• Journal of Korean Institute of Industrial Engineers
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• v.43 no.3
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• pp.176-183
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• 2017

In recent years, studies about multitasking becomes more important. During multitasking, operators can feel frustration when they are interrupted during the task and frustration can affect operator's emotional state and performance. However there is no research on measuring the frustration quantitatively in multitasking environment. In this paper, we suggested quantitative measurement of frustration during multitasking. In order to measure the frustration, we made a mathematical representation with emotional decay model and the initial intensity of frustration based on cognitive closure theory. The amount of initial intensity could be represented as the ratio of actual remaining time to expected remaining time. By the experiment, we measured the frustration during the experiment and compared this values with values of frustration dimension of NASA-TLX. Finally we got the linear regression model with a good accuracy ($R^2=0.986$). This study contributes to measuring the emotion quantitatively by the relation of expected and actual remaining time in multitasking environment.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.79-84
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• 2023

Due to the rapid performance improvement of smartphones, multitasking on mobile platforms has become an essential feature. Unlike traditional desktop or server environments, mobile applications are mostly interactive jobs where response time is important, and some applications are classified as real-time jobs with deadlines. When interactive and real-time jobs run concurrently, memory allocation between multitasking applications is a challenging issue as they have different time requirements. In this paper, we study how to allocate memory space when real-time and interactive jobs are simultaneously executed in a smartphone to meet the multitasking requirements between heterogeneous jobs. Specifically, we analyze the memory size required to satisfy the constraints of real-time jobs and present a new model for allocating memory space between heterogeneous multitasking jobs. Trace-driven simulations show that the proposed model provides reasonable performance for interactive jobs while guaranteeing the requirement of real-time jobs.

• Informatization Policy
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• v.21 no.4
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• pp.59-80
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• 2014

As usage of smartphone increases, it causes various social issues including smartphone addiction. This study, therefore, examined the impact of smartphone users' multitasking behaviors, satisfaction and flows on the smartphone addiction. Also, this research investigated the impacts of users' characteristics such as their polychronicity, innovativeness, and self-efficacy on their multitasking behaviors. The results of this study can be summarized as follows: users' polychronicity showed positive impact on their multitasking behaviors while innovativeness and self-efficacy did not. In addition, users' multitasking behaviors had significantly positive effects on satisfaction, flow and addiction. Users' satisfaction, however, did not show the direct impact on addiction while the positive impact on the flow, which in turn had direct effects on addiction. In sum, users' polychronicity and their multitasking behavior can be considered one of the most important predictors directly and indirectly through flow explaining smartphone addiction.

• Journal of Convergence for Information Technology
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• v.12 no.5
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• pp.51-61
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• 2022

The purpose of this study was to identify whether need of cognition have a mediating effect on media multitasking on learning engagement in online education. The subjects were 168 university students who had received online education for more than one semester, and data were collected using questionnaires on media multitasking, need of cognition, and learning engagement. For data analysis, correlation, hierarchical regression and Sobel test were performed using SPSS WIN 20.0 program. As a result of the study, there was a significant positive correlation between media multitasking and learning engagement (r=.52, p<.001), and a between need of cognition and learning engagement(r=.39, p<.001). Media multitasking had a significant effect on learning participation (β=.52, p<.001), and explanatory power was 27%. When need of cognition was input, the effect of media multitasking on learning engagement decreased (β=.52 ⟶ .44), so it was found that need of cognition had a partial mediating effect in the effect of media multitasking on learning engagement(Z=2.77). p<.01). Therefore, it is suggested to use online contents with learning to organizing a g a lesson syllabus for distance education, and to organize activities promoting media multitasking activities. Also, it is proposed to develop and apply various teaching strategies to promoting the need of cognition in the distance education.

• The KIPS Transactions:PartA
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• v.17A no.2
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• pp.73-80
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• 2010

As the power of hardware has improved, there have been numerous researches in processing concurrently using multitasking method. The incremental evaluation is the evaluation method of reevaluating only affected parts instead of reevaluating overall program when the program has been changed. It is necessary to do more studies that improve the efficiency of concurrent incremental evaluation to do multitasking using multi-threading of Java not to do in parallel using multiprocessor. In this paper, the dependency in the dependency chart is based on the attribute that describes the real value of the variable that directly affects the semantics, thereby doing efficient evaluation. So using the dependency, this paper presents the concurrent incremental evaluation algorithm for Java Languages and proves its correctness, analyzing the efficiency of concurrent incremental evaluation by the simulation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.43-48
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• 2022

Recently, due to the rapid performance improvement of smartphones and the increase in background executions of mobile apps, multitasking has become common on mobile platforms. Unlike traditional desktop and server apps, response time is important in most mobile apps as they are interactive tasks, and some apps are classified as real-time tasks with deadlines. In this paper, we discuss how to meet the requirements of heterogeneous multitasking in managing memory of real-time and interactive tasks when they are executed together on a smartphone. To do so, we analyze the memory requirement of real-time tasks, and propose a model that has the ability of allocating memory to multitasking tasks on a smartphone. Trace-driven simulations with real-world storage access traces captured by heterogeneous apps show that the proposed model provides reasonable performance for interactive tasks while guaranteeing the requirement of real-time tasks.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.9_10
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• pp.429-440
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• 2008

As embedded system becomes more complex, software development becomes more important in the entire design process. Most embedded applications consist of multi -tasks, that are executed in parallel. So, dataflow model that expresses concurrency naturally is preferred than sequential programming language to develop multitask software. For the execution of multitasking codes, operating system is essential to schedule multi-tasks and to deal with the communication between tasks. But, it is needed to execute multitasking code without as when the target hardware platform cannot execute as or target platforms are candidates of design space exploration, because it is very costly to port as for all candidate platforms of DSE. For this reason, we propose the serialized multitasking code generation technique from dataflow specification. In the proposed technique, a task is specified with dataflow model, and generated as a C code. Code generation consists of two steps: First, a block in a task is generated as a separate function. Second, generated functions are scheduled by a multitasking scheduler that is also generated automatically. To make it easy to write customized scheduler manually, the data structure and information of each task are defined. With the preliminary experiment of DivX player, it is confirmed that the generated code from the proposed framework is efficiently and correctly executed on the target system.

• Journal of Digital Policy
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• v.3 no.1
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• pp.1-10
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• 2024

The purpose of this study was to identify the predictors of self-regulated learning strategies among nursing university students. Data were collected from 212 nursing university students in G metropolitan city and K city. The SPSS WIN 23.0 version program was used for data analysis. The data were analyzed using Pearson's correlation coefficient and multiple regression. There were significant correlations between media multitasking and self-regulated learning strategies(r=.45, p<.001), learning flow and self-regulated learning strategies(r=.59, p<.001), and media multitasking and learning flow(r=.32, p<.001). Friendship satisfaction, media multitasking and learning flow explained 45% of the variance for self-regulated learning strategies. To increase the self-regulated learning strategies among nursing university students, it is necessary to develop multiple interventions that enhance friendship satisfaction, media multitasking and learning flow.

• Journal of KIISE:Computing Practices and Letters
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• v.6 no.2
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• pp.173-186
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• 2000

Real-time embedded systems should accommodate many kinds of hardware platforms and resource management policies that vary depending on their operating environments and purposes. It is not an easy job to adapt a multitasking kernel to new system services and hardware platforms, as the kernel must strictly satisfy constraints on its size and performance. This paper describes the design and implementation of an object-oriented multitasking framework that can be reused for implementing microprocessor-based real-time embedded multitasking kernels, In this kernel framework, those parts that can vary depending on hardware platforms and system resource management policies are separated into the hot spots and encapsulated by abstract classes. Our framework thus can be effectively used to implement microprocessor-based real-time embedded kernels that demand high portability and adaptability.

• The Transactions of the Korea Information Processing Society
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• v.4 no.11
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• pp.2711-2720
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• 1997

Multitasking and microtasking on the CRAY machine provides still another way to improve computational power. Since CRAY-2 has 4 processors we can achieve speedup up to 4 properly designed algorithms. In this paper we present two parallelizations of linear system solution in the CRAY-2 with multitasking and microtasking library. One is the LU decomposition on the dense matrices and the other is the iterative solution of large sparse linear systems with the preconditioner proposed by Radicati di Brozolo. In the first case we realized a speedup of 1.3 with 2 processors for a matrix of dimension 600 with the multitasking and in the second case a speedup of around 3 with 4 processors for a matrix of dimension 600 with the multitasking and in the second case a speedup of around 3 with 4 processors for a matrix of dimension 8192 with the microtasking. In the first case the speedup is limited because of the nonuniform vector lenghts. In the second case the ILU(0) preconditioner with Radicati's technique seem to realize a reasonable high speedup with 4 processors.