Dismal: A Spreadsheet for Sequential Data Analysis and HCI Experimentation

Ritter, Frank E.; Wood, Alexander B.

doi:10.1037/e449412006-001

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…The task was created in the Dismal 3 spreadsheet (see Figure 3) that was initially developed to analyze process models and sequential behavioral data (Ritter & Larkin, 1994;Ritter & Wood, 2005). The Dismal spreadsheet task in this study consists of fourteen sequential subtasks shown in Figure 4.…”

Section: The Dismal Spreadsheet Taskmentioning

confidence: 99%

Learning, Forgetting, and Relearning for Keystroke- and Mouse-Driven Tasks: Relearning Is Important

Kim

Ritter

2014

Human–Computer Interaction

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We investigate performance change arising through learning, forgetting, and relearning. Participants learned a spreadsheet task with either keystrokedriven (keyboard, n D 30) or mouse-based menu-driven (mouse, n D 30) commands. Their performance confirmed the power law of practice. The keyboard users learned to complete the task faster than the mouse users on the last learning session (Day 4). At a 6-day retention interval, the mouse users were observed to forget more-they took more time to complete the task than the keyboard users. Of interest, the participants in the two modality groups showed no reliable differences in their forgetting under the retention of 12 and 18 days. With additional practice, the mouse group users with the 6-day retention relearned more-they reliably reduced the time to complete the task in comparison to the paired keyboard group. These results help understand why people may choose to use a mouse-driven graphical user interface rather than a keystroke-driven interface: People choosing to use a mouse-based menu-driven interface may not need to use a knowledge-in-the-head strategy but knowledge-in-the-world, and may be doing so because this strategy provides better relearning, rather than because it is faster or easier initially or because it is better for learning or forgetting. These results provide a richer explanation of why menu-driven interfaces (knowledge-in-the-world) are more ubiquitous and suggests when they can be replaced, for example, where use is infrequent but often enough that forgetting does not substantially occur. Our results provide preliminary suggestions for choosing optimal training strategies and supporting these strategies in terms of the three stages of learning and forgetting. ) is a cognitive scientist with an interest in human-computer interaction and cognitive modeling; he is a Research Scientist at the University of Central Florida. Frank E. Ritter (frank.ritter@psu.edu) is a cognitive scientist with an interest in human-computer interaction and cognitive modeling; he is a Professor in the College of Information Sciences and Technology, and in CSE and Psychology at the Pennsylvania State University.Color versions of one or more of the figures in the article can be found online at www.tandfonline. com/hhci.

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Section: The Dismal Spreadsheet Taskmentioning

confidence: 99%

Learning, Forgetting, and Relearning for Keystroke- and Mouse-Driven Tasks: Relearning Is Important

Kim

Ritter

2014

Human–Computer Interaction

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“…In the area of qualitative data analysis, many software tools address the need for supporting the transcription of raw data into sequences of encoded events, for example, Dismal (Ritter & Larkin, ; Ritter & Wood, ), NVivo, INTERACT, InfoScope, MORAE, MarShapa (Sanderson et al ., ) and MacVisSTA. As such, these tools relate to our collection system as described in Section .…”

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confidence: 99%

Supporting activity modelling from activity traces

et al. 2011

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We present a new method and tool for activity modelling through qualitative sequential data analysis. In particular, we address the question of constructing a symbolic abstract representation of an activity from an activity trace. We use knowledge engineering techniques to help the analyst build an ontology of the activity, that is, a set of symbols and hierarchical semantics that supports the construction of activity models. The ontology construction is pragmatic, evolutionist and driven by the analyst in accordance with their modelling goals and their research questions. Our tool helps the analyst define transformation rules to process the raw trace into abstract traces based on the ontology. The analyst visualizes the abstract traces and iteratively tests the ontology, the transformation rules and the visualization format to confirm the models of activity. With this tool and this method, we found innovative ways to represent a car-driving activity at different levels of abstraction from activity traces collected from an instrumented vehicle. As examples, we report two new strategies of lane changing on motorways that we have found and modelled with this approach

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Dismal: A Spreadsheet for Sequential Data Analysis and HCI Experimentation

Cited by 2 publications

References 4 publications

Learning, Forgetting, and Relearning for Keystroke- and Mouse-Driven Tasks: Relearning Is Important

Learning, Forgetting, and Relearning for Keystroke- and Mouse-Driven Tasks: Relearning Is Important

Supporting activity modelling from activity traces

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