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An illustration of the power-law relationship for the development of Professor Asimov's professional writing skills. (a) The number of books Professor Isaac Asimov wrote as a function of time in months. (b) The time to complete 100 books as a function of practice, plotted with logarithmic coordinates on both axes. From Ohlsson, S. (1992). The learning curve for writing books: Evidence of Professor Asimov. Psychological Science 3, 380–382. With permission from Wiley-Blackwell.
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Developing high-level problem-solving skill is critical to successfully perform a variety of tasks in both formal (e.g., school and work) and informal (e.g., home) settings. One way to understand how people acquire such skills is to examine research on expertise in problem solving. In this article, we provide an integrative review of the psychologi...
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... exact relationship has been shown to be a very general phenomenon and has been observed in a variety of activities from learning to roll cigars to learning to solve math problems (see Proctor and Dutta, 1995 for a review). See Figure 3 for a real-world example of the power-law relationship. ...
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... Problems' complexity and the difficulty of solving them are also strengthened by their unclear and sometimes conflicting objectives, the accessibility of multiple or unpredictable solutions, and sometimes unknown aspects. Furthermore, complex problems are characterized by many variables involved with a high degree of connectivity between them, multiple conflicting objectives, and uncertainty (Johnson, Albizri, Harfouche, & Fosso-Wamba, 2022;Nokes, Schunn, & Chi, 2010). Moreover, they often have a more extensive scope and multiple A normative reference frame on expertise approaches has been defined in France with the NF X50-110 standard (Huver, Loisel, Peyrouty, Pineau, Tuffery, Peyrouty, & Chanay, 2011) ''Quality in expertise activities'' and at the European level with the CNS EN 16775 standard ''Expertise activities -General requirements for expertise services''. ...
The hypothesis-driven methodology is a cognitive activity used in expertise processes to solve problems with limited knowledge and understanding. Although some organizations have standardized this approach to guide humans in carrying out expertise in enterprises, it lacks appropriate tools to assist experts in carrying out this cognitive activity, tracking understanding, or capturing the reasoning steps and the knowledge produced during the process. To acquire, share and reuse experts' knowledge applied during expertise processes while assisting humans in bringing understanding to complex problems, this study introduces a human-machine collaborative framework that formalizes experts' knowledge from the hypothesis-driven methodology described in the France standard NF X50-110 of ''Quality of expertise activity''. This framework utilizes Hypothesis Theory extended with qualitative doubt and a systematic reasoning process to generate a hypothesis exploratory graph (HEG). The proposed approach makes it easier to carry out expertise processes through a human-machine collaboration, offers a means to share and reuse knowledge from expertise, and provides expertise processes evaluation mechanisms. Furthermore, an experiment conducted on a use-case of expertise process verifies the feasibility and effectiveness of the approach.
... Delivering a series of empirically informed technical processes, procedures, and checklists might neatly align with evidence-based protocols or skills learned through formative education; however, does the ends justify the means (Collins et al., 2015), and is simply doing what you know as a practitioner good enough? Alongside the 'doing' procedural-based knowledge (Nokes et al., 2010) that practitioners possess, they are expected to apply this knowledge to situations in unique, novel contexts requiring individualised and considered solutions. For this reason, the ability to apply cognitive skills to dynamic, environmentally derived problems becomes necessary. ...
... There may be an onus on Practitioners to utilise data and justify their methods and approaches, and this is what comes through in the responses. If this is the case, then this will be either anticipatory, therefore drawing on skilled intuition or procedural knowledge (Nash & Collins, 2006;Nokes et al., 2010) to predict based on 'knowing' or retrospective, in that the justification is created through data visualisation based on what has happened (Milkman et al., 2009). Either way, this would suggest that practitioners rely on procedures, unpacking ready-made solutions through pre-determined processes to familiar problems. ...
... When practitioners face complex problems, we might expect them to generate novel or innovative solutions to overcome them (Fiore et al., 2017). In some cases, this was reported and is to be expected, especially if problems are truly complex and difficult to solve (Nokes et al., 2010). Where problems are simple and yet creativity is applied, this might suggest the practitioner has a level of freedom, lower risk in deploying different strategies or low accountability to the result (Proudfoot et al., 2007). ...
Practitioners operating in performance sports are required to problem solve, enabling them to offer tailored performance solutions while making expert decisions with high precision. Problem-solving and decision-making are intertwined and tangled in practice. However, the reality is that practitioners require two independent skill sets. This study aims to investigate performance practitioner’s approaches to problem-solving and decision-making, analysing the meta-cognitive skills required by multidisciplinary team (MDT) practitioners to be effective in their daily practice. Using a 71-statement Likert scale survey, 115 performance- and medical-related MDT practitioners were surveyed to gain insight into their strengths and perceptions of how they think about problem-solving and decision-making in their work. We tabulated descriptive data and created heat maps to visualise correlations between responses. Findings suggest that practitioners rely on a mixed bag of approaches, cognitively toggling between problem types, approaches, and decision styles. In this study, practitioners preferred skilled procedural doing and intuitive expertise to overcome simple problems over rationalistic, logical innovation to address complex problems. Findings suggest the need for MDT practitioners to differentiate between problem types, problem-solving approaches, and decision-making styles while deepening our comprehension of practitioners’ expertise. It offers insight into the cognition that forms the foundation of their approaches, providing a valuable perspective.
... Increasing experience and understanding in a domain substantially alters the way new problem situations are perceived, represented, and categorized [23]. In this work, we set out to study a specific form of portable knowledge that is associated with expertise: the formation of abstract mental representations of key causal models. ...
... Gaining understanding and expertise changes the way new problem situations are perceived, represented, and categorized [23]. Categorization is critical, as it determines what schemata and procedures are subsequently activated [23]. ...
... Gaining understanding and expertise changes the way new problem situations are perceived, represented, and categorized [23]. Categorization is critical, as it determines what schemata and procedures are subsequently activated [23]. That is why categorization according to underlying principle, typical in experts, is likely to activate conceptual knowledge and procedures on how to solve such situations, whereas categorization in terms of superficial features is likely to activate knowledge of situations that cannot be solved by the same procedure, which can, in the worst case, be misleading [9]. ...
The ability to recognize key causal models across situations is associated with expertise. The acquisition of schema-governed category knowledge of key causal models may underlie this ability. In an experimental study (n = 183), we investigated the effects of promoting the construction of schema-governed categories and how an enhanced ability to recognize the key causal models relates to performance in complex problem-solving tasks that are based on the key causal models. In a 2 × 2 design, we tested the effects of an adapted version of an intervention designed to build abstract mental representations of the key causal models and a tutorial designed to convey conceptual understanding of the key causal models and procedural knowledge. Participants who were enabled to recognize the underlying key causal models across situations as a result of the intervention and the tutorial (i.e., causal sorters) outperformed non-causal sorters in the subsequent complex problem-solving task. Causal sorters outperformed the control group, except for the subtask knowledge application in the experimental group that did not receive the tutorial and, hence, did not have the opportunity to elaborate their conceptual understanding of the key causal models. The findings highlight that being able to categorize novel situations according to their underlying key causal model alone is insufficient for enhancing the transfer of the according concept. Instead, for successful application, conceptual and procedural knowledge also seem to be necessary. By using a complex problem-solving task as the dependent variable for transfer, we extended the scope of the results to dynamic tasks that reflect some of the typical challenges of the 21st century.
... Learning tasks refer to situations when learners actively engage with the learning materials which require them to integrate domain-specific knowledge and procedures with more general critical thinking and analysis skills [62], such as solving complex problems [40] or scientific inquiry tasks [93]. For tasks in different domains, there are behavioural patterns that appear with different levels of practice. ...
... For tasks in different domains, there are behavioural patterns that appear with different levels of practice. For example, experts in a domain may show deeper categorization of a problem, use forward-thinking strategies in their execution of the solution or iteratively evaluate intermediate solutions [62]. In these tasks, learners have a large number of operators or actions available in the problem space, or learning environment, making the combinations leading to a solution difficult to observe. ...
... In these tasks, learners have a large number of operators or actions available in the problem space, or learning environment, making the combinations leading to a solution difficult to observe. Furthermore, defining and describing such combinations is not trivial, since they are "automated", or stored in one's memory as procedural learning, making it difficult to explain by experienced individuals [62]. As a consequence, the assessment and monitoring of learners are particularly difficult for teachers, especially considering the scale provided by educational technologies and learning platforms [32]. ...
Describing and analysing sequences of learner actions is becoming more popular in learning analytics. Nevertheless, the authors found a variety of definitions of what a learning sequence is, of which data is used for the analysis, and which methods are implemented, as well as of the purpose and educational interventions designed with them. In this literature review, the authors aim to generate an overview of these concepts to develop a decision framework for using sequence analysis in educational research. After analysing 44 articles, the conclusions enable us to highlight different learning tasks and educational settings where sequences are analysed, identify data mapping models for different types of sequence actions, differentiate methods based on purpose and scope, and identify possible educational interventions based on the outcomes of sequence analysis.
... The ability to effectively solve complex engineering problems can be the difference between project success and failure, but problem solving differs based on expertise. Experts are known to employ different problem-solving strategies compared to novices [2,3]. Experts' greater information processing capacity [4] allows them to approach a problem in a non-systematic manner [5]. ...
... These exceptional insights into expert performance can only be gathered by using a contrastive research approach that compares participants of two or more expertise levels to "find out the most particular and striking characteristics of expert performance" (Boshuizen et al., 2020, p. 5). This contrastive approach is the central research method in expertise research, comparing individuals with low and high performance in a specific domain to understand what facets of performance change during skill development, such as better memory (Sala & Gobet, 2017), more accurate problem-solving behaviors (Nokes et al., 2010), or in our case, engagement in purposeful practice associated with improved performance. ...
Although sales performance is well researched, relatively little is known about what salespeople actively do to improve and achieve superior reproducible performance. We extend existing research by examining purposeful practice, a core concept from expertise research, as a potential contribution to skill development in sales, a heretofore unexplored framework. The exploratory research sets out for a better understanding of purposeful sales practice and potential activities that successful professionals engage in for self-improvement using a sequential two-phase research approach. By comparing different expertise level groups, particular characteristics of expert performance are identified and can be used so less-skilled salespeople become better.
... Additionally, when an individual is navigating numerous pages to find information, cognitive fatigue can be increased reducing user motivation. This can lead to frustration because it takes mental resources to locate the system information (Nielsen, 1993;and Nokes, 2010). Therefore, finding information under current state of the art methods is less then desirable. ...
This paper describes a new informational navigation tool to augment user learning and/or knowledge transference strategies for complex Human-System-Integrative (HSI) machines. The device is designed to augment a reduction in the amount of time required to navigate machine operating manuals when searching for operational information, including a) systems and systems integration, b) normal operational procedures c) abnormal operational procedures d) emergency operational procedures, and d) machine limitations. The device can be applied to many different domains including Academia (textbooks), Automotive, Aviation, Medical, Nuclear, Space, and any other domain requiring complex Human-Machine-Interaction (HMI). Current methods using paper and digital technologies are challenging to circumnavigate; they can make learning unreasonably problematic and laborious; and can require a large amount of the user’s time. As a result, operators can lose efficiency in time and cognitive development when utilizing current state-of-the-art methods. To improve learning resources, the Enhanced User-System Learning Interface (EUSLI), an Advanced Interactive Media (AIM), was developed. Preliminary tests using professional airline pilots (the users) and aircraft manuals (the knowledge transfer system) as the testing experiment, demonstrated an efficiency increase of 65.5% when compared to state-of-the-art methods. Additionally, results indicate increases in cognitive resources, cognitive compatibility, situation awareness, usability, and enjoyment (an emotive factor); and decreases in user fatigue and workload. This paper describes the qualitative and quantitative data and analysis of the research conducted in association with previous research of the Enhanced Pilot Learning Interface published with AHFE in 2018. The results indicate the current research study verified the findings of the previous experiment, with enhanced formative information to be included in the experimentation's conclusions.
... Research into effective problem-solving has a longstanding tradition, originating with the founding work of Herbert Simon and Allen Newell (Chi, 2011;Newell & Simon, 1972;Nokes et al., 2010;Ohlsson, 2012;Reed, 2016;Van Merrienboer, 2013). This line of research initially focused mainly on abstract puzzles but has shifted in time to problems that contain more realistic contexts and require more domain-specific content knowledge. ...
... These studies have shown that differences in problem-solving competency are generally not dependent on having a better memory or on general problem-solving skills. Instead, the consensus is that problem-solving proficiency is determined to a large extent by if and how a problem-solver has organised knowledge into productive knowledge structures or cognitive schemas (Chi, 2011;Nokes et al., 2010;Ohlsson, 2012). Proficient problem-solvers appear to have cognitive schemas containing abstract elements that they can attune to concrete problems. ...
... Hierarchical problem-solving can be divided into three sub-processes that are facilitated by a cognitive schema: problem abstraction, problem reduction and problem refinement (Chi et al., 1982;Nokes et al., 2010;Ohlsson, 2012;Reed, 2016). Different names are used for these processes in different studies, in this study we consistently use the terms given above. ...
Promoting problem-solving in students is an important aim of secondary science education. There is a mismatch, however, between the complex, ill-structured nature of realistic scientific problems, versus the well-structured problems students are generally confronted with. The current study investigates a teaching-learning strategy that resolves this mismatch by combining a focus on hierarchical problem-solving strategies for complex, authentic problems with the use of domain-specific question agendas that represent scientific perspectives. Three design principles were applied to develop an exemplary lesson series that was implemented in two Dutch pre-university classes. A pre-/post-test research design was followed in which data was collected in the form of sets of student-generated research questions. Additionally, audio recordings of lessons and student interviews were collected and analysed. Results indicate that participating students became more proficient at applying hierarchical problem-solving strategies like (1) reducing complex ecological problems to more manageable subproblems by formulating productive research questions and (2) identifying types of ecological problems by connecting them to domain-specific question agendas (problem-abstraction). A qualitative analysis of the teaching-learning process and student interviews informed potential refinements of the design principles, namely the use of more diverse contexts and a greater focus on collaborative learning.
... Expertise refers to observable performance and latent competence. Expertise is defined as an individual's very high domain-specific competence which enables a sustained high performance (Boshuizen et al., 2020;Ericsson & Smith, 1991;Ericsson, 2018a;Mieg, 2001;Nokes et al., 2010). Typically, an individual develops expertise in one domain and perhaps becomes an intermediate in related domains but remains a novice in more distant domains. ...
Expertise is featured by continued high performance in a particular domain. Expertise research has primarily focused on absolute expertise in structured domains such as chess and emphasized the significance of deliberate practice for expertise development. We investigated the development of relative expertise in commercial domains as part of ill-structured domains. Due to the ill-structuredness and acknowledging the use of the term expert in organizational practice, we developed a taxonomy to distinguish between four types of experts in the broader sense (relative expert, managerial relative expert, evolved specialist, and native specialist). Eighteen peer-nominated individuals from business-to-business sales departments from four German organizations participated in our interview study. A content analysis was applied using both deductive and inductive categorizations. The interview data clearly corresponds to the concept of progressive problem solving rather than to the concept of deliberate practice. Almost all our respondents referred to either “being thrown in at the deep end” by others (assigned complex tasks) or “jumping in at the deep end” of one’s own accord (self- selected complex tasks). However, the interview partners described features of deliberate practice for novices. In this very early stage of expertise development, more experienced colleagues structure parts of the ill-structured domain and enable deliberate practice while for advanced beginners and later stages expert development rather resembles progressive problem solving. Our results provide implications on how to foster expertise development in ill-structured domains. Possible limitations arise from the small sample, the peer-nomination process, and the retrospective nature of interview data.
... Methods. According to the human problem solving theory [18,19], managers' or experts' decision-making process could be divided into five stages: (1) the decision problem recognition and classification, (2) problem solving space representation, (3) problem solution searching and application, (4) solution evaluation, and (5) satisfied solution storage. During the second stage, experts are able to rapidly find all the relevant dimensions with multiple decision analysis hierarchies to form the problem solving space [20]. ...
Although the competitive advantages brought by intelligent manufacturing technology for enterprises have been preliminarily shown, a lack of matched management capacity still greatly limits its effect. This paper focuses on the cost management capacity problem of intelligent manufacturing enterprises. The multiscale cost data model is established on the basis of the three-dimensional cost system model, which contains actual cost, standard cost, and testing cost. According to the scale transformation theory, we propose the dynamic updating mechanism of standard cost. The key cost center identification methods, respectively, for the production performance assessment scenario (KCCI_PPA) and the business decision-making scenario (KCCI_BDM) are also put forward, which could overcome the subjective determination limitation of initial observation scale in the traditional variable-scale data analysis method. Experiments with both industrial statistical and enterprise real datasets verify the efficiency and accuracy of the proposed KCCI_PPA and KCCI_BDM method.
