Exploring LogiAssist – The Mobile Learning and Assistance Platform for Truck Drivers

Scheffel, Maren; Kirschenmann, Uwe; Taske, Andreas; Adloff, Katja; Kiesel, Maik; Klemke, Roland; Wolpers, Martin

doi:10.1007/978-3-642-40814-4_27

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…Several studies (Di Mitri et al 2017;Gašević et al 2014;Manso-Vázquez et al 2018;Rohloff and Meinel 2018;Scheffel et al 2013;Silva et al 2018) have focused on the reflection phase, in which students evaluate their learning process. For example, the work in Gašević et al (2014) examined the use of video annotation software to analyse graded and non-graded reflection in relation to the linguistic and psychological processes linked to self-reflection annotation in videos.…”

Section: Reflection Phasementioning

confidence: 99%

“…The authors found that this improved students' learning reflection in terms of motivation, planning, goal-setting, design, performance, and engagement. The authors of Scheffel et al (2013), Di Mitri et al (2017) used written feedback to provide prompts to learners to reflect on their learning experiences, while the scheme in Yousef and Khatiry (2021) supported students' awareness, reflection, and learning processes using cognitive and behavioural LA dashboards, and the researchers found that cognitive dashboards had the advantage of promoting awareness and self-reflection, and had an impact on the learning process.…”

Section: Reflection Phasementioning

confidence: 99%

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Informative Feedback and Explainable AI-Based Recommendations to Support Students’ Self-regulation

et al. 2023

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Self-regulated learning is an essential skill that can help students plan, monitor, and reflect on their learning in order to achieve their learning goals. However, in situations where there is a lack of effective feedback and recommendations, it becomes challenging for students to self-regulate their learning. In this paper, we propose an explainable AI-based approach to provide automatic and intelligent feedback and recommendations that can support the self-regulation of students’ learning in a data-driven manner, with the aim of improving their performance on their courses. Prior studies have predicted students’ performance and have used these predicted outcomes as feedback, without explaining the reasons behind the predictions. Our proposed approach is based on an algorithm that explains the root causes behind a decline in student performance, and generates data-driven recommendations for taking appropriate actions. The proposed approach was implemented in the form of a dashboard to support self-regulation by students on a university course, and was evaluated to determine its effects on the students’ academic performance. The results revealed that the dashboard significantly enhanced students’ learning achievements and improved their self-regulated learning skills. Furthermore, it was found that the recommendations generated by the proposed approach positively affected students’ performance and assisted them in self-regulation

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Section: Reflection Phasementioning

confidence: 99%

Section: Reflection Phasementioning

confidence: 99%

Informative Feedback and Explainable AI-Based Recommendations to Support Students’ Self-regulation

et al. 2023

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“…This could include providing feedback on harsh braking events (62) and offering training on hazard perception (63) and defensive driving techniques (64). Training and coaching could be delivered through simulators (65)(66)(67), in-person sessions (68,69), online modules (70), or mobile apps (71). A review by Michelaraki et al concluded that implementing gamification and reward schemes appears to effectively improve safety across various modes of transportation, including truck driving (72).…”

Section: Recommendations and Outlookmentioning

confidence: 99%

Predicting Damage Incidents, Fines, and Fuel Consumption from Truck Driver Data: A Study from the Netherlands

Driessen,

Dodou,

Waard

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Trucks are disproportionately involved in fatal traffic accidents and contribute significantly to CO2 emissions. Gathering data from trucks presents a unique opportunity for estimating driver-specific costs associated with truck operation. Although research has been published on the predictive validity of driver data, such as in the contexts of pay-how-you-drive insurance and naturalistic driving studies, the investigation into how telematics data relate to the negative consequences of truck driving remains limited. In the present study, driving data from 180 truck drivers, collected over a 2-year period, were examined to predict damage incidents, traffic fines, and fuel consumption. Correlation analysis revealed that the number of fines and damage incidents could be predicted based on the number of harsh braking events per hour of driving, whereas fuel consumption was predicted by engine torque exceedances. Our analysis also sheds light on the impact of covariates, including the engine capacity of the truck operated and time of day, among others. We conclude that the damage incidents and fines incurred by truck drivers can be predicted not only from their number of harsh decelerations but also through driving demands that extend beyond the driver’s immediate control. It is recommended that transportation companies adopt a systemic approach to mitigating truck-driving-related expenses.

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Energy-Efficient and Safe Driving Using a Situation-Aware Gamification Approach in Logistics

Klemke

Kravčík

Bohuschke³

2014

Lecture Notes in Computer Science

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Exploring LogiAssist – The Mobile Learning and Assistance Platform for Truck Drivers

Cited by 5 publications

References 8 publications

Informative Feedback and Explainable AI-Based Recommendations to Support Students’ Self-regulation

Informative Feedback and Explainable AI-Based Recommendations to Support Students’ Self-regulation

Predicting Damage Incidents, Fines, and Fuel Consumption from Truck Driver Data: A Study from the Netherlands

Energy-Efficient and Safe Driving Using a Situation-Aware Gamification Approach in Logistics

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