Preparing for skill transfer: a decision tree tool for curriculum design and assessment of virtual offshore emergency egress training

Figures & data

Figure 1. Ideal decision tree for emergency egress

Figure 2. Process used to compare participants’ route selection strategies with ideal DT

Figure 3. Longitudinal experiment timeline

Table 1. Design of transfer scenarios (after Musharraf et al., 2019)

#	Set	Quality of Communication		Proximity to Hazards		Situation Familiarity
U1	1	+	Clear PA with all relevant info	+	Hazard not accessible	+	Starts in cabin
U2		-	Poor PA that lacked info	+	Hazard not accessible	+	Starts in cabin
U3		+	Clear PA with all relevant info	-	Hazard blocking route	+	Starts in cabin
U4		-	Poor PA that lacked info	-	Hazard blocking route	+	Starts in cabin
U5	2	+	Clear PA with all relevant info	+	Hazard not accessible	-	Starts in Bridge
U6		-	Poor PA that lacked info	+	Hazard not accessible	-	Starts in Bridge
U7		+	Clear PA with all relevant info	-	Hazard blocking route	-	Starts in Bridge
U8		-	Poor PA that lacked info	-	Hazard blocking route	-	Starts in Bridge

Table 2. Description of scenario attributes (Smith et al., 2017)

Attributes	Possible Values
End Location	Muster, Lifeboat
Alarm type	None, General Platform Alarm (GPA), Prepare to Abandon Platform Alarm (PAPA)
Route directed by PA	None, 1st, 2nd
Hazard presence	No, Yes
Obstructed route	None, 1st, 2nd
Previous route selected	1st, 2nd

Table 3. Sample participant knowledge base developed from data in phase 1

Scenario	Attributes						Route choice
	End Location	Alarm	Route by PA	Hazard	Obstructed Route	Previous Route
P1	Muster	None	1st	No	None	N/A	Primary
P3 (F1)	Lifeboat	None	1st	No	None	1st	Primary
P3 (F2)	Muster	None	2nd	No	None	1st	Secondary
T1	Lifeboat	None	None	No	None	2nd	Secondary
P4	Muster	GPA	1st	No	None	2nd	Primary
P5	Muster	GPA	None	No	None	1st	Primary
T2	Muster	GPA	1st	No	None	1st	Primary
P6	Lifeboat	PAPA	1st	No	2nd	1st	Primary
T3 (F1)	Muster	GPA	None	No	None	1st	Primary
T3 (F2)	Muster	GPA	2nd	No	1st	1st	Secondary
P7	Lifeboat	PAPA	2nd	Yes	1st	2nd	Secondary
P8 (F1)	Muster	GPA	1st	Yes	2nd	2nd	Primary
P8 (F2)	Lifeboat	PAPA	1st	Yes	2nd	2nd	Primary
T4 (F1)	Muster	GPA	None	Yes	None	1st	Primary
T4 (F2)	Lifeboat	GPA	2nd	Yes	1st	1st	Secondary
T4 (F3)	Lifeboat	PAPA	2nd	Yes	1st	1st	Secondary

Figure 4. Sample participant decision tree developed from the KB shown in.Table 3

Figure 5. Accepted heuristics for emergency egress

Table 4. Decision trees after acquisition (ACQ) and retraining (RET) phases

Type	Decision Rules	% Participants		Compared to Ideal DT
		End Phase 1	End Phase 2
1		60%	68%	Match
2		16%	10%	Match
3	*1 participant had a similar DT but reversed rules for PAPA and None	10%	13%	Match
4		3%	0%	Match
5		3%	0%	Incomplete
6		3%	0%	Incorrect
7		0%	3%	Incorrect
8		0%	3%	Incorrect
	*No behavioural pattern or strategy identified	5%	3%	Trainees not suitable

Figure 6. Boxplot of performance scores for the transfer scenarios

Table 5. Percentage of DTs that predicted participants’ successful performance

Tree Type	Percent DTs predicted successful performance in transfer scenarios
	Near transfer				Far transfer
	U1	U2	U3	U4	U5	U6	U7	U8
All Types	66	85	82	87	13	16	53	16
Type 1	47	66	58	63	5	13	40	8
Type 2	8	8	11	11	0	0	5	3
Type 3	11	11	13	13	8	3	8	5

Figure 7. Revised decision tree for emergency egress

Musharraf, M., Moyle, A., Khan, F., & Veitch, B. (2019). Using simulator data to facilitate human reliability analysis. Journal of Offshore Mechanics and Arctic Engineering, 141(2), 021607. https://doi.org/10.1115/1.4042538

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Smith, J., Musharraf, M., & Veitch, B. (2017). Data informed cognitive modelling of offshore emergency egress behaviour. Proceedings of the 2017 international conference on cognitive modeling. Coventry, UK. https://iccm-conference.neocities.org/2017/ICCMprogram_files/paper_17.pdf

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