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Record W2517858966 · doi:10.21914/anziamj.v57i0.10435

Preparing non-traditional students for engineering degrees

2016· article· en· W2517858966 on OpenAlexaboutno aff
Nadine Adams, Clinton Hayes, Antony Dekkers, Josua Pienaar

Bibliographic record

VenueANZIAM Journal · 2016
Typearticle
Languageen
FieldMathematics
TopicMathematics Education and Programs
Canadian institutionsnot available
Fundersnot available
KeywordsBachelorGovernment (linguistics)Economic shortageEngineering educationMathematicsMathematics educationEngineeringPolitical scienceEngineering management

Abstract

fetched live from OpenAlex

Engineering employment in Australia is cyclic in nature. Australian Government reports indicate that in the past five years there has been a threefold increase in the average number of candidates for engineering positions and a doubling in the proportion of vacancies filled. Until relatively recently there was a surplus in engineering positions; making engineering an attractive career option for students. Students tend to decide on their study direction based on the present economic climate, thus the present downturn in the resource sector and the reduction of engineering positions may result in another shortage of engineering graduates in five years' time. Previous shortages in qualified engineers, combined with the Australian Government's widening participation agenda, have attracted many non-traditional students to pursue engineering degrees. The number of non-traditional students entering the Bachelor of Engineering at Central Queensland University has more than doubled between 2011 and 2014. As engineering bachelor degrees have mathematics prerequisites or assumed knowledge, non-traditional students use enabling programmes to gain entry into these degrees at Central Queensland University. In this study we examine the effectiveness of enabling mathematics units preparing non-traditional students for a bachelor of engineering degree. References N. Adams, A. Dekkers, and S. Elliott. Supportive frameworks that increase mathematical knowledge and confidence in students enrolled in bridging mathematics courses. In Proc. Int. Conf. Mathematics, Science and Technology Education, Kruger National Park, South Africa, 2012. University of South Africa. http://hdl.cqu.edu.au/10018/928333. N. Adams and C. Hayes. Does teaching with a tablet pc enhance the teaching experience and provide greater flexibility. In Australasian Tablets in Education Conference. Monash University, Dec. 2009. http://hdl.cqu.edu.au/10018/917340. N. M. Adams, C. J. Hayes, S. Elliott, A. J. Dekkers, D. F. Johnston, and R. Dodd. Transformative learning: Increasing the confidence of enabling mathematics students. Int. J. Sci. Math. Tech. Learn. 21(2-3):19–29, 2015. http://ijlsmtl.cgpublisher.com/product/pub.266/prod.86. J. A. Athanasou and I. Lamprianou. A teacher's guide to assessment. Sense Publishers, 2002. https://www.sensepublishers.com/catalogs/bookseries/other-books/a-teachers-guide-to-educational-assessment/ Queensland Study Authority. Mathematics B senior syllabus, 2014. https://www.qcaa.qld.edu.au/downloads/senior/snr_maths_b_08_syll.pdf. W. Binney and C. Martin. How do rural students choose their higher education institutions? Two regional Australian cases. J. Institut. Res. Austral. 6:74–80, 1997. http://www.aair.org.au/articles/volume-6-no-2/6-2-how-do-rural-students-choose-their-higher-education-institutions-two-regional-australian-cases M. Brueckner, A. Durey, R. Mayes, and C. Pforr. The mining boom and Western Australia's changing landscape: Towards sustainability or business as usual? Rural Society, 22(2):111–124, 2013. doi:10.5172/rsj.2013.22.2.111. Central Queensland University, Australia. Student demographics. CQUniversity Intranet, 2015. G. E. Davis and M. A. McGowen. Formative feedback and mindful teaching of undergraduate mathematics. In Proc. 30th Conf. Int. Group Psychology of Mathematics Education, 241, 2006. http://www.cees.mak.ac.ug/sites/default/files/ED496931.pdf. Department of Employment. Labour market information portal, 2015. http://lmip.gov.au/default.aspx?LMIP. S. Doyle. STEPS: Celebrating 20 Years 1986–2006. Technical Report, Central Queensland University, 2006. http://hdl.cqu.edu.au/10018/1013477 T. Drewes and C. Michael. How do students choose a university? An analysis of applications to universities in Ontario, Canada. Res. High. Edu., 47(7):781–800, 2006. doi:10.1007/s11162-006-9015-6. D. Dunning, C. Heath, and J. M. Suls. Flawed self-assessment implications for health, education, and the workplace. Psychol. Sci. Pub. Interest, 5(3):69–106, 2004. doi:10.1111/j.1529-1006.2004.00018.x. Engineers Australia. Inquiry into the shortage of engineering and related employment skills. Technical Report, Senate Education Employment and Workplace Relations References Committee, 2012. https://www.engineersaustralia.org.au/sites/default/files/shado/Representation/Government%20Submissions/2012/engineers_australia_submission_to_senate_skills_shortage_inquiry_-_march_2012.pdf. Australian Government. Budget, 2015. http://www.budget.gov.au/2015-16/index.htm. J. Hattie. Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge, 2008. doi:10.4324/9780203887332. Labour Market Research, and Analysis Branch, Department of Employment. Labour market reserch–-engineering professions. Technical Report, 2015 https://docs.employment.gov.au/system/files/doc/other/ausengineeringprofessions.pdf. G. S. May and D. E. Chubin. A retrospective on undergraduate engineering success for underrepresented minority students. J. Engineer. Edu., 92(1):27–39, 2003. doi:10.1002/j.2168-9830.2003.tb00735.x. B. McDonald and D. Boud. The impact of self-assessment on achievement: The effects of self-assessment training on performance in external examinations. Assess. Edu.: Principles, Policy, Practice, 10(2):209–220, 2003. doi:10.1080/0969594032000121289. G. A. Miller. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychol. Rev. 101(2):343, 1994. doi:10.1037/0033-295x.101.2.343. A. Porter and S. Denny. Building leadership capacity for development and sharing of mathematics learning resources across disciplines and universities. Technical Report, 2013. http://www.olt.gov.au/project-building-leadership-capacity-uow-2007. D. Robson, W. Abell, and T. Boustead. Scaffolding for learning equation solving. In Crossing divides. Proc. 32nd Ann. Conf. Mathematics Education Research Group of Australasia. Palmerston North, NZ, 2009. http://www.merga.net.au/node/38?year=2009. TechSmith Corporation. Camtasia studio version 8.6.0, 2015. https://www.techsmith.com. V. Validakis. Australia entering phase three of mining boom, 75,000 jobs to be cut. Australian Mining, July 2014. https://australianmining.com.au/news/australia-entering-phase-three-of-mining-boom-75000-jobs-to-be-cut-2/. J. J. van Merrienboer and J. Sweller. Cognitive load theory and complex learning: Recent developments and future directions. Edu. Psychol. Rev. 17(2):147–177, 2005. doi:10.1007/s10648-005-3951-0.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0360.015

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.103
GPT teacher head0.367
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2016
Admission routes1
Has abstractyes

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