RRLS AUTHORS

• Focus will be on interest, belonging, performance/competence, and recognition in the comparison
• As Ju and Zhu (2023) define, engineering identity is someone who recognizes and fully accepts their career and their eagerness to pursue the engineering field. 
• Schell et al. (2018) stated that students who identify themselves as engineers have a higher likelihood of graduating.
• Firstly, a study wherein first-generation students were reported as 15.7% much more than other non-first-generation students (Eagan et al., 2015).
• It puts them with different expectations and needs than other non-first-generation (Becker et al., 2017).
•  Some students lacked exposure or preparation before college enrollment (Atherton, 2014)
• In contrast, students who are not the first in their generation have lower chances of students dropping out of their career (Fabiano, 2022)
• Fabiano (2022) also stated that there are differences in status; non-first-generation students are less likely to drop out.
• In addition, 82% of the students who have both parents with a bachelor’s degree had the highest graduation rate (Nam, 2023). 
• Identity of engineering students can be influenced by parents and teachers through recognition. Another factor that can affect is the interest as to how the environment influences the students on how they understand and develop motivation in the process of being an engineer. (Rodriguez et al., 2021).
•  With the rapid and continuous development of engineering identity, it can be a measure in determining the influential factors affecting one’s career over time (Lockhart & Rambo-Hernandez, 2023).
• According to McCave (2014), greater levels of engineering identity indicates a higher chance of pursuing an engineering major as it shows the manifestation of one’s persistence in the field of engineering.
• Moreover, one's engineering identity and motivation shows how the relationship of one another influence their perception of being an engineer, thus, continuing interest in the field after the first year (Godwin et al., 2020)
• One case study mentioned about the long-term effects of one’s engineering identity, wherein it showed the lack of interest and participation of a student towards the engineering course she took as she doesn't consider herself being identified as an engineer, thus, lacking performance during classes (Godwin & Potvin, 2016).
•  Revelo (2019) also found out that identifying and developing an engineering identity can affect the retention of information and persistence in the field, making it a big factor in their performance.
• Other studies focused on different factors like the mental health of engineering students' identity and the relation of how they feel included in an engineering setting helping their development in different engineering programs (Jensen & Cross, 2021).
• Another study is about the engineering identity and how their mindset grows over time. Wherein when their growth mindset greatly increases, so as their engineering identity (Henderson et al., 2017).
• However, one study by Co-Enarsico (2021) discussed the identity of nursing students and looked at how much they identify themselves with their profession. It collected data through their reflection, which focuses on whether they “act like” a nurse that shows how they can improve and enhance communication through different perspectives which improves the quality of care.
• the researchers will be utilizing the IV-DV model wherein the independent variable depicts a causal relationship with the dependent variable being measured.
• The research design that the researchers will employ is the causal-comparative design. The two independent variables namely first-generation and non-first-generation engineering college students to be compared will subsequently be evaluated through the dependent variable, which pertains to their engineering identity in terms of recognition, interest, performance/competence, and belonging.
• This study will only focus on investigating and comparing the engineering identity development of first-generation (FG) and non-first-generation (NFG) first-year engineering college students in Bulacan State University, S.Y. 2023-2024. The study will be conducted on a sample population of first-year engineering college students.
• First-generation (FG) students are those who come from families where their parents did not complete a four-year college degree (The Center, 2017)
• Non-first-generation (NFG) students are students having parents who have graduated from college (Darrah et al., 2022).
• Engineering identity reflects the acceptance of students and their recognition of engineering and it also refers to the manner in which students position themselves and are perceived by others as individuals who engage in the field (Ju & Zhu, 2023; Henderson et al., 2023).
• In this study, causal-comparative design will be utilized. In causal-comparative research, the researcher aims to determine if the dependent and the independent variable have a causal link (Çobanoğlu, 2023).
• At its core, it is aimed at determining and analyzing causal relationships between variables, particularly in situations where the researcher is unable to actively manipulate the variables (Costello, 2023).
• In this research design, because the researcher has no control over the independent variable, the relationship between the independent and dependent variables is generally suggested (Maheshwari, 2018).
• the researchers seek to determine a causal relationship between first-generation and non-first-generation first-year engineering college students and their engineering identity, analyzing whether the variables suggest a causal connection between each other, through comparing the engineering identity of the sample under the two different groups.
• Sample sizes between 30 and 500 work well for most research. However, non-probability sampling makes it tricky to figure out the right sample size because it does not allow the calculation of the margin of error or confidence interval (Arize, 2023).
• According to Aransiola (2023), volunteer sampling is a non-probability sampling method involving participants that voluntarily choose to be part of a study.
•  In reference to Arize's (2023) given range of number of respondents, the researchers decided that the sample size of both groups will be 100, allotting a number of 50 respondents for each group.
• The researchers will only seek participants over the internet via public postings and let the respondents self-select themselves
• questionnaire consists of a 4-point Likert Scale ranging from strongly disagree to strongly agree
• The survey items were compiled on the basis of a number of previous quantitative surveys for the four different dimensions of engineering identity as stated previously (Verdin et al., 2018; Choe & Borrego, 2019; Godwin, 2016).