Context-Based Collaborative Learning while Literature Review

Can Literature review be faster and more rigorous? With the combined effort of human collaboration and machine intelligence it can!

Collaborative learning is when two or more people work towards a common goal with shared responsibilities. This method is based on the belief that people can teach each other to maximize learning and output. It reinforces the fact that learning is a community process and people are able to learn and understand more when they interact with each other. In a classroom, on the top of textbook-based learning practices, if a teacher encourages students to discuss what could be the reasons for let’s say ‘what happens when water is added to acid’ or vice versa. Students can discuss and understand the mechanism and come up with the logic behind adding acid to water and not the other way around. It will be more engaging and more fulfilling to each student as they will find themselves to be actively involved in the process of solution-finding. If a group of people interested in a similar kind of question, trying to answer a similar kind of problem were to discuss what each of them knows then most likely they can indulge in an exchange of knowledge and draw reasonable conclusions from the discussion in lesser time while teaching something new to each other.

In the current digital age, while doing the literature review, researchers encounter many matches for their keywords. It is very difficult to find the ones which have useful information. These days, there is personalized feed created according to your search keywords provided by Google Scholar, Mendeley, etc. which makes you wonder about the potential of artificial intelligence in the field of research.

A software…

Which understands more than just keywords,

Which understands the context of your searches and gives results accordingly.

That would be truly revolutionary!

This would ease the process of literature review manifolds as one will have a dedicated software trying to find the research articles of their use which will reduce the time one spends on just finding the right literature. Imagine how during academic and research collaborations, the research output increases because of the fact that there are collective goals and every person involved have a specific set of skills which they bring to the table for a successful realization of those goals. Similarly, this can be equated with collaborating with an intelligent machine which understands your input more than the mere words, it understands what you want to say and produces output according to that. It will not only increase your productivity but also the scientific rigor of your literature survey. For example, if your search keywords are, “Dementia” and “health care”, the results will include all articles with both of the above-mentioned keywords. But with context-based collaborative learning tool, the tool will read the highlighted text and produce results on the basis of what is being said there.

In the year 2018 alone, 226,831 reviews were published, according to Scopus. That clearly indicates that each year, so many man-hours go into literature survey, analyzing the literature, drawing out logical conclusions and research gaps from it. Since the literature survey is the first step of the process of research, we need better tools to be able to function optimally and contribute significantly to this quest for knowledge. The programmers and scientific community alike need to take stock of the situation and come up with such tools or software to produce more relevant search results for better literature reviews.

Hi, I am Shafali Garg. This is my first blog as a guest author in The Academic Rollercoaster.