Empirical evaluation of 20 web form optimization guidelines

Seckler, Mirjam; Heinz, Silvia; Bargas-Avila, Javier A.; Opwis, Klaus; Tuch, Alexandre N.

doi:10.1145/2468356.2468695

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…Note that preliminary data on this study has been presented as "Work-in-progress" at CHI 2013 [28]. In comparison to this publication, we expand these data by investigating more participants and extending our analysis of user performance data (error analysis, eye tracking data, Keystroke Level Model comparison) and of questionnaire data and interviews.…”

Section: Acknowledgmentsmentioning

confidence: 94%

Designing usable web forms

Seckler

Heinz

Bargas-Avila

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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This study reports a controlled eye tracking experiment (N = 65) that shows the combined effectiveness of 20 guidelines to improve interactive online forms when applied to forms found on real company websites. Results indicate that improved web forms lead to faster completion times, fewer form submission trials, and fewer eye movements. Data from subjective questionnaires and interviews further show increased user satisfaction. Overall, our findings highlight the importance for web designers to improve their web forms using UX guidelines.

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Section: Acknowledgmentsmentioning

confidence: 94%

Designing usable web forms

Seckler

Heinz

Bargas-Avila

et al. 2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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“…Such requirements capture the input data that shall be collected for certain types of users; they are fulfilled by setting the required/optional property of the corresponding fields in a data entry form. In other words, the required fields (also called mandatory fields [46]) of a form collect input information considered as important to the stakeholders who plan to use the collected information; the absence of this information could affect the application usage. On the contrary, optional fields collect information that is nice to have but whose absence is acceptable.…”

Section: Data Entry Formsmentioning

confidence: 99%

Learning-based Relaxation of Completeness Requirements for Data Entry Forms

Belgacem,

Li,

Bianculli

et al. 2024

ACM Trans. Softw. Eng. Methodol.

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Data entry forms use completeness requirements to specify the fields that are required or optional to fill for collecting necessary information from different types of users. However, because of the evolving nature of software, some required fields may not be applicable for certain types of users anymore. Nevertheless, they may still be incorrectly marked as required in the form; we call such fields obsolete required fields. Since obsolete required fields usually have “not-null” validation checks before submitting the form, users have to enter meaningless values in such fields in order to complete the form submission. These meaningless values threaten the quality of the filled data, and could negatively affect stakeholders or learning-based tools that use the data. To avoid users filling meaningless values, existing techniques usually rely on manually written rules to identify the obsolete required fields and relax their completeness requirements. However, these techniques are ineffective and costly. In this paper, we propose LACQUER, a learning-based automated approach for relaxing the completeness requirements of data entry forms. LACQUER builds Bayesian Network models to automatically learn conditions under which users had to fill meaningless values. To improve its learning ability, LACQUER identifies the cases where a required field is only applicable for a small group of users, and uses SMOTE, an oversampling technique, to generate more instances on such fields for effectively mining dependencies on them. During the data entry session, LACQUER predicts the completeness requirement of a target based on the already filled fields and their conditional dependencies in the trained model. Our experimental results show that LACQUER can accurately relax the completeness requirements of required fields in data entry forms with precision values ranging between 0.76 and 0.90 on different datasets. LACQUER can prevent users from filling 20% to 64% of meaningless values, with negative predictive values (i.e., the ability to correctly predict a field as “optional”) between 0.72 and 0.91. Furthermore, LACQUER is efficient; it takes at most 839 ms to predict the completeness requirement of an instance.

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“…Designing user-friendly form fields is one of the most critical aspects of overall user experience with Web forms . Web forms lacking usability result in a significant level of user dissatisfaction thus increasing the drop-out rate, preventing the collection of the required data (Seckler, Heinz, Bargas-Avila, Opwis & Tuch, 2013), and decreasing the number of logins to a Web application (Thompson & Torabi, 2007). The usability of form fields vary greatly depending on how they are designed, but minor changes to the form design make it possible to increase the interaction speed, reduce user errors, and improve user satisfaction (Seckler et al, 2014).…”

Section: Web Formsmentioning

confidence: 99%

“…Das, McEwan, and Douglas (2008) used an eyetracking system to examine the usability of label positions in Web forms and found that right-aligned labels provided the shortest completion time. In another study conducted with 23 participants, optimized Web forms were found to produce faster data entry, fewer errors, less eye fixation, and higher user satisfaction (Seckler et al, 2013). In their usability study, Strohl et al (2015) optimized the design of existing forms according to the following facets of user experience: usefulness, usability, accessibility, credibility, findability, and desirability.…”

Section: Eye-tracking Research In Web Formsmentioning

confidence: 99%

User-Friendly Locations of Error Messages in Web Forms: An Eye Tracking Study

İnal

2016

JEMR

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Error messages presented to users are one of the most important elements of Web forms. Error messages are embedded in different parts of the forms available on the Internet and presented in various formats. One of the measures of a user-friendly error message design is the ability to easily capture users’ attention and facilitate fast error correction. In this empirical study, I tested four different locations of error messages frequently used in Web forms on 32 participants. In addition, I analysed the participants’ interactions with error messages through their eye movements. The results of the study showed that the participants spotted the error message fastest when it was displayed on the right side of the erroneous input field. When error messages displayed further the input field users have less saccades to and fixations on error messages compared to those located near to this field, suggesting that less effort has been spent to understand the given message. However, group mean dif-ferences were not statistically significant for form completion time, error recognition time, the number of saccades, and error correction time.

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Empirical evaluation of 20 web form optimization guidelines

Cited by 5 publications

References 12 publications

Designing usable web forms

Designing usable web forms

Learning-based Relaxation of Completeness Requirements for Data Entry Forms

User-Friendly Locations of Error Messages in Web Forms: An Eye Tracking Study

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