scholarly journals Designing Training Programs to Introduce Emerging Technologies to Future Workers—A Pilot Study Based on the Example of Artificial Intelligence Enhanced Robotics

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2876
Author(s):  
Janika Leoste ◽  
Tiia Õun ◽  
Krista Loogma ◽  
José San Martín López
Keyword(s):  
Artificial Intelligence ◽  
Innovation Process ◽  
Work Place ◽  
Technology Enhanced Learning ◽  
Specific Work ◽  
Negative Attitudes ◽  
Training Course ◽  
Self Confidence ◽  
Usable Knowledge ◽  
Enhanced Learning

Implementing an Emerging Technology (ET) is a difficult task due to people lacking ET-related knowledge and skills or having skeptical and negative attitudes towards the ET. As learners construct their understanding about an ET and develop related skills by actually passing through the ET Innovation Process (IP) stages (Awareness, Acceptance and Adoption), it could be useful to provide them with training that imitates certain IP stages. Using Artificial Intelligence Enhanced Robotics (AIER) as the example ET, we designed a two-day workshop to lead learners (n = 16) through the AIER IP Awareness stage, and a six-week training course with eight contact days to simulate the AIER IP Acceptance stage to learners (n = 10). Using online surveys and quantitative content analysis methods we confirmed that the workshop format increased the AIER-related self-confidence and general knowledge in 78% of participants, while the training course helped more than half of the participants to construct usable knowledge about a specific AIER and to see its possibilities in their specific work-place contexts. This paper is the pilot of using the Technology-Enhanced Learning Innovation Process (TELIP) model, first tested on a STEAM innovation, outside the educational context, for developing appropriate training approaches for specific ET IP stages.

scholarly journals Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

2020 ◽  
Vol 10 (3) ◽  
pp. 1042 ◽  
Author(s):  
Juan L. Rastrollo-Guerrero ◽  
Juan A. Gómez-Pulido ◽  
Arturo Durán-Domínguez
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Technology Enhanced Learning ◽  
Learning Collaborative ◽  
Academic Tasks ◽  
Learning Contexts ◽  
The Relationship ◽  
Enhanced Learning

Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things. These are benefited by the automation of many processes involved in usual students’ activities which handle massive volumes of data collected from software tools for technology-enhanced learning. Thus, analyzing and processing these data carefully can give us useful information about the students’ knowledge and the relationship between them and the academic tasks. This information is the source that feeds promising algorithms and methods able to predict students’ performance. In this study, almost 70 papers were analyzed to show different modern techniques widely applied for predicting students’ performance, together with the objectives they must reach in this field. These techniques and methods, which pertain to the area of Artificial Intelligence, are mainly Machine Learning, Collaborative Filtering, Recommender Systems, and Artificial Neural Networks, among others.

Locus of Control, Safety Attitudes and Involvement in Hazardous Events in Indian Army Aviators

2013 ◽  
Vol 3 (1) ◽  
pp. 9-18 ◽  
Author(s):  
Catherine Joseph ◽  
Suhasini Reddy ◽  
Kanwal Kashore Sharma
Keyword(s):  
Locus Of Control ◽  
Future Research ◽  
Research Needs ◽  
Negative Attitudes ◽  
Human Errors ◽  
Safety Attitudes ◽  
Positive Correlation ◽  
Self Confidence

Locus of control (LOC), safety attitudes, and involvement in hazardous events were studied in 205 Indian Army aviators using a questionnaire-based method. A positive correlation was found between external LOC and involvement in hazardous events. Higher impulsivity and anxiety, and decreased self-confidence, safety orientation, and denial were associated with a greater number of hazardous events. Higher external LOC was associated with higher impulsivity, anxiety, and weather anxiety and with lower self-confidence, safety orientation, and denial. Internal LOC was associated with increased self-confidence, safety orientation, and denial. Hazardous events and self-confidence were higher in those involved in accidents than those not involved in accidents. Future research needs to address whether training can effectively modify LOC and negative attitudes, and whether this would cause a reduction in, and better management of, human errors.

Cultures of Inquiry in Technology Enhanced Learning and Beyond

2011 ◽  
Vol 4 (2) ◽  
pp. 291-311 ◽  
Author(s):  
Patrick Carmichael
Keyword(s):  
Research And Development ◽  
Educational Research ◽  
Technology Enhanced Learning ◽  
Common Problems ◽  
Culture Of Inquiry ◽  
Enhanced Learning ◽  
Educational Research And Development

Interdisciplinary working is often understood as involving individuals or teams from different disciplines to engage with common problems, but this has proved to be an enduring challenge. An alternative framing of interdisciplinary working is Hall's ‘culture of inquiry’, in which it is conceptualised as narrative creation in an environment of formative critique. This paper explores the relevance and applicability of this idea to educational research and development, specifically in the context of purportedly interdisciplinary TEL projects. It draws on the author's experience in projects in which multiple narratives — pedagogical, technological and social — have the potential to contribute to both to individual and collective understanding and the development of new practice.

Faculty Development: A Stage Model Matched to Blended Learning Maturation

2011 ◽  
Vol 15 (1) ◽  
Author(s):  
Michael L. Fetters ◽  
Tova Garcia Duby
Keyword(s):  
Faculty Development ◽  
Blended Learning ◽  
Lessons Learned ◽  
Technology Enhanced Learning ◽  
Stage Model ◽  
Years Of Experience ◽  
Development Programs ◽  
Curriculum Innovation ◽  
Enhanced Learning

Faculty development programs are critical to the implementation and support of curriculum innovation. In this case study, the authors present lessons learned from ten years of experience in faculty development programs created to support innovation in technology enhanced learning. Stages of curriculum innovation are matched to stages of faculty development, and important lessons for success as well as current challenges are delineated and discussed.

scholarly journals Monitoring, awareness and reflection in blended technology enhanced learning: a systematic review

2017 ◽  
Vol 9 (2/3) ◽  
pp. 126 ◽  
Author(s):  
María Jesús Rodríguez Triana ◽  
Luis P. Prieto ◽  
Andrii Vozniuk ◽  
Mina Shirvani Boroujeni ◽  
Beat A. Schwendimann ◽  
...  
Keyword(s):  
Systematic Review ◽  
Technology Enhanced Learning ◽  
Enhanced Learning

Research on the strategy of intelligent analysis to improve sports person psychological experience in the era of artificial intelligence

2021 ◽  
pp. 1-11
Author(s):  
Lei Wu ◽  
Juan Wang ◽  
Long Jin ◽  
P. Hemalatha ◽  
R Premalatha
Keyword(s):  
Artificial Intelligence ◽  
Vital Role ◽  
Performance Ratio ◽  
Psychological Experience ◽  
The Public ◽  
Usable Knowledge ◽  
Intelligent Analysis ◽  
Accuracy Ratio ◽  
Depression Level ◽  
Predictive Research

Artificial intelligence (AI) is an excellent potential technology that is evolving day-to-day and a critical avenue for exploration in the world of computer science & engineering. Owing to the vast volume of data and the eventual need to turn this data into usable knowledge and realistic solutions, artificial intelligence approaches and methods have gained substantial prominence in the knowledge economy and community world in general. AI revolutionizes and raises athletics to an entirely different level. Although it is clear that analytics and predictive research have long played a vital role in sports, AI has a massive effect on how games are played, structured, and engaged by the public. Apart from these, AI helps to analyze the mental stability of the athletes. This research proposes the Artificial Intelligence assisted Effective Monitoring System (AIEMS) for the specific intelligent analysis of sports people’s psychological experience. The comparative analysis suggests the best AI strategies for analyzing mental stability using different criteria and resource factors. It is observed that the growth in the present incarnation indicates a promising future concerning AI use in elite athletes. The study ends with the predictive efficiency of particular AI approaches and procedures for further predictive analysis focused on retrospective methods. The experimental results show that the proposed AIEMS model enhances the athlete performance ratio of 98.8%, emotion state prediction of 95.7%, accuracy ratio of 97.3%, perception level of 98.1%, and reduces the anxiety and depression level of 15.4% compared to other existing models.

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