NASA-TLX–based workload assessment for academic resource recommender system

Afridi, Ahmad Hassan; Mengash, Hanan Abdullah

doi:10.1007/s00779-020-01409-z

Cited by 7 publications

(3 citation statements)

References 48 publications

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“…[75], [67], [81], [42], [68], [44], [82], [23], [45], [83], [31], [13], [77], [1], [35], [46], [47], [48], [14], [69], [74], [72], [51], [52], [88], [85], [87] Normalized Discounted Cumulative Gain (NDCG) 𝑁𝐷𝐶𝐺@𝐾 = 𝐷𝐶𝐺@𝑘 𝐼𝐷𝐶𝐺@𝐾 [7], [67], [22], [40], [70], [49], [76], [6], [89], [90], [91], [92] Precision 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛(𝑢) = [39], [81], [22], [28], [12], [48], [76], [71], [89], [36], [97], [95], [98], [92...…”

Section: Below Inmentioning

confidence: 99%

Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, Datasets

Saifudin,

Widiyaningtyas

2024

IEEE Access

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Recommender systems are needed with the presence of the internet and social media. The benefits that are felt in the recommender system can make it easier for users to find suitable products and recommend other products, specifically with lots of information. Recommender systems continue to develop over time. This has led many researchers to continue to find the latest approach and evaluation techniques by comparing the performance of previously existing recommender systems. The main approaches that are often used in recommender systems are Content-Based Filtering (CBF), Collaborative Filtering (CF), and Hybrid Filtering (HBF). This time, we focus on conducting a Systematic Literature Review (SLR) of several research articles and analyzing methods for algorithms developed in building recommender systems. The SLR method consists of three stages: planning, implementation, and reporting. The research used as a comparison is between 2019-2023 using various existing data sets. There were 72 primary studies where the Collaborative Filtering method was used in 46 studies, Content-Based Filtering was used in 11 studies, and the Hybrid Filtering method was used in 15 studies. The results of this SLR process show the advantages and disadvantages of each method and type of evaluation developed in building a recommender system. Apart from that, several challenges arise with various existing problems. However, the model-based collaborative filtering method is one method that can minimize the problems of cold start, data sparsity, and scalability.

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Section: Below Inmentioning

confidence: 99%

Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, Datasets

Saifudin,

Widiyaningtyas

2024

IEEE Access

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“…These subscales represent different sources of workload experienced by operators during task performance, with three subscales focusing on the demands imposed by the task (mental, physical, and temporal demand) and the other three subscales emphasizing the interaction of the operator with the task (performance, effort, and frustration level (Lowndes et al, 2020). The weights or loads assigned to each of these subscales are crucial in computing the overall workload score, with the NASA TLX providing a structured framework for integrating these various load sources to obtain a comprehensive assessment of mental workload in different work environments (Afridi et al, 2020).…”

Section: Sources Of Load (Weights)mentioning

confidence: 99%

Mental workload analysis of Industrial Engineering students in Cebu Technological University: National Aeronautics and Space Administration-Task Load Index

Castilla,

Vasquez,

Agbay

et al. 2024

Int. J. Res. Publ. Rev.

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“…The NASA TLX survey is a multi-dimensional questionnaire designed to determine the task load of a participant performing a task (Hart and Staveland 1988;Hart 2006). Various research communities have adopted it as a reliable measure of task load (Afridi and Mengash 2020;Cecil et al 2021). Said et al demonstrated that a modified NASA TLX survey accurately reflects the assumed influences of various covariates on perceived workload (Said et al 2020).…”

Section: Surveysmentioning

confidence: 99%

The human experience of comprehending source code in virtual reality

et al. 2022

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Virtual reality (VR) is an emerging technology used in various domains such as medicine, psychotherapy, architecture, and gaming. Recently, software engineering researchers have started to explore virtual reality as a tool for programmers. However, few studies examine source code comprehension in VR. This paper explores the human experience of comprehending source code in VR and compares it to source code comprehension in a desktop environment. We conducted a study with 26 graduate student programmers. We measured actual productivity, perceived productivity and used the NASA Task Load Index (TLX) survey to measure various factors such as mental demand, physical demand, temporal demand, performance, effort, and frustration. We found that the programmers experienced more physical demand, effort, and overall task load when reading and comprehending code in VR. However, we did not observe any statistically significant differences in the programmers’ measured productivity or perceived productivity between VR and desktop comprehension.

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NASA-TLX–based workload assessment for academic resource recommender system

Cited by 7 publications

References 48 publications

Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, Datasets

Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, Datasets

Mental workload analysis of Industrial Engineering students in Cebu Technological University: National Aeronautics and Space Administration-Task Load Index

The human experience of comprehending source code in virtual reality

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