HAL Id: hal-01574127
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Submitted on 11 Aug 2017
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Towards Adaptive Dashboards for Learning Analytic:
An Approach for Conceptual Design and implementation
Inès Dabbebi, Sébastien Iksal, Jean-Marie Gilliot, Madeth May, Serge Garlatti
To cite this version:
Inès Dabbebi, Sébastien Iksal, Jean-Marie Gilliot, Madeth May, Serge Garlatti. Towards Adaptive Dashboards for Learning Analytic: An Approach for Conceptual Design and implementation. 9th In- ternational Conference on Computer Supported Education (CSEDU 2017), Apr 2017, Porto, Portugal.
pp.120-131, �10.5220/0006325601200131�. �hal-01574127�
Towards Adaptive Dashboards for Learning Analytic An Approach for Conceptual Design and implementation
Dabbebi Ines
12, Iksal Sebastien
1, Gilliot Jean-Marie
2, May Madeth
1and Garlatti Serge
21
UBL, University of Maine, LIUM Laboratory, Laval-LeMans, France
2
UBL, Telecom Bretagne, LABSTICC Laboratory, Brest, France
{ines.dabbebi, sebastien.iksal, madeth.may}@univ-lemans.fr, {jm.gilliot, serge.garlatti}@telecom-bretagne.eu
Keywords: Learning analytic dashboard, Dashboard generator, Dashboard model, User’s needs, Context.
Abstract: Designing Learning Analytic (LA) dashboards can be a challenging and complex task when dealing with abundant data generated from heterogeneous sources with various uses. On top of that, each dashboard is designed in accordance with the user’s needs and their observational objectives. Therefore, understanding the context of LA and its users is compulsory as it is part of the dashboard design approach.
Our research effort starts with an exploratory study of different contextual elements that could help us define what an adaptive dashboard is and how it fulfills the user’s needs. To do so, we have conducted a needs assessment to characterize the user profiles, their activities, their visualization preferences and objectives when using a dedicated dashboard. In this paper, we introduce a conceptual model, which will be used to generate a variety of LA dashboards. Our main goal is to provide users with adaptive dashboards, generated accordingly to their context of use while satisfying the users’ requirements. We also discussed the implementation process of our first prototype as well as further improvements.
Human has been producing an overwhelming amount of data and information on the Web (John et al., 2016). In a learning situation, every time a learner or a teacher interacts with the learning envi- ronment such as taking an online class in a MOOC (Massive Open Online Course) or signing into their virtual learning environment; he leaves behind him a significant amount of digital footprint or traces of his actions (Greller and Drachsler, 2012). Through bet- ter use of this data, teachers can be able to adapt their courses and learners can use it to change their learning behaviors. As for the researchers, they are more inter- ested in extracting new knowledge and in exploring the phenomena of learning data exploitation (Verbert et al., 2013). However, in the meantime, information expand faster than our capacity to understand them (Speier et al., 1999). Consequently, it becomes much harder for users to observe, to control and to adjust their learning process. For example, while MOOCs helps teachers to reach thousands of students simul- taneously (de Waard, 2015), it also creates a major challenge for them to follow the interactions occurred throughout a learning session effectively. To address the problem of information overload, the adoption of a learning analytic (LA) process is required. LA helps the measurement, collection, analysis and reporting
of data about learners and their contexts, along with a purpose of understanding and optimizing learning and the environments in which it takes place (Siemens et al., 2011). The development of this LA process rep- resents a core objective of HUBBLE project (HUman Observatory Based on analysis of e-Learning traces)
1. This project aims at creating a national observatory with the scope of building and sharing extensive data analysis processes based on traces generated from e- learning environments. This process offers ways to collect a critical mass of digital traces and to calcu- late a significant set of data called indicators (Iksal and Choquet, 2007). Therefore, the visualization of these indicators in the right way can help different users such as decision makers (teachers, designers, administrators or policy) to extract various facts about a particular learning situation. They can then draw meaningful conclusions about their different decision contexts. Moreover, the visualization task can sup- port other users such as researchers and analysts to share, capitalize and reuse various tools and models, and also dashboard models.
Being part of HUBBLE project, the scope of our research work covers the design of reusable dash-
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