Characterising users’ task completion process in learning-related tasks:A search pace model

2021 ◽  
pp. 016555152110605
Author(s):  
Chang Liu ◽  
Xiaoxuan Song ◽  
Preben Hansen

This study investigated users’ searching, reading and writing interactions and their activity transitions during task completion process when users were collecting information for learning-related search tasks. Task completion process was defined as the process users started to search till the time when they have collected enough information to accomplish the search tasks. The data analysis was conducted from a new process perspective through synthesising macro- and micro-process levels. Four evenly distributed stages were divided according to the total task completion time in each search session. Our results demonstrated that users generally experienced three sub-processes during task completion process: exploration, accumulation and composition/reporting. Exploration sub-process is basically the first quarter of the total task completion time, during which users often issue more queries and visit more search engine result pages (SERPs) to collect information, and the dominant activity transition is switching between searching and reading; accumulation sub-process is mainly the second and third quarters of the total task completion time, during which they visit more unique content pages, have more revisits per content page, and they switch between reading and writing activities frequently; the last stage is composition/reporting sub-process, which is dominated by writing, and users often switch between writing and reading, and between writing and searching. Based on these findings, we propose a search pace model to describe how users proceed from the beginning to the end of task completion process in these three sub-processes. The methodology applied has been proved to be effective to examine users’ interaction behaviours from the process perspective on both the micro- and macro-levels. The findings of this article help us understand how users proceed their dynamic searching, reading and writing behaviours for learning-related tasks, and also have implications for the design of search systems that support learning-related tasks.

Author(s):  
Auður Anna Jónsdóttir ◽  
Ziho Kang ◽  
Tianchen Sun ◽  
Saptarshi Mandal ◽  
Ji-Eun Kim

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.


Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1774
Author(s):  
Ming-Chin Chuang ◽  
Chia-Cheng Yen ◽  
Chia-Jui Hung

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.


Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 395
Author(s):  
Chien-Hsiung Chen ◽  
Miao Huang

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.


2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fanghai Gong

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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