scholarly journals Artificial intelligence, real-time feedback and workplace learning analytics to support in situ complex problem-solving: a commentary

2020 ◽  
Vol 37 (5) ◽  
pp. 267-277
Author(s):  
Maarten de Laat ◽  
Srecko Joksimovic ◽  
Dirk Ifenthaler
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Real Time ◽  
Workplace Learning ◽  
Learning Analytics ◽  
Complex Problem ◽  
Complex Problem Solving ◽  
Feedback Systems ◽  
Content Type

PurposeTo help workers make the right decision, over the years, technological solutions and workplace learning analytics systems have been designed to aid this process (Ruiz-Calleja et al., 2019). Recent developments in artificial intelligence (AI) have the potential to further revolutionise the integration of human and artificial learning and will impact human and machine collaboration during team work (Seeber et al., 2020).Design/methodology/approachComplex problem-solving has been identified as one of the key skills for the future workforce (Hager and Beckett, 2019). Problems faced by today's workforce emerge in situ and everyday workplace learning is seen as an effective way to develop the skills and experience workers need to embrace these problems (Campbell, 2005; Jonassen et al., 2006).FindingsIn this commentary the authors argue that the increased digitization of work and social interaction, combined with recent research on workplace learning analytics and AI opens up the possibility for designing automated real-time feedback systems capable of just-in-time, just-in-place support during complex problem-solving at work. As such, these systems can support augmented learning and professional development in situ.Originality/valueThe commentary reflects on the benefits of automated real-time feedback systems and argues for the need of shared research agenda to cohere research in the direction of AI-enabled workplace analytics and real-time feedback to support learning and development in the workplace.

Making sense: unleashing social capital in interdisciplinary teams

2017 ◽  
Vol 2 (3) ◽  
pp. 118-133 ◽  
Author(s):  
Lauren H. Bryant ◽  
Sherry Booth Freeman ◽  
Alan Daly ◽  
Yi-Hwa Liou ◽  
Suzanne Branon
Keyword(s):  
Social Capital ◽  
Problem Solving ◽  
Interdisciplinary Approach ◽  
Complex Problem ◽  
Interdisciplinary Teams ◽  
Sense Making ◽  
Complex Problem Solving ◽  
Educational Innovations ◽  
Content Type ◽  
The Creation

Purpose Previous attempts to solve complex problems in the field of education have often focused on one disciplinary perspective. This impedes the creation of meaningful solutions and lasting change. While an interdisciplinary approach has the potential for complex problem solving, it has often proven difficult. The purpose of this paper is to apply social capital and sense-making lenses to facilitate complex problem-solving on a large, interdisciplinary, National Science Foundation funded team. Design/methodology/approach Social network analysis (SNA) and interviews allowed for the examination of the existing underlying social structures of the project team, and the ways in which these underlying structures were impacting the team’s ability to leverage its own social capital. Findings Findings demonstrated that decentralized, low levels of weekly and daily collaboration may constrain the team’s capacity for collective sense-making and its ability to achieve coherence around project goals. Practical implications Using SNA to systematically study the underlying network structure of a team, with the intention to use that data to drive change, can allow teams to shape their networks over time to allow for sense-making and successful collaborations. It may be that, while large teams are studying their intended phenomena, they should also make time to study themselves. Social implications Increasing the successfulness of large teams stands to positively impact researchers’ abilities to create workable solutions to intractable problems. Originality/value While SNA is a popular approach to understanding school districts and the spread of educational innovations, this study uses SNA to understand the creation of solutions and innovations.

Complex Problem Solving and Worked Examples

2009 ◽  
Vol 23 (2) ◽  
pp. 129-138 ◽  
Author(s):  
Florian Schmidt-Weigand ◽  
Martin Hänze ◽  
Rita Wodzinski
Keyword(s):  
Problem Solving ◽  
Cognitive Load ◽  
Learning Experience ◽  
Worked Examples ◽  
Complex Problem ◽  
Complex Problem Solving ◽  
Strategic Learning ◽  
Learning Behavior ◽  
Worked Example ◽  
8Th Grade

How can worked examples be enhanced to promote complex problem solving? N = 92 students of the 8th grade attended in pairs to a physics problem. Problem solving was supported by (a) a worked example given as a whole, (b) a worked example presented incrementally (i.e. only one solution step at a time), or (c) a worked example presented incrementally and accompanied by strategic prompts. In groups (b) and (c) students self-regulated when to attend to the next solution step. In group (c) each solution step was preceded by a prompt that suggested strategic learning behavior (e.g. note taking, sketching, communicating with the learning partner, etc.). Prompts and solution steps were given on separate sheets. The study revealed that incremental presentation lead to a better learning experience (higher feeling of competence, lower cognitive load) compared to a conventional presentation of the worked example. However, only if additional strategic learning behavior was prompted, students remembered the solution more correctly and reproduced more solution steps.

The Prediction of Problem-Solving Assessed Via Microworlds

2016 ◽  
Vol 32 (4) ◽  
pp. 298-306 ◽  
Author(s):  
Samuel Greiff ◽  
Katarina Krkovic ◽  
Jarkko Hautamäki
Keyword(s):  
Working Memory ◽  
Problem Solving ◽  
Complex Problem ◽  
Two Dimensions ◽  
Assessment Instruments ◽  
Complex Problem Solving ◽  
Visual Spatial ◽  
Rule Knowledge ◽  
Rule Application ◽  
Fluid Reasoning

Abstract. In this study, we explored the network of relations between fluid reasoning, working memory, and the two dimensions of complex problem solving, rule knowledge and rule application. In doing so, we replicated the recent study by Bühner, Kröner, and Ziegler (2008) and the structural relations investigated therein [ Bühner, Kröner, & Ziegler, (2008) . Working memory, visual-spatial intelligence and their relationship to problem-solving. Intelligence, 36, 672–680]. However, in the present study, we used different assessment instruments by employing assessments of figural, numerical, and verbal fluid reasoning, an assessment of numerical working memory, and a complex problem solving assessment using the MicroDYN approach. In a sample of N = 2,029 Finnish sixth-grade students of which 328 students took the numerical working memory assessment, the findings diverged substantially from the results reported by Bühner et al. Importantly, in the present study, fluid reasoning was the main source of variation for rule knowledge and rule application, and working memory contributed only a little added value. Albeit generally in line with previously conducted research on the relation between complex problem solving and other cognitive abilities, these findings directly contrast the results of Bühner et al. (2008) who reported that only working memory was a source of variation in complex problem solving, whereas fluid reasoning was not. Explanations for the different patterns of results are sought, and implications for the use of assessment instruments and for research on interindividual differences in complex problem solving are discussed.

scholarly journals Working Memory, Fluid Reasoning, and Complex Problem Solving: Different Results Explained by the Brunswik Symmetry

2021 ◽  
Vol 9 (1) ◽  
pp. 5
Author(s):  
André Kretzschmar ◽  
Stephan Nebe
Keyword(s):  
Working Memory ◽  
Problem Solving ◽  
Cognitive Abilities ◽  
Latent Variables ◽  
Complex Problem ◽  
Equation Modeling ◽  
Complex Problem Solving ◽  
Symmetry Principle ◽  
Fluid Reasoning ◽  
The Impact

In order to investigate the nature of complex problem solving (CPS) within the nomological network of cognitive abilities, few studies have simultantiously considered working memory and intelligence, and results are inconsistent. The Brunswik symmetry principle was recently discussed as a possible explanation for the inconsistent findings because the operationalizations differed greatly between the studies. Following this assumption, 16 different combinations of operationalizations of working memory and fluid reasoning were examined in the present study (N = 152). Based on structural equation modeling with single-indicator latent variables (i.e., corrected for measurement error), it was found that working memory incrementally explained CPS variance above and beyond fluid reasoning in only 2 of 16 conditions. However, according to the Brunswik symmetry principle, both conditions can be interpreted as an asymmetrical (unfair) comparison, in which working memory was artificially favored over fluid reasoning. We conclude that there is little evidence that working memory plays a unique role in solving complex problems independent of fluid reasoning. Furthermore, the impact of the Brunswik symmetry principle was clearly demonstrated as the explained variance in CPS varied between 4 and 31%, depending on which operationalizations of working memory and fluid reasoning were considered. We argue that future studies investigating the interplay of cognitive abilities will benefit if the Brunswik principle is taken into account.

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