Note:

In this section, I present the area I chose to focus on and uncited justifications. Indeed, these ideas are founded upon my own contemplations, and those discussed by the scholars I have attached in the reference list.

Research Methods Outline

The aim is to build a CIR interface that facilitates, asynchronous and synchronous, remote and co-located collaboration by focusing on the UI instead of algorithms. Simply, this research lies in the group of studies that research the depth of mediation, with a focus on the user interface. Another unique aspect is the focus on users and their interactions with the CIR interface instead of on the CIR system details, for example, the relevance of documents or ranking models. I propose a group of students working on a literature search for a systematic review. The process is explained in the section about the evaluation of the system. Figure 1 below presents a classification of the current dimensions of studying Collaborative Information Retrieval.

Figure 1( A summary of the dimensions of studying CIR as is evident from research, source: Author)

The overall purpose of the research study is to build and evaluate a CIR interface. The research methods, techniques and strategies deployed in the study are informed by the dimensions in which collaborative information retrieval is studied. Specifically, they are intent, depth of mediation, concurrency and location. Like most scholastic studies, this investigation is founded on gaps that were identified in each of the dimensions.

Concerning intent, this study focuses on explicit intent where users take deliberate actions to collaborate-therefore, in this context implicit collaboration is incorporated to a small extent. Aside from the fact that implicit intent is mostly employed in social information retrieval where users’ attributes and their relationships impact the search outcomes of others, there is little foundational research to rely on. Further, considering that the end-users of the CIR interface is a group of students who are tasked to research a topic, inferring information from their attributes such as gender, age and role in the group is not a priority of the ongoing study.

By contrast, focusing on explicit intent is necessary as it is expected that the group will commit to consciously team up on the task. Even though, the current investigation does not specifically focus on the implicit intent of users, crucial features of implicit intent are deployed in the CIR interface to further support explicit collaboration and add a level of flexibility of the system. Even more, including features of implicit collaboration adds scalability to the CIR interface making it suitable for scenarios where there might be different levels in the group in terms of access to information and roles. For instance, a team leader is expected to initiate the collaborative activities thus, outcomes of other users may need to be inferred from the team leader’s search activities without his/her deliberate actions.

The depth of mediation refers to the approach through which the CIR interface facilitates collaborative searching. Definitively, research contemplates that collaboration can be facilitated through the user interface (UI), at the algorithmic level, and through the use of independent communication tools. Notably, all these approaches are meant to address the sharing of information, awareness, persistence and division of labor which are the domains through which collaboration can be achieved in any computerized system. In line with the preceding elaborations, the current CIR interface will facilitate collaboration primarily through the user interface – this will involve shared workspaces, communication tools, task performance tracking.  The use of independent communication tools to facilitate collaboration is more oriented towards individual IR rather than CIR thus, it is not considered in this investigation.

Deviating from past investigations that focus on one aspect of concurrency, the CIR interface in this investigation will facilitate both synchronous and asynchronous collaboration. Markedly, users will be aware of whether others are logged in or not and what their activities have been. Further, both remote and co-located collaboration is considered and it is analyzed in the questionnaire.  There are many investigations that narrow down to specifics concerning co-location and concurrency, however, in the mobile context they are interesting dimensions to investigate. For instance, assuming the students are aware of each other’s activities and presence in the system, the study will be able to understand how users perceive co-location and concurrency (through the questionnaire) and how they actually tend to interact with the system (from the logs). Notably, this presents opportunities for an in-depth study of the interactions between users and the CIR interface.

Preview

The methods section takes a two-pronged approach. Firstly, the section presents the entirety of the details of building the CIR interface. Afterwards, it details the evaluation of the CIR interface- this involves offering the CIR interface to a group of users with a common information need and collecting feedback from them. Notably, the feedback is collected through questionnaires with questions on specific areas of interest.  Another approach would be to simulate ideal users in the CIR interface-it involves using test collections. Nonetheless, the first approach is more feasible because the aim is to understand how users perceive its effectiveness and not how the actual CIR system and algorithms retrieve relevant documents.

Building the CIR Interface

A.     Key considerations

i.                    Input

There is a wide range of input that CIR interfaces can accept. Further, mobile devices present a wide range of input capabilities outside the conventional desktop environment. While both desktops and mobile devices support input in the form of text, speech, and a wide range of multimedia, mobile phones provide additional capabilities through sensors. Because this study is specific to the mobile platform, it is a priority to leverage the sensor capabilities to optimize the CIR interface. These capabilities may include but not limited to screen orientation, size, and graphic specific details. Even though the system is poised to accept a wide range of multimedia, the primary focus is on text-based queries. The other forms of input are considered secondary in this investigation.

ii.                  Presentation

Unlike most studies that focus on the algorithmic dimension of how CIR is occurring, the current investigation is engraved on the user interface. The decision is motivated largely by the relatively high numbers of studies on the underpinnings of CIR than those of how users interact with the CIR interface. In this study, it is proposed that the most plausible approach to evaluate a CIR interface is to query the users themselves. Therefore, the presentation which encompasses the graphical interface that users see is a key concern of this investigation.  The aim is to understand how users perceive collaborating on their mobile devices and document how the overall task was achieved.

iii.                Frameworks of CIR

Because this investigation does not focus on building a CIR system from scratch it ought to be founded on a robust CIR framework. In detail, a framework in the programming context is a prebuilt prototype of the CIR interface.

iv.                 Technologies to build the CIR interface

From the research, it is evident that the user interface is a key dimension of focus on how the study intends to facilitate collaboration. A wide range of technologies, tools and protocols exist however, this study relies on Java.

II.         Evaluating the CIR interface

The aim is to measure the effectiveness of the CIR interface to facilitate collaboration. Instead of simulating the system with test collection, this study delves further into the user dimension. In this context, a group of students will be collaborating on collecting relevant literature for a systematic review. The studies will need to meet a minimum inclusion criterion and receive an appraisal from at least three members. After the literature has been selected, they will need to identify key characteristics of the studies and classify them. They will also be expected to share insights about the studies they interact with to identify themes. Notably, the tasks involve identifying and classifying. The sources of literature are scholarly databases such as google scholar and journals. To make it more interesting, the CIR interface will track task completion and user activities transparently.

Process

Offer the CIR interface to a group of students until the task is completed.

Offer the group of students with a questionnaire

Collect logs of CIR interface and draw insights

Collect the results of the questionnaire and draw insights.

References

Aditya, P. & Counts, S., 2011. Identifying topical authorities in microblogs. Proceedings of the Conference on Web Search and Data Mining (WSDM’11), p. 45–54.

Anon., 2008. Efficiency Issues in Information Retrieval Workshop. Glasgow, UK, s.n., pp. 1-62.

Behrend, A., Reichartz, F., Dorau, C. & Manthey, R., n.d. Data Stream Analysis for Location-Aware Collaborative Information Retrieval.

Borlund, P., 2003. The IIR evaluation model: a framework for evaluation of interactive information retrieval systems. Information Research, April.8(3).

Clough, P. & Sanderson, M., 2013. Evaluating the performance of information retrieval systems using test collections. Information Research, 18(2), pp. 1-13.

Crestani, F., Mizzaro, S. & Scagnetto, I., 2019. Mobile Information Retrieval, s.l.: s.n.

Crestani, F., Mizzaro, S. & Scagnetto, I., 2019. Mobile Information Retrieval.

Dumais, S., Grudin, J., Poltrock, S. & Bruce, H., 2000. Collaborative Information Retrieval (CIR). New Review of Information Behaviour Research, 117(1), pp. 298-298.

Foley, C. & Smeaton, A., 2010. Division of labour and sharing of knowledge for synchronous collaborative information retrieval. Inf Process. Manage, 46(6), p. 762–772.

Grudin, J., 1994. Groupware and social dynamics: eight challenges for developers. Communications of the ACM, 37(1), p. 92–105.

Hansen, P. & Järvelin, K., 2005. Collaborative Information Retrieval in an information-intensive domain. Information Processing & Management, 41(5), pp. 1101-1119.

Htun, N. N., Halvey, M. & Baillie, L., 2017. An Interface for Supporting Asynchronous Multi-Level Collaborative Information Retrieval. Conference on Conference Human Information Interaction and Retrieval, p. 225–234.

Karunakaran, A., Reddy, M. C. & Spence, P. R., 2013. Toward a model of collaborative information behaviour in organizations. Journal of the American Society for Information Science and Technology, December, 64(12), pp. 2437-2451.

Kelly, D., 2009. Methods for Evaluating Interactive Information Retrieval Systems with Users. Foundations and Trends® in Information Retrieval, 3(1-2), pp. 1-224.

Naderi, H. & Rumpler, B., 2006. PERCIRS: a PERsonalized Collaborative Information Retrieval System. Hammamet, Conference: Actes du XXIVème Congrès INFORSID.

Naderi, H. & Rumpler, B., 2010. PERCIRS: a system to combine personalized and collaborative information retrieval. Journal of Documentation, 66(4).

Reddy, M. & Jansen, B. J., 2007. A model for understanding collaborative information behaviour in context: A study of two healthcare teams. Information Processing & Management, 44(1), pp. 256-273.

Shah, C. & Leeder, C., 2016. Exploring Collaborative Work Among Graduate Students Through the C5 Model of Collaboration: A Diary Study. Journal of Information Science (JIS), 42(5), pp. 609-629.

Si, L. & Jin, R., 2011. Machine learning for information retrieval. Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR.

Smeaton, A., Foley, C., Byrne, D. & Jones, G., 2008. iBingo Mobile Collaborative Search. Irish Research Council for Science.

Soulier, L. & Lynda, T.-L., 2017. On the collaboration support in Information Retrieval. ACM Computing Surveys, 50(4), p. 1.

Tamayo, S. C., Fernández-Luna, J. M., Huete, J. F. & Pérez-Vázquez, R., 2009. A proposal for an experimental platform on Collaborative Information Retrieval. s.l., s.n., pp. 485-493.

Tamine, L. & Soulier, L., 2016. Collaborative Information Retrieval: Concepts, Models and Evaluation. European Conference on Information Retrieval: Advances in Information Retrieval, pp. 885-888.

Teevan, J., Morris, M. R. & Bush, S., 2009. Discovering and using groups to improve personalized search. New York, ACM, p. 15–24.

Yang, H., Sloan, M. & Wang, J., 2014. Dynamic information retrieval modelling. Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’14), Volume 1920.