השוואת שיטות

	AttrakDiff/UEQ	NASA-TLX
תחום	אינטראקציה אדם-מחשב	אינטראקציה אדם-מחשב
משפחה	Hypothesis test	Hypothesis test
שנת המקור	2003	1988
הוגה השיטה	Marc Hassenzahl (AttrakDiff), Martin Schrepp (UEQ)	Sandra Hart and Lowell Staveland
סוג	Questionnaire measuring pragmatic and hedonic quality dimensions	Multi-dimensional post-task questionnaire for measuring subjective mental workload
מקור מכונן	Hassenzahl, M. (2003). The thing and I: Understanding the relationship between user and product. In M. A. Blythe, K. Overbeeke, A. F. Monk, & P. C. Wright (Eds.), Funology (pp. 31–42). Kluwer Academic Publishers.	Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In P. A. Hancock & N. Meshkati (Eds.), Human Mental Workload (pp. 139–183). Elsevier.
כינויים	Hedonic Quality Assessment, Pragmatic vs. Hedonic, UEQ	Task Load Index, TLX, NASA-TLX
קשורות	4	4
תקציר	AttrakDiff and the User Experience Questionnaire (UEQ) are assessment instruments for measuring user experience across multiple dimensions. AttrakDiff, developed by Marc Hassenzahl, evaluates the tension between pragmatic quality (functionality, usability, does the system do what I need?) and hedonic quality (beauty, emotional engagement, does it delight me?). The UEQ, developed by Schrepp and colleagues, extends this framework with additional dimensions including efficiency, perspicuity, stimulation, and novelty. Both instruments provide quantitative post-use assessment, complementing task-based usability testing with holistic experience evaluation.	The NASA Task Load Index (TLX) is a multi-dimensional subjective workload assessment tool developed at NASA Ames Research Center by Sandra Hart and Lowell Staveland in the 1980s. TLX measures perceived mental workload across six dimensions—mental demand, physical demand, temporal demand, performance, effort, and frustration—allowing researchers and practitioners to understand the cognitive and affective burden of tasks and interfaces. The instrument is widely used in human factors, cognitive engineering, and HCI to identify task bottlenecks and evaluate system designs.