An Investigation of Using a Tablet Computer for Searching on the Web and the Influence of Cognitive Load

Debue, Nicolas; Oufi, Nora; Leemput, Cécile Van De

doi:10.20982/tqmp.16.3.p226

Cited by 6 publications

(4 citation statements)

References 46 publications

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“…As suggested by the cognitive learning theory, part of the reasons why students do not store information correctly is their ineffectiveness to process the acquired information via a computer screen [50]. The usage of Tablets may also pose a technological hindrance during information capturing since special skills will be required to enhance performance [51].…”

Section: Effects Of Reading From the Computer Screen On The Resultsmentioning

confidence: 99%

Quantitative Analysis Between Blackboard Learning Management System and Students’ Learning

Darko¹

2022

J. Engg. Res. & Sci.

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Proper use of the Blackboard Learning Management System (LMS) motivates students to engage with their studies but the students within Material Science and Engineering (MSE) often use these LMSs to copy mathematical derivations, scientific information and submit coursework tasks without spending much time interacting with the system. Quantitatively, there is a piece of missing information on how interaction with the Blackboard LMS influences students' performances. Statistical evaluations were made by using the average times students spent on Blackboard and their final examination grades for their three-year Bachelor's degree period. There was a linear positive correlation between the time students spent on the LMS and their grades. Observations also show that students engage more with LMS at certain periods within a week. It was recognised that the more students engage with the Blackboard, the more they construct information for themselves. This result provides a quantitative analysis that gives evidence of how time spent on LMS supports students' learning.

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Section: Effects Of Reading From the Computer Screen On The Resultsmentioning

confidence: 99%

Quantitative Analysis Between Blackboard Learning Management System and Students’ Learning

Darko¹

2022

J. Engg. Res. & Sci.

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“…Searching for pertinent videos on Google or YouTube can be very time-consuming [38]. For students and some teachers who do not yet have a good understanding of geosciences, their YouTube or Google search for geoscience topics can find misinformation-filled videos (e.g., creationist, flat Earth, climate change deniers) and can have their learning impeded by watching these [39]. Search requires knowledge of the search space, especially for videos because, in contrast with scientific papers on Google Scholar, these mostly are not peer-reviewed.…”

Section: Gegvl Designmentioning

confidence: 99%

Google Earth Geoscience Video Library (GEGVL): Organizing Geoscience Videos in a Google Earth Environment to Support Fieldwork Teaching Methodology in Earth Science

et al. 2022

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Fieldwork teaching methodology (FTM) and active learning are effective strategies for geoscience education. However, traditional field trips require significant resources, time, physical abilities, and the expertise of teachers. In this study, we provide a supplementary virtual field trip experience by showing how different kinds of geoscience videos can be spatially organized into one digital interactive virtual environment. Here, we present the Google Earth Geoscience Video Library (GEGVL) which uses Google Earth and location-specific videos about Earth events, to create a virtual field-based learning experience. Using Google Earth, GEGVL organizes field-based videos by location and links pertinent non-field-based videos, and allows users to roam the globe in search of geoscientific videos that are pertinent to them or their students. Currently, GEGVL contains 150 videos organized into ten different geoscience disciplines: Plate Tectonics, Minerals, Structural Geology, Metamorphism, Magmatism, Hydrology, Environmental Science, Sedimentology, Paleontology, and Paleomagnetism. Despite stability challenges with Google Earth integration, results of user surveys among lower-division undergraduates show that the design logic of GEGVL is a promising virtual field-based learning organizer for increasing students’ interest in and helping them learn about Earth sciences.

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“…The assessment of efficiency is also conducted based on the number of clicks that users perform upon the website whilst completing a particular task. The count for number of clicks refers to the time duration from the start of each task along to the associated completion [22].…”

Section: Cognitive Load Metricsmentioning

confidence: 99%

COG Tool: An Automated Cognitive Measurement of Workload for Mobile Event Logging

Saleh

Enaizan

2022

Int. J. Interact. Mob. Technol.

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Cognitive workload has been defined as the cognitive processing amount needed by a user in using an application. Cognitive load theory has been gaining increasing levels of importance within usability evaluations for mobile applications and the user interface. The PACMAD model has proven that cognitive workload (CL) can be seen as being a primary usability evaluation attribute in relation to mobile applications. Once the tasks that have been assigned to users within the environment of the mobile application have been performed by the evaluator, he/she subjectively measures the CL. Typically, subjective measurements are taken at a point following completion of a task and so they are static. Since these methods are static in nature, they are inappropriate for assessment of dynamic changes to the cognitive loads through user interaction. Robust measurement of the cognitive load of a user within real-time is not a simple, trivial undertaking. Lots of research studies have tried to assess the CL of users through use of various subjective measurements, though it is often the case that those approaches are unsuitable for use with real-life applications because of the high degree of intrusiveness. Automated measurement is seen as being a field that is relatively unexplored, particularly if used in relation to CL for mobile applications. The COG tool (CL automated tool) is proposed in this paper since it supports the interaction logging of the user for derivation of CL metrics that are based upon both secondary and primary tasks. Several strengths are provided by the automated measurement of CL, and it fills the gaps of a static nature of its non-objective, automated (subjective) counterpart. Within this research, in order for the study aim to be satisfied, objective CL metrics have been used by the researcher, i.e., task time, task success rate, number of touches, number of errors, duration on help, help visit count, total effort percentage and reaction on help. A total of 68 participants were recruited for the experiment, with them using an AAU mobile application through performance of a task that was predefined without there being any interference of a moderator or researcher. The findings showed that the proposed tool could fulfill the expected cognitive load monitoring functions amongst user interaction, collection of cognitive metrics and the logging of CL metrics that could be analyzed further.

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An Investigation of Using a Tablet Computer for Searching on the Web and the Influence of Cognitive Load

Cited by 6 publications

References 46 publications

Quantitative Analysis Between Blackboard Learning Management System and Students’ Learning

Quantitative Analysis Between Blackboard Learning Management System and Students’ Learning

Google Earth Geoscience Video Library (GEGVL): Organizing Geoscience Videos in a Google Earth Environment to Support Fieldwork Teaching Methodology in Earth Science

COG Tool: An Automated Cognitive Measurement of Workload for Mobile Event Logging

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