The disorderly and disrupted movement of passengers within train stations are key concerns in rail transport, especially where there are increasing numbers of passengers, coupled with often out-dated, adapted station spaces. With careful planning and design, different characteristics of lighting can be employed to address problems relating to the movement and behaviour of passengers in railway environments. This study aims to offer an approach to identify new concepts for lighting-based interventions to influence passenger movement behaviours within train stations. Behaviourally orientated lighting literature was reviewed, providing the knowledge base to inform a series of engagement activities with transport stakeholders and lighting technologists, to understand problematic behaviours and how these might be resolved through targeted lighting design. In combining findings from the literature with insights from rail and transport-related industry stakeholders and lighting specialists, a number of potential opportunities for novel applications of lighting have been identified. Six scenarios are developed that illustrate these opportunities for potential lighting-based interventions to influence train passenger movement and behaviour. These scenarios can be used to inform the direction of further research and consideration of how different lighting characteristics can affect rail passenger behaviours.
Features of lighting that can influence people’s behaviours have been identified in an earlier study, along with six scenarios where these could be applied to solve problems with movements through railway stations. The current paper describes the development and testing of novel lighting interventions for three of these scenarios, with two new products controlled by the Internet of Things technology integrated with operational railway systems. The first uses projected light to indicate preferred platform waiting locations. The second uses chasing light-emitting diode lighting along a staircase to encourage bi-directional movements. The field study has been carried out in real-world operational railway settings. An evaluation has been based on a theory-based approach to consider whether the lighting functions as intended and whether people react in anticipated ways. The study found that the lighting interventions have been successfully implemented, and there are indications of favourable responses from passengers, though these have been small effects. The approach to evaluation also assists with diagnosis of weaknesses in the initial concepts and determination of the situational factors that can compete with the behaviour influencing effect of the lighting. This enables refinement and further product development. Practical challenges in implementing trials in this type of operational setting have been identified.
Artificial intelligence tools for education (AIEd) have been used to automate the provision of learning support to mainstream learners. One of the most innovative approaches in this field is the use of data and machine learning for the detection of a student’s affective state, to move them out of negative states that inhibit learning, into positive states such as engagement. In spite of their obvious potential to provide the personalisation that would give extra support for learners with intellectual disabilities, little work on AIEd systems that utilise affect recognition currently addresses this group. Our system used multimodal sensor data and machine learning to first identify three affective states linked to learning (engagement, frustration, boredom) and second determine the presentation of learning content so that the learner is maintained in an optimal affective state and rate of learning is maximised. To evaluate this adaptive learning system, 67 participants aged between 6 and 18 years acting as their own control took part in a series of sessions using the system. Sessions alternated between using the system with both affect detection and learning achievement to drive the selection of learning content (intervention) and using learning achievement alone (control) to drive the selection of learning content. Lack of boredom was the state with the strongest link to achievement, with both frustration and engagement positively related to achievement. There was significantly more engagement and less boredom in intervention than control sessions, but no significant difference in achievement. These results suggest that engagement does increase when activities are tailored to the personal needs and emotional state of the learner and that the system was promoting affective states that in turn promote learning. However, longer exposure is necessary to determine the effect on learning.
This article explores how 145 photographs collected from 20PowerPoint lectures in undergraduate psychology at 16 UK universities were integrated with lecturers' speech. Little is currently known about how lecturers refer to the distinct types of photographs included in their presentations. Findings show that only 48 photographs (33%) included in presentation slides were referred to explicitly by exploring their features to make a point related to the lecture content, with only 14 of these used to invite student questioning. Most photographs (97 or 67%) represent a case of 'unprobed representations', that is, either 'embedded' in the talk as 'illustrations' of the speech topic or not referred to at all. A taxonomy of uses that lecturers made of the photographs in their slideshows was created through adapting a Peircean semiotic analysis of the photograph-speech interaction. The implications in terms of lecturer and student engagement with the photographic material are discussed, arguing the case for more Critical Semiotic Exploration of photographs in HE practice.
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