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Exploring the effectiveness of a novel teaching approach for information and academic literacies in a first year engineering unit

2015· article· en· W2296539364 sur OpenAlexaboutno aff
Mariette LeRoux, Fiona Jones, Nicholas M. K. Tse, Daniel McGill, Azadeh Safari, Sudipta Chakraborty

Notice bibliographique

RevueProceedings of The Australian Conference on Science and Mathematics Education (formerly UniServe Science Conference) · 2015
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueLibrary Science and Information Literacy
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésInformation literacyTransferable skills analysisSession (web analytics)Relevance (law)Process (computing)Computer scienceUnit (ring theory)Engineering educationMathematics educationLiteracyPsychologyPedagogyHigher educationEngineeringWorld Wide WebEngineering management
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

First year Engineering students tend to lack key information and academic literacy skills, which results in poor writing and language use, the use of a limited range of sources and poor referencing in their assignments. In the 2014 graduate outlook survey, 48% of graduate employers ranked communication skills as the most important selection criterion when recruiting graduates. Transferable skills are becoming increasingly important, not just to produce a more adaptable work force, but to inspire lifelong students who will continuously learn and improve. In the Macquarie University Engineering Program these transferable skills are introduced early in the degree using enquiry based methodology in a core first year Engineering unit. Tutors play a pivotal role in this process, facilitating repeated practice and acting as mentors for the students. To emphasise the importance of information and academic literacy as the first step in educating Engineering students, librarians developed a series of ‘research studios’ based on Baratta, Chong and Foster’s work (2011) which were run during tutorial sessions in week 4 of session 1. As Engineering students typically have active, sensing, inductive and visual learning styles (Young, 2012, p. 22) an activity based approach was used to help students self-discover and practice. This was supplemented with an online language activity created by the learning skills department. The following learning outcomes were addressed:  recognising when information is needed  appreciating the relevance of different types of resources for their field  identifying the most efficient search strategy to locate relevant information of a high standard  critically evaluating information sources  using appropriate academic language  using the correct format of reporting  referencing correctly and ethically Over 300 students attended tutorials held in library classrooms. Each ‘research studio’ was held in a different room and facilitated by a different library staff member, with groups of students moving from room to room at the conclusion of each 40 minute session. Library staff members provided short instruction, with most of the tutorial time devoted to hands-on activities, small group work and discussion. A large first year core unit was chosen to pilot this approach in order to be representative of the Engineering student population. Evaluation data shows that all of the activities had positive effects on student learning. The online language activity recorded a high number of hits. Student feedback indicated that the activity-based approach to developing information skills helped to consolidate understanding. Tutor feedback indicated that the quality of assignments submitted following the program was improved over previous sessions. To facilitate integrating these literacies into the unit, tutors will provide input into reviewing the exercises and will be trained to facilitate the learning activities co-designed by librarians and learning skills specialists in a blended learning format. As there was some feedback that exercises were too easy, the input from tutors will help pitch the training at the appropriate level and also provide valuable subject specific context. This presentation shares the results of this unique collaboration and its impact on student test results. It will address the information and academic literacy skills that Engineering students require to succeed in their academic and professional endeavours. REFERENCES Ali, R., Abu Hassan, N., Daud, M. Y. M., & Jusoff, K. (2010). Information literacy skills of engineering students. International Journal of Research and Reviews in Applied Sciences, 5(3), 264-270. http://eprints.utm.my/37881/2/IJRRAS_5_3_08.pdf Baratta, M., Chong, A & Foster, J.A. (2011). The research studio: integrating information literacy into a first year engineering science course. American Society for Engineering Education Conference, Vancouver, Canada. file:///C:/Users/mariette.leroux/Downloads/ASEE2011TheResearchStudioFinal%20(2).pdf Fosmire, M & Radcliffe, D. (2013). Integrating information into the Engineering design process. West Lafayette, IN.: Perdue University Press. http://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1030&context=purduepress_ebooks Lindsay, E. (2015). Graduate outlook 2014: employer’s perspectives on graduate recruitment in Australia. Melbourne, Vic.: Graduate Careers Australia. http://www.graduatecareers.com.au/wp-content/uploads/2015/06/Graduate_Outlook_2014.pdf Young, S.J. (2012). Engineering. in O’Clair and Davidson, J. (eds) The busy librarian’s guide to information literacy in science and engineering. Chicago, IL: Association of College and Research Libraries Proceedings of the Australian Conference on Science and Mathematics Education, Curtin University, Sept 30th to Oct 1st, 2015, page X, ISBN Number 978-0-9871834-4-6.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,017
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,036

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,017
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,002
Communication savante0,0040,003
Science ouverte0,0030,005
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,177
Tête enseignante GPT0,347
Écart entre enseignants0,169 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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Même revueProceedings of The Australian Conference on Science and Mathematics Education (formerly UniServe Science Conference)Même sujetLibrary Science and Information LiteracyTravaux en français237 207