Manual assembly learning, disability, and instructions: an industrial experiment

Figures & data

Table 1. Overview of empirical studies on the effect of the form of instruction and disability on assembly performance (n/a = significance not assessed, * = significant difference, otherwise non-significant difference).

#	Contribution	N	Type of disability	Industrial use case?	Pre-training	Ranking of instruction (or disability) per dependent variable
1	Furniss et al. (1999) (1)	1	Mental (Vineland Scale = 15%)	Yes	Two cycles w/o support	CT (n/a)
						1. Screen-based with audio prompts, 2. w/o prompts
						Error rate (n/a)
						1. with prompts, 2. w/o prompts
2	Furniss et al. (1999) (2)	1	Mental (Vineland Scale = 40%)	Yes	Two cycles w/o support	CT (n/a)
						1. Screen-based, 2. Paper-based
						Error rate (n/a):
						1. Paper-based, 2. Screen-based
3	Korn, Schmidt, and Hörz (2013)	40	Cognitive and/or physical	No	n/a	CT:
						1. Projection, 2. Screen-based
						Error rate:
						1. Screen-based, 2. Projection
4	Lancioni et al. (2013)	2	Intellectual (profound) disability and total blindness	No	One cycle with mentor guidance	Completion rate (n/a):
						1. Audio prompts, 2. Human assistance
						Error rate (n/a):
						1. Audio prompts, 2. Human assistance
5	Lancioni et al. (2014)	3	Intellectual (profound) and visual impairment, and deaf	No	One cycle with mentor guidance	Completion rate of correct assembly (n/a):
						1. Light cues and stimulation device
						2. Human assistance
6	Funk et al. (2015)	15	Cognitive (PI = 5–10%, 15–35%, or 40%+)	No	n/a	Time per part*:
						1. Projection, 2. Screen-based
						Errors per part*:
						1. Projection, 2. Screen-based
7	Funk, Mayer, and Schmidt (2015)	62	Mental (PI = 5–10%, 15–35%, or 40%+)	Yes	One cycle while instructed verbally	CT:
						1. Contour, 2. Pictorial, 3. No visualisation, 4. Video
						(Disability affected negatively*) Error rate:
						1. Contour, 2. Video, 3. Pictorial, 4. No visualisation
						(Disability affected negatively*)
8	Korn, Funk, and Schmidt (2015)	10	Mild cognitive, and partly mild physical	Yes	No, subjects were familiar with product	CT:
						1. Projection, 2. Screen-based
						Error rate:
						1. Screen-based, 2. Projection
9	Korn et al. (2016)	5	Mental (PI = 5%, 10%, 20%, 20%, and 35%)	Yes	30 min of assembly	CT:
						1. Projection (with gamification)
						2. Screen-based
						(* for PI = 35%, for PI = 5% opposite rank)
10	Kosch et al. (2018)	15	Cognitive (PI = 5–10%, 20–35%, or 40%+)	No	One cycle with paper instruction	CT:
						1. Projected*, 2. Auditory, 3. Haptic*
						Error rate:
						1. Projected, 2. Auditory, 3. Haptic
11	(Bosch et al. 2020) (1)	17	One or more unspecified disabilities per worker	Yes	n/a	CT at the beginning:
						1. Human assistance only, 2. Human and projection
						CT at the end:
						1. Human and projection, 2. Human only
12	(Bosch et al. 2020) (2)	19	Unspecified disabilities per worker	Yes	n/a	Learning speed (n/a): 1. Human and projection, 2. Human only
13	(Vanneste et al. 2020)	44	Cognitive (various levels or non-disabled)	Yes	One demo cycle with oral explanation	CT:
						1. Paper-based, 2. Oral, 3. Projection
						Error rate:
						1. Projection*, 2. Paper-based, 3. Oral
						Independence:
						1. Projection*, 2. Paper-based, 3. Oral*
						(Disability affected all parameters negatively)
	This study	24	Illiterate, psychosocial, cognitive	Yes	No	Paper-based, animations, projection, and adaptive projection are compared in terms of
						CT in the first cycle
						CT when learning plateaus
						Retention in the plateau phase

Figure 1. Forms of instruction studied with the elements inside a box.

At heart of the figure is paper instructions’ element box including text and image. Animations add GIFs, annotations, and next step. Projection adds guide light. Adaptive projection shows or hides these elements based on the experience level.

Figure 2. Example of operator experience level (adaptivity of instructions) based on algorithm.

In this scatter chart, each point represents step group cycle time from cycle 1 to 33. A trend line illustrates how operator’s experience level improves from level 1 to 3 through cycles.

Figure 3. Effect of form of instruction on mean and SD (2σ) of CT in each cycle.

Paper instructions and animations result in longer first cycle time and steeper initial learning curve. Each curve levels out until cycle 15. With paper instructions, within-group variance is very high in the first cycle and with adaptive projection later.

Figure 4. Effect of type of disability on mean and SD (2σ) of CT in each cycle.

Illiterate has longest first cycle time. Illiterate and psychosocially disabled people have steeper initial learning curve. Cognitively disabled people learn much slower, and their cycle times show high variation. Each curve levels out until cycle 15.

Table

Principle	Explanation	Guideline
Avoiding extraneous load
Worked examples	A demonstration that illustrates the steps to complete the task.	Use some steps as worked examples and others as practice problems and gradually move on to mere practice problems.
Focus attention	Directing cognitive resources toward relevant content in the instructions.	Use cues (e.g. arrows) and signals (e.g. bolding) to focus attention on relevant visual and textual content.
Split attention	Cognitive load resulting from integrating two or more dependent sources of visual information that are physically separated.	Integrate explanatory text and related visuals close to each other.
Modality	Working memory has a channel for both auditory and visual information. With dual modality, performance on the primary task is not significantly depressed.	Present text in auditory rather than written mode when integrated with other visual material, such as figures, animations, or video.
Redundancy	Identical information formats that do not contribute to schema acquisition or automation interfere with learning. Less is more.	Break information down to essentials. Eliminate extraneous text, visuals, and audio. Eliminate redundancy in information delivery modes.
Performance aids	Many tasks do not require accessing knowledge from memory but can use various reference resources to supplement internally stored knowledge and skills.	Use graphics alone when the task can be effectively communicated visually. Use arrows or other motion cues rather than text to depict motion. Present graphics from the visual perspective of the learner.
Fading of memory support	Fade the support as learners gain expertise.	Gradually remove memory support (instructions) as training progresses.
Managing intrinsic load
Pre-training	Allowing the learner to build schemata or prior knowledge about the process (concept and components of a system).	Segment a system into components for meaningful learning. Visually segregate and name each component. Represent their state change in the context of the whole system.
	Allowing the learner to build schemata or prior knowledge about procedure (steps to complete the actual task).	Two strategies for steps: (1) teaching, practising, and again teaching with supportive information; (2) teaching supportive information and then teaching each step hands-on.
Learners control over pacing	Giving learners the opportunity to assimilate new content at their own pace.	Implement self-pacing in instructions with ‘continue’ or ‘next’ buttons. If not possible, try to develop instructionally paced environments.
Promoting germane load
Self-explanation	A mental dialogue at a worked example that helps learners to understand the example and build a schema from it.	Get learners to engage in good self-explanation while they read instructions.
Automaticity	The built schemas need to be automated so that cognitive resources are freed to allow the construction of more complex schemas.	Help learners to automate skills by giving them many opportunities to rehearse new knowledge and skills, through hands-on practice or mental rehearsal of the steps.

Furniss, Fred, Alison Ward, Giulio Lancioni, Nelson Rocha, Bernardo Cunha, Philip Seedhouse, Pedro Morato, and Neil Waddell. 1999. “A Palmtop-Based Job Aid for Workers with Severe Intellectual Disabilities.” Technology and Disability 10 (1): 53–67. doi:10.3233/TAD-1999-10106.

 Google Scholar

Korn, Oliver, Albrecht Schmidt, and Thomas Hörz. 2013. “The Potentials of In-Situ-Projection for Augmented Workplaces in Production.” In CHI ‘13 Extended Abstracts on Human Factors in Computing Systems, 979–984. New York: ACM. doi:10.1145/2468356.2468531.

 Google Scholar

Lancioni, Giulio E., Nirbhay N. Singh, Mark F. O’Reilly, Vanessa A. Green, Doretta Oliva, and Francesca Campodonico. 2013. “Two Men with Multiple Disabilities Carry out an Assembly Work Activity with the Support of a Technology System.” Developmental Neurorehabilitation 16 (5): 332–339. doi:10.3109/17518423.2012.736419.

 PubMed Web of Science ®Google Scholar

Lancioni, Giulio E., Nirbhay N. Singh, Mark F. O’Reilly, Jeff Sigafoos, Gloria Alberti, Viviana Perilli, Dominga Laporta, Francesca Campodonico, Doretta Oliva, and Jop Groeneweg. 2014. “People with Multiple Disabilities Learn to Engage in Occupation and Work Activities with the Support of Technology-Aided Programs.” Research in Developmental Disabilities 35 (6): 1264–1271. doi:10.1016/j.ridd.2014.03.026.

 PubMed Web of Science ®Google Scholar

Funk, Markus, Andreas Bächler, Liane Bächler, Oliver Korn, Christoph Krieger, Thomas Heidenreich, and Albrecht Schmidt. 2015. “Comparing Projected In-Situ Feedback at the Manual Assembly Workplace with Impaired Workers.” In Proceedings of the 8th ACM International Conference on Pervasive Technologies Related to Assistive Environments, 1–8. New York: ACM. doi:10.1145/2769493.2769496.

 Google Scholar

Funk, Markus, Sven Mayer, and Albrecht Schmidt. 2015. “Using In-Situ Projection to Support Cognitively Impaired Workers at the Workplace.” In Proceedings of the 17th International ACM SIGACCESS Conference on Computers & Accessibility - ASSETS ‘15, 185–192. New York: ACM. doi:10.1145/2700648.2809853.

 Google Scholar

Korn, Oliver, Markus Funk, and Albrecht Schmidt. 2015. “Towards a Gamification of Industrial Production.” In Proceedings of the 7th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, 84–93. New York: ACM. doi:10.1145/2774225.2774834.

 Google Scholar

Korn, Oliver, Johannes Lang, Andreas Korge, Haris Causegic, and Albrecht Schmidt. 2016. “Gamification of a Workday: A Study on the Effects in Sheltered Employment.” In CHI '16 Extended Abstracts on Human Factors in Computing Systems, 3114–3121. San Jose, CA: ACM. doi:10.1145/2851581.2892283.

 Google Scholar

Kosch, Thomas, Markus Funk, Albrecht Schmidt, and Lewis L. Chuang. 2018. “Identifying Cognitive Assistance with Mobile Electroencephalography.” Proceedings of the ACM on Human-Computer Interaction 2 (EICS): 1–20. doi:10.1145/3229093.

 Google Scholar

Bosch, Tim, Gu van Rhijn, Frank Krause, Reinier Könemann, Ellen S. Wilschut, and Michiel de Looze. 2020. “Spatial Augmented Reality.” In Proceedings of the 13th ACM International Conference on Pervasive Technologies Related to Assistive Environments, 1–7. New York: ACM. doi:10.1145/3389189.3397975.

 Google Scholar

Vanneste, Pieter, Yi Huang, Jung Yeon Park, Frederik Cornillie, Bart Decloedt, and Wim van den Noortgate. 2020. “Cognitive Support for Assembly Operations by Means of Augmented Reality: An Exploratory Study.” International Journal of Human-Computer Studies 143: 102480. doi:10.1016/j.ijhcs.2020.102480.

 Web of Science ®Google Scholar

Data availability statement

The data that supports the findings of this study is available from the corresponding author upon reasonable request.