Set of four distinct data sets constructed by a statistician Francin Anscombe in 1973. All four sets have almost the same statistical observations, included mean, variance, correlation and number of elements (11), however they have different distributions which results in distinguishments in plotting on the graph.

Although particular points on the graph of each set are not corresponding, each dataset produces the same statistical properties and is represented with the similar linear function. This quartet model is used to show importance of data visualization and plotting datasets on the graph. Even though each dataset generate similar statistical properties, in fact it looks different on the graph. Thus, datasets should be firstly analyzed graphically, before performing relationship analyse and interpretation.

It is not exactly known how Francinc Anscombe constructed this model, however nowadays there are more models with similar properties, which is identical statistics and dissimilar graphics.

[1] Matejka, J., Fitzmarice, G., (2017). Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing

[2] VL 11-02 | Advanced Topics in HCI: Data Visualization, HCI SoSe 2021

An intelligent system is based on artificial Intelligence. Interactive intelligent systems include humans as an actor which interact with these intelligent systems. To have an interaction between an intelligent system and a human we often need a graphical user interface (GUI).

To design Interactive intelligent systems we have to combine two design paradigms. The first paradigm is the “focus on interaction design” the second is “focus on algorithm design”. The combination is called “computer collaboration design”. It is defined as: “The focus is on both, algorithms and human, thus, the goal is to enable a collaboration that consider the “best” of both”[1].

Resource:

[1] HCI-11-01 Advanced Topics in Human-Computer Interaction, p. 3-5, https://blogs.fu-berlin.de/hci1-sose2021/lu-11-advanced-topics-in-human-computer-interaction/ , visited 04.07.2021 08.30 am

Ziele von Datenvisualisierung

Das Ziel von Datenvisualisierung besteht darin, Daten so darzustellen, dass ein Mensch fähig ist, in ihnen Muster, Trends, Korrelationen oder Anomalien zu erkennen. Hierdurch sollen neue Erkenntnisse gewonnen werden können.[1]

Einsatz von Datenvisualisierung

Visualisierungen müssen allerdings sehr bewusst erstellt werden. Es ist wichtig zu verstehen, wie der Zusammenhang zwischen den Informationen, die Visualisiert werden und den Erkenntnissen die der Mensch daraus gewinnt ist. Ein besseres Design für die Visualisierung hilft Menschen dabei, besser zu kommunizieren und die Daten im Endeffekt zu verstehen. Beispiele für Designentscheidungen für die Darstellung von Daten, die zu Missverständnissen führen können sind Getting Over the Rainbow, Problems With 3D, Data on the Move und Show, Don’t Tell.

[1]: Frei übersetzt nach HCI-11-02 Advanced Topics in HCI: Data Visualization, Seite 2

Quelle: HCI-11-02 Advanced Topics in HCI: Data Visualization, S. 2,5,7. https://blogs.fu-berlin.de/hci1-sose2021/lu-11-advanced-topics-in-human-computer-interaction/, abgerufen 02.07.2021 um 14:00 Uhr

Reflective Design (Sengers et al., 2005) is a design paradigma based on the slow technology movement, which porposes „A design agenda for technology aimed at reflection and moments of mental rest rather than efficiency in performance[1]“. Reflective design emphasizes the socialtechnological elements in a product design, which doesn’t only mean to design a product with pure focus on high efficiency as well as effectivity, but also to design a product which assist the users to proactively interact with product/system while using it.

Sengers et al. (2005) defines reflective design as „a practice which combines analysis of the ways in which technologies reflect and perpetuate unconscious cultural assumptions, with design, building, and evaluation of new computing devices that reflect alternative possibilities“[2].

Reflective Design provides a framework for reconsidering the design that allows designers to rethink and examine their preassumptions about the systems they create, their design principles regarding target users as well as the social impact of chosen technologies. It also provides a toolkit that enables the users to be part of this reflective process.

Principles of Reflective Design [2]:

(1) Designers should use reflection to uncover and alter the limitations of design practice;

(2) Designers should use reflection to re-understand their own role in the technology design process;

(3) Designers should support users in reflecting on their lives;

(4) Technology should support skepticism about and reinterpretation of its own working;

(5) Reflection is not a separate activity from action but is folded into it as an integral part of experience;

(6) Dialogic engagement between designers and users through technology can enhance reflection.

Reflective Design Strategies [2]:

(1) Provide for interpretive flexibility;

(2) Give users license to participate;

(3) Provide dynamic feedback to users;

(4) Inspire rich feedback from users;

(5) Build technology as a probe;

(6) Invert metaphors and cross boundaries.

References:

[1] VL 11-3_HCI_Advanced_Topics_Reflective_Technologies, Page 5, HCI SoSe 2021, access at 23:39, 02.Jul.2021

[2]Sengers, P., Boehner, K., David, S., & Kaye, J. J. (2005, August). Reflective design. In Proceedings of the 4th decennial conference on Critical computing: between sense and sensibility (pp. 49-58).