A Learning Ecosystem for Linemen Training based on Big Data Components and Learning Analytics

Linemen training is mandatory, complex, and hazardous. Electronic technologies, such as virtual reality or learning management systems, have been used to improve such training, however these lack of interoperability, scalability, and do not exploit trace data generated by users in these systems. In this paper we present our ongoing work on developing a Learning Ecosystem for Training Linemen in Maintenance Maneuvers using the Experience API standard, Big Data components, and Learning Analytics. The paper describes the architecture of the ecosystem, elaborates on collecting learning experiences and emotional states, and applies analytics for the exploitation of both, legacy and new data. In the former, we exploit legacy e-Learning data for building a Domain model using Text Mining and unsupervised clustering algorithms. In the latter we explore self-reports capabilities for gathering educational support content, and assessing students emotional states. Results show that, a suitable domain model for personalizing maneuvers linemen training path can be built from legacy text data straightforwardly. Regarding self reports, promising results were obtained for tracking emotional states and collecting educational support material, nevertheless, more work around linemen training is required

The Use of Learning Analytics Interactive Dashboards in Serious Games: A Review of the Literature

The learning analytics in serious games, corresponds to a subject in increasing demand in the educational field. In this context, there is a need to study how data visualizations found in the literature are adopted in learning analytics in serious games. This paper presents a Systematic Literature Review (SLR) on how the evolution of studies associated with the use of learning analytics interactive dashboards in serious games is processed, seeking to investigate the characteristics of using dashboards for viewing educational data. A bibliometric analysis was carried out in which 75 relevant studies were selected from the Scopus, Web of Science, and IEEExplore databases. From the data analysis, it was observed that in the current literature there is a reduced number of studies containing the main actors in the learning process, as follows: teachers/instructors, students/participants, game developers/designers, and managers/researchers. In the vast majority of investigated studies, data visualization algorithms are used, where the main focus takes into account only actors, such as teachers/instructors and students/participants

Summer 2017

https://digitalcommons.augustana.edu/augustanamagazine/1003/thumbnail.jp

GVSU Press Releases, 2019

A compilation of press releases for the year 2019 submitted by University Communications to news agencies concerning the people, places, and events related to Grand Valley State University

Illinois State Magazine, May 2011 Issue

Alumni magazine for Illinois State University, May 2011 issue.https://ir.library.illinoisstate.edu/ism/1009/thumbnail.jp

Tracking Systems in Team Sports: A Narrative Review of Applications of the Data and Sport Specific Analysis.

Seeking to obtain a competitive advantage and manage the risk of injury, team sport organisations are investing in tracking systems that can quantify training and competition characteristics. It is expected that such information can support objective decision-making for the prescription and manipulation of training load. This narrative review aims to summarise, and critically evaluate, different tracking systems and their use within team sports. The selection of systems should be dependent upon the context of the sport and needs careful consideration by practitioners. The selection of metrics requires a critical process to be able to describe, plan, monitor and evaluate training and competition characteristics of each sport. An emerging consideration for tracking systems data is the selection of suitable time analysis, such as temporal durations, peak demands or time series segmentation, whose best use depends on the temporal characteristics of the sport. Finally, examples of characteristics and the application of tracking data across seven popular team sports are presented. Practitioners working in specific team sports are advised to follow a critical thinking process, with a healthy dose of scepticism and awareness of appropriate theoretical frameworks, where possible, when creating new or selecting an existing metric to profile team sport athletes