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

Abstract

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.

Research Landscape of Artificial Intelligence and e-Learning: A Bibliometric Research

“…Amongst them, educational data mining, systems, educational games, and tutoring systems reflect that the core research orientation in the field of education is to focus on the construction of a complete educational system. Through educational data mining ( Santos and Boticario, 2015 ; Yang et al, 2021 ), learning analytics ( Franzoni et al, 2020 ) and other data collection and analysis methods, the behavioral patterns and emotional differences of personalized students can be analyzed to improve the management ability of the system. Based on suggestions in the literature on the multi-dimensional and overall research direction, social networks can be supported by computer programming and educational artificial intelligence games to promote the development of artificial intelligence applications in online education ( Kuk et al, 2017 ; Petit et al, 2018 ).…”

“…From 2010 to 2015, the application of AI mainly focused on recommending suitable learning paths for learners and improving their learning benefits through intelligent learning ( Kurilovas et al, 2015 ). However, research from 2016 to 2021 focused on developing new technological applications to assess the learning status of individual students and providing them with helpful learning methods (e.g., statistical learning, data mining and decision trees) ( Samarakou et al, 2018 ; Kavitha and Lohani, 2019 ; Franzoni et al, 2020 ). Conversely, compared to recommended applications before 2015, the research in 2016 also focused on applying artificial intelligence technology to integrate teaching and adopted optimized methods to improve the teaching methods of online learning to assist students in learning, as well as to evaluate and optimize learning effects ( Barlybayev et al, 2020 ; Xie and Mai, 2021 ).…”

“…Before 2016, the topics mainly focused on the use of a recommendation system to evaluate students’ learning effects to further propose the design of new online learning course modules Kurilovas et al, 2014 , 2015 ; Stantchev et al, 2015 ). However, since 2017, the research focus has been on the influence of artificial intelligence applications on teaching status ( Jalal and Mahmood, 2019 ; Kavitha and Lohani, 2019 ; Franzoni et al, 2020 ). In addition, the top three most-cited journals (co-citation analysis) are Expert Systems with Applications, Computers in Human Behavior, and Journal of Network and Computer Applications.…”

A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System

“…reduce dropout, and assessing new skillsets." Moreover, TAI systems can contribute to the learning design research field [42] by producing technological tools that are easy to share and reuse [43]. Best practices on dashboard visualization or data management can be shared among practitioners and engineers to produce other systems compliant with TAI requirements.…”

A Hands-On Laboratory for Intelligent Control Courses

“…Recent research proposes new tools for learning. Franzoni et al [33] proposed an innovative visual tool for continuous analysis of students in real time. Particularly, in the Faculty of Engineering, Universidad Autónoma de Querétaro research has been carried out denoting the advantages it has for students to implement educational strategies that include theoretical and practical knowledge [34,35].…”