READY Experimental Datasets

		Experimental Dataset
User-Oriented Resource-Adaptivity

Experiment on Execution of Instruction Sequences

Date: 1999
Short Description:
The picture below shows the screen that subjects worked with throughout the experiment. They used a mouse to click on the buttons labeled with digits and on the large OK button.

Their primary task was to execute sequences of prerecorded spoken instructions, for example "Set X to 3", each sequence comprimising 2, 3 or 4 steps. Each sequence was presented by the system in either stepwise (each partial step on its own) or bundled (all steps together) mode. In stepwise mode, after executing a single instruction, the subject had to signal completion by clicking on the OK button in order to receive the instruction for the next step.
On half of the trials, the large rectangle at the top of the screen provided a situational distraction which was intended to increase the congnitive load the subject was suffering from while performing the primary task. At more or less regular intervals, the rectangle took on a color that alternated between red and green in random order. Whenever the same color appeared twice in succesion, the subject was to press the space bar.
For more details on the experimental setting, subjects and results, the reader is referred to the publications listed below.

Datasets:

Variable Description
CaseID unique case identifier

SubjectID unique subject identifier

Number_of_Steps total number of steps in the instruction sequence

Presentation_Mode mode of presentation: bundled or stepwise

Secondary_Task presence or absence of a distractional task (yes/no)

Number_of_Presses total number of clicks to perform the whole task

Number_of_Flashes total number of flashes of the secondary task

Error_in_Task presence or absence of errors concerning the primary task (yes/no)

Execution_Time execution time of the current task (ms)

Indiv_Ave_Execution_Time a subject's average execution time for all tasks during the whole experiment (ms)

Inter_Task_Pause_Time pause length bewteen two consecutive tasks (ms)

Indiv_Ave_Pause_Time a subject's average inter-task pause length during the whole experiment (ms)

Error_in_Flash_Reactions presence or absence of errors concerning the secondary task (yes/no)

Relevant Publications:
- Jameson, A., Großmann-Hutter, B., March, L., & Rummer, R. (2000). Creating an empirical basis for adaptation decisions. In Lieberman, H. (Ed.), IUI2000: International Conference on Intelligent User Interfaces. New York: ACM.
  Abstract: How can an adaptive intelligent interface decide what particular action to perform in a given situation, as a function of perceived properties of the user and the situation? Ideally, such decisions should be made on the basis of an empirically derived causal model. In this paper we show how such a model can be constructed given an appropriately limited system and domain: On the basis of data from a controlled experiment, an influence diagram for making adaptation decisions is learned automatically. We then discuss why this method will often be infeasible in practice, and how parts of the method can nonetheless be used to create a more solid basis for adaptation decisions.
  Portable Document Format (PDF) GZipped PostScript Uncompressed PostScript
- Großmann-Hutter, B., Jameson, A., & Wittig, F. (1999). Learning Bayesian networks with hidden variables for user modeling. In Proceedings of the IJCAI99 Workshop ``Learning About Users''. Stockholm, July 31st 1999.
  Abstract: We present issues and initial results of our research into methods for learning Bayesian networks for user modeling on the basis of empirical data, focusing on issues that are especially important in the context of user modeling. These issues include the treatment of theoretically interpretable hidden variables, ways of learning partial networks and combining them into a single network, ways of taking into account the special properties of datasets acquired through psychological experiments, and ways of increasing the efficiency and effectiveness of the learning algorithms.
  Portable Document Format (PDF) GZipped PostScript Uncompressed PostScript

Variable	Description
CaseID	unique case identifier
SubjectID	unique subject identifier
Number_of_Steps	total number of steps in the instruction sequence
Presentation_Mode	mode of presentation: bundled or stepwise
Secondary_Task	presence or absence of a distractional task (yes/no)
Number_of_Presses	total number of clicks to perform the whole task
Number_of_Flashes	total number of flashes of the secondary task
Error_in_Task	presence or absence of errors concerning the primary task (yes/no)
Execution_Time	execution time of the current task (ms)
Indiv_Ave_Execution_Time	a subject's average execution time for all tasks during the whole experiment (ms)
Inter_Task_Pause_Time	pause length bewteen two consecutive tasks (ms)
Indiv_Ave_Pause_Time	a subject's average inter-task pause length during the whole experiment (ms)
Error_in_Flash_Reactions	presence or absence of errors concerning the secondary task (yes/no)