camp. Groups names were created by the fabrication librarian todistinguish between each of them. In the mechanical camp, we had group A, group B and groupC. In the exploration camp, we had group D, group E and group F.Makerspace sessions were led by Rebecca, with assistance from the makerspace specialist andone student worker. Three employees were needed to roam around the makerspace for eachsession, providing assistance as needed to the groups as they fabricated using various machinesand tools. There were two engineering camp counselors at each session, helping to managestudent behavior and occasionally aided in generating ideas for fabrication and using hand tools.There was no formal assessment conducted during the makerspace sessions and
1Preparing Engineering Graduate Students to Engage in Scholarly Communications Dianna E. B. Morganti Angie Dunn ASEE Annual Conference 2 Abstract The typical engineering degree plan has several important gaps when reviewed againstthe research lifecycle. These gaps are often filled in by students learning ad hoc, by overworkedfaculty over numerous mentoring sessions, or often by the engineering research librarians inworkshops and consultations. Purposeful incorporation of a curriculum that fills those gaps,though, can
Paper ID #37021Mapping Graduate Student Workshops to Career Readiness FrameworksSeth Vuletich, Colorado School of Mines Seth Vuletich is the Scholarly Communications Librarian the Colorado School of Mines. Seth provides specialized support to graduate students through all stages of the research lifecycle. Prior to entering the field of librarianship, Seth was a professional woodworker and earned a bachelor’s degree in geology from the University of Colorado, Boulder. Seth earned his Master’s in Library and Information Science from the University of Denver in 2021.Ms. Brianna B. Buljung, Colorado School of Mines
from knowing who would be completingthe survey (e.g., individuals with non-technical backgrounds may not feel comfortable answeringspecific questions). However, each multiple-choice question received at least 194 responses fromthe 201 participants. The open-ended questions relating to the survey content received aminimum of 122 responses with the “Next steps” questions (those designed to assist with thesnowballing distribution method) receiving a minimum of 53 respondents.The survey was created using Google Forms and consisted of eight sections: an introduction tothe survey (including Graphic 1 shared in Appendix B), career connection to engineering,student education, course specifics, course logistics, course value, everyday use, and next
,” Library Trends, vol. 53, no. 1, pp. 129-155, Summer 2004.[6] A. Priestner, A Handbook of User Experience Research & Design in Libraries, Lincolnshire,UK: UX in Libraries, 2021.[7] L. Horowitz, “Unpacking Assessment,” ACRLog, https://acrlog.org/2011/12/27/unpacking-assessment/ (retrieved Jan. 21, 2024).[8] M.J. Bitner, A.L. Ostrom, & F.N. Morgan, “Service Blueprinting: A Practical Technique forService Innovation,” California Management Review, vol. 50, no. 3, pp. 66-94, Spring 2008.[9] S. Markless & D. Streatfield, Evaluating the Impact of Your Library, 2nd Ed., London, UK:Facet Publishing, 2013.[10] B. Canovan & M. Zogas, “Engaging Patrons: Budgeting Your Time and Money,” presentedat the ILA/ACRL Spring Conference, IA, USA, May 24
CiteScore were extracted and reformatted. In the reformatted tabular structure as inTable 1 of Appendix B, column headings were thousands of topics as predictors and CiteScore;each row (also called observation) represented a publication record. The value of each topic waseither 1 or 0, indicating whether each publication contains a certain topic or not. Linearregression was employed to predict CiteScore using topics as predictors. The threshold of theoccurrence of each topic was set to 0.1% of the total number of publications, resulting in 538unique topics. The selection of threshold values has trade-offs. If the threshold value is too small,overfitting in linear regression would occur because there would be a large number of topics aspredictors
. In Procedings of the Conference of the International Group for the Psychology of Mathematics Education; International Group for the Psychology of Mathematics Education, 1996; Vol. 4, pp 187--194.(4) Zieffler, A.; Garfield, J.; Alt, S.; Dupuis, D.; Holleque, K.; Chang, B. What Does Research Suggest About the Teaching and Learning of Introductory Statistics at the College Level? A Review of the Literature. J. Stat. Educ. 2008, 16 (2), 8. https://doi.org/10.1080/10691898.2008.11889566.(5) Kahneman, D.; Tversky, A. Subjective Probability: A Judgment of Representativeness. Cognit. Psychol. 1972, 3 (3), 430–454.(6) Konold, C. Informal Conceptions of Probability. Cogn. Instr. 1989, 6 (1), 59–98.(7) Shewhart, W. A. Economic
] Available: DOI: 10.1515/9781503633919.[2] B. McGillivray and G. M. Tóth, Applying Language Technology in Humanities Research:Design, Application, and the Underlying Logic, 1st ed. Palgrave Macmillan, 2020. [E-book]Available: DOI: 10.1007/978-3-030-46493-6.[3] R. Oldenburg, The Great Good Place: Cafes, Coffee Shops, Community Centers, BeautyParlors, General Stores, Bars, Hangouts and How They Get You Through the Day, 1st ed. NewYork: Paragon House, 1989.[4] P. Mehta and A. Cox, "At Home in the Academic Library? A Study of Student Feelings of"Homeness", The New Review of Academic Librarianship, vol. 27-1, pp. 4-37, 2021.https://doi.org/10.21900/j.jloe.v3.956.[5] S. Sinclair and G. Rockwell, “Summary,” Voyant Tools. https://voyant-tools.org/?corpus
-basedevaluation of factors like organization, description and critique of current state of the art,discussion of current debates, and recommendations for future research. An IL-related item isincluded for References, but the description is much higher level than the customized VALUErubric used for the study, since it is only one of many factors on which the students’ grades arebased. The full assignment prompt is included in Appendix B. Provided the reports and otherdata were gathered as part of regular class activities, and no student identifying information wasconnected to any of the results, IRB approval was not required for this study.The initial VALUE rubric for this project had previously been customized from AACU’soriginal version for a prior
,” Ithaka S+R, Jan. 2019. doi: 10.18665/sr.310885.[11] S. Parker, “Research Data Sharing in Engineering: A Report on Faculty Practices and Preferences Prior to the Tri-Agency Policy,” in 2023 ASEE Annual Conference & Exposition Proceedings, Baltimore, Maryland: ASEE Conferences, Jun. 2023, p. 44112. doi: 10.18260/1-2--44112.[12] C. Tenopir et al., “Changes in Data Sharing and Data Reuse Practices and Perceptions among Scientists Worldwide,” PLOS ONE, vol. 10, no. 8, p. e0134826, Aug. 2015, doi: 10.1371/journal.pone.0134826.[13] B. Suhr, J. Dungl, and A. Stocker, “Search, reuse and sharing of research data in materials science and engineering—A qualitative interview study,” PLOS ONE, vol. 15, no. 9, p
Association Conference, Jun. 18-21, 2020. [Online]. Available: https://ojs.library.queensu.ca/index.php/PCEEA/article/view/14145[12] P. Sharma, K. Kumar, and P. Babbar, “Embedded librarianship: Librarian faculty collaboration,” Journal of Library & Information Technology, vol. 34, no. 6, pp. 455-460, Nov. 2014.[13] M. Stoeckle, B. Lenart, and J. E. Murphy, “A Text Analysis of Four Levels of Librarian Involvement and Impact on Students in an Inquiry-Based Learning Course,” Partnership, vol. 17, no. 1, pp. 1–17, 2022, doi: 10.21083/partnership.v17i1.6574.[14] B. Pati, and S. Majhi, “Pragmatic implications of embedded librarianship in academics: A review of eminent literatures,” Library Hi Tech News, vol
on this project will involve exploring additional years of data to detectlonger-term trends along with a second phase involving semi-structured interviews withengineering students to see if their perceived experiences are in sync with the findings of thisstudy.AcknowledgementsI would like to thank Dr. Brooke Coley and graduate students A. Lili Castillo, Ulises TrujilloGarcia, and Himani Sharma in the EESD program at ASU for their expertise, assistance, andfeedback during the coding process for this study. I would also like to thank Deidre Kirmis atASU Library for her help in retrieving the transcripts used for the study.References[1] B. Schembera and J. M. Durán, “Dark data as the new challenge for big data science and the introduction of
practices and promotion of open data in science,” Sci Ed, vol. 6, no. 1, pp. 3–9, Feb. 2019, doi: 10.6087/kcse.149.[2] D. R. Berg and K. E. Niemeyer, “The case for openness in engineering research,” F1000 research, vol. 7, pp. 501–501, 2018. [Online]. Available: https://bit.ly/3SH2PpQ[3] A. Johri, S. Yang, M. Vorvoreanu, and K. Madhavan, “Perceptions and Practices of Data Sharing in Engineering Education,” Advances in engineering education, vol. 5, no. 2, 2016, [Online]. Available: https://bit.ly/3Ur54hJ[4] B. Suhr, J. Dungl, and A. Stocker, “Search, reuse and sharing of research data in materials science and engineering—A qualitative interview study,” PLoS ONE, vol. 15, no. 9 September, Sep. 2020, doi: 10.1371/journal.pone
: Identifying information literacy skills for a successful transition from student to professional,” Science & Technology Libraries, vol. 31, no. 1, pp. 124–132, Jan. 2012, doi: 10.1080/0194262X.2012.648104.[6] A. Head, “Learning curve: How college graduates solve information problems once they join the workplace.” Oct. 16, 2012. doi: 10.2139/ssrn.2165031.[7] C. Tenopir and D. W. King, Communication patterns of engineers, 1st ed. Wiley, 2003. doi: 10.1002/0471683132.[8] AAC&U, “The career-ready graduate: What employers say about the….” Accessed: Dec. 12, 2023. [Online]. Available: https://www.aacu.org/research/the-career-ready-graduate- what-employers-say-about-the-difference-college-makes[9] B. Otis and L. Whang
Paper ID #41008The Role of University Research Libraries on Improving Education in Science,Technology, Engineering, Arts and Mathematics: A Focus on InstitutionalCollaborative CultureDr. Jason M. Keith, Mississippi State University Jason Keith is the Dean and Earnest W. and Mary Ann Deavenport, Jr. Chair in the Bagley College of Engineering at Mississippi State University, a position he has held since March, 2014. Keith received his B.S. in Chemical Engineering from The University of Akron and his Ph.D. from the University of Notre Dame. Keith is a Fellow of ASEE.Lis Pankl, Mississippi State University
and Arts students’ search processes. We mayexpect that undergraduate students experience cognitive complexity with more advanced searchtechniques, like proximity searching, truncation, wildcards, or Boolean expressions, for example.However, analysis reveals that undergraduate students experience cognitive complexity in basicelements of library research: a) deciding which terms to use, b) knowing if they are searching inthe right place, c) examining each article to weed out less relevant articles, and d) evaluating thequality of a source. Our findings reveal a sizable disconnect between what librarians may expectare basic elements of the search process and what students experience as cognitively complex.Introduction As public internet
process.Six questions were written supported by this prompt to satisfy our original goals as seen in Table3:Table 3. First questions designed around the Engineering Grand Challenges. 1.) Using ChatGPT and your own expertise, define the following terms: a.) Power b.) Renewable energy c.) Heat island d.) Photovoltaic array e.) Albedo f.) Angle of attack 2.) Using ChatGPT and your own expertise, provide some background information about renewable energy projects which have been started in New York City the last 5 years. 3.) Using ChatGPT and your own expertise, examine the following facets of the scholarly conversation occurring around renewable energy and New York City
] C. Wapner, “3D Printing Policy Considerations through the Library Lens,” OITP Perspectives, no. 3, 2015, https://www.ala.org/advocacy/sites/ala.org.advocacy/files/content/advleg/pp/pub/perspectives- 3D_Library_Policy-ALA_OITP_Perspectives-2015Jan06.pdf .[11] E. Lenton and C. Dineen, “Set it and Forget it (Almost): How We Make DIY 3D Printing Work in Our Library,” Public Services Quarterly, vol. 12, no. 2, pp. 179–186, Apr. 2016, doi: 10.1080/15228959.2016.1168725.[12] S. B. Nagle, “Maker Services in Academic Libraries: A Review of Case Studies,” New Review of Academic Librarianship, vol. 27, no. 2, pp. 184–200, Apr. 2021, doi: 10.1080/13614533.2020.1749093.[13] C. Benjes-Small, L. M. Bellamy, J. Resor-Whicker, and L
. 27, 2023).[22] M. Tsugawa, B. Webster, S. Solanki, A. Cuellar, and C. M. Spence, “Examination of Ableist Educational Systems and Structures that Limit Access to Engineering Education through Narratives,” American Society for Engineering Education, Aug. 2022, [Online]. Available: https://peer.asee.org/collections/2022-asee-annual-conference-exposition[23] M. E. Spencer and S. B. Watstein, “Academic Library Spaces: Advancing Student Success and Helping Students Thrive,” portal: Libraries and the Academy, vol. 17, no. 2, pp. 389– 402, 2017, doi: 10.1353/pla.2017.0024.[24] B. E. Eshbach, “Supporting and engaging students through academic library programming,” The Journal of Academic Librarianship, vol. 46, no. 3, p
Paper ID #38580Using Decision-based Learning to Develop Expert Information LiteracyBehaviors in Engineering UndergraduatesMr. David Pixton, Brigham Young University David Pixton is a subject liaison at the Harold B. Lee Library at Brigham Young University. In this role, he is responsible for providing research training and assistance to students and faculty within the majority of engineering and technology fields offered at the university. He holds degrees in Mechanical Engineer- ing and Library & Information Science. David’s current research is focused on improving learning in a library environment, including the
ICPSRmember institutions.B.3. Government Data SourcesGovernments around the world house myriad datasets on their websites. Many of these sitesoffer filtering options by Data Type or Format. See Appendix B for a non-comprehensive list ofgovernment and international data sources.Clinical TrialsAccessing biomedical research, both peer-reviewed and GL alike, remains critical for thosewithin or tangential to the medical community—serving to fill knowledge gaps and shed light onclinical outcomes. A vital component of biomedical GL resides in clinical trials. Defined as “anyresearch study that prospectively assigns human participants or groups of humans to one or morehealth-related interventions to evaluate the effects on health outcomes,” clinical trials
structure for an essay). Students acknowledged the challenges of usingChatGPT more broadly and emphasized its usefulness as a tool for topic development andpossibly reviewing, but not for research or writing.Samples of the Activity At the start of the second part of the activity, students were shown side-by-side samplesof narratives produced by humans and by the AI bot, and were asked to identify which one waswhich through an online poll. This first poll generated the baseline pre-activity data. Figure 1.shows a sample of the narratives shown to students. Students had to pick whether A or B wasgenerated by AI. Figure 1: Two narratives presented to students side-by-side. A was generated by a large language model AI, and B was
andpublishing patterns.Journal articles and conference proceedings were originally planned as the formats to beexamined but as the research proceeded it was determined that a) there was sufficient material toexamine the patterns focusing only on the journals and b) that publishing in conference venueswas potentially different enough to warrant a separate study. Finally, as neither of the authorswas fluent enough in other languages to warrant including them, only articles published entirelyin English were included, i.e. an English abstract alone was insufficient for a study to beincluded. This filter was applied inconsistently by the database vendors so some of the originalnumbers include papers that were written in a language other than English but
Education, vol. 103, no. 1, pp. 45-76, 2014.[5] A. Henry and L. Stieglitz, "An Examination of Systematic Reviews in the Engineering Literature," in 2020 ASEE Virtual Annual Conference Content Access, 2020.[6] M. Phillips, J. B. Reed, D. Zwicky, and A. S. Van Epps, "A scoping review of engineering education systematic reviews," Journal of Engineering Education, 2023.[7] K. Kolaski, L. R. Logan, and J. P. A. Ioannidis, "Guidance to best tools and practices for systematic reviews," Systematic Reviews, vol. 12, no. 1, p. 96, 2023/06/08 2023, doi: 10.1186/s13643-023-02255-9.[8] N. R. Haddaway, M. J. Grainger, and C. T. Gray, "Citationchaser: A tool for transparent and efficient forward and backward citation
. Conf. 2019, Minneapolis, MN,USA. B. Steiz, Ed. https://doi.org/10.26207/9z0c-7955.[13] X. Lei, “The impact of emotion management ability on learning engagement of collegestudents during COVID-19,” Frontiers in Psychology, vol. 13, Aug. 2022,https://doi.org/10.3389/fpsyg.2022.967666.[14] K. Phillips, “IF I APPLY: Identifying Bias and Resource Credibility,” Penn State UniversityLibraries, 2019. https://guides.libraries.psu.edu/IFIAPPLY.[15] R. S. Nickerson, “Confirmation bias: A ubiquitous phenomenon in many guises,” Rev. ofGeneral Psychol., vol. 2, no. 2, 175-220, 1998.[16] E. Roles, K. Phillips, and S. Thomas, “LibGuides: IF I APPLY - a source evaluation tool:Home,” IF I APPLY - A source evaluation tool, 2016. https://libguides.marshall.edu
, the functionality ofsearch interfaces used to access databases varies widely.The characteristics of databases for large, complex search strategies can vary, as well. Forexample, in Gusenbauer & Haddaway’s [5] evaluation of 28 databases for suitability of use insystematic reviews, they explored variation across databases in terms of: (a) functionality ofBoolean operators (AND, OR, and NOT), (b) options for and functionality of search fieldlimiters (e.g., title, abstract, and/or keyword searching), (c) maximum search string length and/orsearch limits based on number of characters or Boolean operators, (d) ability to truncate searchterms, (e) ability to search using proximity operators, and (f) ability to search for exact termsand/or turn
extracurricular nature of these research opportunities. Bibliography[1] B. P. Chang and H. N. Eskridge, “What Engineers Want: Lessons Learned from Five Years of Studying Engineering Library Users,” presented at the 2015 ASEE Annual Conference & Exposition, Jun. 2015, p. 26.1721.1-26.1721.17. Accessed: Feb. 24, 2023. [Online]. Available: https://peer.asee.org/what-engineers-want-lessons-learned-from-five-years-of-studying- engineering-library-users[2] J. de la Cruz, A. Winfrey, and S. Solomon, “Navigating the Network: An Exploratory Study of LGBTQIA+ Information Practices at Two Single-Sex HBCUs | de la Cruz | College & Research Libraries,” Mar. 2022, doi: https://doi.org/10.5860/crl.83.2.278.[3] F. Albarillo, “Information
digitallibrary and ignore others, except maybe Google Scholar. TABLE IV DATABASES SEARCHED WITH RESULTS DATABASE SEARCH STRATEGIES Results Index any EV terms 8* A B C D E F available articles ? ? ACM Digital Library
transfer student success," Reference Services Review, vol. 45, no. 3, pp. 511-526, 2017.[13] L. R. Coats and A. E. Pemberton, "Transforming for our transfers: the creation of a transfer student services librarian," Reference Services Review, vol. 45, no. 3, pp. 485- 497, 2017.[14] L. W. Roberts, M. E. Welsh, and B. Dudek, "Instruction and outreach for transfer students: A Colorado case study," College & Research Libraries, vol. 80, no. 1, p. 94, 2019.[15] J. Kohout-Tailor and S. R. Schilf, "Residential transfer students and the university library: A needs assessment," Journal of Academic Librarianship, Article vol. 49, no. 3, pp. N.PAG-N.PAG, 2023, doi: 10.1016/j.acalib
Waterloo, “University of Waterloo rankings and reputation,” UndergraduatePrograms, https://uwaterloo.ca/future-students/university-of-waterloo-ranking (accessed Jan. 18,2024).[2] University of Waterloo, “Student Full-time Equivalents (FTE),” Institutional Analysis andPlanning, https://uwaterloo.ca/institutional-analysis-planning/university-data-and-statistics/student-data/student-full-time-equivalents-fte (accessed Jan. 18, 2024).[3] C.L Dym, P. Little and E.J Orwin, Engineering design: a project-based introduction, 4th Ed.Hoboken, N.J.: Wiley, 2014.[4] B. Friedman and D.G. Hendry. Value Sensitive Design: Shaping Technology with MoralImagination. Cambridge, Massachusetts: MIT Press, 2019.[5] C. Titus, C. B. Zoltowski, and W. C. Oakes, “Designing