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 Integration of biosensor to a window-based control system for user emotion detection to static and dynamic visual contents of webpages

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fatima M. Isiaka ◽  
Awwal Adamu ◽  
Zainab Adamu
Keyword(s):  
Control System ◽  
Eye Movement ◽  
User Experience ◽  
Completion Time ◽  
Dynamic Control ◽  
Task Completion ◽  
Time Interval ◽  
Task Completion Time ◽  
Content Type ◽  
Dynamic Control System

Purpose Basic capturing of emotion on user experience of web applications and browsing is important in many ways. Quite often, online user experience is studied via tangible measures such as task completion time, surveys and comprehensive tests from which data attributes are generated. Prediction of users’ emotion and behaviour in some of these cases depends mostly on task completion time and number of clicks per given time interval. However, such approaches are generally subjective and rely heavily on distributional assumptions making the results prone to recording errors. This paper aims to propose a novel method – a window dynamic control system – that addresses the foregoing issues. Design/methodology/approach Primary data were obtained from laboratory experiments during which 44 volunteers had their synchronized physiological readings – skin conductance response, skin temperature, eye movement behaviour and users activity attributes taken by biosensors. The window-based dynamic control system (PHYCOB I) is integrated to the biosensor which collects secondary data attributes from these synchronized physiological readings and uses them for two purposes: for detection of both optimal emotional responses and users’ stress levels. The method’s novelty derives from its ability to integrate physiological readings and eye movement records to identify hidden correlates on a webpage. Findings The results from the analyses show that the control system detects basic emotions and outperforms other conventional models in terms of both accuracy and reliability, when subjected to model comparison – that is, the average recoverable natural structures for the three models with respect to accuracy and reliability are more consistent within the window-based control system environment than with the conventional methods. Research limitations/implications Graphical simulation and an example scenario are only provided for the control’s system design. Originality/value The novelty of the proposed model is its strained resistance to overfitting and its ability to automatically assess user emotion while dealing with specific web contents. The procedure can be used to predict which contents of webpages cause stress-induced emotions to users.

Selection of manipulator system for multiple-goal task by evaluating task completion time and cost with computational time constraints

2013 ◽  
Vol 27 (4) ◽  
pp. 233-245 ◽  
Author(s):  
Yanjiang Huang ◽  
Lounell B. Gueta ◽  
Ryosuke Chiba ◽  
Tamio Arai ◽  
Tsuyoshi Ueyama ◽  
...  
Keyword(s):  
Completion Time ◽  
Time Constraints ◽  
Computational Time ◽  
Task Completion ◽  
Task Completion Time ◽  
Multiple Goal ◽  
Selection Of

Emotion detection on webpages using biosensors integrated to a window-based dynamic control system

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fatima Isiaka ◽  
Salihu Aish Abdulkarim ◽  
Kassim Mwitondi ◽  
Zainab Adamu
Keyword(s):  
Control System ◽  
Eye Movement ◽  
User Experience ◽  
Completion Time ◽  
Dynamic Control ◽  
Task Completion ◽  
Task Completion Time ◽  
Content Type ◽  
Stress Levels ◽  
Dynamic Control System

PurposeDetecting emotion on user experience of web applications and browsing is important in many ways. Web designers and developers find such approach quite useful in enhancing navigational features of webpages, and biomedical personnel regularly use computer simulations to monitor and control the behaviour of patients. On the other hand, law enforcement agents rely on human physiological functions to determine the likelihood of falsehood in interrogations. Quite often, online user experience is studied via tangible measures such as task completion time, surveys and comprehensive tests from which data attributes are generated. Prediction of users' emotion and behaviour in some of these cases depends mostly on task completion time and number of clicks per given time interval. However, such approaches are generally subjective and rely heavily on distributional assumptions making the results prone to recording errors.Design/methodology/approachThe authors propose a novel method-a window dynamic control system that addresses the foregoing issues. Primary data were obtained from laboratory experiments during which forty-four volunteers had their synchronised physiological readings, skin conductance response (SCR), skin temperature (ST), eye movement behaviour and users’ activity attributes taken using biosensors. The window-based dynamic control system (PHYCOB I) is integrated to the biosensor which collects secondary data attributes from these synchronised physiological readings and uses them for two purposes. For both detection of optimal emotional responses and users' stress levels. The method's novelty derives from its ability to integrate physiological readings and eye movement records to identify hidden correlates on a webpage.FindingsResults show that the control system detects basic emotions and outperforms other conventional models in terms of both accuracy and reliability, when subjected to model comparison that is, the average recoverable natural structures for the three models with respect to accuracy and reliability are more consistent within the window-based control system environment than with the conventional methods.Research limitations/implicationsThe paper is limited to using a window control system to detect emotions on webpages, while integrated to biosensors and eye-tracker.Originality/valueThe originality of the proposed model is its resistance to overfitting and its ability to automatically assess human emotion (stress levels) while dealing with specific web contents. The latter is particularly important in that it can be used to predict which contents of webpages cause stress-induced emotions to users when involved in online activities.

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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