AASK - Aircraft accident statistics and knowledge: A database of human experience in evacuation, derived from aviation accident reports

Computer based mathematical models describing the aircraft evacuation process have a vital role to play in aviation safety. However such models have a heavy dependency on real evacuation data in order to (a) identify the key processes and factors associated with evacuation, (b) quantify variables and parameters associated with the identified factors/processes and finally (c) validate the models. The Fire Safety Engineering Group of the University of Greenwich is undertaking a large data extraction exercise from three major data sources in order to address these issues. This paper describes the extraction and application of data from one of these sources - aviation accident reports. To aid in the storage and analysis of the raw data, a computer database known as AASK (aircraft accident statistics and knowledge) is under development. AASK is being developed to store human observational and anecdotal data contained in accident reports and interview transcripts. AASK comprises four component sub-databases. These consist of the ACCIDENT (crash details), FLIGHT ATTENDANT (observations and actions of the flight attendants), FATALS (details concerning passenger fatalities) and PAX (observations and accounts from individual passengers) databases. AASK currently contains information from 25 survivable aviation accidents covering the period 4 April 1977 to 6 August 1995, involving some 2415 passengers, 2210 survivors, 205 fatalities and accounts from 669 people. In addition to aiding the development of aircraft evacuation models, AASK is also being used to challenge some of the myths which proliferate in the aviation safety industry such as, passenger exit selection during evacuation, nature and frequency of seat jumping, speed of passenger response and group dynamics. AASK can also be used to aid in the development of a more comprehensive approach to conducting post accident interviews, and will eventually be used to store the data directly.

The AASK Database V3.0: a database of human experience during aircraft evacuation incidents

This paper describes recent developments with the Aircraft Accident Statistics and Knowledge (AASK) database. The AASK database is a repository of survivor accounts from aviation accidents developed by the Fire Safety Engineering Group of the University of Greenwich with support from the UK CAA. Its main purpose is to store observational and anecdotal data from the actual interviews of the occupants involved in aircraft accidents. Access to the latest version of the database (AASK V3.0) is available over the Internet. AASK consists of information derived from both passenger and cabin crew interviews, information concerning fatalities and basic accident details. Also provided with AASK is the Seat Plan Viewer that graphically displays the starting locations of all the passengers - both survivors and fatalities - as well as the exits used by the survivors. Data entered into the AASK database is extracted from the transcripts supplied by the National Transportation Safety Board in the US and the Air Accident Investigation Branch in the UK. The quality and quantity of the data was very variable ranging from short summary reports of the accidents to boxes of individual accounts from passengers, crew and investigators. Data imported into AASK V3.0 includes information from 55 accidents and individual accounts from 1295 passengers and 110 crew.

Student Experience of E-Learning (SEEL)

The Student Experience of E-Learning project (SEEL) was an institutional response to the university’s HEA/JISC Benchmarking exercise (Ryan and Kandler, 2007). The study had a social constructivist approach which recognised the importance of listening to the student voice (JISC 2007) within the University of Greenwich context, to interpret the student experience of e-learning. Nearly 1000 students responded to an online survey on their approaches to, and their use of, learning technology. The quantitative and qualitative questions used included identifying study patterns, using specific online tools, within the context of learning and beyond, and student’s attitudes towards using e-learning in their studies. Initially, individual responses to questions were analysed in depth, giving a general indication of the student experience. Further depth was applied through a filtering mechanism, beginning with a cross-slicing of individual student responses to produce cameos. Audio logs and individual interviews were drawn from these cameos. Analysis of the cameos is in progress but has already revealed some unexpected results. There was a mismatch between students’ expectations of the university’s use of technology and their experiences and awareness of its possible use in other contexts. Students recognised the importance of social interaction as a vehicle for learning (Vygotsky 1978, Bruner 2006) but expressed polarised views on the use of social networking sites such as Facebook for e-learning. Their experiences in commercial contexts led them to see the university VLE as unimaginative and the tutors’ use of it as lacking in vision. Whereas analysis of the individual questions provided a limited picture, the cameos gave a truer reflection of the students lived experiences and identified a gulf between the university’s provision and the students’ expectation of e-learning and their customary use of technology. However it is recognised that the very nature of an online survey necessarily excludes students who chose not to engage, either through lack of skills or through disillusionment and this would constitute a separate area for study.