The usability and user experience evaluation of web-based online self-monitoring tool: Case study human-computer interaction course

Santoso, Harry Budi; Fadhilah, Suci; Nurrohmah, Isnaeni; Goodridge, Wade H.

doi:10.1109/iuser.2016.7857946

Cited by 7 publications

(9 citation statements)

References 5 publications

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“…In this study, we considered user experience (UX) as the major criteria for evaluating the DDKAT. UX describes the quality of interaction between the participants and the computer system [58]. Normally, in a real-world situation, different users may have different feelings for the same product due to varying personal experiences.…”

Section: System Evaluationmentioning

confidence: 99%

“…There are many research frameworks for state-of-the-art evaluation of user experience, such as the Questionnaire for User Interaction Satisfaction, System Usability Scale, Standardized User Experience Percentile Rank Questionnaire, Software Usability Measurement Inventory, AttrakDiff, and User Experience Questionnaire. Among these frameworks, the User Experience Questionnaire (UEQ) is the most widely used evaluation framework for interactive products for the following reasons: (1) it provides a thorough evaluation by accounting for both user experience and classical usability aspects, (2) it interprets results accurately, (3) it is free to use and has no fees, (4) it is reliable and easy to apply, (5) it requires low effort, and (6) it provides fast, direct, and valid quantitative measurements of UX [58]- [61].…”

Section: System Evaluationmentioning

confidence: 99%

“…The UEQ includes 26 items that are grouped into six scales: attractiveness, the appeal of the product; perspicuity, the ease of product use; efficiency, the speed with which it solves tasks; dependability, the user's control over product interaction; stimulation, the user's reactions to the product; and novelty, how innovative the product is [58] and [61]. These scales are further categorized into two quality measures, called pragmatic quality (PQ) for task-related quality aspects, and hedonic quality (HQ) for non-task related quality aspects.…”

Section: System Evaluationmentioning

confidence: 99%

“…These scales are further categorized into two quality measures, called pragmatic quality (PQ) for task-related quality aspects, and hedonic quality (HQ) for non-task related quality aspects. PQ accounts for perspicuity, efficiency, and dependability, while the HQ covers stimulation and novelty [58]. Each item in the UEQ comprises a pair of terms (adjectives) with opposite meanings and a 7-point Likert scale [61].…”

Section: System Evaluationmentioning

confidence: 99%

“…The queries of the students were addressed after the demonstration. Finally, the UEQ survey was given to all students, and they were asked to fill it out based on their understanding [58]. After 102 intermediate users evaluated the DDKAT and completed the UEQ, the results were automatically generated using a data analysis tool 8 [66].…”

Section: Intermediate User Evaluationsmentioning

confidence: 99%

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A Data-Driven Knowledge Acquisition System: An End-to-End Knowledge Engineering Process for Generating Production Rules

Ali

Khan

et al. 2018

IEEE Access

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Data-driven knowledge acquisition is one of the key research fields in data mining. Dealing with large amounts of data has received a lot of attention in the field recently, and a number of methodologies have been proposed to extract insights from data in an automated or semi-automated manner. However, these methodologies generally target a specific aspect of the data mining process, such as data acquisition, data preprocessing, or data classification. However, a comprehensive knowledge acquisition method is crucial to support the end-to-end knowledge engineering process. In this paper, we introduce a knowledge acquisition system that covers all major phases of the cross-industry standard process for data mining. Acknowledging the importance of an end-to-end knowledge engineering process, we designed and developed an easy-to-use data-driven knowledge acquisition tool (DDKAT). The major features of the DDKAT are: (1) a novel unified features scoring approach for data selection; (2) a user-friendly data processing interface to improve the quality of the raw data; (3) an appropriate decision tree algorithm selection approach to build a classification model; and (4) the generation of production rules from various decision tree classification models in an automated manner. Furthermore, two diabetes studies were performed to assess the value of the DDKAT in terms of user experience. A total of 19 experts were involved in the first study and 102 students in the artificial intelligence domain were involved in the second study. The results showed that the overall user experience of the DDKAT was positive in terms of its attractiveness, as well as its pragmatic and hedonic quality factors. INDEX TERMSKnowledge engineering, data mining, features ranking, algorithm selection, decision tree, production rule, user experience. I. INTRODUCTIONKnowledge systems have come a long way, from manual knowledge curation to automatic data-driven knowledge generation. The major drivers of this transition were the size and complexity of data. Since large datasets cannot be efficiently analyzed manually, the automation process is essential [2].Initially in this process of knowledge automation, knowledge engineers followed ad-hoc procedures [3]. Later on, more systematic methodologies were devised, which can be referred to as data-driven knowledge acquisition systems.Knowledge extraction from structured sources such as databases is an active area of research in the information

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Section: System Evaluationmentioning

confidence: 99%

Section: System Evaluationmentioning

confidence: 99%