Effects of Varied Surgical Simulation Training Schedules on Motor-Skill Acquisition

2019 ◽  
Vol 27 (1) ◽  
pp. 68-80 ◽  
Author(s):  
Frank E. Ritter ◽  
Martin K.-C. Yeh ◽  
Yu Yan ◽  
Ka-Chun Siu ◽  
Dmitry Oleynikov
Keyword(s):  
Skill Acquisition ◽  
Completion Time ◽  
Surgical Simulation ◽  
Task Completion ◽  
Time On Task ◽  
Task Completion Time ◽  
Motor Skill Acquisition ◽  
Training Schedule ◽  
Task Time ◽  
Surgery Simulator

There have been many studies to evaluate the effect of training schedules on retention; however, these usually compare only 2 drastically different schedules, massed and distributed, and they have tended to look at declarative knowledge tasks. This study examined learning on a laparoscopic surgery simulator using a set of procedural or perceptual-motor tasks with some declarative elements. The study used distributed, massed, and 2 hybrid-training schedules that are neither distributed nor massed. To evaluate the training schedules, 23 participants with no previous laparoscopic experience were recruited and randomly assigned to 1 of the 4 training schedules. They performed 3 laparoscopic training tasks in eight 30-minute learning sessions. We compared how task time decreased with each schedule in a between-participants design. We found participants in all groups demonstrated a decrease in task completion time as the number of training sessions increased; however, there were no statistically significant differences in participants’ improvement on task completion time between the 4 different training schedule groups, which suggested that time on task is more important for learning these tasks than the training schedule.

Effects of Automation Type on Human Performance in Proofreading Tasks

2018 ◽  
Vol 62 (1) ◽  
pp. 1140-1140 ◽  
Author(s):  
Heejin Jeong ◽  
Jangwoon Park ◽  
Jaehyun Park ◽  
Byung Cheol Lee
Keyword(s):  
Task Performance ◽  
Skill Acquisition ◽  
Completion Time ◽  
Human Performance ◽  
Task Completion ◽  
Strong Positive Correlation ◽  
Task Completion Time ◽  
Perceived Trust ◽  
Mental Demand ◽  
Study Results

Automation is ubiquitous and indispensable in modern working environments. It is adopted and used in not only advanced industrial- and technology-oriented operations, but also ordinary home or office computational functions. In general, automated systems aim to improve overall work efficiency and productivity of labor-intensive tasks by decreasing the risk of errors, and cognitive and physical workloads. The systems offer the support for diverse decision-making processes as well. However, the benefits of automation are not consistently achieved and depend on the types and features of automation (Onnasch, Wickens, Li, & Manzey, 2014; Parasuraman, Sheridan, & Wickens, 2000). Possible negative side effects have been reported. Sometimes, automation may lead to multi-tasking environments, which allows operators to be distractive with several tasks. It ultimately prolongs task completion time and causes to neglect monitoring and follow-up steps of the pre-processing tasks (Endsley, 1996). Furthermore, the operators who excessively depend on automation are easily deteriorated in skill acquisition, which is necessary for the emergency or manual operations. Thus, inconsistent performance in automation is a major issue in successful adoption and trust in automation (Jeong, Park, Park, & Lee, 2017). This paper presents an experimental study that investigates the main features and causes of the inconsistency in task performance in different types of automation. Automated proofreading tasks were used in this study, which is one of the most common types of automation we experience in daily life. Based on the similar algorithm of the auto-correct function in Microsoft Word, a custom-built program of five proofreading tasks, including one non-automated and four automated proofreading tasks, were developed using Visual Studio 2015 C#. In the non-automated task used as a reference for individual difference, participants were asked to manually find a typographical error in a sentence. In the automated tasks, auto-correcting functions are provided in two levels (i.e., low and high) of automation and two statuses (i.e., routine and failure of automation). The type of automation is defined as the combinations of a status and a level. Participants identified typographical errors by only an underlined word at the low-level automation, whereas an underlined word with a possible substituting word was given at the high-level. Additionally, in the routine automation status, a correct substituting word is provided. On the other hand, a grammatically incorrect word is given in the failed automation status. Nineteen participants (11 females and 8 males; age mean = 33.8, standard deviation = 19.1) took part in this study. Results of statistical analyses show a clear advantage in high-routine automation, in terms of both task completion time and accuracy. While task performances of high & routine automation types are quite obvious in both task completion time and accuracy, those in the failed automation types are mixed and indistinguishable. Different levels and statues of failed automation do not much influence task performance. Moreover, task completion time and mental demand are strongly correlated, and the accuracy rate and perceived trust show a strong positive correlation. The approaches and outcomes of the current study can provide some insights into the human-automation interaction systems that support human performance and safety, such as in-vehicle warning systems and automated vehicle controls.

scholarly journals An Instrumental Assessment of Touch'n Go eWallet Mobile App

2021 ◽  
Vol 15 (06) ◽  
pp. 4
Author(s):  
Azham Hussain ◽  
Emmanuel O.C. Mkpojiogu ◽  
Fazillah Mohmad Kamal ◽  
Rohazna binti Wahab ◽  
Noor Halawati Che Meh
Keyword(s):  
User Satisfaction ◽  
Completion Time ◽  
User Feedback ◽  
Mobile App ◽  
Task Completion ◽  
Time On Task ◽  
Completion Rates ◽  
Task Completion Time ◽  
Instrumental Assessment

<p>This study assessed the instrumentality of Touch ’n Go eWallet mobile app at selected areas in University Malaysia Perlis (UniMAP) and Politeknik Tuanku Syed Sirajuddin (PTSS) Perlis, Malaysia, in July and August 2019. Fifteen staff from the two institutions was selected as participants. The purpose of the test was to assess the usability of the app and get user feedback to improve the instrumental quality of the application in order to meet user satisfaction and their experience. This report contains the participants’ feedbacks, task completion rates, ease or difficulty of task completion, time on task, errors, and recommendations for improvements. This study used 4 tasks to assess the instrumental quality of Touch ‘n Go eWallet mobile app. Overall, the outcome of the study revealed that the app is generally usable and instrumental to assisting users accomplish their electronic wallet goals. There is however some observed issues in the app that require fixing to enhance the instrumental quality of app.</p>

The Effects of Language Barriers and Time Constraints on Online Learning Performance: An Eye-Tracking Study

2021 ◽  
pp. 001872082110109
Author(s):  
Auður Anna Jónsdóttir ◽  
Ziho Kang ◽  
Tianchen Sun ◽  
Saptarshi Mandal ◽  
Ji-Eun Kim
Keyword(s):  
Online Learning ◽  
Eye Movement ◽  
Completion Time ◽  
Language Use ◽  
Time Pressure ◽  
Time Constraints ◽  
Task Completion ◽  
Esl Students ◽  
Task Completion Time ◽  
Eye Fixation

Objective The goal of this study is to model the effect of language use and time pressure on English as a first language (EFL) and English as a second language (ESL) students by measuring their eye movements in an on-screen, self-directed learning environment. Background Online learning is becoming integrated into learners’ daily lives due to the flexibility in scheduling and location that it offers. However, in many cases, the online learners often have no interaction with one another or their instructors, making it difficult to determine how the learners are reading the materials and whether they are learning effectively. Furthermore, online learning may pose challenges to those who face language barriers or are under time pressure. Method The effects of two factors, language use (EFL vs. ESL) and time constraints (high vs. low time pressure), were investigated during the presentation of online materials. The effects were analyzed based on eye movement measures (eye fixation rate—the total number of eye fixations divided by the task duration and gaze entropy) and behavioral measures (correct rate and task completion time). Results The results show that the ESL students had higher eye fixation rates and longer task completion times than the EFL students. Moreover, high time pressure resulted in high fixation rates, short task completion time, low correct rates, and high gaze entropy. Conclusion and Application The results suggest the possibility of using unobtrusive eye movement measures to develop ways to better assist those who struggle with learning in the online environment.

scholarly journals Bandwidth-Aware Rescheduling Mechanism in SDN-Based Data Center Networks

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1774
Author(s):  
Ming-Chin Chuang ◽  
Chia-Cheng Yen ◽  
Chia-Jui Hung
Keyword(s):  
Data Center ◽  
Completion Time ◽  
Network Performance ◽  
Data Locality ◽  
Task Completion ◽  
Data Center Networks ◽  
Task Completion Time ◽  
Data Packets ◽  
Network Bandwidth ◽  
Data Process

Recently, with the increase in network bandwidth, various cloud computing applications have become popular. A large number of network data packets will be generated in such a network. However, most existing network architectures cannot effectively handle big data, thereby necessitating an efficient mechanism to reduce task completion time when large amounts of data are processed in data center networks. Unfortunately, achieving the minimum task completion time in the Hadoop system is an NP-complete problem. Although many studies have proposed schemes for improving network performance, they have shortcomings that degrade their performance. For this reason, in this study, we propose a centralized solution, called the bandwidth-aware rescheduling (BARE) mechanism for software-defined network (SDN)-based data center networks. BARE improves network performance by employing a prefetching mechanism and a centralized network monitor to collect global information, sorting out the locality data process, splitting tasks, and executing a rescheduling mechanism with a scheduler to reduce task completion time. Finally, we used simulations to demonstrate our scheme’s effectiveness. Simulation results show that our scheme outperforms other existing schemes in terms of task completion time and the ratio of data locality.

scholarly journals The Impact of Notification Modality and Ambient Sound on Users’ Mobile Interaction

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 395
Author(s):  
Chien-Hsiung Chen ◽  
Miao Huang
Keyword(s):  
Completion Time ◽  
Sampling Method ◽  
Sound Level ◽  
Task Completion ◽  
Maze Task ◽  
Task Completion Time ◽  
Mobile Interaction ◽  
Ambient Sound ◽  
Convenience Sampling ◽  
The Impact

This study investigated the impacts of different notification modalities used in low and high ambient sound environments for mobile phone interaction. Three different notification modalities—Shaking Visual, Shaking Visual + Vibration, and Vibration—were designed and experimentally tested by asking users to conduct a maze task. A total of 72 participants were invited to take part in the experiment through the convenience sampling method. The generated results indicated that (1) the notification modality affects participants’ task completion time, (2) the error rate pertinent to the number of notifications is positively related to the participants’ task completion time, and (3) the ambient sound level and notification modalities impact the overall experience of the participants. The main contributions of this study are twofold. First, it verifies that the multi-dimensional feature of a Shaking Visual + Vibration synesthesia notification design is implementable. Second, this study demonstrated that the synesthesia notification could be feasible for mobile notification, and it was more perceptible by the users.

scholarly journals Workflow Scheduling Based on Mobile Cloud Computing Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fanghai Gong
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Completion Time ◽  
Mobile Cloud Computing ◽  
Task Completion ◽  
Workflow Scheduling ◽  
Mobile Cloud ◽  
Task Completion Time ◽  
Computing Machine

In recent years, cloud workflow task scheduling has always been an important research topic in the business world. Cloud workflow task scheduling means that the workflow tasks submitted by users are allocated to appropriate computing resources for execution, and the corresponding fees are paid in real time according to the usage of resources. For most ordinary users, they are mainly concerned with the two service quality indicators of workflow task completion time and execution cost. Therefore, how cloud service providers design a scheduling algorithm to optimize task completion time and cost is a very important issue. This paper proposes research on workflow scheduling based on mobile cloud computing machine learning, and this paper conducts research by using literature research methods, experimental analysis methods, and other methods. This article has deeply studied mobile cloud computing, machine learning, task scheduling, and other related theories, and a workflow task scheduling system model was established based on mobile cloud computing machine learning from different algorithms used in processing task completion time, task service costs, task scheduling, and resource usage The situation and the influence of different tasks on the experimental results are analyzed in many aspects. The algorithm in this paper speeds up the scheduling time by about 7% under a different number of tasks and reduces the scheduling cost by about 2% compared with other algorithms. The algorithm in this paper has been obviously optimized in time scheduling and task scheduling.

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