“It’s Freedom to Put Things Where My Mind Wants”: Understanding and Improving the User Experience of Structuring Data in Spreadsheets

Chalhoub, George; Sarkar, Advait

doi:10.1145/3491102.3501833

Cited by 11 publications

(6 citation statements)

References 79 publications

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“…The researchers then independently re-coded the same sample with the codebook. Manual inspection showed poor inter-rater agreement, consistent with initial open coding rounds in prior studies [11,14,106]. The two researchers then discussed disagreements and ambiguities and revised the codebook.…”

Section: Data Segmentation and Analysissupporting

confidence: 69%

“What It Wants Me To Say”: Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models

Liu¹,

Sarkar²,

Negreanu³

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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Code-generating large language models map natural language to code. However, only a small portion of the infinite space of naturalistic utterances is effective at guiding code generation. For non-expert end-user programmers, learning this is the challenge of abstraction matching. We examine this challenge in the specific context of data analysis in spreadsheets, in a system that maps the user's natural language query to Python code using the Codex generator, executes the code, and shows the result. We propose grounded abstraction matching, which bridges the abstraction gap by translating the code back into a systematic and predictable naturalistic utterance. In a between-subjects, think-aloud study (n=24), we compare grounded abstraction matching to an ungrounded alternative based on previously established query framing principles. We find that the grounded approach improves end-users' understanding of the scope and capabilities of the code-generating model, and the kind of language needed to use it effectively. CCS CONCEPTS• Human-centered computing → Natural language interfaces; Interactive systems and tools; Empirical studies in HCI .

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Section: Data Segmentation and Analysissupporting

confidence: 69%

“What It Wants Me To Say”: Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models

Liu¹,

Sarkar²,

Negreanu³

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…The picture has not changed at the time of writing. Even in praxisware, which often aims to contain as large a portion of common and professional workflows as possible, it is often observed how auxiliary tools are used to finesse, subvert, combine, marshall, and supplement the information workflow in conjunction with the "primary" tool [15,46], with ensuing frictions.…”

Section: What We Lose When Software Is 'Easy'mentioning

confidence: 99%

“…It shifts the nature of the task itself. A spreadsheet user who knows a priori how to write formulas will approach the same task differently from one who does not; starting with planning the data that must go in the spreadsheet and structuring it appropriately [15]. By the time the spreadsheet is open and ready, the "task" has already taken on a vastly different nature for the two users.…”

Section: Towards a Doctrine Of Negotiated Complexitymentioning

confidence: 99%

Should Computers Be Easy To Use? Questioning the Doctrine of Simplicity in User Interface Design

Sarkar

2023

Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems

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That computers should be easy to learn and use is a rarely-questioned tenet of user interface design. But what do we gain from prioritising usability and learnability, and what do we lose? I explore how simplicity is not an inevitable truth of user interface design, but rather contingent on a series of events in the evolution of software. Not only does a rigid adherence to this doctrine place an artificial ceiling on the power and flexibility of software, but it is also culturally relative, privileging certain information cultures over others. I propose that for feature-rich software, negotiated complexity is a better target than simplicity, and we must revisit the ill-regarded relationship between learning, documentation, and software. CCS CONCEPTS• Human-centered computing → HCI theory, concepts and models; HCI theory, concepts and models; Interaction design theory, concepts and paradigms; • Social and professional topics → Cultural characteristics; History of computing; Computing education; Computing and business.

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“…Recent research, such as Huang et al (2020), has focused on developing new techniques like WARDER for effective spreadsheet defect detection by utilizing validity-based cell cluster refinements. Chalhoub and Sarkar (2022) investigate the user experience of structuring data in spreadsheets and provide insights into how users think about data organization, which can help inform future spreadsheet design improvements.…”

Section: Artificial Intelligence For Engineering Design Analysis and ...mentioning

confidence: 99%

A user model to directly compare two unmodified interfaces: a study of including errors and error corrections in a cognitive user model

Tehranchi,

Bagherzadeh,

Ritter

2023

AIEDAM

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User models that can directly use and learn how to do tasks with unmodified interfaces would be helpful in system design to compare task knowledge and times between interfaces. Including user errors can be helpful because users will always make mistakes and generate errors. We compare three user models: an existing validated model that simulates users’ behavior in the Dismal spreadsheet in Emacs, a newly developed model that interacts with an Excel spreadsheet, and a new model that generates and fixes user errors. These models are implemented using a set of simulated eyes and hands extensions. All the models completed a 14-step task without modifying the system that participants used. These models predict that the task in Excel is approximately 20% faster than in Dismal, including suggesting why, where, and how much Excel is a better design. The Excel model predictions were compared to newly collected human data (N = 23). The model’s predictions of subtask times correlate well with the human data (r2 = .71). We also present a preliminary model of human error and correction based on user keypress errors, including 25 slips. The predictions to data comparison suggest that this interactive model that includes errors moves us closer to having a complete user model that can directly test interface design by predicting human behavior and performing the task on the same interface as users. The errors from the model’s hands also allow further exploration of error detection, error correction, and different knowledge types in user models.

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“It’s Freedom to Put Things Where My Mind Wants”: Understanding and Improving the User Experience of Structuring Data in Spreadsheets

Cited by 11 publications

References 79 publications

“What It Wants Me To Say”: Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models

“What It Wants Me To Say”: Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models

Should Computers Be Easy To Use? Questioning the Doctrine of Simplicity in User Interface Design

A user model to directly compare two unmodified interfaces: a study of including errors and error corrections in a cognitive user model

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