27. Didactics - Learning and Teaching
Paper
Investigation of STEM Subject and Career Choices of Lower Secondary School Students in a City in Northern Norway
Mona Kvivesen, Saeed Manshadi, Stig Uteng
UiT The Arctic University of Norway,
Presenting Author: Kvivesen, Mona;
Manshadi, Saeed
Economic development worldwide requires specialists in the STEM disciplines – science, technology, engineering and mathematics (Mohtar, 2019). Research shows that there is great interest in STEM disciplines among primary school children but that this interest decreases at lower secondary school. The attitudes of lower secondary school students depend on the environment and the people around them, like teachers, friends and parents (Tomperi et al., 2020).
Gender differences influence motivation for STEM education and careers, and most researchers agree with the existence of gender inequality in STEM fields (Delaney & Devereux, 2019; Diekman et al., 2017; Master, A., 2021, Moss-Racusin, 2018). According to Master (2021), children belonging to a gender group with negative STEM stereotypes tend to doubt their abilities, making it difficult to develop an interest in this area. These processes begin in preschool age and intensify later in school years and carrier choices. Delaney and Devereux (2019) believe that the effects of these processes are shown by the different choices of subjects and grades in secondary school. Several studies have attempted to identify factors that contribute to the development of the gender gap in STEM, such as differences in lifestyles, support for shared goals, and access to appropriate role models and mentors (Diekman et al., 2017; Master, A., 2021; Moss-Racusin, 2018; Kiernan et al., 2022).
Research on students’ career choices is based on social cognitive career theory (SCCT), which explores students’ interest in STEM subjects and examines the interactions between self-efficacy, goals and expected results (Lent et al., 2000). These three variables enable people to influence their professional development. SCCT also includes variables that influence personal control over a career.
In this paper, we focus on students’ STEM subject and career aspirations in a city in Northern Norway. This is a further investigation of an international study in which we investigated STEM subjects and career aspirations (Tomperi et al., 2022).
The research questions are as follows:
1. Which STEM subjects do students from a city in Northern Norway have interest in?
2. What influences students’ orientation towards a particular STEM discipline as their future career?
3. Do gender differences exist in the students’ orientation towards certain STEM disciplines as their future career?
This paper uses an adapted version of the STEM Career Interest Survey (STEM-CIS) to investigate the interest in STEM subjects and careers of students in lower secondary schools in a city in Northern Norway. STEM-CIS is derived from SCCT (Lent et al., 2000). The SCCT framework includes three models of career development: interest, choice and performance. The interest model examines the ways self-efficacy and output expectations develop students’ interest, while the choice model explores the ways interest, self-efficacy, and output expectations influence choice goals, which then motivate choice actions (Lent, 2013).
Personal inputs, such as gender, grade, family and school, influence individuals’ learning experiences, which in turn affect their self-efficacy and outcome expectations. Factors that are influenced by personal inputs also affect interest, goals and actions. Guided by SCCT, the STEM-CIS was developed to measure the six key constructs of self-efficacy, personal goals, expectation of results, interest in, contextual support and individual inputs (Kier et al., 2014).
Methodology, Methods, Research Instruments or Sources UsedIn this study, we adapted the STEM-CIS survey developed by Kier et al. (2019) to investigate lower secondary school students’ orientation towards STEM disciplines and their future career choices. The students accessed the extended STEM-CIS online by using a mobile, tablet or computer under the supervision of their teachers. The students participating in the study were aged 13–16 years, which is the age of lower secondary school in Norway. Of the 273 students who participated in the survey, 129 were boys and 144 were girls; all students attended the same lower secondary school in a city in Northern Norway.
A descriptive survey model was used as a quantitative research method. Data were analysed using the statistical programming environment R (R Core Team, 2019). The results were interpreted with a significance level of 0.05. As the data did not have a normal distribution (kurtosis and skewness values were not zero and the Kolmogorov-Smirnov tests were significant (p < .05) for all variables), we used the Mann-Whitney Wilcoxon U test to analyse the STEM-CIS scores according to gender. The original STEM-CIS (Kier et al., 2014) consists of 44 items and four subscales (science, mathematics, technology and engineering). However, as were also interested in the sub-disciplines in science (biology, chemistry, geology and physics), the survey consists of 77 items and seven subscales (biology [B], chemistry [C], geology [G], physics [P], mathematics [M], technology [T], and engineering [E]. Each discipline-specific subscale contains 11 items that address six social cognitive career dimensions: self-efficacy (items 1–2), personal goals (items 3–4), outcome expectations (items 5–6), interests in (items 7–8), contextual supports (items 9 & 11), and personal inputs (item 10). Scores were obtained using a five-point Likert scale, with response options ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). Higher scores reflect a greater perceived value of the subject. The overall reliability value α was 0.97 (N = 77).
Conclusions, Expected Outcomes or FindingsAccording to SCCT, self-efficacy affects outcome expectations and together they influence interests. Students are likely to develop an interest, choose to pursue the subjects of interest and, as a result, perform better at activities in subjects in which they have stronger self-efficacy (Lent, 2000). The results show that students’ interest is at a medium level (2.8 < mean rank value < 3.2) in most of the STEM subjects, except for biology and chemistry, which reported a lower level. The students reported high self-efficacies in science and mathematics and a medium level for the other subjects. For the outcome expectations dimension, all subjects showed a medium level, except for mathematics, where the students reported a high level. For the personal goals dimension, the students reported a high level for mathematics and a medium level for the other subjects. In the contextual support dimension, students showed a medium level for all subjects, except science, for which the students reported a low level. For personal inputs, the students showed a medium level of self-efficacy for all subjects.
When we compared the results by gender, we found significant differences between boys and girls in the personal goals dimension for biology and technology, where girls had a higher level than boys for biology and a lower level in technology than boys. In biology and chemistry, girls showed higher levels than boys in outcome expectations, but boys showed higher levels in the same dimension for technology. There was also a significant difference in contextual support for technology. Here, boys showed higher levels than girls. These are trends we expected and fit with the result from Kiernan et al. (2022), who reported that boys prefer technology subjects while girls prefer biology.
ReferencesDelaney, J. M., & Devereux, P. J. (2019). Understanding gender differences in STEM: Evidence from college applications. Economics of Education Review, 72, 219–238. https://doi.org/10.1016/j.econedurev.2019.06.002
Diekman, A. B., Steinberg, M., Brown, E. R., Belanger, A. L., & Clark, E. K. (2017). A goal congruity model of role entry, engagement, and exit: Understanding communal goal processes in STEM gender gaps. Personality and Social Psychology Review, 21(2), 142–175. https://doi.org/10.1177/1088868316642141.
Kier, M. W., Blanchard, M. R., Osborne, J. W., & Albert, J. L. (2014). The development of the STEM career interest survey (STEM-CIS). Research in Science Education, 44, 461–481. https://doi.org/10.1007/s11165-013-9389-3
Kiernan, L., Walsh, M., & White, E. (2022). Gender in technology, engineering and design: Factors which influence low STEM subject uptake among females at third level. International Journal of Technology and Design Education, 1–24. https://doi.org/10.1007/s10798-022-09738-1
Lent, R. (2013). Social cognitive career theory. In S. D. Brown & R. W. Lent (Eds.), Career development and counselling: Putting theory and research to work (pp. 115–146). John Wiley & Sons.
Lent, R. W., Brown, S. D., & Hackett, G. (2000). Contextual supports and barriers to career choice: A social cognitive analysis. Journal of Counselling Psychology, 47(1), 36–49. https://doi.org/10.1037/0022-0167.47.1.36
Master, A. (2021). Gender stereotypes influence children’s STEM motivation. Child Development Perspectives, 15(3), 203–210. https://doi.org/10.1111/cdep.12424
Mohtar, L. E., Halim, L., Rahman, N. A., Maat, S. M., Iksan, Z. H., & Osman, K. (2019). A model of interest in stem careers among secondary school students. Journal of Baltic Science Education, 18(3), 404–416. https://doi.org/10.33225/JBSE/19.18.404
Moss-Racusin, C. A., Sanzari, C., Caluori, N., & Rabasco, H. (2018). Gender bias produces gender gaps in STEM engagement. Sex Roles, 79, 651–670. https://doi.org/10.1007/s11199-018-0902-z.
R Core Team. (2019) R: A language and environment for statistical computing. R Core Team, Vienna, Austria.
Tomperi, P., Ryzhkova, I., Shestova, Y., Lyash, O., Lazareva, I., Lyash, A., Kvivesen, M., Manshadi, S., & Uteng, S. (2020). The three-factor model: A study of common features in students’ attitudes towards studying and learning science and mathematics in the three countries of the North Calotte region. LUMAT International Journal on Math, Science and Technology Education, 8(1), 89–106. https://doi.org/10.31129/LUMAT.8.1.1369
Tomperi, P., Kvivesen, M., Manshadi, S., Uteng, S., Shestova, Y., Lyash, O., Lazareva, I., & Lyash, A. (2022). Investigation of STEM subject and career aspirations of lower secondary school students in the North Calotte Region of Finland, Norway, and Russia. Education Sciences, 12(3), 192. https://doi.org/10.3390/educsci12030192
27. Didactics - Learning and Teaching
Paper
Impact Analysis of Programs Offered by STEM Learning Centres: Outcomes for Students and Teachers
Linda Hobbs, Seamus Delaney, George Aranda, Peta White, Jerry Lai
Deakin University, Australia
Presenting Author: Hobbs, Linda;
Delaney, Seamus
Science, Technology, Engineering and Mathematics (STEM) has become an important policy agenda in many countries around the world to increase international economic competitiveness (Kärkkäinen & Vincent-Lancrin, 2013). With the recognition that teachers are critical to successful learning (Baker‐Doyle & Yoon, 2011; Darling-Hammond, 2000; Hattie, 2011) and as schools grapple with how to introduce STEM into their curriculum (Education Council, 2015), it is becoming increasingly essential to develop processes and programs that support and sustain teacher and school change (Office of the Chief Scientist, 2016a). STEM learning centres play a vital role as part of the STEM education ecosystem (Schugurenzky, 2000; Traphagen & Traill, 2014) in providing specialist learning experiences for students and teachers to compliment school curricula. Depending on their purpose and structure, STEM learning centres can offer informal and non-formal learning opportunities and may be integrated into formal learning as part of school programming. Some STEM learning centres are part of the outreach strategy for universities, such as the University of Arizona STEM Learning Center, which engage local school students in STEM-related programs in partnership with a range of organisations. In comparison, in Italy the Fondazione Golinelli is funded privately by a philanthropic foundation since 1998, providing STEM experiences for students from early childhood through to adulthood. Different again is the LUMA Centre Finland, which is a large multi-university organisation that collaborates with private and public institutions to research, develop and implement non-formal, out-of-school and extra-curricular LUMA activities. Industry collaboration and design-based pedagogy are core foci (Aksela et al., 2021).
In Victoria, Australia, the Tech Schools are specialised, purpose-built STEM learning centres that are hosted, owned and operated by universities or Technical and Further Education (TAFE) institutions, but funded by the Victorian Department of Education and Training (the Department). Currently consisting of a network of ten Tech Schools operating under a single Tech School ‘model’, Tech Schools are designed to provide learning programs that are developed in partnership with local industry partners to suit local contexts and needs, and are aligned to the Victorian school curriculum. Each Tech School offers different programs. Teachers are offered a range of professional development opportunities, whilst the wider community interacts through events, after school programs, and access to the facility’s resources. Like the other STEM learning Centres mentioned, Tech schools are not schools, but centres that are accessed by local secondary schools to supplement their STEM programs.
We are conducting a longitudinal evaluation of the Tech Schools Initiative in 2019-2023. The evaluation uses a Theory of Innovation based on Jäger’s (2004) wave model of innovation, which identified three pillars of innovation: content, structure and people. This presentation will focus on one part of the evaluation: the effects of the innovative content arising through the student programs. The research question is: What differential effects do Tech School programs have for participating students and teachers? Six categories of programs were devised in order to undertake a program impact analysis:
Category 1. Programs with industry (Industry-based technologies and involved Industry and community partners);
Category 2. Programs focused on problem solving and design-based challenges (Design and problem-based learning);
Category 3. Programs focused on skill building (Skill development);
Category 4. Programs with blended delivery modes or locations (Located at the school, host, industry and community, online);
Category 5. Programs focused on networking and deep engagement (Networking, Weeks or months in duration, Located at Tech School or industry); and
Category 6. Programs focused on enhancing senior studies (Skill development, Career pathways, Senior school year levels [Year 11 and 12]).
Methodology, Methods, Research Instruments or Sources UsedThe broader evaluation comprises research methods designed to capture, explore and understand the unique ways Tech Schools operate in practice, how they deliver teaching and learning that meets student and teacher needs, and how they influence the broader STEM and school ecosystem. A longitudinal four-year data collection strategy was developed to be broad, capturing data from all stakeholders (i.e., Tech School staff; partner school principals, teachers and students; industry and community partners; host representatives) from each of the ten Tech Schools; and deep as data collected to construct case studies of five Tech Schools. A suite of tools for data collection was co-designed, piloted and validated by Deakin, the Department and Tech School Directors during 2019 and 2020. The tools include surveys and interviews with each stakeholder group. The analyses have focused on outcomes for students, teachers, schools, industry and community partners and hosts; partnerships elements; and the nature of innovation occurring through the Tech Schools model.
The programs have been examined in various ways in 2020, 2021 and 2022. In 2022, an analysis of programs was conducted using all student programs listed on the Tech School websites. The purpose was to identify features of programs relating to program intentions, structures (e.g., timing, location), and stakeholder involvement that might have differential impact for students and teachers and therefore point to best practice. These features were combined to form six program categories. This presentation will provide an overview of the impacts associated with six program categories and then showcase the outcomes of programs categories that represent their most valuable contribution to students and teachers.
A program impact analysis used data from a student attitude survey, teacher reflection survey, student exit surveys, and student interviews. The survey items produced largely ordinal data from multiple choice/Likert scale questions. Quantitative datasets were analysed using descriptive statistics to look for varying associations between variables.
Qualitative analysis of interviews included representing the espoused outcomes for the students for programs for which there was adequate quantitative data as well as data from the student interviews where students had attended those programs.
Conclusions, Expected Outcomes or FindingsLooking across the programs, there are some common features that can have similar or different effects, depending on the program category. The presentation will show how general capabilities, designing and problem solving, technology, industry representation and connection, curriculum content connection, and the online and school delivery modes influence outcomes. The predominant features of each category that were drawn out by the data will be highlighted. Some key points that will be detailed in the presentation include:
• Teacher capacity to teach STEM is most influenced when they use Tech School-devised pre- and post-lessons that prepare students for, and follow up after, the Tech School visit.
• Where programs are specifically designed to represent or connect with industry or the emphasis is on careers and local industry, there is greater impact on student awareness of STEM and STEM industries, and some impact on interest in STEM studies and pathways.
• Technology, design thinking, and collaboration often co-occur in programs, and the effects are generally that students and teachers are more aware and proficient with the design process, enjoy the collaboration and value its contribution to complex and novel solutions, and that technology helps students learn.
The program categorisation provides a useful delineation of programs that can be offered at STEM learning centres. Understanding the effects of these for identifying best practice and where to place funding and effort in terms of program design, resourcing and delivery is useful for STEM education organisations operating outside of but integrated into the formal school structure. Tech Schools have become a valuable part of the STEM education ecosystem in the areas where they exist in Victoria because of the range of programs available and their currency to young peoples’ future, teachers’ capacity for STEM teaching, and the pathways into local STEM careers.
ReferencesAksela, M., Lundell, J. & Ikävalko, T. (Eds.) LUMA Finland. Together we are more. LUMA Centre FInland. https://www.luma.fi/en/download/luma-finland-together-we-are-more/ Accessed December 16 2021.
Baker‐Doyle, K. J., & Yoon, S. A. (2011). In search of practitioner‐based social capital: A social network analysis tool for understanding and facilitating teacher collaboration in a US‐based STEM professional development program. Professional Development in Education, 37(1), 75-93. doi:10.1080/19415257.2010.494450
Darling-Hammond, L. (2000). Teacher quality and student achievement: A review of state policy evidence. Education Policy Analysis Archives, 8(1), 1-44. https://doi.org/10.14507/epaa.v8n1.2000
Education Council (2018). Optimising STEM Industry-School Partnerships: Inspiring Australia’s Next Generation Final Report. Canberra: Education Council. https://www.chiefscientist.gov.au/sites/default/files/2019-11/optimising_stem_industry-school_partnerships_-_final_report.pdf
Hattie, J. (2011). Visible Learning for Teachers: Maximizing Impact on Learning. Abingdon, UK: Taylor & Francis Ltd
Jäger, M. (2004). Transfer in Schulentwicklungsprozessen. Wiesbaden: VS Verlag für Sozialwissenschaften.
Kärkkäinen, K. & Vincent-Lancrin, S. (2013). Sparking Innovation in STEM Education with Technology and Collaboration: A Case Study of the HP Catalyst Initiative. OECD Education Working Papers, No. 91, OECD Publishing. http://dx.doi.org/10.1787/5k480sj9k442-en. Accessed 1 November 2021
Office of the Chief Scientist (2016a). STEM Program Index 2016. Canberra: Commonwealth of Australia. https://www.chiefscientist.gov.au/sites/default/files/SPI2016_release.pdf Acessed 21 December 2021.
Schugurenzky, D. (2000). The forms of informal learning: Towards a conceptualization of the field. Centre for the Study of Education and Work, OISE/UT. https://tspace.library.utoronto.ca/handle/1807/2733 Accessed 1 November 2021
Traphagen, K. & Traill, S. (2014). Working Paper: How Cross Sector Collaborations are Advancing STEM Learning. Noyce Foundation. https://smile.oregonstate.edu/sites/smile.oregonstate.edu/files/stem_ecosystems_report_execsum_140128.pdf
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