by Sarah Nguyen
Back in March, during bloggERS’ Making Tech Skills a Strategic Priority series, we distributed an open survey to MLIS, MLS, MI, and MSIS students to understand what they know and have experienced in relation to technology skills as they enter the field.
To be frank, this survey stemmed from personal interests since I just completed an MLIS core course on Research, Assessment, and Design (re: survey to collect data on current landscape). I am also interested in what skills I need to build/what class I should sign up for my next quarter (re: what tech skills do I need to become hire-able?). While I feel comfortable with a variety of tech-related tools and tasks, I’ve been intimidated by more “high-level”computational languages for some years. This survey was helpful for exploring what skills other LIS pre-professionals are interested in and which skills will help us make these costly degrees worth the time and financial investment that is traditionally required to enter a stable archive or library position.
The survey was open for one month on Google Forms, and distributed to SAA communities, @SAA_ERS Twitter, the Digital Curation Google Group, and a few MLIS university program listservs. There were 15 questions and we received responses from 51 participants.
Results & Analysis
Here’s a superficial scan of the results. If you would like to come up with your own analyses, feel free to view the raw data on GitHub.
The most popular technology-related skill that students are interested in learning is data management (manipulating, querying, transforming data, etc.). This is a pretty broad topic as it involves many tools and protocols which can vary between a GUI or scripts. A separate survey that does a breakdown of specific data management tools might be in order, especially since these types of skills can be divided into specialty courses, workshops, which then translates into a specific job position. A more specific survey could help demonstrate what types of skills need to be taught in a full semester-long course, or what skills can be covered in a day-long or multi-day workshop.
It was interesting to see that even in this day and age where social media management can be second nature to many students’ daily lives, there was still a notable interest in understanding how to make this a part of their career. This makes me wonder what value students have in knowing how to strategically manage an archives’ social media account. How could this help with the job market, as well as an archival organization’s main mission?
Looking deeper into the popular data management category, it would be interesting to know the current landscape of knowledge or pedagogy in communicating with IT (e.g. project management and translating users’ needs). In many cases, archivists are working separately from but dependently on IT system administrators, and it can be frustrating since either department may have distinct concerns about a server or other networks. In June’s NYC Preservathon/Preservashare 2019, there was mention that IT exists to make sure servers and networks are spinning at all hours of the day. Unlike archivists, they are not concerned about the longevity of the content, obsolescence of file formats, or the software to render files. Could it be useful to have a course on how to effectively communicate and take control of issues that can be fuzzy lines between archives, data management, and IT? Or as one survey respondent said, “I think more basic programming courses focusing on tech languages commonly used in archives/libraries would be very helpful.” Personally, I’ve only learned this from experience working in different tech-related jobs. This is not a subject I see on my MLIS course catalog, nor a discussion at conference workshops.
The popularity of data management skills sparked another question: what about knowledge around computer networks and servers? Even though LTO will forever be in our hearts, cloud storage is also a backup medium we’re budgeting for and relying on. Same goes for hosting a database for remote access and/or publishing digital files. A friend mentioned this networking workshop for non-tech savvy learners—Grassroots Networking: Network Administration for Small Organizations/Home Organizations—which could be helpful for multiple skill types including data management, digital forensics, web archiving, web development, etc. This is similar to a course that could be found in computer science or MLIS-adjacent information management departments.
I can’t say this is statistically significant, but the inverse relationship between 15.7% who have not/will not take a technology-focused course in their program, compared to 78.4% of respondents who are not aware of the difference between scripting and programming is eyebrow raising. According to an article in PLOS Computational Biology, the term “script” means “something that is executed directly as is”, while a “program[… is] something that is explicitly compiled before being used. The distinction is more one of degree than kind—libraries written in Python are actually compiled to bytecode as they are loaded, for example—so one other way to think of it is “things that are edited directly” and “things that are not edited directly” (Wilson et al 2017). This distinction is important since more archives are acquiring, processing and sharing collections that rely on the archivist to execute jobs such as web-scraping or metadata management (scripts) or archivists who can build and maintain a database (programming). These might be interpreted as trick questions, but the particular semantics and what is considered technology-focused is something modern library, archives, and information programs might want to consider.
Figure 4 illustrates the various ways students tackle new technologies. Reading the f* manual (RTFM) and Searching forums are the most common approaches to navigating technology. Here are quotes from a couple students on how they tend to learn a new piece of software:
- “break whatever I’m trying to do with a new technology into steps and look for tutorials & examples related to each of those steps (i.e. Is this step even possible with X, how to do it, how else to use it, alternatives for accomplishing that step that don’t involve X)”
- “I tend to google “how to….” for specific tasks and learn new technology on a task-by-task basis.”
In the end, there was overwhelming interest in “more project-based courses that allow skills from other tech classes to be applied.” Unsurprisingly, many of us are looking for full-time, stable jobs after graduating and the “more practical stuff, like CONTENTdm for archives” seems to be a pressure felt in-order to get an entry-level position. Not just entry too; as continuing education learners, there is also a push to strive for more—several respondents are looking for a challenge to level up their tech skills:
- “I want more classes with hands-on experience with technical skills. A lot of my classes have been theory based or else they present technology to us in a way that is not easy to process (i.e. a lecture without much hands-on work).”
- “Higher-level programming, etc. — everything on offer at my school is entry level. Also digital forensics — using tools such as BitCurator.”
- “Advanced courses for the introductory courses. XML 2 and python 2 to continue to develop the skills.”
- “A skills building survey of various code/scripting, that offers structured learning (my professor doesn’t give a ton of feedback and most learning is independent, and the main focus is an independent project one comes up with), but that isn’t online. It’s really hard to learn something without face to face interaction, I don’t know why.”
It’ll be interesting to see what skills recent MLIS, MLS, MIS, and MSIM graduates will enter the field with. While many job postings list certain software and skills as requirements, will programs follow suit? I have a feeling this might be a significant question to ask in the larger context of what is the purpose of this Master’s degree and how can the curriculum keep up with the dynamic technology needs of the field.
- Potential bias: Those taking the survey might be interested in learning higher-level tech skills because they do not already know the skills, while those who are already tech-savvy might avoid a basic survey such as this one since they already know the skills. This may put a bias on the survey population consisting of mostly novice tech students.
- More data on specific computational languages and technology courses taken are available in the GitHub csv file. As mentioned earlier, I just finished my first year as a part-time MLIS student, so I’m still learning the distinct jobs and nature of the LIS field. Feel free to submit an issue to the GitHub repo, or tweet me @snewyuen if you’d like to talk more about what this data could mean.
Wilson G, Bryan J, Cranston K, Kitzes J, Nederbragt L, Teal TK (2017) Good enough practices in scientific computing. PLoS Computational Biology 13(6): e1005510. https://doi.org/10.1371/journal.pcbi.1005510
Sarah Nguyen is an advocate for open, accessible, and secure technologies. While studying as an MLIS candidate with the University of Washington iSchool, she is expressing interests through a few gigs: Project Coordinator for Preserve This Podcast at METRO, Assistant Research Scientist for Investigating & Archiving the Scholarly Git Experience at NYU Libraries, and archivist for the Dance Heritage Coalition/Mark Morris Dance Group. Offline, she can be found riding a Cannondale mtb or practicing movement through dance. (Views do not represent Uovo. And I don’t even work with them. Just liked the truck.)