Toward a Characterization of Adaptive Systems

Feigh, Karen M.; Dorneich, Michael C.; Hayes, Caroline C.

doi:10.1177/0018720812443983

Cited by 170 publications

(70 citation statements)

References 84 publications

(105 reference statements)

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“…Such group awareness in a virtual setting is also seen in many other large-scale online environments, such as Wikipedia and Stack Overflow, where people help each other as virtual groups with no seeming reward Faraj, 2000, 2005;Forte et al, 2009). Our findings suggest the possibility of enhancing group output through design interventions that address individual behavioral plasticity to ultimately modulate spontaneous adaption of collective dynamics (Feigh et al, 2012;Barg-Walkow and Rogers, 2016). For example, by exposing workers to social information about a distribution of other workers, the collective task designer or administrator could dynamically influence workers' task choice in a desired direction.…”

Section: Discussionsupporting

confidence: 63%

“…Just as the specific motivational factors contributing to the division of labor are presently unclear, the role of social information on group coordination in crowdsourcing remains elusive. An improved understanding of these aspects could inform design interventions for collective tasks (Feigh et al, 2012;Nov et al, 2014). Such interventions could modulate social signals to elicit the dynamic assignment of people to selected tasks toward optimized collaboration in a shared effort.…”

Section: Introductionmentioning

confidence: 99%

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The Influence of Social Information and Self-expertise on Emergent Task Allocation in Virtual Groups

et al. 2018

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Dynamic group coordination facilitates adaptive division of labor in response to group-level changes. Yet, little is known about how it can be operationalized in online collaborations among individuals with limited information about each other. We hypothesized that simple social information about the task distribution of others can elicit emergent task allocation. We conducted an online experiment where participants analyze images of a polluted canal by freely switching between two tasks: creating keyword-based tags for images and categorizing existing tags. During the task execution, we presented experimentally manipulated information about the contrasting group-level task distributions. Participants did not change the effort allocation between the tasks when they were notified that the group is deficient in workers in the task they intrinsically prefer. By contrast, they allocated more effort to the less preferred task than they would intrinsically do when their intrinsic effort allocation counterbalances the current distribution of workers in the group. Such behavioral changes were observed more strongly among those with higher skills in the less preferred task. Our results demonstrate the possibility of optimizing group coordination through design interventions at the individual level that lead to spontaneous adaption of division of labor at the group level. When participants were provided information about the group-level task distribution, they tend to allocate more effort to the task against their intrinsic preference.

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Section: Discussionsupporting

confidence: 63%

Section: Introductionmentioning

confidence: 99%

The Influence of Social Information and Self-expertise on Emergent Task Allocation in Virtual Groups

et al. 2018

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“…Adaptive systems or user-adaptive systems are defined as systems that modify their behavior and output according to different user characteristics or features such as preferences, personality, emotion, and skills. Adaptive systems can be described as "the technological component of joint humanmachine systems that can change their behaviour to meet the changing needs of their users, often without explicit instructions from their users" (Feigh, Dorneich, & Hayes, 2012, p. 1008). Jameson also defines an adaptive system as "an interactive system that adapts its behavior to individual users on the basis of processes of user model acquisition and application that involve some form of learning, inference, or decision making" (Jameson, 2009, p. 106).…”

Section: Background Adaptivity In E-learning Systemsmentioning

confidence: 99%

“…There are different workstreams in AESs. The development of adaptive models and frameworks represents one such workstream (Feigh et al, 2012;Knutov, 2012). Student modeling represents a more focused stream that involves the representation, storing, and maintenance of student characteristics such as learning style, knowledge level, and motivation (Chrysafiadi & Virvou, 2013;Normadhi et al, 2019).…”

Section: Background Adaptivity In E-learning Systemsmentioning

confidence: 99%

Effective Adaptive E-Learning Systems According to Learning Style and Knowledge Level

Alshammari

Qtaish

2019

JITE:Research

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Aim/Purpose: Effective e-learning systems need to incorporate student characteristics such as learning style and knowledge level in order to provide a more personalized and adaptive learning experience. However, there is a need to investigate how and when to provide adaptivity based on student characteristics, and more importantly, to evaluate its value in learning enhancement. This study aims to bridge that gap by examining the effect of different modes of learning material adaptation and their sequences to the learning style and knowledge level of students in e-learning systems. Background: E-learning systems aim to provide acceptability and interactivity between students, instructors, and learning content anytime and anywhere. However, traditional systems are typically designed for generic students irrespective of individual requirements. Successful e-learning systems usually consider student characteristics such as learning style and knowledge level to provide more personalized and adaptive student-system interaction. Methodology: A controlled experiment was conducted in a learning context with 174 subjects to evaluate the learning effectiveness of adaptivity in e-learning systems. Contribution: The main contributions of the paper are threefold. First, a novel adaptive approach is proposed based on a specific learning style model and knowledge level. Second, the approach is implemented in an e-learning system to teach computer security, the application domain. Third, a rigorous experimental evaluation of the learning effect of the adaptive approach is offered. Findings: The results indicate that adaptation according to the combination of learning style and knowledge level produces significantly better learning gains, both in the short-term and medium-term, than adaptation according to either trait individually. Recommendations for Practitioners: Practitioners should consider the combination of learning style and knowledge level when delivering and presenting learning material to their students. Recommendation for Researchers: Researchers should consider sound educational models when designing adaptive e-learning systems. Also, rigorous and carful experimental design evaluations should be taken into account. Impact on Society: Universities and e-learning industries can benefit from the proposed adaptive approach and the findings in designing and developing more personalized and adaptive e-learning systems. The incorporation of student characteristics, especially learning style and knowledge level, may be used to enhance learning. Future Research: The experiment might be duplicated with a focus on longer-term learning gains by including more subjects and more learning resources. Also, the study might be expanded to application domains other than computer security. Moreover, other variables such as student satisfaction, motivation, and affective state might be explored to further the understanding of the effect of adaptivity on learning gains.

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“…A traditional closed-loop adaptive system includes three principle elements (Feigh et al, 2012): (1) measurement of workload in real time to act as triggers for adaptations, (2) decision logic to decide when to turn on and off automated adaptations based on the triggers, and (3) the adaptations themselves in of form of changes to the automation and human-machine interface. This work takes a novel approach of replacing the decision logic, normally performed by the automation, with human decision logic.…”

Section: The Crew Workload Managermentioning

confidence: 99%

Evaluation of the Display of Cognitive State Feedback to Drive Adaptive Task Sharing

et al. 2017

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This paper presents an adaptive system intended to address workload imbalances between pilots in future flight decks. Team performance can be maximized when task demands are balanced within crew capabilities and resources. Good communication skills enable teams to adapt to changes in workload, and include the balancing of workload between team members This work addresses human factors priorities in the aviation domain with the goal to develop concepts that balance operator workload, support future operator roles and responsibilities, and support new task requirements, while allowing operators to focus on the most safety critical tasks. A traditional closed-loop adaptive system includes the decision logic to turn automated adaptations on and off. This work takes a novel approach of replacing the decision logic, normally performed by the automation, with human decisions. The Crew Workload Manager (CWLM) was developed to objectively display the workload between pilots and recommend task sharing; it is then the pilots who “close the loop” by deciding how to best mitigate unbalanced workload. The workload was manipulated by the Shared Aviation Task Battery (SAT-B), which was developed to provide opportunities for pilots to mitigate imbalances in workload between crew members. Participants were put in situations of high and low workload (i.e., workload was manipulated as opposed to being measured), the workload was then displayed to pilots, and pilots were allowed to decide how to mitigate the situation. An evaluation was performed that utilized the SAT-B to manipulate workload and create workload imbalances. Overall, the CWLM reduced the time spent in unbalanced workload and improved the crew coordination in task sharing while not negatively impacting concurrent task performance. Balancing workload has the potential to improve crew resource management and task performance over time, and reduce errors and fatigue. Paired with a real-time workload measurement system, the CWLM could help teams manage their own task load distribution.

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Toward a Characterization of Adaptive Systems

Abstract: The framework provided in this article provides a tool for organizing and informing past, present, and future research and development efforts in adaptive systems.

Cited by 170 publications

References 84 publications

The Influence of Social Information and Self-expertise on Emergent Task Allocation in Virtual Groups

The Influence of Social Information and Self-expertise on Emergent Task Allocation in Virtual Groups

Effective Adaptive E-Learning Systems According to Learning Style and Knowledge Level

Evaluation of the Display of Cognitive State Feedback to Drive Adaptive Task Sharing

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