Predictors of academic success for Māori, Pacific and non-Māori non-Pacific students in health professional education: a quantitative analysis

Tertiary institutions internationally aim to increase student diversity, however are struggling to achieve equitable academic outcomes for indigenous and ethnic minority students and detailed exploration of factors that impact on success is required. This study explored the predictive effect of admission variables on academic outcomes for health professional students by ethnic grouping. Kaupapa Māori and Pacific research methodologies were used to conduct a quantitative analysis using data for 2686 health professional students [150 Māori, 257 Pacific, 2279, non-Māori non-Pacific (nMnP)]. The predictive effect of admission variables: school decile; attending school in Auckland; type of admission; bridging programme; and first-year bachelor results on academic outcomes: year 2–4 grade point average (GPA); graduating; graduating in the minimum time; and optimal completion for the three ethnic groupings and the full cohort was explored using multiple regression analyses. After adjusting for admission variables, for every point increase in first year bachelor GPA: year 2–4 GPA increased by an average of 0.46 points for Māori (p = 0.0002, 95% CI 0.22, 0.69), 0.70 points for Pacific (p < 0.0001, CI 0.52, 0.87), and 0.55 points for nMnP (p < 0.0001, CI 0.51, 0.58) students. For the total cohort, ethnic grouping was consistently the most significant predictor of academic outcomes. This study demonstrated clear differences in academic outcomes between both Māori and Pacific students when compared to nMnP students. Some (but not all) of the disparities between ethnic groupings could be explained by controlling for admission variables.

Introduction
Tertiary institutions internationally aim to widen participation to ensure graduating cohorts meet the needs of the diverse communities in which they operate (Bowes et al. 2013;The Sullivan Commission 2004;Whiteford et al. 2013). However, tertiary institutions are failing to achieve equitable academic outcomes and show on-going trends of underachievement for indigenous and ethnic minority students in tertiary programmes (Garvey et al. 2009;Madjar et al. 2010a;The Sullivan Commission 2004;Zorlu 2013). Razack et al. (2015) note that despite strategic commitments to widening participation globally, emphasis on academic performance in determining admission to medical school inevitably excludes those we are aiming to 'include' (Razack et al. 2015). Ethnic disparities are particularly concerning for universities offering health professional programmes that aim to contribute to health equity targets by graduating more workforce-ready ethnic minority health professionals (Curtis et al. 2014b;Kaehne et al. 2014;Ratima et al. 2007). Indigenous and ethnic minority peoples are under-represented within health professions globally and this limits sector ability to provide a culturally safe, competent and appropriate healthcare (Curtis et al. 2012b;Health Workforce Advisory Committee 2003;The Sullivan Commission 2004;Whiteford et al. 2013).
A broad mix of factors that help or hinder academic success for indigenous and ethnic minority students in tertiary health study have been identified internationally: academic preparation (including secondary school academic achievement, exposure to science subjects, meeting tertiary admission prerequisites, and having clear career goals); socioeconomic status; availability of role models and mentors; family support; work/life balance; access to childcare; financial support; clear career information; student support systems; support to transition and first year academic results and environments (Gardner 2005;Jensen 2011;Loftin et al. 2012;Orom et al. 2013). These findings align with literature specific to Māori and Pacific health professional students in New Zealand (Curtis et al. 2012b(Curtis et al. , 2014bMorunga 2009;Ratima et al. 2008;Sapoaga et al. 2013;Wikaire and Ratima 2011;Wilson et al. 2011).
Recruitment and retention initiatives have been implemented internationally that aim to identify and address barriers to success for indigenous and ethnic minority health professional students (e.g. scholarships to support financial barriers, admission quotas, provision of pastoral support staff, bridging/foundation programmes to address gaps in academic preparation, additional tutorials, mentoring programmes, provision of indigenous student study space) (Curtis et al. 2012b). However, political and societal backlash that sees these interventions as 'special treatment' and institutional resistance to change has limited both the ability to implement required changes and the effectiveness of interventions t (Hesser et al. 1998;Towns et al. 2004). Whilst some promising early results have been reported (i.e. increased enrolment numbers, improved academic performance in year 1 of degree-level study), there is a lack of literature investigating ethnic disparities in programme academic performance and graduation (Curtis et al. 2012b).
Further detailed understanding of which factors influence not only indigenous and ethnic minority student academic success but are also associated with ethnic differences in academic outcomes in tertiary health programmes is necessary to achieve educational and health equity targets. Whilst qualitative studies have offered deeper insight into factors that help and hinder students, there is a need to understand which factors might be operating in different ways (Curtis et al. 2014b). In addition, quantitative analyses in some studies have been limited by small numbers of enrolled students from ethnic minority groups, a focus on first-year academic outcomes, and a lack of direct comparison between ethnic groups. Quantifying differences in exposure to helping and hindering factors for different ethnic groups is expected to contribute to enhanced targeted support (Curtis et al. 2015a).
Quantitative studies that investigate predictors of academic success for health professional students generally has been completed, however, the majority of international literature in this area has tended to focus on total (predominantly white) student cohorts as indigenous and ethnic minority student numbers are often too small to allow sufficient power for statistical analysis or comparison with other ethnic groups (Andriole and Jeffe 2010;Mills et al. 2009;Utzman et al. 2007). Ethnicity is often positioned as a 'predictor' variable rather than deliberate comparison exploring reasons for disparities between ethnic groups and it is difficult to generalise international findings to both a New Zealand and Māori/Pacific context. In addition, inconsistencies in identification, data collection, definition and grouping of student ethnicity (for example, indigenous status, low-income status, country of birth, self-identified ethnicity, experiences of colonisation) across studies internationally limit generalisation of findings to a local context. Social accountability of health sectors and tertiary education providers requires particular attention to be focused on understanding the unique needs of indigenous and ethnic minority health professional students in order to better meet their needs and support their academic success. It is timely to complete detailed quantitative analysis of predictive factors to explore how each concept might impact on academic success for different ethnic groups. It is unknown if these reasons for inequities can explain all differences in academic outcomes between ethnic groups.

New Zealand context
In New Zealand (NZ), Māori (the indigenous peoples of NZ) and the Pacific population (made up of more than 40 different ethnic groups that have their own culture, language, history and links to NZ) experience higher health need and yet there is a critical shortage of Māori and Pacific health professionals (Māori make up 14.9% of the population, 3% of doctors, 6% of nurses, 2% of pharmacists and 5% of dentists, Pacific make up 7.4% of the NZ population, 1% of doctors, 0.2% of pharmacists, 0.6% of dentists and 2.2% of nurses) (Cram 2014;Curtis et al. 2012bCurtis et al. , 2014aMinistry of Health 2004, 2011Statistics New Zealand 2014). Māori and Pacific higher education students show higher attrition rates, lower participation rates and are underrepresented in bachelor level programmes compared to non-Māori students (Education Counts 2010).
The FMHS at the University of Auckland (UoA), New Zealand offers health professional degree-level programmes and is committed to Māori and Pacific health workforce development through its' Vision 20: 20 programme (The University of Auckland 2014a), providing comprehensive Māori and Pacific student academic and pastoral support (Curtis and Reid 2013). Despite promising increases in recruitment numbers, disparities in academic outcomes between Māori and Pacific, and nMnP students in FMHS remain (e.g. FMHS first year bachelor course completion rates in 2014 were 76.8% for Māori, 61.8% for Pacific and 82.8% for the total cohort) (The University of Auckland 2014b).
This project aimed to investigate predictors of academic success for Māori and Pacific students compared to nMnP enrolled in Bachelor of Health Sciences, Nursing and Pharmacy programmes within the FMHS, UoA. Findings from this study may help to inform medical and other health professional schools internationally that aim to both widen participation and ensure equitable academic outcomes. Findings from this study may offer generalizable findings for international contexts particularly given that indigenous and ethnic minority health professional students internationally may have experiences. Understanding the reasons for inequities between ethnic groups is important to monitor institutional performance against equity targets and contribute to developing indigenous and ethnic minority student support initiatives.

Objectives
The main objective of this study was to identify important predictors of academic success (or failure) for Māori, Pacific and nMnP students by: 1. Exploring the predictive effect of FMHS student admission variables and early academic outcome variables on academic results. 2. Investigating how ethnicity is related to student academic outcomes after controlling for the effects of admission process variables.

Methodology
A Kaupapa Māori Research (KMR) approach, inclusive of Pacific methodology, was utilised for this study (Pihama 2001;Smith 1997Smith , 2012. KMR is an established indigenous research methodology that informs the research question being asked, the methods that are chosen to explore the research question and how the research findings are interpreted (Smith 2012;Smith and Reid 2000). Within this research, the application of Kaupapa Māori and Pacific theoretical principles includes: a commitment to Māori and Pacific leadership and control over the research process to ensure the foregrounding of Māori and Pacific priorities (this includes the need to critique structural power imbalances that may be operating within institutions) (Pihama 2001;Smith 1997Smith , 2012; ensuring positive outcomes for Māori and Pacific research participants and communities (i.e. identifying unique characteristics of Māori students to inform tailoring of educational programmes and support services by institutions to better meet the specific needs of these students), and interpreting findings from a Māori and Pacific worldview (that acknowledges health and educational outcomes for Māori and Pacific students are influenced by broad social, cultural, historical, political and economic contexts) (Smith 1997). Given this positioning, the authors expect that the analysis and recommendations from this research will require institutional change rather than requiring students to change themselves. The application of Kaupapa Māori Research positioning within a quantitative context can be seen within the project's commitment to maximise 'equal explanatory power' and 'equal analytical power' for data analyses between Māori/Pacific and nMnP participants (e.g. the inclusion of multiple years to enhance sample sizes by ethnicity; use of high quality ethnicity data) (Cormack and Robson 2010;Te Rōpū Rangahau Hauora a Eru Pōmare 2002).
This project has also been informed by Pacific methodological approaches including that of 'talanoa' (Vaioleti 2006). Talanoa is referred to as a Pacific research methodology (similar to Kaupapa Māori), whereby the research is mutually beneficial, upholds Pacific aspirations and ensures researchers are accountable to participants. The input of Pacific Health researchers and methodology of talanoa (Vaioleti 2006) allowed for meaningful exploration of and advocacy for issues amongst both Māori and Pacific students. The research acknowledges the similar effects of social impacts on health and education for Pacific and Māori peoples (Ministry of Health 2004). Pacific representation within the project team and a project advisory group acknowledges mutual expertise of these parties in Pacific health research and values Pacific knowledge and decision-making contribution (Naepi 2015).

Research setting
This research was located within Te Kupenga Hauora Māori (Department of Māori Health and Office of Tumuaki, UoA, NZ) with senior Māori health researcher leadership. Oversight by an advisory group of Māori, Pacific, academic and administrative faculty staff with expertise pertaining to the research topic.

Participants
All students enrolled in year two of the BHSc, BNurs, or BPharm programmes within FMHS, UoA (2002 who had had sufficient time to complete the programme. Students who were currently enrolled in 2014 (i.e. still studying towards completion) or for whom the minimum time required to complete their programme had not passed were excluded.

Study design
An observational study design was used. Secondary individual student demographic, admission and academic results data from 2001 to 2013 was sourced from Student Services Online (SSO) (the UoA web-based centralised student data management system). Multiple regression analysis was used to test: how predictor variables were related to academic outcome variables for each of the Māori, Pacific and nMnP ethnic groupings; and, how ethnicity was related to predictor variables and/or student academic outcomes.

Ethnicity variable of interest
Student ethnicity was categorised as Māori, Pacific and nMnP ethnic groupings. Selfidentified ethnicity was automatically categorised into Māori, Pacific, Asian, European, and Other ethnic groupings within SSO using a prioritisation protocol prior to obtaining the data for the purposes of this study (Education Counts 2012). Prioritisation of Māori ethnicity (as the ethnicity of first priority) when multiple ethnicities are selected ensures representation of all individuals who identify as Māori within analysis outcomes (Cormack and Robson 2010). However, those who may have identified with both Māori and Pacific ethnicity will be counted in the Māori group only-therefore, reducing the Pacific group numbers. The Asian, Other and European ethnicity categories were combined into one nMnP comparator grouping (note that students who self-identified as New Zealander were included within the Other category) given that these ethnicity categories demonstrate similar outcomes within this context. Māori and Pacific ethnic categories remained separate given that different impacts on academic outcomes may be occurring for Pacific and Predictors of academic success for Māori, Pacific and… 303 Māori students. Maintaining separate ethnic categories for Māori and Pacific students aligns with Māori positioning as indigenous peoples and rights as Treaty of Waitangi partners.

Predictor variables
Predictor variables were chosen based on available SSO data and a predictors of success model that foregrounds significant concepts that may impact on indigenous (Māori) and ethnic minority (Pacific) student success (Wikaire et al. 2016). Concepts within the model include: demographic; socioeconomic status; academic preparation; transitioning; early academic results; and, the tertiary environment.

Demographic
Demographic variables included gender, age at admission and year of admission into year 2 (2002-2012). 1

Socioeconomic status
NZ secondary school decile rating categories: low (1-3) (high deprivation), medium (4-7), and high (8-10) (low deprivation) were used to measure/represent socioeconomic status (Mills et al. 2009). High decile represents schools with a high proportion of students who reside in areas of low deprivation (high socioeconomic status). Home schooled or overseas schools were coded as missing.

Academic preparation
Academic preparation was measured using secondary school results that reflect FMHS bachelor degree entry requirements. With a new system having been introduced in 2005, this data was only available for approximately half of the full student cohort and therefore have been excluded from this analysis. A separate analysis using this data is reported elsewhere (Wikaire 2015).

Transitioning
Attended school in Auckland (yes, no), type of admission (school leaver, alternative admission) and bridging programme (yes, no) were used to measure transitioning via relocation (moving to live in Auckland for study purposes) or transition via varying pathways between secondary school and FMHS bachelor-level enrolment respectively. School leaver was defined as enrolment in bachelor level FMHS study in the year immediately following secondary school. All other students were classified as alternative admission. Completion of a UoA bridging foundation programme that aims to bridge the 'gap' between secondary and tertiary education contexts was recorded.

Early academic results
Early academic results were measured using first year bachelor grade point average (GPA) (average 'grade' attained by each student in the first year of bachelor level study across eight courses) (0-9) and passing all courses (i.e. no 'fail' grades) in the first year of bachelor study (yes, no). Early academic results were modelled as being both predicted by admission variables and predictive of longer term programme academic outcomes.

Tertiary environment
Note that there is a lack of measured variables representing tertiary environment factors (e.g. curriculum). Therefore, although the tertiary environment is conceptualised as impacting on student success in the 'predictors of success model', variables measuring these factors were not routinely collected within the SSO system and hence were not included within the analysis in this study.

Early academic outcomes
Early academic outcomes were measured using first year bachelor GPA (0-9) and first year bachelor passed all (eight) courses (yes, no).

Programme outcomes
Programme outcomes included: graduation from the intended programme (i.e. the programme in which students originally enrolled in year 2) (yes, no); graduated in the minimum time (yes, no) (3 years for BHSc and BNurs, 4 years for BPharm); and Year 2-4 programme GPA (0-9) (the average grade achieved over all courses from year two until programme completion).

Optimal programme completion
Ideally, institutions aim for students to achieve optimal academic success via a combination of programme completion, achieving high grades and completing the programme in the minimum time. An optimal completion outcome was developed to explore the combined overall academic success for students using four outcome levels in hierarchical order: 1. Optimal completion = graduated from intended programme (yes) and graduated in minimum time (yes)with and an overall A grade average (i.e. C6.6) across the programme); or 2. Sub-optimal completion with higher grades = graduated from intended programme (yes) with an overall B grade average or higher (i.e. C3.6); or 3. Sub-optimal completion with lower grades = graduated from intended programme (yes) with an overall C grade average (i.e. 1-3.5); or 4. Non-completion = those students who for varying reasons did not complete their intended programme.
Category 2 (sub-optimal with high grades) was used as the reference category in the analysis as this was the category with the most students and can thus be taken to represent a 'typical' completion outcome.

Analysis
Statistical analysis was performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). All statistical tests were two-sided at a 5% significance level. A statistical analysis plan was developed a priori that incorporated key predictor and outcome variables of interest using multiple regression models. Continuous variables were summarised descriptively in mean and standard deviation (SD), and the predictive effect of admission variables were tested using linear regression model with an overall F-test, followed by t-tests comparing the mean differences and 95% confidence intervals to the reference level. Binary categorical variables were presented in frequencies and percentages and were tested using logistic regression with a logit link. The optimal completion outcome with four defined categories was tested using nominal logistic regression with a logit link and separate odds ratios were obtained for each comparison category. Odds ratios and 95% confidence intervals were reported with associated p values using Chi square tests.
As shown in Fig. 1, pre-defined predictor variables were added to the baseline model (#1) in sequential order (#2-5). Consistent with Kaupapa Māori methodology that locates indigenous peoples at the centre of enquiry, Māori and Pacific ethnic groupings were foregrounded throughout the data analysis (Walter and Andersen 2013). The first stage of analysis involved running separate models for each ethnic grouping to identify the predictive effect of admission variables and early academic outcomes for Māori, Pacific and nMnP student groupings separately. Significant predictors were identified in the model with a p value\0.05, and the results on final full model are presented. Regression analyses were next conducted using the total cohort including three ethnic groupings in the same  Fig. 1 Multiple regression analysis plan that included a baseline (model 1) and the sequential inclusion of additional predictor variables (models 2-5). Predictor variables were predefined. Analysis was completed for the full cohort including three ethnic groupings in the same model so that a group comparison could be carried out that compared Māori/Pacific to nMnP students and repeated for Māori, Pacific and nMnP ethnic groupings separately. The analysis was completed for each of the listed academic outcomes model to investigate how ethnic disparities were related to admission process and student academic outcomes. Differences in academic outcomes and the change in these differences were observed between ethnic groupings with the sequential addition of predictor variables to each model. The results of unadjusted model, baseline model and full model are reported.

Ethics
Ethical approval for this project was granted by the UoA Human Participants Ethics Committee (Ref 8110). As per ethics protocols, written informed consent was not required for this research project due to the use of secondary administrative data sources. All secondary data obtained from these datasets were de-identified by an independent research member with no student contact or teaching responsibilities and data analysis occurred via a coding system.

Results
A total of 2686 students were included in this study including 5.6% Māori (n = 150), 9.6% Pacific (n = 257) and 84.9% nMnP (n = 2279) students. Descriptive summary data are presented in Table 1 for all predictor and academic outcome variables. Differences in unadjusted predictor and academic outcome variables between ethnic groupings have previously been discussed elsewhere (Wikaire et al. 2016). Tables 2, 3 present the analysis results for each of the three ethnic groupings of interest (Māori, Pacific and nMnP) as well as the full cohort which compares the three ethnic groupings in the same model. All models beyond the baseline (#2-5) controlled for age, year of admission and gender (i.e. baseline model #1).  Total cohort comparison analysis Table 2 and 3 present the linear and logistic regression analysis results for the full cohort which includes the three ethnic grouping comparison (Māori, Pacific, nMnP). The main variable of interest is 'ethnic grouping' where results are presented comparing 1) Māori to nMnP, and 2) Pacific to nMnP (reference group). Significant differences in first year bachelor GPA, year 2-4 programme GPA and likelihood of graduation are demonstrated between ethnic groupings, even after controlling for all pathway variables. First year bachelor GPA was also a significant predictor of years 2-4 programme GPA and graduation.
Year 2-4 programme GPA Year 2-4 programme GPA was on average 0.86 points lower for Māori than nMnP students in the unadjusted model (p \ 0.0001, CI -1.14, -0.57) however this difference was accounted for by pathway variables (model #5).

Graduated from intended programme
The odds of graduating from the intended programme were 43% lower for Māori than nMnP students with adjustment for all pathway variables (p = 0.0083, OR 0.57, CI 0.38, 0.87) (model #5).

Graduated in minimum time
The odds of graduating in the minimum time were significantly lower for Māori compared to nMnP in unadjusted and baseline models only however became non-significant when adjusting for pathway variables.

Optimal completion
In Table 3, the unadjusted model shows that Māori students were less likely to achieve an optimal completion (p = 0.0258, OR 0.52, CI 0.29, 0.92), whilst more likely to achieve a sub-optimal completion with low grades (p = 0.0161, OR 1.89, CI 1.13, 3.19) or a noncompletion outcome (p = 0.0213, OR 1.58, CI 1.07, 2.34) relative to achieving a suboptimal completion with high grades, when compared to nMnP students. After adjusting for first year bachelor results (model #5), the difference in graduation outcomes between Māori and nMnP students was no longer significant. This suggests that the first year of bachelor study is an important determinant of disparity in overall academic success between Māori and nMnP students.

Comparison of Pacific to nMnP students
First year bachelor GPA In Table 2, model #4, after adjusting for all pathway variables, the difference in mean first year bachelor GPA was 1.35 points lower for Pacific than nMnP students (p \ 0.0001, CI -1.65, -1.05).
Year 2-4 programme GPA Table 2 shows that Pacific students achieved a year 2-4 programme GPA that was on average 1.73 points lower in the unadjusted model (p \ 0.0001, CI -1.95, -1.51) and reduced but remained 0.57 points lower than that of nMnP students in model #5 (p \ 0.0001, CI -0.78, -0.36).

Graduated from intended programme/Graduated in minimum time
In unadjusted models, Pacific students had 38% lower odds of graduating (p \ 0.0001, OR 0.62, CI 0.46, 0.82) and 61% lower odds of graduating in the minimum time (p \ 0.0001, OR 0.39, CI 0.28, 0.56) when compared to nMnP. Differences in likelihood of graduating and graduating in minimum time between Pacific and nMnP students were no longer significant after adjustment for first year bachelor results, bridging programme and type of admission.

Optimal completion
In the unadjusted model, the odds of achieving an optimal completion were eight times lower (p \ 0.0001, OR 0.15, CI 0.06, 0.33) the odds of achieving a sub-optimal completion with low grades were more than four times higher (p \ 0.0001, OR 4.21, CI 3.00, 5.93) and the odds of non-completion were nearly two times higher (p \ 0.0001, OR 1.72, CI 1.25, 2.35) relative to a sub-optimal completion with high grades for Pacific students when compared to the same outcome for nMnP students (Table 3). After adjustment for all pathway variables, the odds of achieving an optimal completion were lower (p = 0.0315, OR 0.38, CI 0.16, 0.92) and the chance of achieving a sub-optimal completion with low grades was higher (p = 0.0211, OR 1.76, CI 1.09, 2.86) relative to a sub-optimal completion with high grades for Pacific students when compared to the same outcome for nMnP students.

Summary of full cohort comparison
Overall, the disparity in first year bachelor GPA between both Māori and nMnP, and Pacific and nMnP was not explained by pathway variables. Similarly, the disparity between Māori and nMnP in likelihood of graduating and between Pacific and nMnP in year 2-4 programme GPA and chances of optimal completion remained significant after adjusting for pathway variables. Although unadjusted models showed Māori had lower year 2-4 GPA, lower odds of graduating in minimum time and 'less optimal' completion than nMnP, these ethnic disparities in academic outcomes were explained by predictor variables indicating the importance of admission variables in determining programme outcome disparities. Overall, these results show that after adjusting for all predictor variables, there was no difference in graduating from intended programme or graduating in the minimum time between Pacific and nMnP students.

Sub-cohort analysis Predictors of academic outcomes for Māori
Bridging programme and first year bachelor GPA were significant predictors of academic outcomes for Māori students. Table 2 (model #4), after adjusting for pathway variables, Māori students who had completed a bridging programme achieved a first year bachelor GPA that was on average 0.83 points lower than those Māori students who did not (p = 0.0165, CI -1.50, -0.15). 2

As shown in
Year 2-4 programme GPA Year 2-4 programme GPA was on average 0.46 points higher for every 1 point increase in first year bachelor GPA for Māori students (p = 0.0002, CI 0.22, 0.69) (model #5) after adjusting for pathway variables.

Graduated from intended programme/Graduated in minimum time
After adjustment, none of the predictors investigated showed significant effects on graduating or graduating in the minimum time for the Māori student grouping (model #5).

Optimal completion
For the optimal completion outcome (Table 3), after controlling for all pathway variables, the odds of achieving an optimal programme completion relative to sub-optimal completion with high grades were 2.85 times higher (p = 0.0314, CI 1.1, 7.37) with every 1-point increase in first year bachelor GPA for Māori students.

Predictors of academic outcomes for Pacific
Type of admission and first year bachelor GPA were important predictors of Pacific student academic outcomes.

First year bachelor GPA
In Table 2, model #4, after controlling for school decile, attending an Auckland school, and bridging programme participation, alternative admission Pacific students had a first year GPA that was on average 0.8 points lower (p = 0.0041, CI -1.40, -0.27) than Pacific school leavers.
Year 2-4 programme GPA In model #5, after controlling for all pathway variables, for every 1-point increase in first year GPA Pacific students achieved a year 2-4 programme GPA that was on average 0.7 points higher (p \ 0.0001, CI 0.52, 0.87).

Graduated from intended programme
For every 1 point increase in first year bachelor GPA, Pacific students had 1.57 times higher odds of graduating (p = 0.0079, OR 1.6, CI 1.13, 2.19).

Graduated in minimum time
None of the investigated predictors had a significant effect on graduating in minimum time for the Pacific student grouping when included in the same model.

Optimal completion
For every 1 point increase in first year bachelor GPA, Pacific students were less likely to achieve sub-optimal completion with low grades (OR 0.57, CI 0.38, 0.84) or non-completion (OR 0.47, CI 0.32, 0.70) than sub-optimal completion with high grades (p = 0.0003 with optimal outcome removed from the dataset due to low numbers of students0010, model #5).

Predictors of academic outcomes for nMnP
For the nMnP student grouping, entering as a school leaver, having completed a bridging programme, and failing to pass all courses in the first year were predictive of 'lower' academic outcomes whilst gaining entry via alternative admission and achieving a higher first year bachelor GPA were predictive of 'higher' academic outcomes for nMnP students.

First year bachelor GPA
After controlling for pathway variables, nMnP students who entered via alternative admission had a first year GPA that was on average 0.24 points higher (p = 0.0434, CI 0.01, 0.48) than for school leavers, whilst those who had completed a bridging foundation programme had a first year GPA that was on average 0.73 points lower (p = 0.0004, CI -1.13, -0.32) than those who had not.

Graduated from intended programme
NMnP students who did not pass all courses in their first year of bachelor study were less likely to graduate from their intended programme (p \ 0.0001, OR 0.42, CI 0.30, 0.59) ( Table 2, Model #5) than those who did.

Graduated in minimum time
Those nMnP students who had completed a bridging foundation programme had lower odds of graduating in the minimum time (p = 0.0015, OR 0.35, CI 0.18, 0.67) than those who had not. For every 1 point increase in first year bachelor GPA, nMnP students were more likely to graduate in the minimum time (p \ 0.0001, OR 1.41, CI 1.25, 1.58) ( Table 2).

Optimal completion
For every 1 point increase in first year bachelor GPA, nMnP students had higher odds of achieving an optimal programme completion (p \ 0.0001, OR 1.95, CI 1.77, 2.15), and had lower odds of achieving a sub-optimal completion with low grades (p \ 0.0001, OR 0.49, CI 0.41, 0.58) than of achieving a sub-optimal completion with high grades (Table 3). NMnP students who did not pass all courses in their first year of bachelor study were more likely to achieve non-completion relative to sub-optimal completion with high grades (p \ 0.0001, OR 2.75, CI 1.92, 3.94) than those who passed all first year bachelor courses ( Table 3).

Summary of sub-cohort analysis
Overall, predictors of achieving a higher first year bachelor GPA were not attending a bridging programme for Māori and entering as a direct school leaver for Pacific students. Achieving a higher first year bachelor GPA was the most significant predictor of achieving a higher year 2-4 programme GPA for both Māori and Pacific students and increased the likelihood of graduating for nMnP. Achieving a higher first year bachelor GPA was also predictive of a higher chance of optimal completion for Māori and nMnP, and lower likelihood of 'less optimal' completion overall, for Pacific and nMnP. Passing all courses in the first year of bachelor study was also important for optimal completion for nMnP students.

Discussion
This study demonstrates that common predictors of academic success operate differently for Māori, Pacific and nMnP student groupings. While most of the predictor variables investigated showed significant predictive effects on academic outcomes for all student groups in unadjusted models, the impact of school decile (in early models not shown) and bridging programme participation was particularly important for Māori and nMnP students when controlling for other factors. Type of admission was particularly important for Pacific and nMnP students. The first year of bachelor level study was highly predictive of subsequent academic outcomes for all student groups investigated. Variables explored did not explain all of the differences in outcomes between ethnic groupings, thereby foregrounding the need to explore additional tertiary factors that may be operating.

Bridging foundation programmes
Mixed results were found when exploring the association between bridging programme participation and academic outcomes. This is consistent with expectations that our results may reflect both the negative impact of known academic and transitioning gaps that exist for students entering bridging programmes (Benseman et al. 2006), as well as positive effects of the bridging programme itself (i.e. aiming to address these gaps) (Madjar et al. 2010b). Note that completion of the FMHS bridging programme (CertHSc), and higher achievement (CertHSc GPA) within this programme has previously been shown to be positively predictive of achieving a higher first year bachelor GPA in FMHS programmes (Curtis et al. 2015b). The findings suggest that bridging programmes may help to address, but cannot 'immunise' Māori and Pacific students from, the impacts of academic and transitioning gaps prior to admission.

Daily struggles in tertiary environments-transitioning into racialised 'climates'
This study reinforces the importance of the first year of bachelor study as having a significant impact on academic results throughout tertiary health programmes. First year academic results were both predicted by pre-tertiary factors, and predictive of programme results. This aligns with known literature that discusses how the first year of health study is often an unsafe and daunting experience where students can be culturally isolated, experience racial discrimination, and are submerged in large (often 900?) class sizes in predominantly white institutions (Curtis et al. 2012a(Curtis et al. , 2014bMadjar et al. 2010a;Orom et al. 2013) and is noted as a key transitioning point through which Māori and Pacific students must overcome multiple challenges (Madjar et al. 2010b;Sapoaga and Van der Meer 2011;Sapoaga et al. 2013). Some of the inequities in academic outcomes between Māori or Pacific and nMnP students were explained by investigated predictors. The positioning of these predictor variables prior to or early on in tertiary education contexts does not dismiss the responsibility of the tertiary institution to address factors that may be operating to impact on Māori and Pacific student success throughout later years (year 2 onwards) in health professional study. In addition, some disparities in academic outcomes identified in this study remain unexplained. There is evidence that negative experiences in tertiary health study continue throughout the programme (Curtis et al. 2014b;Garvey et al. 2009), hence even equitable academic outcomes do not guarantee culturally safe and enjoyable experiences through tertiary study (Kaehne et al. 2014;Zorlu 2013). Mayeda et al. (2014) interviewed Māori and Pacific students across UoA who shared examples of both overt and subtle racist remarks often made by nMnP student peers and teaching staff.

Individual versus institutional focus
Common explanations for why Māori and Pacific students have lower educational outcomes are generally focussed on the individual student (F. Harris 2008) as opposed to the institution (Kasuya et al. 2003;Kay-Lambkin et al. 2002;Rich et al. 2011) citing 'cultural factors', institutional or cultural climate as avenues of explanation for disparities (Andriole and Jeffe 2010;Woolf et al. 2013). However, these explanations position the institution, although responsible for ensuring students success, as invisible to critique in the 'blame' frame (Bishop and Glynn 1999;Martin-McDonald and McCarthy 2008). Routinely collected data within the UoA take a similar approach meaning individual student-focused variables are more likely to be available for analysis rather than institutional measures.
Hesser, Cregler and Lewis (1998) studied medical school admission for African American students and stated that ''a substantial portion of unexplained variance … can be attributed to … static versus changing personal philosophies and commitments held by key institutional figures pertaining to the promotion of, opposition to, or indifference towards racial-ethnic diversity'' (p. 191)). A critical analysis requires measurement of institutional factors that may be predictive of student outcomes (e.g. the proportion of Māori and Pacific staff, culturally relevant course content, interventions that address racial discrimination, effect of class timetabling on part-time employment, and potential for flexible learning).

Academic preparation
The impact of secondary school results is theorised to have a significant effect on academic outcomes for this cohort however the ability to carry out this work for the full cohort was not possible due to a change in academic achievement systems in 2005 (with school results only available for half of the cohort). A separate analysis using on the cohort for whom school results were available has been completed and have been reported elsewhere (Wikaire 2015).

Strengths
This study includes a comprehensive quantitative analysis of student data that has not previously been undertaken in a NZ context. The use of Kaupapa Māori methodology, informed by Pasifika methodology, is a particular strength of the project as it foregrounds Māori and Pacific worldviews and allows data analysis in a way that may not otherwise have been completed (Smith 2012;Vaioleti 2006). This has allowed comparison of ethnic groups and exposes racism and privileging of particular ethnic groups over others (Borell et al. 2009). By adopting a non-deficit analysis, this research acknowledges that it is not 'ethnicity' that is to blame for academic disparities, but something about the environments created by institutions and the associated experiences that privilege some ethnic groups over others (Harris et al. 2013). This means moving beyond simple description to interrogation of why these experiences are occurring and how these environments manifest such phenomena.

Limitations
This study was limited by the available data that are routinely collected by the UoA and the way in which they are collected, limiting the ability to analyse and report on student data by ethnicity in various ways (Education Counts 2012). The ability to explore the impact of socioeconomic status using a relevant measure was also limited by available data. Hence, the study used school decile as a proxy for socioeconomic status and acknowledges that this may limit the study findings (Engler 2010).

Interpretation issues
Pre-prioritisation of the data means that students who identify with both Māori and Pacific ethnic groups will be included in the Māori group only. Considering the importance of the first year of tertiary study as highlighted within relevant literature and the present study findings, this study does not include students who may have entered the first year of bachelor study, but did not continue their academic pathway in year two, therefore eliminated the stories of those students who experienced academic difficulty that led to attrition in year one.
In NZ the UoA FMHS, and Vision 20:20 in many ways is leading innovation and success in Māori and Pacific students in this area (The LIME Network 2013). Given this context, combined with this particular institution, the findings of this study may represent inflated results in some areas. For example, high entry criteria may mean that the students within this sample are likely to represent those school leavers with higher academic achievements than the national average. The Māori and Pacific students in this sample therefore also demonstrate academic outcomes that have been influenced (we assume positively) by the multifaceted recruitment and retention interventions in the FMHS context.

Conclusion
The findings of this research demonstrate the impact school results and socio-demographic factors have on academic outcomes at tertiary level. Importantly, these factors impact negatively on Māori and Pacific student outcomes to a greater extent than for nMnP students. This research shows that the impact of pre-tertiary and early academic variables extends further than the first year of bachelor level study and that these variables do not fully explain the differences observed in academic outcomes for Māori and Pacific students. This highlights the need for tertiary institutions to extend focus beyond admission and transitioning phases, and provide on-going support for Māori and Pacific students throughout tertiary health programmes. Furthermore, use of new and innovative teaching and learning approaches that cater for students from lower socio-economic and indigenous backgrounds is warranted. Critiquing what role the tertiary institution can play in the elimination of academic inequities for Māori and Pacific students studying towards health professional degrees is necessary.


A STUDY OF E-LEARNING: INDIAN PERSPECTIVE

: Education has become a basic need of a human being. Every Individual must need to take education to become a self-developed person. Also, Modern Education provides promised employment which results in betterment of economy of a country. E-learning has provided a meaningful way of education. This study lights up on the aspects related toe-learning solutions which also includes the study e-learning pertaining to Education and society and the changes which are being faced in educational systems. This study then provides a summarization about the impact safe-learning on education and society .

And using various technologies.

Impact of E-Learning on Education:
 E-learning makes the class rooms interactive which convert the School into learning environment.  Interactive classrooms lead to funny way to learn.  E-learning helps in active participation of students and teachers.  The automate nature of E-learning provide animations for different concept which in turn helps in gaining knowledge better.  E-learning and teaching also provide teachers with a large database of questions and also capable of helping to overcome the problem.  Teachers can also upload content online; create question paper and examine student's performance.  As ICT becomes integrated with the curriculum, it provides an audio-visual mode of learning that automatically develops the memory of the students.  E-learning has different types of contents live animations, videos, selfexplanatory diagrams, quizzes, eBooks and past year questions papers, all of which are regularly updated.  It provides effective teaching and learning means in classroom for teachers and students with user-friendly GUI. One of the best example of impact of e-learning on education in India is National Program on Technology Enhanced Learning (NPTEL), which is being funded and initiated by Human Resource Development (HRD) ministry. The six major engineering have streams been covered in this project for undergraduate level. The main purpose of NPTEL are, to make lectures in a video format for broadcasting that provides quality content to create e-learning material that would be make in such a way to meet the needs of engineering and other professional course across the country [8].
To make e-learning material available in the internet for the video lectures to compliment class room teaching. The NPTEL has developed curriculum based video courses. This is undertaken by seven IITs, IISc Bangalore as Partner Institutions (PI) and some other selected premier institutions as Associate Partner Institutions (API). In addition to this, a number of core courses common to all Engineering programs such as mathematics, physics, chemistry, management, Electronics, language etc, have also been included [9]. The main aim of the project NPTEL is to facilitate the competitiveness of industry in the global markets by improving the quality of education. Another objective of NPTEL is to make high quality learning material available to students across the country by using the advances in information and communication technology. The target group for this project consists of students and faculty of institutions offering undergraduate programs in India. A formal Memorandum of Understanding (MoU) between five IITs, Four IIMs and CMU established a Virtual Centre for Technology Enhanced Learning (VCTEL).

Social Impact of E-Learning:
The biggest impact of access to e-learning on various members of society could be significant. Apart from the improved skills, source of income and employment effects on graduates. Others impacts of education are improved health and prosperity, specifically among girls and women. This section on societal impacts briefly considers these types of impacts of education and then considers more closely the relationship between e-learning and society-specifically how the social content can affect the capacity of e-learning programs to furnish their potential benefits to all students. Social aspects such as living in a rural place, a person or a student speaking a different language can all get access and use of e-learning application. E-learning model is getting popular in recent years in the name of in-line education [10 -12].
One of the best impacts of e-learning on society is that of improved health. Because of this, one of aims of Development Goal Five regarding improving health is to promote girls who are going school, particularly primary school. As stated in the Goal (UNICEF 2011),"Educating girls for six years or more drastically and consistently improves their prenatal care, postnatal care and childbirth survival rates. Educating mothers also greatly cuts the death rate of children under five. Educated girls have higher self-esteem, are more likely to avoid HIV infection, violence and exploitation, and to spread good health and sanitation practices to their families and throughout their communities [13]. And an educated mother is more likely to send her children to school."Further impacts that specifically post-primary education has been to have In close reducing poverty, delaying girl's marriage, and consolidating decision making power. Secondary education plays a vital role in preparing for a long-term learning perspective. These are all the reasons why governments have been investing in compulsory primary school education and expanded secondary school education [14].The education feed by the secondary schools may not be accessible to all parts of society or all areas of a country, hence e-learning has been found to affect this access to education. Research has shown that a lack in access to ICT created gap between countries, and an even larger gap subsist within countries between rural and urban areas, between women and men, and between poor and rich [15].
E-Learning has the ability to play a transitive role in a society. Culture also can play an important role affecting how technology and e-learning are adopted and adapted and how successful they actually are improving learning. The capable transformative role of e-learning often runs against educationalist choice for teaching in traditional ways that do not affect life in classrooms. Educators tend to use e -learning aspect in culturally familiar ways that may lower their effectiveness.
Language also can affect the e-learning programmes and specifically when learning software [16]. Learning materials and Internet is in a standard language in which many students and teachers are not good. In India for example, students switch from learning in their respective mother tongue in primary school to English as the language of first preference in secondary school. The students often took some English language classes in primary school, but many are not very well versed to learn into an English-only mode [17]. At the same time the most used language on the Internet is English, and most software and learning materials are in English. Students and teachers with limited knowledge of English may be marginalized. E-Learning can thus require to be becoming proficient in reading a second language before its capability can be met. However, e-learning and the Internet can also be a good motivator for students to learn English and other foreign languages popular on internet, is working to diminish the language barrier [18][19][20][21].

Conclusions:
We have now realized the critical importance of education for economic and social development. Schools and colleges are turning into e-learning rapidly. Many schools and colleges have started teaching e -learning programs. This paper is attempted to light upon a research on e-learning impacts and to know significant practices to add new and on-going e-learning programs. The teacher is important to thee-learning environment. For the students to have a successful e-learning experience the teachers would need to adopt new perspectives. These transitions may impact of the teaching skills; they will require new set of skills and abilities to prepare for e-learning environment. The implementation of e-learning in an official setting requires further strategic planning. Changing the educational teaching through technology requires use of effective implementation tactics. Implementing any type of approaches that involves transition and differ how people work can overcome difficulties for an Organization. This paper has clearly provide the information as to how e-learning has helped the education and the society for the betterment of people and country which in turn helps to emancipate all in general. 6. References:


extfieldsofstudy: Engineering Physics

text:
The Effect of STEM Learning through the Project of Designing Boat Model toward Student STEM Literacy

STEM Learning focusses on development of STEM-literate society, the research about implementation of STEM learning to develope students’ STEM literacy is still limited. This study is aimed to examine the effect of implementation STEM learning through the project of designing boat model on students STEM literacy in energy topic. The method of this study was a quasi-experiment with non-randomized pretest-posttest control group design. There were two classes involved, the experiment class used Project Based Learning with STEM approach and control class used Project-Based Learning without STEM approach. A STEM Literacy test instrument was developed to measure students STEM literacy which consists of science literacy, mathematics literacy, and technology-engineering literacy. The analysis showed that there were significant differences on improvement science literacy, mathematics technology-engineering between experiment class and control class with effect size more than 0.8 (large effect). The difference of improvement of STEM literacy between experiment class and control class is caused by the existence of design engineering activity which required students to apply the knowledge from every field of STEM. The challenge that was faced in STEM learning through design engineering activity was how to give the students practice to integrate STEM field in solving the problems. In additional, most of the students gave positive response toward implementation of STEM learning through design boat model project.

Introduction
Science integration with other fields in learning has done by some of developed countries by developing STEM (Science, Technology, Engineering and Mathematics) education. The purpose of STEM education is to develop STEM-Literate society, the 21st century STEM education requires students as future citizens to apply knowledge from STEM discipline in real life [1]. STEM literacy as an ability to identify, to apply, and to integrate the concept of science, technology, engineering, and mathematics to innovate, and to solve the complex problems [2]. Those two definitions have the same idea that STEM Literacy is the ability of students in applying knowledge from STEM discipline to solve real-world problems. The integration of STEM in learning can be applied through design engineering activities. Engineering design can increase students' abilities to solve complex problems [3].
Several studies show that STEM lesson can be reached through Project-Based Learning (PBL). PBL increased engagement, classroom culture and interest in STEM [4], PBL can develop students creativity [5][6] [7]. Characteristics of PBL STEM are an ill-defined task and a well-defined outcome. The students received contextual assignments to solve some problems by integrating the concepts of science, engineering, and mathematics. [10]. Studies on learning that can improve STEM literacy are still limited, some of which focus on improving one aspect of literacy. One study revealed that PBL learning can improve students science literacy on aspects of competence and attitudes, it also gives some recommendation to integrate Project-Based Learning with STEM approach in the lesson [11]. One of the STEM literacy development research proposed STEM literacy can be achieved through Scientific Imagineering through AR which consists of imagining, studying and researching, designing, developing, presenting and evaluating steps. Based on the evaluation of experts, experts strongly agree that Scientific Imagineering through AR can improve STEM literacy [12]. However, this research has not discussed the results of implementation Scientific Imagineering through AR in the learning process and how the achievement of student STEM literacy.
The study on learning to improve STEM Literacy of students needs to be implemented, one of which is by integrating STEM approach in the learning process. Thus, this study tried to investigate the achievement of student STEM literacy through STEM learning. In this study, Ill-defined tasks are given to the students by providing the challenge of how to apply the principle energy transf formation in the process to make a boat model which move in the fast and stable way by integrating the concept of science, technology, engineering, and mathematics.
Energy concept learning through engineering design activities expect students to apply the concept of energy conversions in designing steam-rubber band powered boat. Concepts of energy conversions and energy conservation laws are applied when students design boats to move faster. Energy can be changed from one form into other forms. Elastic potential energy is the energy stored in elastic materials as the result of their stretching or compressing. Elastic potential energy can be stored in rubber bands. The elastic potential energy (PE) stored in the rubber band is: Where, x is the distance of rubberband stretches from its equilibrium (m) and k is rubberband constant (N/m). In case rubber band powered boat, the value of x influence by the number of winds. The potential energy stored on the rubber band will turn into kinetic energy that can move the boat. The kinetic energy (KE) of rubber band powered boat is given by: Where, m is mass of boat (kg) and v is velocity (m/s). Based on equations (1), (2) and the law of conservation of energy, it can be concluded that to increase the kinetic energy of the object can be done by increasing the potential energy. Through engineering design activities, students can propose several ways to obtain maximum kinetic energy of the boat. Students can propose many ideas and approaches to solve the problems with more than one possible solution to implement. [13].

Research Design and Participant
This study is a quasi-experiment study with nonrandomized pretest-posttest control group design [14]. In this study, experiment group was given Project Based Learning treatment with STEM approach and control group was given Project Based Learning treatment without STEM approach. The participants of this study were 8th-grade students in a junior high school in Kuningan-West Java. Fifty-six students were divided into two classes, 28 students in experiment class and 28 students in control class.

Data Sources and Analysis
The measurement of students STEM literacy used multiple choice problem solving, which consist of science literacy, mathematics literacy, and technology and engineering literacy. The instrument of science and mathematics literacy test was developed based on indicators of science and mathematics literacy assessment which is developed by Programme for International Student Assesment (PISA), whereas instrument of technology and engineering literacy test was developed based on indicators of technology and engineering literacy which is developed by National Assesment Educational Progress (NAEP). The instrument of STEM literacy test was validated by five expert judgements, the data of validation result were analyzed with CVR (Content Validity Ratio) method [15]. The result used CVR method, STEM literacy test shows Instrument Content Validity Ratio was 0.97. The result of instrument reliability which analyzed by SPSS 22 shows that coefficient alpha Cronbach's was 0.71.
The enhancement of students' STEM literacy is known by analyzing normalized gain (<g>) on experimental group and control group [16]. Furthermore, to examine the singificant differences of STEM literacy improvement, the data were processed with IBM Statistics SPSS 22 software. In comparing the two groups, effect size Cohens (d) can be computed by subtracting the mean of the second group from the mean of the first group and dividing by pooled standard deviation of both groups [17]. In addition, to get information about students' respond toward implementation of STEM Project-Based Learning, the researcher gave a questionnaire to the students and analyzed percentage and its interpretation from every statement that was given.

Experimental and Control Group Treatments
Learning stage of the experimental class used STEM-Project based learning model consisting of reflection, research, discovery, application and communication [18]. These learning steps correspond to engineering design process that include identify the problems, research, ideate, analyse ideas, build, test and refine, and communicate [19]. Both the experimental and control classes are assigned to make steam-rubber band powered boat. The main difference is in the experimental class is redesign activity to get best boat performance. Engineering design activity required students to apply their understanding of science, mathematics and technology in making projects.

Result and Discussion
The data analysis of STEM literacy improvement among the students is classified based on the STEM literacy components, namely: science, technology engineering, and mathematics. The difference of the STEM literacy improvement between the class applying the STEM Project-based learning and the class applying Project-based learning without the STEM approach can be seen in Table 2. *α = 0,05 **p-value<0,05 Table 1 shows that, in general, the STEM literacy improvement among the students in the experimental class is higher than the control class. The mean difference test results indicate that there is a significant difference in STEM literacy improvement between the control class and the experimental class. The highest improvement was achieved in the literacy components of technologyengineering, while the lowest increase was achieved in the literacy component of mathematics. From all components of STEM literacy, PBL-STEM Learning had a large effect (effect size  0.8).
The improvement in the literacy component of science consisted of students' ability to explain scientific phenomena as well as interpreted data and evidence scientifically. The class which taught by PBL-STEM was trained to be able to predict scientific phenomena by designing boat activities undertaken at the discovery stage. The challenge the teachers faced in boat designing activities was the lack of students' knowledge and experience related to project tasks. The activity of design drawing trained students' scientific imagineering since they had to be able to predict what would happen if they made the design [20]. The process of scientific imagineering was based on prerequisite knowledge (the concept of force, pressure, density), and energy concepts that they obtained at the research stage. The application of the scientific concept in boat design was closely related to the indicator of technology-engineering literacy that i.e. to understand the principles of technology and develop solutions to achieve goals. Figure 1 shows an example of a boat design sketch that students have made.  [3]. Students need to apply the concept of science, technology-engineering, and mathematics in designing and making products. At each stage of product manufacturing, students conceptualize related science. This experience enables students to build understanding of the relationships between concepts [22]. Engineering design activities in PjBL-STEM learning can help students understand the content of science over a long period of time [23].
The STEM literacy in the literacy components of technology-engineering consists of indicators of understanding the principles of technology and developing solutions to achieve goals. The technologyengineering literacy assessment examines students to be able to propose solutions and alternatives, selecting the right materials and troubleshoot malfunctions. Activities that train students' technologyengineering literacy are carried out at the application stage. The activity of boat modeling that apply the principle of energy change requires students to be able to think critically and creatively in overcoming any obstacles found in the boat making process. Students should think how to overcome the leakage of steam systems, create larger steam pressures, make steering propellers, and modify other components to improve boat performance. Therefore, students need creativity and critical thinking to develop solutions to achieve goals. This is consistent with previous research stating that PjBL-STEM learning can enhance student creativity, students' thinking skills, and problem-solving skills [23]. STEM-based learning can encourage students to improve the quality of planned products [24]. Figure 2 shows an example of boat products that students have made.  The answers of student 1 and 2 show that students have applied the concept of energy conservation law. In this case, to obtain maximum kinetic energy, students had to provide maximum potential energy by increasing the number of propeller rotations and increasing the amount of rubber used. Increasing the number of rotation of the blades means increasing the amount of rubber length (x); meanwhile, adding the amount of rubber used means adding the value of the rubber constant (k). it was similar with the student 3 who proposed solutions by increasing the number of candles burned; it meant increasing the amount of heat provided to accelerate the production of steam. Student 4 proposed solutions by minimizing the steam channel. It meant that students have been able to apply concepts related to style and pressure. To produce a large thrust power, great pressure is required. Efforts to increase the pressure is conducted by minimizing the area of the steam channel. Engineering design activities show that in STEM learning, students can propose more than one solutions to make the boat move faster [13]. One of the differences in treatment between the experimental class and the control class was the existence of a boat racing competition conducted at the end of the lesson. This boat racing competition required students to redesign the boat in order to have the best performance. Previous researches have shown that engineering design competitions can improve students' enthusiasm in learning. The winners of the competition could apply the concept well and design the boat based on their own knowledge and used the strategy to achieve their goals [25] The research results in the mathematical literacy indicate that there is a significant difference of mathematical literacy between the experimental class and the control class. Some activities of integrating mathematics in boat making are deciding boat shape design, using mathematical equations in managing boat making cost, measuring distance and time, calculating boat speed, and making charts. This result is in accordance with the theory that one of the advantages of PjBL-STEM learning is to improve the learning outcomes of mathematics. [18]. Mathematical literacy is one of the foundations in STEM education; mathematics gives a role to train students in analyzing data and providing explanations based on the available data [21].
Based on the research results described previously, it can be concluded that STEM learning through designing boat project gives a large effect in improving the science literacy, technologyengineering literacy, and mathematical literacy. The obstacle experienced by teachers in applying STEM learning was the lack of readiness of students in boat design activities due to weak knowledge and experience of students related to the project tasks. This led the time required for boat designing activities was longer than the planned time.
Based on the analysis results of students' responses of STEM PBL, it can be concluded that students were more motivated and interested in learning science after experiencing a boat design learning. Most of the students were enthusiastic in designing activities and making boat model and participating in boat racing competitions. The experience in engineering design activities was new for students; therefore, they need to be trained on how to integrate science, technology, engineering and mathematics in solving problems.

Conclusion
Based on the results of the analysis and discussion, STEM learning through the boat designing project that has been implemented has a significant effect on the improvement of students' STEM literacies in the literacy component of science, mathematics, and technology-engineering. The highest improvement in STEM literacy was obtained in the literacy component of engineering-technology since the students experienced in the engineering design process. Students' knowledge understanding related to the project task influences the engineering design activities. Therefore, it is important to identify the students' initial understanding before assigning the project tasks so that teachers can reemphasize the concepts required for project works. Mostly students show a positive response to the STEM learning that has been implemented; in addition, students were more motivated in learning science.