Procedural Instructions, Principles, and Examples

Eiríksdóttir, Elsa; Catrambone, Richard

doi:10.1177/0018720811419154

Cited by 135 publications

(43 citation statements)

References 81 publications

(198 reference statements)

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“…The results from Chi (2009) and Hill and Levenhagen (1995) support the claim that self-explanations improve articulation and that improved articulation can improve problem solving. Eiriksdottir and Catrambone (2011) argue that integrating information from general and specific instructions can improve the application of general knowledge and transfer of specific knowledge. Lemari e et al (2008) argue that meaningful labels of text improves organization of information.…”

Section: Resultsmentioning

confidence: 99%

“…If learners connect information sharing the same label, then they could more easily integrate information presented in each type of instruction. Though expository text is important, learners can have difficulty applying abstract instructions to problems (Eiriksdottir & Catrambone, 2011;VanLehn, Jones, & Chi, 1992). Worked examples are an important part of instruction because they provide specific information about how to apply concepts to problem solving (Catrambone, 1998;Trafton & Reiser, 1993).…”

Section: Expository Textmentioning

confidence: 99%

“…Eiriksdottir and Catrambone (2011) argue that worked examples are preferred over expository text because they take less effort to understand and apply to closely related problems. The concrete features that make worked examples easier to understand also tie them to a particular context.…”

mentioning

confidence: 99%

“…The concrete features that make worked examples easier to understand also tie them to a particular context. Therefore, when learners rely too heavily on worked examples, they are commonly unable to glean conceptual information about the procedure from these concrete examples, which can inhibit transfer to novel problems (Bassok & Holyoak, 1989;Eiriksdottir & Catrambone, 2011). For this reason, much research has focused on designing worked examples in a way that promotes conceptual understanding and transfer (e.g., Catrambone, 1998;Mayer, 2009;Renkl, 2002).…”

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confidence: 99%

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Improving problem solving with subgoal labels in expository text and worked examples

Margulieux

Catrambone

2016

Learning and Instruction

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a b s t r a c tIn highly procedural problem solving, procedures are typically taught with context-independent expository text that conceptually describes a procedure and context-dependent worked examples that concretely demonstrate a procedure. Subgoal labels have been used in worked examples to improve problem solving performance. The effect of subgoal labels in expository text, however, has not been explored. The present study examined the efficacy of subgoal labeled expository text and worked examples for programming education. The results show that learners who received subgoal labels in both the text and example are able to solve novel problems better than those who did not. In addition, subgoal labels in the text appear to have a different, rather than an additive, effect on learners compared to subgoal labels in the example. Specifically, subgoal labels in the text appear to help the learner articulate the procedure, and subgoal labels in the example appear to help the learner apply the procedure.

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Section: Resultsmentioning

confidence: 99%

Section: Expository Textmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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Improving problem solving with subgoal labels in expository text and worked examples

Margulieux

Catrambone

2016

Learning and Instruction

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“…Furthermore, assembly tasks can be practiced in virtual environments [4]. Additional enhancements regarding pedagogical concepts enhance the suitability for learning [7].…”

Section: Mixed Realitymentioning

confidence: 99%

In-Factory Learning – Qualification For The Factory Of The Future

Quint

Mura

Gorecky

2015

ACTA Universitatis Cibiniensis

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The Industry 4.0 vision anticipates that internet technologies will find their way into future factories replacing traditional components by dynamic and intelligent cyber-physical systems (CPS) that combine IntroductionThe arising connection between the real physical world (like our homes and factories) and the digital world (support by internet technologies) brings more and more intelligent objects into our everyday lives and work contexts. The so-called Internet of Things facilitates, for instance, smart homes, which can adjust heat, light, and ventilation autonomously in order to optimize both energy consumption and comfort. The Internet of Things implies a revolution for the future factory environment, too. Following the Industry 4.0 vision [1] we expect the digitalisation to promote intelligent devices, which are able to learn from experiences, to communicate with each other, and to take decisions towards selfoptimization. Alongside this extensive automation, the human worker has been acknowledged as the most flexible entity in the production system, who plans, controls, manages, and trouble shoots. Thus, the demands for the employees' broad and interdisciplinary skills increase simultaneously.High complexity and various interdependencies result from the involved smart objects, which may act in a dynamic way instead of being static and predictable. Furthermore, production workers will initially have difficulties to keep track of the digital information behind the physical production environment. The challenge is that the digital mechanisms are invisible in nature. Professions in manufacturing or electronic engineering have to be enriched by interdisciplinary competences from computer science in order to make employees cope with their new requirements. Therefore, innovative and appropriate qualification is needed.In the light of the mentioned learning challenges within smart factories and other environments, we suggest a comprehensive approach combining current trends from human-machine interaction and insights from psychological and pedagogical science with the aim of developing interactive learning environments integrating real and virtual world.

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Animation‐guided family empowerment program on perioperative care after neurosurgery: A randomized controlled trial for preventing respiratory complications

Lv,

Zhao,

et al. 2024

Health Care Science

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BackgroundThis study aimed to evaluate the effectiveness of using animation as education material for family empowerment program on perioperative care for caregivers whose children were to undergo neurosurgery.MethodsA total of 204 caregivers were randomly assigned to either the face‐to‐face oral nursing educated group (Oral Group) or the animation‐assisted nursing educated group (Animated Group). The nursing education primarily focused on instructing caregivers about the manual vibration method. The primary outcome of interest in this study was participants' knowledge level, collected by a 10‐item questionnaire. Secondary outcomes included child patients' clinical data, including hospitalization days, treatments, and signs of pneumonia.ResultsParticipants in the Animated Group exhibited significantly higher accuracy in perioperative care knowledge assessment, and patients in this group had a lower chance of requiring atomization therapy compared to the Oral Group.ConclusionsThe animation‐assisted nursing education program effectively enhances pediatric caregivers' knowledge, reduces respiratory complications after surgery, and offers valuable insights for future studies on the use of such programs to instruct caregivers.

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Procedural Instructions, Principles, and Examples

Cited by 135 publications

References 81 publications

Improving problem solving with subgoal labels in expository text and worked examples

Improving problem solving with subgoal labels in expository text and worked examples

In-Factory Learning – Qualification For The Factory Of The Future

Animation‐guided family empowerment program on perioperative care after neurosurgery: A randomized controlled trial for preventing respiratory complications

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