Engineering Students and Professionals Report Different Levels of Information Literacy Needs and Challenges
Bibliographic record
Abstract
A Review of: Phillips, M., Fosmire, M., Turner, L., Petersheim, K., & Lu, J. (2019). Comparing the information needs and experiences of undergraduate students and practicing engineers. The Journal of Academic Librarianship, 45(1), 39-49. https://doi.org/10.1016/j.acalib.2018.12.004 Abstract Objective – To compare the levels of information literacy, needs, and challenges of undergraduate engineering students with those of practising engineers. Design – Electronic survey. Setting – Large land grant university in the Midwestern United States and multiple locations of a global construction machinery manufacturing company (locations in Asia Pacific, Europe, North America). Subjects – Engineering undergraduates and full-time engineers. Methods – Two voluntary online surveys distributed to (a) students in two undergraduate engineering technology classes and one mechanical engineering class; and (b) to engineers in an online newsletter. None of the questions on the survey were mandatory. Because the call for practising engineers generated a low response rate, direct invitations were sent in batches of 100 to randomly selected engineers from a list provided by the human resources department of the company participating in the study. The surveys were similar but not identical and included multiple choice, Likert scale, and short answer questions. Data analysis included two-sided unpaired sample t-tests (quantitative data) and deductive and inductive content analysis (qualitative data). Main Results – There were 63 students and 134 professional engineers among the respondents. Survey response rates were relatively low (24.3% for students; approximately 4.5% for employees). Students rated themselves higher overall and significantly higher than did engineers on the questions “know where to look for information” (students M = 5.3; engineers M = 4.2) and “identifying the most needed information” (students M = 5.5; engineers M = 4.8) (mean values reported on a 7-point scale). Neither group rated themselves highly on “reflecting on how to improve their performance next time” or “having a highly effective structure for organizing information,” though engineers in North America rated themselves significantly higher than those in Asia Pacific on organizing information, knowing where to look for information, and using information to make decisions. Both students and engineers reported often using Google to find information. The library was mentioned by one-half of engineers and one-third of students. Engineers reported consulting with peers for information and making more use of propriety information from within their companies, while students reported using YouTube videos and online forums, as well as news and social media. More than half of students (57%) reported having enough access to information resources, while 67% of engineers felt that they lacked sufficient access. The most common frustration for both groups was locating the information (45% of student responses; 71% of engineer responses). Students reported more frustration with evaluating information (17%) compared to engineers (9%). Conclusion – Engineering students and professional engineers report differences in their levels of confidence in finding information and differences in the complexity of the information landscape. Engineering librarians at the university level can incorporate this knowledge into information literacy courses to help prepare undergraduates for industry. Corporate librarians can use this information to improve methods to support the needs of engineers at all levels of employment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".