In a #blogjune post from earlier this week, Sharon asked the following questions in relation to the way libraries provide information to clients:
Should we be like the farmers and start educating people about the process of getting the information to them? What does it involve? How do we go about it? What skills are needed? What hurdles do we jump to get the information? Or do they really need/want to know?
This is one of the biggest differences between academic and health libraries that I’ve found in my short time as a health librarian. In my role as a Research Librarian in a university library, I did show clients how I found information for them, in order for them to learn how to do it themselves. This was a natural part of working in an educational institution. However, the situation is quite different in the health library where I currently work. We provide information as a service – the client (usually a busy, time-poor clinician) submits their literature search request, we search the relevant resources and find some results, and then we send those results to the client. The same applies to requests for journal articles.
This is what I was referring to when I included “black box” in the title of this post. The processes that we go through in order to find the information that our clients are looking for is hidden from the clients. By contrast, in the academic setting I was providing a “window” into the process so that the clients could understand it better. I’m not saying that none of our clients want to learn how to find information – I have sat with a couple of them and showed them how to construct a good search strategy. However, these have tended to be the exception rather than the rule. There are times when health librarians do need to provide clients with an insight into how the information was found, e.g. when they’re assisting a team who are preparing a systematic review. On the whole though, the information retrieval process remains a mystery to our clients. Again, I’m not saying this as a bad thing, it’s just the way that things are and it seems to work for everyone concerned.
In what has become a bit of #blogjune tradition for me, today’s fourth post is about our night out at the Vivid festival. We took the boys in to the city last night, having done a bit of reconnaissance a week or so ago without them. The highlight for them was the display on Customs House, which was very much aimed at kids, with dinosaurs, gnomes and giant snails featuring in the show.
After watching the Customs House show a couple of times, we headed off to the MCA and checked out the nearby installations. They were pretty interactive, although the queue was too long at some of them. The boys had fun with the large lit-up drums, and they enjoyed checking out the glowing “spider web”. The big pig was also an attraction. We stopped here for dinner, which included cupcakes for dessert to celebrate my wife’s birthday.
We had two very tired boys with us by the end of the night, and they slept well once we got home. I’m pretty sure we’ll be going back to Vivid next year.
Last week was Library and Information Week (LIW) for 2015. My workplace prepared a series of events to celebrate. We hosted training sessions or webinars from several vendors who updated us on their products. On Thursday morning we hosted a morning tea in the library as part of Australia’s Biggest Morning Tea, which raised over $100 for the Cancer Council. Clients also came along to the training sessions that we ran – one on EndNote, and another that I led looking at measuring and improving research impact.
During LIW, we also took the opportunity to visit the other hospital we serve, where the staff only have virtual access to library services. We planned a day of training sessions covering accessing resources, searching, EndNote, and my research impact presentation, as well as having drop-in sessions where we’d answer any library-related questions that the staff had. Despite having a limited period in which to promote the day, we were happy with the attendance that we got. We will try and visit more regularly from now on, now that we have a full complement of staff in the library.
Another LIW event we were involved in was National Simultaneous Storytime (NSS). Although we didn’t a storytime ourselves, we contacted the Starlight Room and the hospital school to see if they would be interested in participating, and they both did. Unfortunately the date for NSS was the same day I was visiting the other hospital, so I didn’t get to see how it went, but by all accounts it was a success.
Overall I think it was a successful LIW this year, and we’ll be planning events for next year.
The most significant change that has happened to me since last year’s #blogjune is that I started a new job. After 12 years at Macquarie University Library (MUL), I took up a position at the medical library at The Children’s Hospital at Westmead (CHW). I started there in March, and have been very happy that I made the move. MUL was the first library I worked in, and I really enjoyed my time working there. As I mentioned in my farewell speech, I feel very fortunate to have begun my library career in a workplace which offered a great deal of support for its staff. I was lucky enough to work with managers and team leaders who encouraged me to take advantage of opportunities as they arose, and I’m very glad I did. They stood me in good stead when it came to applying for the job at CHW.
As you’d expect, the library at CHW is much smaller than MUL, with a staff of five. This means that everyone does a bit of everything, but still has responsibility for certain things. My role focuses on the management of the electronic resources that the library subscribes to. I make sure that our Serials Solutions information is kept up to date, and also administer the OpenAthens accounts that the hospital staff set up. This is an area of the library that I didn’t have much direct involvement with at MUL – there was a team dedicated to maintaining the records for our electronic resources. However, I’ve managed to pick it up pretty quickly, and I have some sympathy for my former colleagues who I used to refer the problems to.
I have made some contacts in the health library sector over the years, and it has been an area of librarianship that has appealed to me. Although there has always been the threat of budget cuts hanging over them, I felt that health librarians were an innovative group who were always looking out for ways to improve and update the services they offer to their clients. I’m excited to be part of that group, and am looking forward to this next stage in my career.
I can’t believe that I’m about to embark on my fourth year of #blogjune. I think I’ve got enough content to write 30 posts (well, 29 after this one), and hopefully there’ll be a few memes doing the rounds to help me out if I need it. As usual, there’ll be a mixture of library content (but not about academic libraries this time), geocaching content, and whatever else I can think of that’s worthy of a post.
It will be a family effort again this year, with my wife and sons also both taking part. Hopefully we can all come up with some interesting material for you. Happy reading!
Yesterday I attended the third Research Support Community Day (RSC Day) at UNSW, which was held as a satellite event to the Information Online conference. I created a Storify of the event, and it’s available here. I attended the first RSC Day in Brisbane in 2013, and it was interesting to see how the topics have changed over the past two years. Back then the focus was on altmetrics, and how they could best be used by librarians and researchers. Now it seems that altmetrics have become “business as usual”, and research data management is now the hot topic. There was also a focus on evaluating the impact of the support services that libraries provide to researchers, now that such services have been offered for several years. There’s even an ISO standard for “Methods and procedures for assessing the impact of libraries“, and related standards for collecting library statistics and for library performance indicators.
It was interesting day, and I’m glad I got the opportunity to attend. Hopefully these days will continue, as I think they’re a worthwhile event and great for building a network of colleagues.
Today I went out geocaching with our eldest son, as our youngest was back at pre-school. We had four caches on our to-do list, and they were all in the bush so we could have a nice walk too.
The first one we went for was only a short walk from where I parked the car. Tom led the way with the GPSr, and took us to the cache. It wasn’t a particularly large one, but I’d chosen it because it was made of Lego. This certainly made it one of the most unique caches we’d found.
It was a short drive to the parking spot for the next couple of caches. The next find was a nano attached to a sign, so it was fairly straightforward. We headed off along the track towards our next cache. This one was originally published as a large sized cache, but over time it’s been muggled a couple of times, so it’s now a micro. It took a while to find it, and I did get a clue from one of the previous logs.
We kept going along the track towards a playground, which ended up being our lunch stop. It was a park which was close to the house I grew up in, and it’s certainly changed a bit since then. After lunch we headed back to the car, and on the way we made our final find for the day. This was another unique container.
We both had a good day, and will go out next week to do it again. However Tom has asked if there can be more caches and less walking, so I’ll need to see if I can find a nice group of caches that we can go for.
As learning analytics has emerged as a discipline over the last few years, several organisations have been founded with the aim of conducting research in the field as well as bringing together professionals to discus the latest developments. Some of them are listed below:
Overall I found the DALMOOC interesting, and I was certainly introduced to tools and ideas that I can use during my research project. Here is my first ever attempt a concept map for what was covered in the course:
I didn’t really engage with the social learning aspects of the course – I preferred to work through the edX platform in the traditional way. I’ve always been a bit wary and nervous of putting my work out there for my peers to assess, so that’s why I stuck with edX. As far as the structure is concerned, I did find it a little disorienting in the first week, but soon got the hang of it. I didn’t really get much out of the Hangouts – I was expecting that they were going to be a bit more interactive and allow some participation from students, rather than only having the instructors involved.
As a complete newbie to learning analytics I found the content manageable and fairly easy to understand. The exception to this was the unit on prediction modeling and behaviour detection in weeks 5 and 6. I found it all quite technical and confusing, and I didn’t complete any of the assessments during those weeks. It was nice to be exposed to it, but I don’t think it’s an area that I’ll be using in my small research project. The tools that we were introduced to in the DALMOOC were pretty easy to use, and I can see that I’ll find Tableau, Gephi and LightSide useful in my Twitter research, at least at a basic level. On a side note, it was nice to see the work of researchers at other Australian universities was mentioned during the course e.g. Shane Dawson and Lori Lockyer.
The DALMOOC has given me a taste of what’s involved in working with learning analytics, and the tools and techniques that are available. There are certainly opportunities for libraries to get involved and make use of the data that our systems produce.
Text mining is the next type of data analysis that we’re looking at in the Data, Analytics and Learning MOOC. I’m looking forward to the next couple of weeks, as I think that some of these tools and techniques might be useful for my research project, which is based on analysing tweets. Text mining is all about trying to find patterns in large collections of text, and using these patterns as a basis for identifying data that is worth investigating further. It’s this finding patterns in textual data which interests me, as that’s the vision that I’ve got for my Twitter research project.
One of the subareas of text mining is analysing the collaborative learning process that occurs in online courses via the discussion forums. This analysis involves modelling conversational interactions between students , and using those models to find out what it is about conversations that make them valuable for online learning. Based on this understanding it’s then possible to design interventions to support learning in online settings. Analysing conversations in online courses draws on knowledge from a number of fields, such as education, psychology, and sociolinguistics. This knowledge is used to determine the cognitive processes associated with collaborative learning, investigate what conversational interactions look like, and build models of how psychological signals are revealed through language. All this ultimately allows the development of models showing where processes are happening during interactions.
An example of how these models can be used in learning analytics research is assessing some reasons for attrition along the way in MOOCs. The models are based on the analysis of the posts in discussion forums, both from the point of view of individual students and from the overall tone of individual threads. The negativity and positivity of the posts and threads is calculated, and then survival modeling is carried out to determine the probability that a student will have dropped out of the course by the following week.
This sort of detailed modeling is out of scope for my research project, but some of the aspects of conversation analysis could be useful, as many of the interactions between Twitter users could be characterised as conversations. At this stage I think I’ll be learning some useful stuff over the next couple of weeks.
Most of the exercises for this week were concerned with building models in LightSide and comparing their performance.
The first exercise dealt with using different feature spaces within the model and seeing how this affected their performance. The initial model, using unigrams, resulted in an accuracy of 75.9% and a kappa value of 0.518. This is OK, but would including bigrams and trigrams as features improve these results? They might, by providing further context for each word, thus reducing the number of incorrect predictions. By including these extra features, there was a slight improvement in the model – an accuracy of 76.5% and a kappa value of 0.530. However, by increasing the number of features there is a risk of creating a model which overfits the data, and can’t be applied to other data sets. To overcome this there is a Feature Selection tool, which only uses the 3,500 (in this case) most predictive features in the model. The result of using this select group of features was a statistically significant improvement in the quality of the model.