The main focus of this thesis is on data entry performed by rural BPO operators from India. One reason for the extra effort is the lower usability factor of the software used for data entry.
Intrduction: Improving Work Efficiency of Rural- Businesss
Introduction
The word 'usability' also refers to methods to improve ease of use during the design process. Memorability: When users return to the design after a period of disuse, how easily can they recover the skill.
Human Error
- Defining Human Error
Our usability research is therefore interested in "human errors" that are relevant in the context of rural computer users. Clicking on Reset instead of the Send button Entering today's date instead of the date of birth Wrong e-mail address entry when re-entering.
The Taxonomy of Errors
- Error Types
- Performance Level and Error Type
Therefore, three types of errors are (a) errors or omissions, (b) rule-based errors, and (c) knowledge-based errors. This conceptual framework consists of three basic error types such as skill-based errors (and omissions), rule-based errors and knowledge-based errors.
Factors Affecting Performance of Data Entry
We have categorized the operators based on these three dimensions of user experience, which are also related to direct performance-shaping factors (Boring, Griffith, & Joe; 2007) shown in Figures 1-4. The UI factor like field constraints, traces during input, confirmation logic, validation logic, error messages, feedback messages are investigated in this thesis.
Effect of Language on Rural Computer Users
- Language versus Cognitive Thinking Strategy of Rural Users during Data
The main focus of this thesis is on 'data entry' because which is predominantly observed in rural BPOs from India. During data collection, we observed that the majority of operators/workers are trained in their mother tongue (local language) and the graphical user interface (GUI) used for data entry is entirely in English.
Graphical User Interface (GUI) or Software for data entry
The language used during data entry is English, this means that their thinking and conversational language is different and therefore the user interface works. The embedded intelligent methods such as machine learning, probabilistic approach and artificial intelligence are suggested to be used in the design and development of 'error limiting user interface' for data entry.
Broad Research Gap
There are other problems associated with data entry performed in rural BPOs such as transcription process or data entry process (paper to digital) as double entry is expensive and time consuming (Chen K., Chen, Conway, Hellerstein, & Parikh , 2011). ), poor quality of mobile data entry (Patnaik, Brunskill, & Thies, 2009), and failure to address specific field limitations (Broeck et al., 2007). Therefore, to solve the problems such as local language, emotions, data entry errors and poor data entry user interface design, this research study proposed a new graphical user interface (GUI) designed with intelligent widgets.
Scope of the thesis
- Research Questions
RQ1: What is the effect of a newly configured user interface designed with intelligent features like- (i) display of autocomplete suggestions for text field by ranking strategy based on probability, (ii) widget for predictive text input, (iii) radio button pointer with most likely options and (iv) dynamic drop-down split menu, on the accuracy of data entry. Spm and (iv) dynamic drop-down split menu, on data entry speed.
Overview of the Thesis
RQ5: What is the effect of language used on the error rate in the case of (i) English language in forms used for data entry, (ii) Indian (Marathi) language and (iii) mixed language (i.e. RQ8: What is the effect of learned expertise in using a certain system – on the speed and accuracy of data entry.
State of the Art Literature Survey: Understanding Nature of the
- Introduction
- Data Entry Error
- Numerical Data Entry and Errors
- Text Data Entry and Errors
- Use of Interactive Devices in Rural Indian Context
- Extended Literature Study on Intelligent features in User Interface
- Literature on Influence of Emotion on Data Entry
- Study of Sensitive Variables
- Consolidated theory/ concept from Literature
- Research Questions and Objectives
- Research Questions
- Objective of the Study
- Conclusion
The effect of intelligent user interface features on data entry operators has been reported. This study leads to the development of the probabilistic approach to data entry.
Exploring the Potential and Influence of Errors during Data Entry
Introduction
Pilot Study 1: Numerical Data Entry
- Research Hypotheses
- Methodology
- Participants
- Instruments
- Experiment Design and Variables
- Procedure in Detail
- Result and Discussion
- Types of Errors
- Task completion time
- Other observations and findings
- Discussion
- Conclusion from Pilot Study 1
Pilot Study 2: Text Data Entry
- Research Hypotheses
- Research Design
- Participants
- Instruments Used
- Experiment Design and Variables
- Procedure
- Results and Discussion
- Types of Errors and Error Rate
- Task completion time
- Discussion
- Conclusion from Pilot Study 2
Pilot Study 3: Effect of Emotion on Data Entry
- Hypotheses
- Methods
- Participants
- Instrument and Materials
- Stimuli
- Research Design
- Experimental Variables
- Experimental Design
- Procedure
- Results and Discussion
- Emotion Manipulation
- Discussion
- Conclusion from Pilot Study 3
Conclusion
Consolidation of all Pilot Studies
Error Limiting Intelligent Interface for Date Entry (ELIIDE)- a Tool
Introduction
For example, (1) special character and text entry are not allowed in 'mobile number field' and (2) if user accidentally enters 'nine' digits which are not allowed, the ELIIDE gives error messages for both incorrect entries. Quantitative probability approach: The ELIIDE is supported with the specially designed widgets with quantitative probability. Dynamic widgets: The ELIIDE implements the design of dynamic drop-down list for data entry.
Flexible: ELIIDE offers a flexible audio support feature that is optional for advanced users.
Block Diagram of ELIIDE - tool
Development Process of ELIIDE Tool
The SQL (Structured Query Language) language was used to manipulate database activities in the backend.
Screenshots of ELIIDE - Tool
- Login Screen
- Data Entry Form
- Error Messages
- Error Report Generation
- Predictive Text Entry Widgets
- Dynamic Drop-down Menu
- Adaptive Feature
- Quantitative Probabilistic Approach
- Generation of Graphs
- Additional Features
- User Performance Report
The 'State' field is implemented with dynamic dropdown design (Section 4.4.6), the 'Date of Birth' field is supported with quantitative probability and bar chart (Section 4.4.8) and the 'Gender' field is provided with numerical probability using percentage and bar chart (section 4.4.8). To check operator performance when entering retracted time data, this option is available on ELIIDE tool. The designed data entry form contains only personal information block (taken from the actual form referred by rural BPO operators) containing seventeen fields.
83 Figure 4-14: Screenshot of additional features such as - the addition of new user, which user data entry report of.
Conclusion
Supports dynamic slide-down menu design No support for probabilistic widgets Supports quantitative probabilistic widgets No support for Adaptive feature Supports adaptive feature.
Experimental Methodology: User Testing, Research Methods,
- Introduction
- User Testing (or Research Methods): Data collection, Participants,
- Data collection methods
- Participants
- Instruments Used
- Experiment Design
- Experiment Variables
- Task Design
- Procedure in details
- Conclusion
The participants were randomly selected for the experiment of data entry on the provided user interfaces, which started with a demographic questionnaire. So we took three different variants of data entry forms, as shown in Figure 5.2. Figure 5.2 below shows the few copies of the data entry forms used in this experiment.
Ninety data entry forms with three groups of thirty each were used in the study.
User Testing / Verification of Designed User Interface- ELIIDE tool
Introduction
This chapter reports on the validation of the ELIIDE tool by actual data entry operators working in rural BPOs. The experimental result highlights the effect of the intelligent function on the performance in terms of speed and accuracy and their subjective evaluation of the intelligent user interface (ELIIDE).
Results and Analysis
- Hypothesis (H 1 )
- Hypothesis (H 2 )
- Hypothesis 3
- Hypothesis 4
- Hypothesis 5
After examining all these classification schemes, we adopted the most suitable ones for this experiment, shown in Table 6-2. The common error observed in the experiment was divided into two broad categories such as text entry errors and number entry errors as shown in Table 6-2. The classification of data entry errors is described below:. In the above example, while typing “PAN card number”, the operator entered “I” instead of “1” which represents a transmission error. The bar graph shown in Figure 6-2 shows the classification of text entry errors observed in the intelligent user interface and the existing user interface created by operators and non-operators.
The bar graph shown in Figure 6-4 depicts the classification of numeric input errors observed in Smart UI and existing UI made by operator and non-operator.
Conclusion
Discussion
Introduction
Discussions
Conclusion, Contribution and Future Work
Introduction
This chapter aims to summarize the main research findings and core contributions of this research, together with its general implications for practice, its limitations and its future scope. Section 8.2 first provides a brief overview and conclusion of the research. Section 8.3 summarizes the consolidated research results. Limitations and general implications of the research results and contributions are elaborated in section 8.5.
Conclusion
Initially, a brief overview and conclusion of the research is stated in Section 8.2, consolidated research findings are summarized in Section 8.3. Section 8.4 presents the core contributions of this research. The computer-based background recording of each participant's interaction with the designed user interface was taken for the calculation of the accuracy and speed. After completing the experiment, the participants were instructed to fill out the post-task questionnaires to express their opinion and experience about the user interface.
The results highlight that intelligent user interface design features affect operator performance in terms of accuracy and speed.
Consolidated Findings of this Research
It can be concluded that the intelligent user interface and the existing interface significantly affect the error rate. Therefore, a user interface designed with intelligent building blocks that address local needs can increase accuracy during data entry. We can conclude that the intelligent user interface and the existing interface significantly affect the time.
We can conclude that the intelligent user interface and existing interface significantly affect cognitive load, system usability, satisfaction, willingness to continue using, and relative benefits.
Major Research Contributions of this Thesis
Therefore, user interface designed with intelligent widgets can reduce cognitive load, increase system usability and satisfaction. We could infer that English, Marathi language data entry task and mixed (combining both English and Marathi) significantly affect accuracy in three task conditions (i) operator using intelligent UI (ii) non-operator using intelligent UI and (iii) operator, that uses intelligent UI. existing user interface. The data entry task may affect accuracy, therefore data entry forms may be either in English or Marathi but not in mixed languages.
The text entry errors are classified into six types: (i) Mistype/spelling/incorrect: substitutions and infractions, (ii) transposition, (iii) doubling, (iv) capitalization, (v) capture, phonetic, misinterpretation and (vi ) omission/wrong field.
Limitations and Generalisations of this Research
- Limitations of this Research
Dynamic: The interface is designed with dynamic widgets that dynamically update meaning rearrangement and highlight the other likely options. The interface adjusts certain features (such as display of audio support as an option for expert users) for the operator based on his past performance. e. The items in this list are sorted into two broad groups as text input errors and numeric input errors.
Numeric input errors are classified into four types as- wrong, reversed, duplicate and missing.
Scope of Future Research
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In Proceedings of the 11th IFIP TC 13 international conference on Human-computer interaction (p. Volume Part II).
