TY  - JOUR
PY  - 2023//
TI  - A field assessment of child abuse investigators' engagement with a child-avatar to develop interviewing skills
JO  - Child abuse and neglect
A1  - Røed, Ragnhild Klingenberg
A1  - Powell, Martine B.
A1  - Riegler, Michael A.
A1  - Baugerud, Gunn Astrid
SP  - e106324
EP  - e106324
VL  - 143
IS  - 
N2  - BACKGROUND: Child investigative interviewing is a complex skill requiring specialised training. A critical training element is practice. Simulations with digital avatars are cost-effective options for delivering training. This study of real-world data provides novel insights evaluating a large number of trainees' engagement with LiveSimulation (LiveSim), an online child-avatar that involves a trainee selecting a question (i.e., an option-tree) and the avatar responding with the level of detail appropriate for the question type. While LiveSim has been shown to facilitate learning of open-ended questions, its utility (from a user engagement perspective) remains to be examined. <br><br>OBJECTIVE: We evaluated trainees' engagement with LiveSim, focusing on patterns of interaction (e.g., amount), appropriateness of the prompt structure, and the programme's technical compatibility. PARTICIPANTS AND SETTING: Professionals (N = 606, mainly child protection workers and police) being offered the avatar as part of an intensive course on how to interview a child conducted between 2009 and 2018. <br><br>METHODS: For descriptive analysis, Visual Basic for Applications coding in Excel was applied to evaluate engagement and internal attributes of LiveSim. A compatibility study of the programme was run testing different hardware focusing on access and function. <br><br>RESULTS: The trainees demonstrated good engagement with the programme across a variety of measures, including number and timing of activity completions. Overall, knowing the utility of avatars, our results provide strong support for the notion that a technically simple avatar like LiveSim awake user engagement. This is important knowledge in further development of learning simulations using next-generation technology.<p />  <p>Language: en</p>
LA  - en
SN  - 0145-2134
UR  - http://dx.doi.org/10.1016/j.chiabu.2023.106324
ID  - ref1
ER  -