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The influences of student- and schoolhouse-level factors on applied science undergraduate educatee success outcomes: A multi-level multi-schoolhouse report

International Journal of STEM Education volume 9, Article number:23 (2022) Cite this commodity

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Abstract

Background

Given the relatively low graduation and retentiveness charge per unit in undergraduate engineering programs in the United States, the factors that influence educatee success outcomes need to exist examined. However, express research systematically studied both student- and school-level factors and how they influenced undergraduate technology student success outcomes. We gathered responses from 458 technology undergraduate students in a cross-sectional multilevel multi-school (14 schools) survey. These 14 schools included both large country universities and liberal arts colleges. The survey measured various educatee-level factors, including demographic, skills, and personality variables, along with seven school-level factors, such as student–kinesthesia ratio and schoolhouse type (i.e., public versus private). The information were analyzed using the hierarchical multilevel modeling approach.

Results

The results showed that female person students reported better outcomes than male person students, racial minority students reported meliorate outcomes than White students, but first-generation students reported poorer outcomes. Communication competency was associated with student learning outcomes, GPA, and plan satisfaction, whereas conflict management preferences were not significantly correlated with any educatee success outcomes. The results of the school-level factors' influences on pupil success outcomes were non consequent, simply some factors, such equally educatee–faculty ratios and diversity charge per unit, were significantly related to some student outcomes.

Determination

Technology education is a complex, multi-faceted issue that requires more collaborative and systematic research. We promise our findings assist educators empathize the dissimilar factors that could potentially influence engineering students and inform improve plan design and policymaking.

Introduction

Student success issue is arguably one of the important topics in didactics research. Student success outcome is largely divers every bit a variable that assesses how well students are prepared to accomplish their electric current and future academic, personal, and professional goals (Kuh et al., 2006). In recent years, more than engineering education inquiry has studied student success outcomes given the relatively low graduation and retention rate in engineering science programs compared to the national charge per unit of students across all majors in the U.s.a. (Marra et al., 2012). Various types of higher education organizations accept made all-encompassing efforts in recruiting more students to enroll in engineering education (Van den Bogaard, 2012). As a result, undergraduate engineering enrollment has been on the rising in the past ten years, with 622,502 full-time undergraduate engineering students enrolled in 2018 (Roy, 2019). Notwithstanding, a large number of students transfer out of technology majors or drib out of the university prior to graduation. Over the last 60 years, the average engineering programme graduation rates have consistently hovered effectually a critically depression fifty% in the United States (Geisinger & Raman, 2013).

Previous studies in applied science education identified a set of factors that could straight or indirectly influence student success outcomes, such as gender, race, and family educational background (Bossart & Bharti, 2017; Fletcher et al., 2021; Smith & Lucena, 2016). However, those factors are mainly at the educatee level, and schoolhouse-level factors such equally school type (e.g., private vs. public) and diversity charge per unit are not well studied. In addition, many studies recruited students from a express range of schools past only including students from i blazon of school. Although some enquiry studies on students from multiple schools (Marbouti et al., 2021), there is a sparse representation of school diversity, such equally the inclusion of technology students from liberal arts schools. Notwithstanding, the number of liberal arts engineering science students has been quickly growing in the United States (Koshland, 2010). To expand the electric current literature, this written report systematically examines the influences of student- and school-level factors on student success outcomes by using a multi-school multilevel approach, and we further include liberal arts applied science students in our multi-school approach.

Literature review

Given the circuitous nature of technology education, previous research used a variety of ways to conceptualize and operationalize educatee success outcomes in engineering education, such as higher grades, satisfaction, and learning outcomes (Bean & Eaton, 2001; Kuh et al., 2006; Marbouti et al., 2021). Although the conceptualization of educatee success outcomes varies profoundly, there is consistent consensus on the importance of understanding them (Kuh et al., 2006). The current report conceptualizes student success outcomes as academic achievement, satisfaction, and learning outcomes, and the study further examines the potential influences of the school and educatee factors on these outcomes. To the best of our cognition, the current study conceptualized student success outcomes these ways every bit they were the most common conceptualizations in the electric current college education literature. To do so, the current study uses instructive perspectives (Tinto, 1987) as the theoretical framework to gain a more comprehensive exam of potentially relevant factors at both student and school levels. Influences on student success outcomes could exist at sociological, organizational, psychological, cultural, and economical levels (Tinto, 1987), which could be categorized into ii levels: pupil and school.

Student-level factors

Pupil-level factors in the current written report are identified from the sociological, economic, psychological, and cultural perspectives that could influence student success outcomes in the instructive perspectives (Tinto, 1987). First, students' demographic characteristics could influence educatee success outcomes, and previous research ofttimes has operationalized demographic characteristics as historic period, gender, race, household income, and family unit educational background (French et al., 2005; Kuh et al., 2006). For example, Blackness students, who are highly underrepresented in engineering educational activity, face pregnant challenges compared to students from other racial/ethnic groups (Fletcher et al., 2021). In addition to race and gender, first-generation students from families where no parents or guardians have earned a baccalaureate degree tend to perform poorer on student success outcomes than those who are non (Martin et al., 2020). In-depth interviews revealed that showtime-generation students majoring in applied science felt that they did not vest in engineering because engineering coursework is distancing them from their family unit members, specifically parents (Smith & Lucena, 2016). Get-go-generation students also reported that they did not have the aforementioned social, cultural, or economic capital equally their peers (Martin et al., 2020). They felt that none of their funds of cognition, bodies of knowledge and skills such as automobile or plumbing repairs that working-course families possess to survive and make a living (González et al., 2006), were validated in their technology education. This fabricated their sense of isolation worse (Smith & Lucena, 2016).

In addition to demographic characteristics, certain relevant skills and personality styles might also influence student success outcomes in undergraduate engineering education, namely, communication competence and conflict direction styles. Students' skills and personality styles are critical for their success in their undergraduate education (Bean & Eaton, 2001). Communication competence refers to the skills of effective and appropriate communication and the ability to utilise and adapt that cognition in various contexts (McCroskey & McCroskey, 1988). Unlike from rudimentary communication skills, communication competence is a more circuitous concept that reflects a student'southward physiological and psychological characteristics and ability to accommodate to various social and cultural contexts (McCroskey & McCroskey, 1988). Student success outcomes in all academic settings are closely related to advice competence because students are frequently required to interact with faculty and peers, especially when seeking data, having in-course discussions, and asking questions (Goldman, 2019). Recent research has shed some light on the importance of advice competency in Stalk education (Wilkins et al., 2015; Yeke & Semerciöz, 2016), so the study further advances the agreement by examining how communication competence impacts student success outcomes.

Another important student-level factor that could influence student success outcomes is disharmonize direction style. Teamwork has been recognized equally an essential skill for engineers as teamwork is regularly integrated into engineering fields (Van den Beemt et al., 2020). The differences in goals, perspectives, and emotions in teamwork could lead to conflicts that negatively affect squad performance and productivity (Liu et al., 2008). How the conflict is handled is more important to the teamwork success than the disharmonize itself (Paul et al., 2004). 5 styles of handling conflict behaviors have been identified in the current literature: collaboration, competition, avoidance, accommodation, and compromise (Liu et al., 2008). The previous research indicates that more collaborative conflict direction styles are more likely to be associated with positive squad performance outcomes (Montoya-Weiss et al., 2001). Avoidance is more likely to hurt team performance because it means that the team cannot bring its full range of resources in decision-making (Chang & Lee, 2013). Satisfactory and successful team performance could motivate students to be more engaged and further heighten students' academic integration (Liu et al., 2008). Thus, sure conflict management styles could either be positively or negatively associated with student success outcomes. Based on the previous literature and the instructive perspectives, the electric current written report asks the following research question about student-level factors:

RQ1: How practise student-level factors, including (a) demographic characteristics, (b) communication competency, and (c) disharmonize management styles influence undergraduate engineering students' success outcomes, including bookish achievement, learning outcomes, and satisfaction?

School-level factors

The schoolhouse-level factors include factors from the organizational and economic levels based on the instructive perspectives (Tinto, 1987). Organizational and economic levels emphasize the school's structural-demographic characteristics and processes that are thought to influence student success outcomes (Kuh et al., 2006). Based on the previous literature, the school-level factors that could influence these experiences include school size, schoolhouse type, student–faculty ratio, tuition, graduation and retentivity rate, and diversity rate. Starting time, the interaction between faculty and students, often measured by the student–faculty ratio, could frequently influence students' experiences and success (Marra et al., 2012). Many schools often employ a low student–kinesthesia ratio as a selling point to those choosing schools for tertiary didactics. Second, pupil peer group characteristics such as racial/ethnic diversity, average socioeconomic status (SES), and age could influence peer experiences, which somewhen could influence student success outcomes (Titus, 2004). Many studies have suggested that diverse peers in the learning environment could improve intergroup relationships and mutual agreement past challenging students to refine their thinking (Morales et al., 2021). These interactions and experiences would influence the style students retrieve, behave, as well as the overall satisfaction with the college feel and perceptions of the campus climate.

Lastly, schoolhouse structural-demographic characteristics could potentially impact student success outcomes. The schoolhouse size and blazon take been specifically linked to pupil success (Titus, 2004). For instance, unlike types of higher pedagogy schools (e.g., public versus private; liberal arts versus large state schools) have drastically dissimilar education missions and policies supporting those missions. Each type of school'southward value could determine the overall schoolhouse surroundings for students and eventually impact their success outcomes (Terenzini & Reason, 2008). Specifically, studies found that students from private liberal arts colleges accept a college level of appointment, whereas students at large public universities were more satisfied with their college experiences (Hu & Kuh, 2002; Kuh & Siegel, 2000). Thus, based on the previous literature, the current report asks the post-obit research question about school-level factors:

RQ2: How do school-level factors, including (a) school type, (b) school size, (c) educatee–faculty ratio, (d) estimated toll of yearly attendance, (e) the 4-year retention rate, (f) the 4-year graduation rate, and (thousand) the multifariousness rate, influence undergraduate engineering science students' success outcomes including academic achievement, learning outcomes, and satisfaction?

The current written report and multilevel approach

The factors that could influence educatee success outcomes are complex and multi-faceted, and the current literature tends to focus on student factors. Even so, as ane would presume, the pupil characteristics and the schoolhouse surroundings would concurrently influence student success outcomes. As many previous instruction research projects have examined, understanding the different levels of factors would more accurately account for variances in student success outcomes (eastward.g., Ma & Klinger, 2000; Ma & Ma, 2014; Titus, 2004). Withal, to the best of our knowledge, no previous study has examined the concurrent influences of both individual determinants and the bookish surround on undergraduate engineering students, particularly those in liberal arts colleges. Thus, the current written report uses a multilevel arroyo to address this gap in the current research, which is particularly suitable for the following reasons. For one, the multilevel approach allows a comprehensive assay of how educatee-level factors touch pupil success outcomes while accounting for the schoolhouse-level variance and vice versa (Ma & Ma, 2014), for example, if nosotros want to examine how the household income affects student GPA. However, students are clustered inside different schools where average household incomes are significantly different between schools. Thus, the multilevel approach accounts for both inside-group and between-group variances where the average household income at that specific school is part of the analysis. Such a linear hierarchical approach could more effectively analyze the naturally nested structure in education information (Raudenbush & Bryk, 2002). Moreover, the multilevel approach tin can examine the magnitudes of influences from each level and would reply the applied question of whether individual student or schoolhouse environs matters more. Thus, we further ask the following research question:

RQ3: How much do student-level factors versus school-level factors insufficiently influence undergraduate engineering students' success outcomes?

Methods

The current study conducted a multi-school survey with undergraduate engineering science students in the Midwestern and Northeastern United states. The study recruited students from 14 college didactics organizations in 2 regions of the The states, ranging from big state universities to liberal arts colleges. Based on the current literature and our enquiry questions, the survey assessed student success outcomes and pupil-level demographic factors, and the inquiry team gathered relevant school-level information by contacting each school.

Procedures

We recruited undergraduate students who were enrolled in an technology program in the Midwestern and Northeastern Us by using convenience sampling. Convenience sampling is a non-probability sampling method that recruits a conveniently available grouping of participants. In this study, a recruitment email was sent to a total of 921 full-time instructors and faculty members at 18 higher educational activity organizations, and nosotros asked the instructor/faculty to share the survey link with their current students. A total of 532 students from 18 different schools responded to the survey, merely we removed responses from four schools from the dataset due to a depression response count (n < x) from each school.

After collecting consent to participate, the survey nerveless demographic data including historic period, sex activity, race, classification (besides known equally yr level; i.e., freshman, sophomore, junior, senior), household income, the name of the school, and whether the pupil was a starting time-generation college educatee (i.e., nobody from the core family unit has e'er graduated from college). Then the survey assessed students' communication competency, conflict management styles, self-reported learning outcomes, current GPA, satisfaction with the university, and satisfaction with the engineering programme. Lastly, nosotros nerveless the student's school electronic mail addresses to send each participant an Amazon gift card every bit bounty and prevent duplicate responses. After the survey, we collected information about the 18 schools which had educatee responses. Nosotros used the information published by National Middle for Education Statistics, and the information included the school blazon (i.east., public or private), university size (i.due east., electric current number of students enrolled), the student–kinesthesia ratio at the schoolhouse, estimated yearly toll of attendance (i.eastward., published tuition based on 24 credit hours per academic year), the 4-year retention rate of the school, the four-year graduation rate of the school, and diversity rate of the school (i.e., per centum of students who identified as non-White/Caucasian).

Participants

Nosotros recruited a total of 514 undergraduate engineering students from 14 different schools. We removed 56 cases with a significant amount of missing information (i.eastward., 50% of the survey response was missing). The final sample consisted of 458 undergraduate engineering students from fourteen different higher didactics schools in the Midwestern and Northeastern United States. The historic period of the students ranged from xviii to 49 (M = 20.93, SD = 2.99). Nigh participants were male (northward = 297, 64.8%). Most participants identified as White/Caucasian (n = 368, fourscore.three%); 22 (4.viii%) identified equally Blackness/African American; 34 (7.four%) identified as Asian; 18 (3.ix%) identified as Latinx; 5 participants (1.1%) identified every bit Heart Eastern; 11 (ii.4%) participants identified as multiracial. The concluding sample had a relatively even distribution among freshmen (n = 112, 24.five%), sophomores (n = 93, 20.3%), juniors (n = 111, 24.2%), and seniors (due north = 141, 30.viii%). Out of the 458 participants, 118 (25.75%) identified as first-generation students. Lastly, participants reported a broad range of household income from less than $10,000 a yr to more than $150,000 a twelvemonth, with the median household income being between $70,000 and $80,000 a twelvemonth.

Survey instruments

Learning outcomes

We measured learning outcomes using self-reported GPA and a sixteen-item survey instrument created based on the Associations of American Colleges and Universities (AACU) guidelines on important learning outcomes for engineering students. The educatee's GPA ranged from i.88 to 4.33 on a iv-indicate system (M = three.45, SD = 0.45). AACU listed 4 categories of essential learning outcomes, including intellectual and applied skills, communication and collaboration skills, personal and social responsibility skills, and advanced learning skills. Following the established tool of cess (Ma & Klinger, 2000), we used 16 items to assess how ofttimes a educatee applied each skill in their engineering education on a 4-point Likert-type scale (1 = never; 4 = often). Higher scores indicated more frequent applications of essential skills learned in technology instruction. The items formed a mensurate (M = 2.98, SD = 0.89) with acceptable reliability (α = 0.87).

Satisfaction

We measured students' satisfaction with their plan and university. We used five pairs of reverse adjectives (i.eastward., bad–good, harmful–beneficial, unimportant–of import, invaluable–valuable, uninspiring–inspiring) on a 7-point semantic differential scale to evaluate students' satisfaction with their programme and university. College scores indicated more than favorable evaluations of their programme and university. The items formed a program satisfaction measure (1000 = 5.71, SD = ane.05) with acceptable reliability (α = 0.85) and a university satisfaction measure (M = 5.44, SD = one.32) with slap-up reliability (α = 0.93).

Student-level factors

We collected students' age, sex activity, race, classification, household income, and whether the student was a first-generation college student. The descriptive statistics of these variables were reported in the Participants section. In addition, we used 12 items from a previously validated scale (McCroskey & McCroskey, 1988) to measure participant's self-perceived communication competence with "various communication contexts (i.e., public, meeting, grouping, dyad) and receivers (strangers, acquaintance, friend)" (McCroskey & McCroskey, 1988, p. four). The items measured participants' self-evaluations of communication competence on a x-point Likert-type calibration (1 = completely Incompetent, 10 = competent). The items formed a mensurate (K = 7.52, SD = 1.52) with peachy reliability (α = 0.92).

The survey measured 5 different conflict management styles (i.e., collaboration, competition, avoidance, accommodation, and compromise) using 15 items. The measures were adopted from previous research on conflict management styles (Liu et al., 2008). Three items were used to mensurate each conflict way on a 4-indicate Likert-blazon scale (1 = rarely, 4 = always). A higher score on a conflict management fashion sub-scale indicated stronger associations with that style. Three items (e.g., "I discuss issues with others to try to find solutions that see everyone's needs.") formed the collaboration sub-calibration (Thousand = i.88, SD = 0.57) with adequate reliability (α = 0.72); three items (eastward.g., "I would argue my case and insist on the advantages of my point of view.") formed the contest sub-scale (M = two.46, SD = 0.61) with acceptable reliability (α = 0.69); three items (east.chiliad., "When I find myself in an argument, I usually say very little and try to leave equally soon every bit possible.") formed the avoidance sub-scale (M = 2.72, SD = 0.73) with acceptable reliability (α = 0.82); three items (e.g., "I try to meet the expectations of others.") formed the adaptation sub-scale (M = 2.02, SD = 0.61) with bully reliability (α = 0.90); three items (e.m., "I try to negotiate and use a give-and-take approach to problem situations.") formed the compromise sub-scale (M = 2.20, SD = 0.57) with great reliability (α = 0.91).

Schoolhouse-level factors

We gathered the information nearly whether the school was a public or individual school, university size, student–kinesthesia ratio at the school, estimated cost of yearly attendance, the 4-year retentivity charge per unit, the 4-year graduation rate, and the diverseness charge per unit. Out of the xiv schools with participants who had more than 10 responses to the survey, 7 (l%) were private schools. The student enrollment size (i.east., academy size) ranged from ane,141 to 46,900 (M = fifteen,336, SD = 14,907). The educatee–faculty ratio of the schoolhouse ranged from eight-students to 1-kinesthesia to xix-students to one-faculty (M = 13.27, SD = three.86). The price of yearly tuition ranged from $8,900 to $58,200 (K = 32,186, SD = 19,093). The 4-year retentiveness rate ranged from 72 to 99% (M = 88.80%, SD = seven.54%). The 4-twelvemonth graduation rate ranged from 53 to 97% (K = 77.87%, SD = 12.74%). The diversity charge per unit ranged from 37 to 79% (M = 64.93%, SD = 13.14%). The full list of information for each school is presented in Tabular array 1.

Table 1 School information

Full size table

Analysis plans

We first dummy-coded race, sexual activity, first-generation higher student condition, and whether the school was private or not. For race, the value 0 indicated that the participant identified as White/Caucasian, and 1 indicated that the participants identified as a race other than White/Caucasian. For sex, the value 0 indicated that the participant identified as male, and 1 indicated the participant identified as female. For student classification, it was measured and treated as an ordinal variable where a higher score indicated a college classification, ranging from freshman to senior. For the first-generation college student status, the value 0 indicated that the participant did not identify as a first-generation college pupil, and 1 indicated the participant identified as a first-generation college student. For private school status, the value of 0 indicated the school was non a private schoolhouse, and i indicated the school was private. We and so ran a series of bivariate correlations betwixt all variables measured at the pupil level to bank check any potential issues of multicollinearity, and no issue of multicollinearity was identified (all r < 0.70). The data were structured to exist students' responses nested within the schoolhouse. Nosotros then used HLM eight.i.iv (by Scientific Software International, INC.; Raudenbush & Bryk, 2002) to build 4 hierarchical linear models (HLMs) to estimate the effects of both student and school-level factors on student success outcomes. Each HLM has a student outcome listed every bit the dependent variable, and the outcomes included self-reported learning outcomes, electric current grade bespeak average (GPA), satisfaction with the academy, and satisfaction with the applied science programme. For each HLM, the offset-level variables included age, nomenclature (treated as an ordinal variable), household income (treated equally an ordinal variable), dummy-coded sex, dummy-coded race, dummy-coded outset-generation college student status, communication competency, and five conflict management styles. All continuous variables (i.e., age, advice competency, conflict direction styles) were entered every bit group mean-centered to avoid loss of the integrity of group comparisons. The second level variables included the dummy-coded private school status, university size, students-to-faculty ratio in the engineering science plan, estimated toll of yearly attendance, the 4-yr retentiveness rate of the engineering program, the 4-yr graduation rate of the applied science program, and the diversity rate of the engineering science program. All variables besides the dummy-coded private school condition were grand hateful-centered for more meaningful interpretations.

Nosotros first built a zilch model with only the dependent variable entered. Then, nosotros added the student-level variables and and so used the estimation of stock-still effects with robust standard errors to determine the significance level of each variable. Nosotros then used information-driven astern deletion to remove the beginning-level variables one by one until all variables in the model were meaning (Ma & Klinger, 2000). The same information-driven backward deletion and iteration processes were repeated for the schoolhouse-level variables. We used astern deletion every bit it was the about accurate approach in HLM when the multilevel models had no clear theoretical guidance (Ma & Klinger, 2000). We present the results of the final models after the backward deletion with the unstandardized coefficient (B) and p-value of each pregnant gene.

Results

Learning outcomes model

At the educatee-level, being a female student (B = 1.75, p < 0.001), having a college household income (B = 0.11, p < 0.001), having a higher classification (B = 0.19, p < 0.01), not being a start-generation student (B =  − i.27, p < 0.001), having better advice competency (B = 0.x, p < 0.05), identified more every bit a disharmonize avoider (B = 1.09, p < 0.001), and identified less as a disharmonize accommodator (B =  − 0.31, p < 0.01) were all positively associated with learning outcomes, while holding all other variables statistically constant. At the school level, being in a private school (B = 1.26, p < 0.001) and having college tuition (per every $10 k; B = 0.28, p < 0.001) were all positively associated with learning outcomes while property all other variables statistically constant. The B value is unstandardized coefficients between two variables and can exist interpreted as the number of units that would alter in the dependent variable when at that place is ane unit of measurement of increase in the contained variable while controlling for all other variables (Raudenbush & Bryk, 2002). For example, our results showed that ane unit increase in students' classification (i.eastward., one level higher) would lead to a 0.19 unit increase in the learning outcomes scores. The proportion of overall variance explained past the model was 28.70%; for student level, the proportion of variance has been explained past the model was 11.99%; for school level, the proportion of variance has been explained past the model was sixteen.72%.

GPA model

At the educatee-level, existence a female educatee (B = 0.25, p < 0.001), not being a first-generation student (B =  − 0.86, p < 0.001), having better communication competency (B = 0.26, p < 0.001) were all positively associated with higher GPA, while holding all other variables statistically abiding. At the school level, being in a private school (B = 1.06, p < 0.001), having a lower student–faculty ratio (B =  − 0.42, p < 0.001), and having a higher diversity rate (B = 2.06, p < 0.001) were all positively associated with higher GPA, while holding all other variables statistically constant. The proportion of overall variance explained by the model was 42.59%; for student level, the proportion of variance explained by the model was 24.61%; for school level, the proportion of variance explained by the model was 17.98%.

Programme satisfaction model

The Chi-square examination of variance components indicated there was non a significant amount (p > 0.50) of variance explained by the school-level factors. Thus, the second level of the model was consequently not specified. At the student-level, being a female student (B = 2.56, p < 0.05), having a higher classification (B = 0.70, p < 0.05), beingness a racial minority student (B = ii.55, p < 0.001), having better advice competency (B = 0.22, p < 0.01), identified more as a disharmonize collaborator (B = 0.99, p < 0.05), identified less as a conflict avoider (B =  − 1.54, p < 0.001), and identified more as a conflict accommodator (B = 1.44, p < 0.001) were all positively associated with higher programme satisfaction, while holding all other variables statistically constant. The proportion of variance explained by the model was 17.88%.

University satisfaction model

At the student-level, existence a beginning-generation student (B = one.22, p < 0.001), beingness a racial minority student (B = iv.63, p < 0.001), identified more than as a conflict compromiser (B = 2.55, p < 0.001) were all positively associated with high university satisfaction, while holding all other variables statistically constant. At the school level, being in a school with more than students (per x g students; B = 0.63, p < 0.001) and having a college retention rate (B = 0.66, p < 0.05) were all positively associated with college university satisfaction while holding all other variables statistically constant. The proportion of overall variance explained by the model was 26.21%; at the student level, the proportion of variance explained by the model was 13.98%; at the school level, the proportion of variance explained by the model was 11.25%. The full results of all four models can be found in Tabular array ii.

Table 2 Results of the terminal hierarchical linear models using backward deletion

Full size table

Give-and-take

The current written report seeks to empathize how several relevant educatee- and school-level factors influence student success outcomes amid undergraduate engineering students. We conducted a multi-school multilevel survey projection to respond the inquiry questions. Overall, both factors were significantly associated with the educatee success outcomes, except that the school-level factors were not correlated with program satisfaction. Some student demographic characteristics and individual skills/traits, particularly students' gender, race, starting time-generation student status, and communication competency, were all significantly related to student success outcomes. Some school-level factors indeed influenced different student success outcomes, but the results remained inconsistent.

The electric current study contributes to the literature in the following ways. Commencement, the factors analyzed and tested in the previous studies mainly existed at the student level, and school-level factors, such every bit school type (e.thou., private vs. public) and diversity rate were not well studied. Our report expands the understanding by including the school-level factors, which indeed had significant impacts on student success outcomes based on our findings. Second, many studies recruited students from a limited range of schools past only including students from i type of school (e.m., large public inquiry-intensive universities). Although some research studies students from multiple schools (Marbouti et al., 2021), there is a thin representation of school diversity, such as the inclusion of technology students from liberal arts schools. Our report makes novel attempts to include a wide variety of unlike types of schools. Lastly, to the all-time of our knowledge, we are the get-go projection that examined educatee success outcomes amongst undergraduate technology students using the multilevel analytical arroyo, which more accurately reflected the theorized data structure of ecological influences on pupil success. We farther expand on some of the intriguing findings and their applied implications in this discussion.

Educatee-level influences

RQ1 asked how the student-level factors would influence student success outcomes. Starting time and foremost, communication competency was consistently associated with all four student success outcomes. Today, advice is an essential component in engineering science didactics, recognized by academicians and manufacture professionals because engineers need to communicate through a growing array of ways to an increasing range of audiences (Yeke & Semerciöz, 2016). The accreditation criteria established by the Accreditation Board for Applied science and Technology (ABET) specifically highlight the importance of advice skills (Hussain et al., 2021). In addition, the development of new communications technologies, an increasingly global marketplace, and an increased emphasis on teamwork all illustrate the need to improve the communication competence of engineers (Tenopir & Rex, 2004). In technology education, the primary focus has been on communication competencies related to technical speaking and writing skills. However, succeeding in an engineering job too requires the ability to communicate, pursue information, maintain relationships, and regulate one's deportment and behavior in various contexts (Sageev & Romanowski, 2001). Thus, we emphasize the importance of collaborative instructions with social science disciplines such equally communication and pedagogy to ameliorate communication competence among future engineers.

Second, female students outperformed male students in GPA and learning outcomes, and they also reported higher university and program satisfaction. These findings are consistent with previous higher and K-12 education research (French et al., 2005; Van den Bogaard, 2012). Many female person students reported that their male peers gave their ideas less credit and failed to trust them with technical piece of work in group projects (Wolfe et al., 2016). This situation might lead to diff learning opportunities, which potentially leads female students to work harder to gain acceptance and equity. Consequently, female students might study and work harder to stay competitive in a male-dominated field (Subri, 2018). Moreover, women prove greater interest than men in solving societal issues. Female students take engaged more actively than male students in non-engineering extracurricular activities (Chachra & Kilgore, 2009). Those activities may meliorate some not-technical skills in leadership and communication, which would positively contribute to student success outcomes (Ro & Knight, 2016).

Third, racial minority students reported higher academy and program satisfaction only did not report better GPA and learning outcomes. Contrary to findings of previous studies, racial minority students in our sample did not have statistically significantly lower GPAs or learning outcomes. Notwithstanding, our results could exist related to the limited number of racial minority students in our sample. Minority students, specially Black students, are less likely to participate in a voluntary survey compared to their White peers (Jang & Vorderstrasse, 2019). The underrepresentation of racial minority students has been a common consequence in engineering education and inquiry (Roy, 2019). Future technology education research should explore and employ persuasive recruiting strategies to more effectively recruit minority students in STEM. More than interestingly, minority students reported college university and program satisfaction than their White peers. This could be accredited to some of the minority student mentoring programs, in which many universities have made agile efforts to better serve minority students. Minority students may have limited professional, social, and financial supports for their written report and career life from their families. Plus, they are more probable to encounter more challenges when they enter a college environment where the predominant racial, ethnic, or religious civilisation differs from their ain (Foor et al., 2007). Constructive mentoring could assist racial and ethnic minority students to advance socially, politically, and economically, specially for those who are besides first-generation students (Atkins et al., 2020).

Lastly, first-generation students reported poorer learning outcomes and lower GPA than students whose parents had obtained at to the lowest degree some higher education. These findings are consistent with previous research, where first-generation students are less engaged and less likely to integrate into their college experiences successfully (State highway & Kuh, 2005). In addition, first-generation students perceive the college environment as less supportive and have slower progress in their intellectual evolution due to a lack of educational aspirations (Pike & Kuh, 2005). Previous research establish that educational aspiration from family is one of the strongest influencers of the showtime-semester GPA among first-generation students, yet they oftentimes did not receive proper back up and understanding from their families (Smith & Lucena, 2016). Offset-generation students have been a growing population in higher didactics over the last 2 decades, but they are less equipped with resource, social support, social upper-case letter, and coping tools (Martin et al., 2020).

School-level influences

The current study reflected the nested construction of factors that influenced student outcomes through report recruitment and analytical approaches. RQ3 sought to compare the differences in influences from pupil-level factors versus school-level factors on student success outcomes. The findings showed that schoolhouse-level factors explained less (compared to pupil-level), merely yet a significant portion of variances in student success outcomes. Schoolhouse-level factors reflect kinesthesia engagement, cultural atmosphere, and the social surround (Kuley et al., 2015). It might not be surprising that the environment around u.s. affects how we function in various contexts, and the education environment is no exception (Thruway et al., 2003). School-level characteristics are the underlying mechanisms that can either exacerbate or supplant pupil-level factors. Moreover, student success outcomes should be viewed as the event of a series of long-term intentional school actions, policies, and practices that gradually influence the larger surroundings.

RQ2 asked how organizational characteristics would influence undergraduate engineering students' outcomes. For one, being in a school that has a higher racial diversity rate was positively associated with a higher average GPA. Such prove indicates the importance of diversity in a college educational activity school and calls for more school actions, policies, and practices that actively include variety-oriented mentorship and support. Such actions, policies, and practices do not only encourage the success of racial minority students in engineering science only could likewise potentially enhance success for all students.

Interestingly, the results showed that having higher tuition was positively associated with learning outcomes. Even though previous research has pointed out how students are particularly concerned with the affordability of their education, engineering students, fifty-fifty those from depression-income households, are willing to invest more in their undergraduate education due to expected high financial returns upon graduation (Geiger, 2004). A previous study constitute that students in engineering were less sensitive to the price of tuition (Shin & Milton, 2008). These students are more than sensitive to the quality of engineering programs, which is visible to students through more avant-garde equipment and the availability of resources often paid through college tuition. However, this does not mean that tuition-related policies will not influence student success outcomes as college students in the U.s.a. today face considerable and historically unprecedented challenges in financing college education (Wolniak et al., 2018). Information technology explains why fiscal assistance policies still appear to play a fundamental role in promoting student outcomes among students from any income level (Tinto & Pusser, 2006). However, the resources and avant-garde equipment tin frequently come up with a pricey tuition fee. Tuition policymaking is circuitous and is sensitive to local, state, and school culture and political contexts (Pusser, 2003). Our findings potentially suggest the viable and effective practice of simultaneously keeping high tuition policies and increasing financial aid as a method to maintain and meliorate student success outcomes, especially for low SES students in engineering.

To our surprise, the findings showed that the student–faculty ratio was negatively associated with student GPA. In other words, students from a school with a higher educatee -faculty ratio reported higher GPAs. Nonetheless, the moderator of this human relationship might be faculty distance, where higher faculty altitude lowered self-efficacy, academic conviction, and GPA among students (Vogt, 2008). Simply because a school has a lower student–kinesthesia ratio does not automatically mean kinesthesia take "closer" interactions with students. For case, larger inquiry-intensive schools now rent more than non-tenure and/or part-fourth dimension instructors to teach undergraduate courses, while more tenure-track faculty members focus on research and graduate pedagogy (American Clan of Academy Professors, 2018). Thus, even the student–faculty ratios could potentially be higher at these larger public research-intensive universities, some of these faculty members might have limited interactions with undergraduate students.

Student–kinesthesia altitude is an individual perception-based variable and could even be faculty-specific. Thus pupil–faculty altitude might be a more than accurate indicator of student outcomes, rather than solely relying on the educatee–faculty ratio. Moreover, big state universities sampled in the current study oft take more than research kinesthesia members who might take fewer interactions with undergraduate students than smaller liberal arts colleges. Policymakers should pay more than attention to interactions, relationships, and distance between students and faculty, every bit student–faculty ratios might not accurately indicate the quality of faculty-and-educatee interactions. Hiring "closer" faculty members that would actively interact, engage, and back up their students might be more effective than hiring more kinesthesia.

Applied implications

Based on our findings, we brand the following suggestions for engineering program evolution and policymaking. First, nosotros recommend that engineering programs consider establishing mentorship programs with higher-performing female and racial minority students equally peer mentors to other underrepresented engineering students. Our findings suggested that some of these female and racial minority students reported better learning outcomes and GPAs compared to their counterparts. These college-performing students might have school and life skills that might exist valuable to their peers and other underrepresented engineering students, such as beginning-generation students. Students volition select their peer mentors based on their preferred identity concordance rather than be paired by the program. Representation and cyclopedia are proven to effectively motivate both female person and minority students in undergraduate mentorship programs (Morales et al., 2021; Zaniewski & Reinholz, 2016). The peer mentorship program will focus on helping students increment social upper-case letter and learn coping skills, such as difficulties with identity-specific challenges and extracurricular affairs (Puccia et al., 2021).

Second, we suggest that engineering programs consider establishing a social network-based mentorship programme that includes first-generation students' family members. As our findings and previous studies have shown, motivating first-generation students has to consider their interpersonal networks and social capitals (Martin et al., 2020). The program can host a serial of "family visit" events, in which families and of import interpersonal contacts of get-go-generation students volition meet and interact with kinesthesia and other students. The program's main purposes would be to educate families and interpersonal contacts nearly engineering nuts so these people could ameliorate understand students' engineering majors and curriculum and teach family members practical tools and resources to support their first-generation students. The goals of the programs are to create a common understanding and shared social uppercase between first-generation students and their families through interactive workshops, fun activities, and peer interactions.

Third, we recommend the evolution of hybrid courses that better integrate communication into technology. Although many engineering curricula offer project-based courses, they mainly focus on engineering pattern and field-specific communications skills outlined by the ABET criteria. It is disquisitional to improving students' overall communication competence and disharmonize management skills to face real-life squad challenges, business demands, and interdisciplinary tasks. Kinesthesia from technology and social science disciplines could collaborate on developing courses and workshops that focus on improving these skills. For instance, social science kinesthesia could give lectures related to leadership, advice, and business direction strategies, while applied science faculty could develop engineering science projects that reflect electric current manufacture demands that crave such skills. Two groups of faculty should interact on designing real-life engineering case studies and interactive activities that could improve communication competency and other social skills.

In improver, some schools offer technology leadership certificate programs that aim to train undergraduate engineering students' professional skills, and then students can exist better prepared for the corporate world. These programs take shown to take great potentials in improving six dimensions of competency amidst undergraduate engineering students, including "communication, innovation, creativity, execution, personal bulldoze, and teamwork" (Paul & Cowe Falls, 2015, p. 1). Further integrating the aforementioned hybrid courses into the leadership certificate programs could enhance the effectiveness of achieving the goals of these programs.

Limitations and future research

The findings of the current study should be interpreted inside its limitations. First, the data were nerveless from students in simply the Midwestern and Northeastern Usa to control for the variance related to regional differences. For instance, students from California might have drastically different educational experiences than those from Michigan. In order to more accurately business relationship for such regional variance, a much larger nationwide sample of students is needed, but such a project, unfortunately, was beyond the scope of the current report.

Second, in that location might be limited randomness in our sample as nosotros did non (also were unable to) recruit students via direct communication, and the survey was forwarded by faculty but. Thus, there could be potential selection bias related to kinesthesia's willingness to share the survey with their students.

Third, GPA is an of import pupil success event that was measured in our current report, but the average GPAs for programs and schools were not controlled for in the current analyses. After repeated requests for such data, many department chairs and program directors were not able to provide such information due to privacy concerns. Consequently, nosotros were not able to include the average GPAs for programs and schools equally school-level variables in our analyses. The lack of such information means we had less control over potential self-choice bias, where students who had college GPAs might be more likely to participate in a survey about their academic performance.

Lastly, there could be cross-factor effects on student success outcomes that nosotros did non explore in the electric current analyses. For example, a educatee might be a first-generation female student or a minority student with poor communication competency. Furthermore, student-level factors, such every bit racial minority status, could interact with school-level factors, such as diversity rate, to have significant impacts on student success outcomes. Exploring and analyzing these more than complex moderations address carve up sets of enquiry questions from the electric current manuscript, and it would require essentially more infinite beyond the length of this manuscript. Thus, we plan to explore such questions in a dissimilar manuscript every bit office of the future efforts of this project, and our future project would employ supervised auto learning algorithms to meliorate understand the classifications of schoolhouse- and student-level factors.

Determination

The current report sought to understand how both student- and school-level factors influence educatee success outcomes through a multi-schoolhouse multilevel arroyo. Engineering education is a complex, multi-faceted effect that requires more collaborative and systematic inquiry. We hope our findings help educators sympathise the different factors that could potentially influence engineering students and inform better program pattern and policymaking.

Availability of data and materials

The datasets used and/or analyzed during the current report are bachelor from the respective author on reasonable request.

Abbreviations

Stem:

Science, engineering science, engineering, and mathematics

SES:

Socioeconomic status

GPA:

Grade point average

AACU:

Associations of American Colleges and Universities

HLMs:

Hierarchical linear models

ABET:

Accreditation Board for Applied science and Engineering science

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Acknowledgements

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Funding

The current projection received funding support from an internal grant from University of Mount Union. The open-access publishing accuse was supported by an internal grant from Academy of Mountain Union and another internal grant from Kennesaw Land University.

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Affiliations

1. School of Engineering, University of Mount Union, 1972 Clark Ave, Alliance, OH, 44601, USA

   Xi Wang

2. School of Communication and Media, Kennesaw State University, m Chastain Road, Kennesaw, GA, 30144, Usa

   Minhao Dai & Robin Mathis

Contributions

XW completed data collections and wrote the Introduction, Literature Review, and Discussion of the manuscript. XW also assisted in the overall proofreading of the manuscript. Physician led data curation and analysis of questionnaire information and wrote the Abstract, Methods, and Results of the manuscript. Doctor also assisted in the overall proofreading of the manuscript. RM assisted in the writing of the discussion section and overall proofreading of the manuscript. All lead authors read and approved the concluding manuscript.

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Correspondence to Xi Wang.

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Wang, X., Dai, M. & Mathis, R. The influences of pupil- and school-level factors on technology undergraduate student success outcomes: A multi-level multi-school report. IJ Stalk Ed 9, 23 (2022). https://doi.org/10.1186/s40594-022-00338-y

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• Received: 03 November 2021

• Accustomed: 18 February 2022

• Published: 05 March 2022

• DOI : https://doi.org/10.1186/s40594-022-00338-y

Keywords

• Undergraduate engineering education
• Student-level factors
• School-level factors
• Student success
• Multilevel assay

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Source: https://stemeducationjournal.springeropen.com/articles/10.1186/s40594-022-00338-y

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