A field assessment of child abuse investigators' engagement with a child-avatar to develop interviewing skills
Peer reviewed, Journal article
Published version
Permanent lenke
https://hdl.handle.net/11250/3074641Utgivelsesdato
2023Metadata
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- Publikasjoner fra Cristin [3465]
- SAM - Institutt for sosialfag [483]
Originalversjon
10.1016/j.chiabu.2023.106324Sammendrag
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 in-
volves 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.
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.
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.
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.