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Open AccessFeature PaperArticle

Artificial Intelligence Visual Metaphors in E-Learning Interfaces for Learning Analytics

1
Department of Mathematics and Computer Science, University of Perugia, 6100 Perugia, Italy
2
Department of Journalism, Hong Kong Baptist University, Hong Kong, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(20), 7195; https://doi.org/10.3390/app10207195
Received: 28 August 2020 / Revised: 8 October 2020 / Accepted: 9 October 2020 / Published: 15 October 2020
(This article belongs to the Special Issue Advances in Artificial Intelligence Learning Technologies)
This work proposes an innovative visual tool for real-time continuous learners analytics. The purpose of the work is to improve the design, functionality, and usability of learning management systems to monitor user activity to allow educators to make informed decisions on e-learning design, usually limited to dashboards graphs, tables, and low-usability user logs. The standard visualisation is currently scarce, and often inadequate to inform educators about the design quality and students engagement on their learning objects. The same low usability can be found in learning analytics tools, which mostly focus on post-course analysis, demanding specific skills to be effectively used, e.g., for statistical analysis and database queries. We propose a tool for student analytics embedded in a Learning Management System, based on the innovative visual metaphor of interface morphing. Artificial intelligence provides in remote learning immediate feedback, crucial in a face-to-face setting, highlighting the students’ engagement in each single learning object. A visual metaphor is the representation of a person, group, learning object, or concept through a visual image that suggests a particular association or point of similarity. The basic idea is that elements of the application interface, e.g., learning objects’ icons and student avatars, can be modified in colour and dimension to reflect key performance indicators of learner’s activities. The goal is to provide high-affordance information on the student engagement and usage of learning objects, where aggregation functions on subsets of users allow a dynamic evaluation of cohorts with different granularity. The proposed visual metaphors (i.e., thermometer bar, dimensional morphing, and tag cloud morphing) have been implemented and experimented within academic-level courses. Experimental results have been evaluated with a comparative analysis of user logs and a subjective usability survey, which show that the tool obtains quantitative, measurable effectiveness and the qualitative appreciation of educators. Among metaphors, the highest success is obtained by Dimensional morphing and Tag cloud transformation. View Full-Text
Keywords: teachers self-evaluation; information visualization; Arttificial Intelligence–based visual interface; learner continuous monitoring; course evaluation; usability teachers self-evaluation; information visualization; Arttificial Intelligence–based visual interface; learner continuous monitoring; course evaluation; usability
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MDPI and ACS Style

Franzoni, V.; Milani, A.; Mengoni, P.; Piccinato, F. Artificial Intelligence Visual Metaphors in E-Learning Interfaces for Learning Analytics. Appl. Sci. 2020, 10, 7195. https://doi.org/10.3390/app10207195

AMA Style

Franzoni V, Milani A, Mengoni P, Piccinato F. Artificial Intelligence Visual Metaphors in E-Learning Interfaces for Learning Analytics. Applied Sciences. 2020; 10(20):7195. https://doi.org/10.3390/app10207195

Chicago/Turabian Style

Franzoni, Valentina; Milani, Alfredo; Mengoni, Paolo; Piccinato, Fabrizio. 2020. "Artificial Intelligence Visual Metaphors in E-Learning Interfaces for Learning Analytics" Appl. Sci. 10, no. 20: 7195. https://doi.org/10.3390/app10207195

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