivation for skilled course) the entire data was subjected to correlation. . To test the null hypothesis that no significant correlation exists between lack of motivation students of Sikkim display for skill courses with identified variables the correlation coefficients have been determined. Correlation matrix obtained has been provided in the table 2 for all the respondents.
From Table-2 we find that Lack of motivation is significantly LAP followed by LACSD, and PGJ. Skill jobs it appears are associated with low academic performers, do not lead to government jobs and low status.
Table 2: Correlation (Overall)
Lack of motivation PGJ RTMO LLO LIC LACSD LAP SLOJ
Correlation 0.34 0.16 0.113 0.094 0.132 0.37 0.258
Significance 0 0.087 0.098 0.214 0.072 0 0
As significant correlation has been found with three variables, the next logical step is to develop a regression model by taking lack of motivation as the dependent variable. Corresponding regression coefficient R is 0.801 and which is fairly high (as value of R ranges from -1 to +1).
Value of R2 is calculated as 0.642 and which indicates that the model explains 64.2% of the variance and which makes the model fairly reliable. Adjusted R2 value is0.638. Corresponding F- value is 149.7. This signifies a strong fitness of the proposed model
The resulting model coefficients ( table 3) are 0.053 (constant), (0.25) LAP, 0.105 (PGT) and 0.076 (SLOJ). The regression equation therefore is given as:
Lack of motivation = 0.053 + LAP (0.25) + PGJ (0.105) + SLOJ (0.706) Table 3: Model Coefficients (Overall)
Model Coefficients t-value Significance
(Constant) 0.053 0.455 0.65
LAP 0.25 24.674 0
PGJ 0.105 5.174 0
SLOJ 0.076 2.862 0.004
It is, however, to be noted that constant has a t-values below 1.96 and that may be interpreted to be insignificant. The overall the perception is that skill development has few takers as these are perceived as meant for low academic performers; are not considered lucrative as government jobs where perhaps salaries are not linked to merit, hard work, uncertainty and instability.
It was hypothesized that regression model will differ across the stake holders. Therefore the regression models have been developed for all the five stake holders namely teachers, students, industry managers, parents and the policy-makers. These have been narrated in subsequent sections.
TEACHERS
For this section the null hypothesis is that no significant correlation are present, as per the belief held by the teachers, amidst lack of motivation students of Sikkim for skill courses with identified variables. Correlations are only for the teacher. Correlation matrix has been provided in the table-4. We find that Lack of motivation is significantly LAP followed by RTMO, LLO and PGJ.
Table 4: Correlation (Teachers) Lack of
motivation PGJ RTMO LLO LIC LACSD LAP SLOJ Correlation 0.217 0.267 0.24 0.131 0.113 0.462 0.134 Significance 0.002 0 0 0.074 0.172 0 0.067
We find that regression coefficient R is 0.773 and which again may be considered fairly high.
Value of R2 is calculated as 0.598. The related F-value is 30.88 and p-value is 0. This indicates a fit model.
The table 5 displays the model coefficients. These are found to be 1.56 (constant), LAP (0.402), LLO (0.206), RTMO (0.25) and PGJ (0.197) . The regression equation therefore is given as:
Lack of motivation = 1.56 + LAP (0.402) + LLO (0.206) + RTMO (0.205) + PGJ (0.197).
Table 5: Model Coefficients (Teachers) Model Coefficients t-value Significance (Constant) 1.56 6.087 0
LAP 0.402 6.026 0
LLO 0.206 3.362 0.001
RTMO 0.25 3.771 0
PGJ 0.197 2.423 0.018
Yet again we find that corresponding value to constant is not significant. As the model depicts all the corresponding t-values of all the variables in the model are above 1.96 it is concluded these are significant. Students, therefore are more divided in their belief regarding what keeps them away from skill-based courses. Students’ beliefs are important as even if these are inaccurate to some extent they still need to be addressed as otherwise the skill courses will remain neglected by the students and their employability will be compromised.
Students: Table 6 provides the correlation coefficients. Lack of students motivation is related to LLO (0.278), RTMO (0.27), LAP (0.25); SLOJ (0.238) and PGJ (0.198).
Table 6: Correlation (Students)
Lack of motivation PGJ RTMO LLO LIC LACSD LAP SLOJ
Correlation 0.198 0.27 0.278 0.073 0.141 0.25 0.238
Significance 0.027 0 0 0.416 0.177 0 0
Yet again significant correlations can be noted to be present between lack of motivation and as many as five independent variables. This provided the motivation to subject the data to regression. It is to be noted that regression coefficient R is 0.814 and R2 is 0.663 F-value is 134.83 and hence it is a fit model.
The table 7 provides values of model coefficients. These are found to be 0.063 (constant), LLO (0.268), RTMO (0.262), LAP (0.219), SLOJ (0.193) and PGJ (0.16) . The regression equation therefore is given as:
Lack of motivation = 0.063 + RTMO (0.262) + LLO (0.268) + LAP (0.219) + SLOJ (0.193) + PGJ (0.16)
Table 7: Model Coefficients (Students)
Model Coefficients t-value Significance
(Constant) 0.063 0.456 0.649
RTMO 0.262 11 0
LAP 0.219 11.494 0
LLO 0.268 11.773 0
SLOJ 0.193 8.427 0
PGJ 0.16 7.969 0
Students’ beliefs are important as even if these are inaccurate to some extent they still need to be addressed as otherwise the skill courses will remain neglected by the students and their employability will be compromised.
Industry Managers: Table 8 provides the correlation coefficients. Industry managers believe that students are not keen to move out of Sikkim and also do not seek skill courses because of relatively lower social standing.
Table 8: Correlation (Industry Managers)
Lack of motivation PGJ RTMO LLO LIC LACSD LAP SLOJ
Correlation 0.361 0.611 0.487 0.123 0.076 0.312 0.102
Significance 0 0 0 0.089 0.482 0 0.112
It is to be noted that regression coefficient R is 0.883 and R2 is 0.779. F-value is 26, therefore, the interpretation yields that model fit, an inference that is validated by the equivalent p-value that is near zero.
The table 9 displays model coefficient values. These are found to be -2.435 (constant), LLO (0.444), RTMO (0.476), LAP (0.362), and PGJ (0.337). The regression equation therefore is given as:
Lack of motivation = (-2.435) + RTMO (0.476) + LLO (0.444) + LAP (0.362) (0.362) + PGJ (0.16)
Table 9: Model Coefficients (Industry Managers) Model Coefficients t-value Significance
(Constant) -2.435 -3.66 0.001
PGJ 0.337 2.712 0.011
LAP 0.362 3.5 0.002
RTMO 0.476 3.689 0.001
LLO 0.444 3.536 0.001
Parents: Parents (Table-10) believe lack of motivation is related to RTMO (0.312), LAP (0.337);
SLOJ (0.744).
Table 10: Correlation (Parents)
Lack of motivation PGJ RTMO LLO LIC LACSD LAP SLOJ
Correlation 0.004 0.312 0.112 0.0961 0.0843 0.337 0.744
Significance 0.933 0 0.072 0.132 0.167 0 0
R value is 0.752 and R2 value is 0.565. F-value is 14.7 and it confirms fitness of model.
Model coefficients (Table-11) are -0.54 (constant), RTMO (0.206), LAP (0.337) and SLOJ (0.673). The regression equation therefore is given as:
Lack of motivation = (-0.54) + RTMO (0.206) + LAP (0.337) + SLOJ (0.673)
Table 11: Model Coefficients (Parents)
Model Coefficients t-value Significance
(Constant) -0.54 -0.945 0.352
LAP 0.337 4.638 0
SLOJ 0.673 4.794 0
RTMO 0.206 2.425 0.021
Policy Makers: In view of the policy makers (Table=12) there are just two variables SLOJ (0.762) and LAP (0.386). In their opinion it is the social view that is holding back the students from the skilled jobs.
Table 12: Correlation (Policy Makers)
Lack of motivation PGJ RTMO LLO LIC LACSD LAP SLOJ
Correlation 0.004 0.107 0.073 0.076 0.113 0.386 0.762
Significance 0.933 0.267 0.342 0.482 0.172 0 0
Value of R and R2 is 0.771 and 0.594 respectively. F-value is 16 and it, therefore, implies that that the model is fit.
The table 13 exhibits the model coefficients. These are found to be 2 (constant), SLOJ (0.5) and LAP (0.337 . The regression equation therefore is given as:
Lack of motivation = 2 + SLOJ (0.5) + LAP (0.337) Table 13: Model Coefficients (Overall)
Model Coefficients t-value Significance
(Constant) 2 4.24 0.001
SLOJ 0.5 4.01 0.002
LAP 0.337 2.712 0.011
CONCLUSION
In this study relative impact of various independent variables on lack of motivation (dependent variable) has been studied. Two important conclusions are (a) that strong correlations exist to explain demotivation through varying independent variables and (b) correlations differ across the stake-holders.
Presence of strong correlations provides with strong basis to develop regression model. These models provide us with the basis to understand the lack of motivation exhibited by the students.
The varying views of stake-holders are important for us to overcome the challenge posed by low motivation in this respect. It is imperative we overcome this challenge so that students can be engaged in skill-based jobs through appropriate training. It is to be found that whereas students consider external factors as the reason for their low preference for skill-based jobs (lack of industrial opportunities and need to relocate outside Sikkim) the teachers hold association of such jobs with low academic performance responsible. It appears this association keeps away relatively good students from such prospects. Students with poor academic performance also shun away such opportunities as their low self-esteem makes the prospects unrewarding and also a conformance of their low capability. To drive away such a notion skill-based jobs should be
made integral to school system with no distinction being made between academically high and low student. It is quite possible that students with high skills (irrespective of low or high academic scores) will develop a natural affinity for the same. Policy makers come up with a relatively simpler model and believe that social stigma against skill-based jobs and its association with low academic performance are solely responsible for such an eventuality. This perhaps is right. In societies where skill-based jobs hold no social stigma there is hardly any reluctance for such jobs. Rather people take pride in their capabilities. The question is how can we remove the social stigma attached to these jobs? Answer perhaps lies in making skill-based jobs economically rewarding. Economic measures can quickly wipe out any social practice as has been recorded many a times. Racial and caste-based distinctions have blurred after some economic parity has been restored.
Concluding statement would be that it is critical to make skilled-jobs acceptable to the today’s youth and this perhaps can be achieved by breaking the barriers identified through a socio- economic reengineering.
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