SensorBricks: a Collaborative Tangible Sensor Toolkit to Support the Development of Data Literacy

Data is often inaccessible for non-expert users, leading to a lack of data transparency and speculation in the understanding and interpretation of data, not providing personal value to individuals. The SensorBricks toolkit was evaluated during 5 workshops (N = 20) to learn how an IoT toolkit can support the development of data literacy. The SensorBricks toolkit introduced data to individuals in a fun, accessible and easy way, creating a low boundary for users to interact with data, sensors and output modalities. The toolkit helped individuals to reflect on the role of data in their environment. The collaborative aspect gave individuals the option to discuss the data and learn from each other’s experiences helping users to create a meaningful interpretation and shared understanding of sensor data. The combined digital and data physicalization data representation gives individuals both awareness and in-detail information, creating an understandable and informative way of presenting data.


INTRODUCTION
Society has become fundamentally data-driven, manifesting itself in business decision algorithms [63], recommendation systems [29], energy consumption [58,74] or domestic technology [108].Data sources are generally inaccessible for non-expert users as they treat end-users as data subjects and not participants [36,90,102].Excluding end users might cause incorrect or misrepresented information, while also reducing the data interaction to a simple communication process.The lack of inclusion in the data interaction process leads to problems such as privacy management, data ownership, and transparency within the data collection [8,102].Therefore, data needs to be classified and presented understandably for novice and non-expert users [17,48,79].To enable end-users to actively manipulate, analyze, and make sense of data, the user has to be at the center of entire the data pipeline.Using a Human-Data Interaction approach supports individuals in building a shared understanding of data and data collection [68,93,110].Therefore, there is a need to develop new tools, methods, and approaches by which non-expert end users are empowered to explore and learn from the use and collection of data.
To place the user at the center of the data collection and provide personal value, individuals should be able to understand the concepts of data and data collection.Data literacy is the ability to read, understand, create, and communicate data as information [30,108].Data literacy has been operationalized in several other fields, such as civic empowerment [9] and education [6,12,59].This process includes both the role of developing data literacy tools that assist novice users in understanding and working with data, focusing on both the development of the intervention itself, as well as the activities around the intervention [108].Not having a sufficient level of data literacy can lead to barriers due to a lack of control and ownership over data [33,36], distrust in the use of data due to a lack of data transparency [102] or misinterpretation of data [57].To provide individuals with the option to manipulate, analyze, discuss, understand, and reflect on the role of data, we use a physical and tangible approach combined with data physicalization [53].This compared to, for example, purely digital applications that require a specific focus and intentionality whereas this paper uses a peripheral tangible programmable interface leading to new forms of data interaction and exploration [49].Tangible and physical interfaces can increase awareness and participation due to the physical properties and affordances [46,53].Tangible awareness is defined here as an object that is designed to enhance awareness and selfreflection among individuals during collaborative activities, based on principles of Tangible User Interfaces (TUI) [87].Combined with (situated) data physicalization, it provides individuals with new possibilities to discover and understand the role of data based on their needs and preferences, potentially increasing their data literacy [48].Tangible data physicalizations bring digital data (e.g., numbers and graphs) into the physical world, helping novice users to create an understanding of a variety of data [53,94].These situated data physicalizations encode and present data into their context of use, to places and people by connecting data with the physical environment [15,107].Situated, tangible data physicalizations can also be used to create a casual information visualization that lends itself to a broad range of user populations-from experts to novices, or from work tasks to more everyday situations [79].
IoT (Internet of Things) toolkits (e.g., Physikit [48] or Loaded Dice [62]) offer an opportunity where tangible and physical aspects can be combined with data physicalization.These toolkits provide individuals with sensors and actuators where they can create, manipulate and explore their own sensor systems and discuss the role of data in, for example, co-design workshops [7].Such toolkits make it possible for novice and non-expert users to build sensor-based systems without requiring experience in electronics and programming.Toolkits can help with collaborative sensemaking where a group derives new insights through an iterative process [78].Collaborative sensemaking between individuals has been shown to improve data literacy skills and is encouraged as a medium to improve data literacy where individuals can learn from each other's experiences [42].Collaborative sensemaking creates a structured way by collaboratively collecting and organizing data and information, creating new knowledge, insights, and understanding for individuals [21,78].Group collaborations have shown to be beneficial for sensemaking, as the group can leverage from each other's skills and insights and share their experience with each other [43,72].IoT toolkits provide this possibility to facilitate and support this collaborative sensemaking by addressing a shared goal or challenge while engaging users from different levels [38].
To focus on data literacy with tangibles and data physicalization, we have developed SensorBricks.SensorBricks (Figure 1) is an IoT-toolkit that takes a Lego-like approach, to create a low entry point for individuals to interact with the toolkit.The toolkit consists of 8 data collection bricks and 6 physicalization bricks to provide individuals with the option to build their own sensor system(s) (Figure 2).The study focuses on: How does the SensorBricks toolkit support the development of data literacy through input and output bricks?The SensorBricks toolkit was evaluated during 5 workshops (N = 20) to answer this question.Our results show, how IoT-toolkits help to support the development of data literacy, by creating a fun, accessible, and easy way, creating a low boundary for users to interact with data, sensors, and output modalities.The collaborative aspect of the toolkit gives individuals the option to discuss the data and learn from each other's experiences helping users to create a meaningful interpretation and shared understanding of sensor data.The combined digital and physical data representation gives individuals awareness and in-detail information, creating an understandable and informative way of presenting data.The produced knowledge is relevant for the development of IoT toolkits to support the development of data literacy.Additionally, we discuss the development of design interventions aiming to introduce individuals to data in a creative, fun, and iterative way.

RELATED WORK
SensorBricks builds on 3 fields in its related work: (i) data literacy, (ii) tangible toolkits and (iii) data physicalization.

Data Literacy in Design
Several definitions are provided for data literacy.Wolff et al., [108] define data literacy as: "the ability to ask and answer real-world questions from large and small data sets through an inquiry process, with consideration of ethical use of data [...] These include the abilities to select, clean, analyse, visualise, critique and interpret data, as well as to communicate stories from data and to use data as part of a design process."(p.23).While Deahl [30] defines data literacy as: "The ability to understand, find, collect, interpret, visualize, and support arguments using quantitative and qualitative data." Based on these definitions, we use data literacy, where data is used as a creative material to read, work with, discuss, understand and reflect on the role of data within design interventions.This approach was initially developed by D'Ignazio and Bhargava [32].They propose that data literacy tools and activities must be focused, guided, inviting, and expendable.
An example where design principles for data literacy are implemented is DataBasic [32].DataBasic consists of 3 digital tools that accompany participatory activities for data literacy learners.The tools are developed as an easy-to-use web tool for beginners that introduces concepts of working with data.Data literacy is currently being used in workshops where the specific needs of users as well as their relation to data are studied [10,75].An example of this can be seen in Data Mural [10] which uses a workshop for participatory and impactful data literacy through visual arts.The examples above show a diverse use of data literacy in both studies and tools.However, a field that is less explored is the use of data literacy in tangible user interfaces or toolkits.Tangible user interfaces can enhance the social collaboration between individuals, creating an opportunity where they share and learn from each other's experiences [109].We, therefore, see an opportunity of using data literacy as a strategy in a tangible user interface to make users reflect on the role of data in a tangible way.
The exploration of data and/or visualization literacy has been the focus of a body of research, that largely focuses on improving the literacy of children (e.g,.[2,35,40,51]), who are considered novices in this field.Our study aligns with some of the principles used in this research.For example, similar to Bae et al., [2], our work employs a hybrid approach and emphasizes fun and playfulness.Fun and playful approaches to improve data and/or visualization literacy can also be found in the works of Huynh et al., [51] and Gäbler et al., [40], which showcase gamified approaches in a digital setting.Finally, similar to the work of Eslambolchilar et al., [35], our approach focuses on tangible interaction in order to improve people's data literacy.While we acknowledge these studies focusing on children, our work bridges the fields of data visualizationwhich is often evaluated by visualization literacy tests (e.g., [13,23,61]) -and playful data literacy for novice users.With novice users, we target adults who are non-expert users and only have little experience with using and analyzing data.Data literacy is crucial for adults to navigate the increasingly data-driven world, enabling them to analyze, interpret, and make informed choices based on data [108].Additionally, we use data physicalization as a representation medium to present this data, meaning that we focus on tangible representation of data, instead of the (digital) graphs, charts, or maps used for visualization literacy tests.Therefore, we rely on data literacy instead to evaluate the toolkit.

Tangible Interfaces as Toolkits for Design
HCI practitioners used tools like Arduino1 , Raspberry Pi2 , and Micro:Bit3 to enhance the accessibility of physical computing, enabling makers to overcome embedded hardware programming challenges.Nonetheless, novice users encounter obstacles in grasping fundamental electronics concepts, such as current direction and circuit routing [11].Tangible toolkits have emerged as interfaces across diverse domains, encompassing programming, problem-solving, education, and communication [60,96].These toolkits empower users to explore and construct context-specific systems, offering a tangible and interactive approach.Examples of this can be seen in toolkits where the form and connection between parts of the system are used as building blocks.AudioCubes [95] is a tangible user interface using a cube-like design to create and explore dynamically alternating sounds by changing, adding, and removing cubes.From an educational perspective, Cube-in [71] is a toolkit that helps grasp the concepts of physical computing by making individuals use different component cubes to learn about electronics, signals, and outputs.
Data -and output modalities related to the data-is a topic that relates itself to tangible interfaces as a toolkit for design.LittleBits [5] provides an open-source library of pre-assembled electronic components, consisting of modules that include power, input (e.g., sliders, toggles), output (e.g., LEDs, vibrations), and wires components.This approach simplifies the prototyping process, ensuring accessibility for individuals who might otherwise feel hesitant about delving into creative electronics.Next, Physikit [48] is a toolkit developed as a physical ambient artifact that can be programmed and configured by non-expert users.The toolkit uses several types of input data and makes the user connect them to the output modules.By taking this approach, users explore what is important to them and change modalities based on their needs.Cubes [86] takes a playful, interactive approach by providing users with 20 cubes with a single input and output.The "Cubes" give users the possibility to explore different modalities through tangible interaction by interacting and combining different cubes.Finally, Loaded Dice [62] are two wireless, Arduino-based 3D-printed cubes specifically crafted for codesign activities with blind and visually impaired individuals.One cube is equipped with a range of sensors (e.g., temperature, light, sound), while the other includes various actuators (e.g., vibration, heat light).These examples show how data and output modalities can be used as a tangible interface, to make users explore and understand what is relevant to them personally.
The tangible toolkits discussed above use several features where users can add, remove, rebuild, or reconfigure the toolkit based on their preferences.Lego is a tool that facilitates these possibilities.Lego developed their own line of Lego Mindstorms which consists of a computer control system, a set of modular sensors, and motors.This system has been used for several explorative systems in education [39] and assistive technology [66], since most people are familiar with Lego and is a system easy to use for novice users.Lego is also seen in toolkits, such as Cubement [26]: a design tool that consists of 7 boxes that individuals can connect with Lego Mindstorms to construct their own tools.Activecubes [56] also draws their inspiration from Lego with a Lego-like tangible user interface for building 3D structures.Additionally, earlier work conducted by Resnick [83][84][85] shows various examples of using Lego such as the Programmable Brick [84] which is a portable computer embedded inside a Lego Brick and has various applications and implications for children's design activities, including autonomous creatures, active environments, and personal science experiments.Resnick et al., also developed the Behavior construction kits [83] which allow children to build behaviors.The construction kit comprises a diverse array of 'behaving machines,' empowering children to explore novel perspectives on computation, programming, and control.These toolkits show how the use of tools such as Lego can help to enrich a toolkit by using something familiar that can easily be built or rebuilt and is suitable for novice users.

Tangible Sensing Artifacts and Data Physicalization
Several tangible design artifacts have been developed to provide individuals with insights about themselves and their environment [3,18,94].These sensing artifacts are seen in setting such as the home [25,48,64], office [17,28,55,89,99], or urban environment [22,31].These artifacts measure a wide variety of parameters such as air quality [28,48,99], temperature [17,48,99], light [17,28,55], noise [17,55,76], personal factors (e.g., productivity) [20,99] or physical (in)activity [16,65,80,81].Examples of artifacts that take a multi-data approach are the earlier mentioned Physikit [48] which measures several environmental data sources and provides users with the possibility to link these to output modalities.Office agents [99] take a similar multi-sensor approach by measuring office-related data sources to give users insights about their office environment.However, these tangible sensor artifacts have a fixed set of data sources, and users lack the option to add or remove sensors that are relevant to their environment.Furthermore, the presented sensing artifacts collect a wide variety of data.These data sources have a variety of units going from lux (light), degrees Celsius (temperature), volts (sounds), or VOC (air quality), often presented in graphs or charts.Novice users have problems reading off these values and making sense of the data [36,102].To make sense of the data, experts need to analyze the data for users to interpret it [36,102].When not including an expert, individuals learn from the data when relating the data pattern to their routines or discussing it collectively.However, this approach leads to assumption-based interpretations leading to speculations on the situatedness of the data [57].Data physicalization can offer a solution to these issues.Data physicalization can help to make data tangible and graspable and overcome some of the limitations of 2D data representation (e.g., situated on paper or a digital screen) [47].Thus, potentially enhancing the engagement with and connection to the data [53,105].Additionally, prior work demonstrates the limitations of purely digital ways of presenting data, including 'display blindness' [69], attention overload through notifications [88], low recall in content [69,73], and lack of social and contextual situated information [16].The tangibility and multiple modalities (e.g., visual and haptic) of physical objects support data analysis and human-computing interaction.The translation from digital data to a physical object can help novice users to create an understanding of the variety of data that is measured in their environment.This approach is already seen in the earlier mentioned Physikit [48] and Office Agents [99] where personal and environmental data is physicalized into a physical object with corresponding output modality which represents the data.When specifically looking at situated physicalization [15,107], examples such as Chemicals in the Creek [77] are seen that use situated data physicalization to engage communities with open government data.Another example is Time-Turner [97] which uses situated physicalization by recording videos of family activities allowing families to review their past activities.Finally, taking a Lego-like approach is Legos [52], where each Lego brick physicalizes different projects and project duration to keep track of individual's time management.Moreover, recent research has shown that creating physicalizations results in a critical reflection of what data are [104], making physicalizations a suitable vehicle for the development of data literacy.
Based on the presented work, we see an opportunity to combine the fields of data literacy, (IoT) toolkit design, and data physicalization in a setting of collaborative sensemaking.These principles are implemented in the SensorBricks toolkit.The Lego-like design of SensorBricks makes it possible for individuals to build their own sensor systems based on their needs and curiosity.The output bricks of the toolkit provide individuals with the possibility to physicalize their data, creating an understandable data representation for nonexpert and novice users.The SensorBricks toolkit was deployed during 5 workshops, to study how an IoT toolkit can support the development of data literacy.

SENSORBRICKS
To support the development of data literacy, we propose a new toolkit called SensorBricks4 .For the design of the SensorBricks toolkit, we defined 4 design principles.In this section, we discuss these, together with the functions, form factor, and used electronics.

Design principles
For the functions and aesthetics of SensorBricks, we set 4 types of design principles, concerning the toolkit, data literacy, and data physicalization.The design principles are based on the literature such as design for appropriation [34], design principles for data literacy [32], tangibility and data physicalization [53], and hybrid data representation [94].
D1 -Appropriation, flexibility, and reconfigurability: Sen-sorBricks is developed as a toolkit that provides users with the option to explore and build a system that fits the context of the user.The flexibility of the Bricks gives the user the option to explore and build their own sensor system, changing the input and output bricks based on their needs [60].The Lego-like approach in the toolkit provides users with the possibility to reconfigure their sensor system continuously based on their environment, curiosity and requirements.Lastly, the toolkit allows appropriation, creating a space of possibilities for design by users where the context of use is expanded for multiple interpretations and provides openness, configurability and, tailorability [34].
D2 -Promoting data literacy: SensorBricks follows the design principles set by D'Ignazio and Bhargava [32] that design literacy tools and activities should be focused, guided, inviting, and expendable.SensorBricks implements this within its design by taking a Lego-form approach.This approach provides users with a low entry point, as most people are familiar with Lego: its is inviting, due to the activities provided with the toolkit via the task cards; and playful with its Lego form to engage and introduce users to meaningful data and modular by offering users a path to learn which data is relevant for them.
D3 -Tangibility: SensorBricks supports (novice) users with data analysis through physicalization by translating data into a physical object.The physicalization is both seen in the construction of the sensor system (adding and removing bricks) and the data presentation (output bricks).SensorBricks follows the concept of data physicalization by creating an understanding of the variety of data for users [53].
D4 -Hybrid data representation: Considering SensorBricks main purpose is to increase data literacy, a hybrid data representation is used.Although the main data output of the sensory system is experienced through the data physicalization output bricks, a tablet is included in the SensorBricks toolkit as well.This way, users can also explore the data through means they are (probably) more familiar with, namely data visualization.

SensorBricks toolkit
Form factor: SensorBricks is a toolkit consisting of 8 input bricks and 6 output bricks.The form of the Bricks is designed as Lego bricks (Figure 2) to give users the possibility to build their own sensor system, by easily adding and removing bricks, giving users the option to explore several brick configurations.SensorBricks are also compatible with normal, commercial Lego bricks.Hence, creating the possibility to use them within the creation of their own sensor system.All input bricks have the same dimensions (48x32x40 mm), which is similar to a standard 4 by 6 Lego brick (length and width).The output bricks have similar dimensions, with some output modules extending these dimensions, due to the output modality.All bricks are 3D printed with PLA.
The complete toolkit (Figure 2), consists of the 14 SensorBricks, 10-port USB HUB, Mi-Fi router, and tablet.The USB HUB is placed in the toolkit to charge the SensorBricks.The MiFi router connects the SensorBricks to the cloud.The tablet is used to visualize the incoming data in the custom-made Adafruit IO dashboard.The wireless nature of the toolkit also gives it the possibility to be used in several locations, outside of the toolkit box.To provide users with an overview of the toolkit an overview card is given, explaining which data source or output modality is collected/linked to the brick.The toolkit also comes with several 'normal' Lego bricks, which provides users with the opportunity to build their own SensorBricks sensor system.
Dashboard: the data dashboard, developed in Adafruit IO, is used to present the numerical values of the sensors and to activate and link the output bricks with the input bricks (red switches in Figure 3).The presented number, both presented in the dashboard and output bricks, is the last measured interval (sampled over a one-minute period).The numerical values on the dashboard present the units of the sensors (e.g., distance in centimeters, light in lux, air quality in VOC).The data in the dashboard is presented via a gauge graph (blue visualizations in Figure 3) Data can also be displayed over time via a times series graph on the dashboard.However, this function was disabled during the workshop due to the short, taskbased setup of the workshop.When more complicated systems are built (several input bricks combined with several output bricks), the authors manually program the brick configuration during the workshop.This is accomplished using a separate controller, programmed by the authors, that links the input and output when participants share their thoughts (think-out-loud).In this way, the more complicated systems can be made without disturbing the participants of the workshop.
Electronics: SensorBricks take a similar approach in electronics (Figure 2) using a Wemos D1 as controller, 400 mah lipo battery as power unit, and micro-USB Li-ion charger combined with a magnetic USB connector for charging purposes.The Sensor-Bricks have a battery life of approximately 6 hours.The 8 input bricks consist of the mentioned electronics and a sensor/software measuring a parameter: SoundBricks (MAX4466), Productivity-Bricks (RescueTime [82]), DistanceBrick (VL53Lox), AirqualityBrick (CCS811), TemperatureBrick (DHT22), LightBrick (TEMT6000), Ac-tivityBrick (MPU6050) and IdentityBrick (RFID).The input bricks measure the sound level (volt), productivity (productive and distracted minutes), distance (millimeter), air quality (CO2 and VOC), temperature (degrees Celsius and humidity percentage), light (lux), activity (steps) and identity (RFID).Data is sent to the cloud at a 1 min interval, to not overload the cloud.For the output bricks, the same basic electronics are used and combined with an output modality: LEDBrick (neopixel), BuzzBrick (piezo), MovementBrick (servo), DigitalBrick (segment display), VibrationBrick (vibration motor), and WindBrick (fan).The data from the input bricks are sent to an Adafruit IO Dashboard, which visualizes the data.The output bricks are subscribed to the same cloud, creating the connection between the input and output bricks.

METHODOLOGY
To evaluate the SensorBricks toolkit and how it supports the development of data literacy, 5 workshops were conducted.The workshops were held in groups consisting of between 3-5 individuals in a closed and controlled setting.The controlled setting gave the opportunity to observe how users would interact with the toolkit, and collaborate and communicate with each other when using the toolkit.During the workshops, participants were introduced to the SensorBricks toolkit and were asked to conduct six tasks to learn how the SensorBricks toolkit can support them in the development of data literacy.The first two tasks were framed as guiding tasks to introduce the toolkit to the participants.The other tasks (tasks 3 -6) were based on daily activities that novice users might encounter during their day and/or when using IoT devices.The tasks asked participants to (i) Measure 3 different light levels, (ii) Measure the temperature in the room and connect this to a data visualisation, (iii) Take 2 input bricks and 2 output bricks and see how the bricks change when you change your environment, (iv) measure the environment of your personal greenhouse which tells you about the perfect circumstances, (v) measure a meeting room to create awareness about a healthy office environment and (vi) Measure your house for security when you leave while going on holiday.The study focuses both on the (potential) change in data literacy level and overall task load while using the bricks.Participants used the SensorBricks during a 2-hour workshop.The study focused on the following phases: phase 1: explanation SensorBricks, introduction of the SensorBricks and the different types of data (input bricks), output (output bricks) and data dashboard, and initial introduction survey; phase 2: workshop and exercises with the SensorBricks based on 6 tasks and in between surveys on data literacy level of individuals and overall task load while using SensorBricks; and phase 3: a semi-structured interview with each group.

Study setup and Data collection
During the workshop, participants received several items (Figure 3): (i) the SensorBricks toolkit consisting of 8 input and 6 output bricks, (ii) 6 task cards, (iii) toolkit overview card, (iv) survey booklet, and (v) Lego bricks to build the sensor system.The task cards consist of 6 exercises going from easy to hard, building on the experience and familiarity individuals gain during the workshop.The booklet consists of two surveys, including the data literacy level (Likert scale 1-7) of individuals (inspired by several data literacy surveys [1,70] and the NASA TLX [27] to measure the task load.The survey on Data literacy level had a Cronbach's Alpha score of  =.950.For the analysis of the NASA TLX a Raw TLX (RTLX) [41] was used by averaging the responses of the participants to create an estimate of the overall workload (scale 1-25).The Nasa TLX data was analyzed in SPSS using Repeated Measures ANOVA using Mauchly's Test, based on the 4 phases of the workshop.In total, all surveys were filled in 4 times: before the 1st task, after completing 2 tasks, after completing 4 tasks, and after completing 6 tasks.After completing the tasks, participants were interviewed to evaluate the usability and user experience of SensorBricks.Additionally, the workshops were filmed, observed, and analyzed.This material was used to learn how the participants experienced SensorBricks.

Participants and Procedures
In total, 20 participants (12 male, 8 female, average age of 26,35) completed the workshop.The study consisted of 5 groups (2 sessions of 3 participants, 1 session of 4 participants, and 2 sessions of 5 participants).Workshops were held at 3 different locations (similar to Figure 3) and had an average duration of 44 min 28 sec (Max: 56 min 25 sec, Min 30 min 24 sec).All participants in the groups knew each other beforehand and had a prior (friendship and/or working) relationship.Participants were contacted via email and a flyer was sent out to the companies for participants willing to join a study.Participants were screened if they did not have a data-focused profession (e.g., data or software scientist) and included individuals from different professions while having similar experiences with using data.Participants did not use the Sensor-Bricks toolkit before the start of the workshop.The video footage and observations were used to analyze the usability aspects of the SensorBricks toolkit -focussing on, the time needed to complete the tasks and brick selection-and the collaboration style between participants.The collaboration style was analyzed to learn about the collaborative sensemaking between individuals.The recorded sessions were analyzed using a coding scheme.The coding focused on collaboration practices and interactions between participants or with the SensorBricks.The coding scheme was based on the work of Wells and Houben [106].They developed their coding scheme using a grounded approach and adapted version of the coding framework of Tang [101].The framework of Wells and Houben was adapted from the interaction with "Mobile AR Interfaces" to the interaction with the SensorBricks.The adapted collaboration styles were: -C1 Active Discussion -Active discussion denoted any faceto-face discussions which included all participants.Due to this, there were limited interactions with the SensorBricks.-C2 Single-Shared View -A single shared view denoted all participants focusing their attention on the SensorBricks.Semi-structured interviews were conducted after each workshop.The interview focused on the following 3 main topics: (i) Usability/toolkit use, (ii) Data literacy, and (iii) Data physicalization.Follow-up questions were asked based on the answers of the participants.The interviews were audio recorded after receiving the participants' consent, after which they were transcribed and analyzed using Reflexive Thematic Analysis [14].Based on an inductive approach, 70 codes were selected.During the second round of coding, the codes were combined into 8 themes, which were afterwards merged into 4 overarching themes.All interviews were held in Dutch, the native language of the participants.Quotes from the interviews were translated into English for this paper.The quotes are coded based on their Session (S) and participant number (P), for example: Session 3 -Participant 4 as S3P4.

RESULTS
In this paper, we evaluate how the toolkit is used as a mediator for data literacy development.During the workshop, we looked at how people used and interacted with the toolkit and SensorBricks, from which we could diver insights into the time it took to complete tasks, and participants' brick selection and collaboration style.Participants reflected on the toolkit itself, support in the development of data literacy, task load, and role of data physicalization.In this section, we report the experience of the participants based on (i) time and brick selection, (ii) collaboration style, (iii) data literacy and task load, and (iv) interviews.

Time and Brick Selection
To learn about the usability of the SensorBricks toolkit and the development in data literacy, the time needed to complete the tasks and the selection of bricks were analyzed.The time needed to complete tasks shows if users become more familiar with the use and data collection of the bricks, while the brick selection indicates which bricks are favorite and/or commonly used.

5.
1.1 Time.The SensorBricks toolkit was used in total for 2 hours, 41 minutes, and 43 seconds.On average, it took the participants 32 min 20 sec to complete all 6 tasks.When looking at the difficulty of the tasks, easy (tasks 1 and 2), intermediate (tasks 3 and 4), and hard (tasks 5 and 6), an increase in time is observed when moving up in the level of difficulty.On average, participants took less time for the 'easy' tasks (first task: 2 min 57 sec and second task: 2 min 55 sec) compared to the 'intermediate' tasks (third task: 6 min 5 sec and fourth task: 6 min 20 sec).A similar increase is seen when looking at the "hard" tasks (fifth task: 8 min 24 sec and sixth task: 7 min 10 sec).

Brick selection.
Throughout the workshop, 141 SensorBricks were used (average of 28.2 bricks per workshop).During the workshop, 72 input bricks (average of 14.4 bricks per session) and 69 output bricks (average of 13.8 bricks per session) were selected (Figure 5).The bricks were used to build 30 sensor systems (5 groups, all completing 6 tasks).The 30 systems consist of 22 single sensor systems (systems that place all input and output bricks into a single system) and 8 dual-sensor systems (systems that have 2 separate systems consisting of output and input bricks) (Figure 4).Systems were considered as a single sensor system when all bricks were placed on a single Lego plate (e.g., Group 1, 4th exercise (Figure 4)), were attached (either to the brick themselves or via Lego bricks, e.g., Group 2, 4th exercise (Figure 4)) or when participants indicated this during the session.
Participants combined input and output bricks during the sessions.Some common combinations of input and output bricks that were seen were the LightBrick combined with the LEDBrick and the AirqualityBrick or the TemperatureBrick combined with the WindBrick.These combinations were made to let the output brick, affect the incoming data by, for example, turning the light of the LEDBrick to increase the incoming light for the LightBrick, S5P4: "You can also use the LEDs to increase the value [of the LightBrick]", S1P1: "When it gets dark, we connect it to the LEDs so that the light [value] goes up" or to improve the air quality or lower temperature by turning on the WindBrick.S5P2: "I put the fan in front of the temperature brick then it cools it down when it's in front of it", S1P1: "You can also couple it to the wind because if it is too high, it can bring it down [CO2 value]." The sensor systems (Figure 4, red squares) provide insight into how the bricks were combined to develop these systems to learn how outputs could potentially influence the data collection.For example, group 5 (task 3), used the MovementBrick combined with Lego to make a fan and the WindBrick to blow fresh air into the AirqualityBrick.In doing so, the group could compare the two output modules and how these affected the input of the AirqualityBrick.This group was also the only group that used the LEDBrick and combined it with the LightBrick during the first task.By connecting the LightBrick to the LEDBrick, participants observed the changes in the data caused by changing the distance between the input and output bricks.Next, the first group tried, when building a greenhouse (task 4), to learn how the WindBrick and LightBrick could improve the overall air quality and light levels in the environment.By breathing into the AirqualityBrick the participants tried to simulate a warm, moist environment and used the WindBrick to remove the warm air.Through experimentation, participants found and observed the connection between input and output bricks.These examples demonstrate how SensorBricks are used as a creative tool to explore, work with, discuss, understand, and reflect on the role of data.

Collaboration Style
The workshops were video recorded and analyzed using a coding scheme to define the collaboration styles during the workshops.Based on the coding scheme, 146 collaboration styles (on average 29.2 styles per workshop) were defined (Figure 5).Analyzing the collaboration styles of the participants shows "Active discussions" (C1) between individuals on the most common collaboration style (ƒ = 56/146, 38.4%)."Single-Shared view" (C2) where individuals have a single shared view, all focusing their attention on the SensorBricks, was indicated as a commonly used collaboration style (ƒ = 48/146, 32.9%) when building a sensor system.Both the group discussion between all participants (C1) and group/shared building of the sensor system (C2) are more commonly seen over the "Disjoint and shared view" (C3, (ƒ = 10/146, 6.9%)) or "Disjoint and distributed view" (C4, (ƒ = 31/146, 21.2%)).The distributed collaboration styles, "Distributed view" (C5, 0%) and "Distributed view with discussion" (C6, (ƒ = 1/146, 0.7%)) were not/barely seen during the workshops.
When analyzing the time spent on the different collaboration styles, a similar pattern is seen where participants spend most of the time on group-related collaboration styles.Participants spent the most time on the "Single-shared view" (C2) where they were building sensor systems together as a group (1 hour 21 min 55 sec).Secondly, participants spent time on "Active discussions" (C1) as a group to discuss the sensor system (41 min).When working disjointly, either being "Disjoint and shared view" (C3, 6 min 45 sec) or "Disjoint and distributed view" (C4, 25 min 39 sec) participants spend less time using those collaboration styles.Distributed collaboration styles "Distributed view" (C5, no time) and "Distributed view with discussion" (C6, 39 sec) were not or barely used during the workshop.
A change in collaboration styles to start the exercise is seen throughout the workshop (Figure 5).At the beginning of the workshop (tasks 1 and 2) participants were more likely to start disjoint when building a sensor system.This is seen in the collaboration styles with C3, "Disjoint and shared view" (ƒ = 2/10, 20.0%) and C4, "Disjoint and distributed view" (ƒ = 5/10, 50.0%) as common styles to start the tasks, over "Active discussions" (C1, ƒ = 3/10, 30.0%).At the end of the workshop (tasks 5 and 6), participants were likely to start as a group by either having an "Active discussion" (C1) as a group (ƒ = 8/10, 80.0%) or directly start building the sensor system with a "Single-shared view" (C2) together as a group (ƒ = 2/10, 20.0%).

Questionnaire: Data Literacy and Task load
Participants were asked to reflect on their data literacy and workload during the workshop.This was done 4 times during the workshop: at the beginning, after completing 2 tasks, after completing 4 tasks and after completing 6 tasks.

Data Literacy.
Participants were asked about their data literacy level during the workshop.This was done via a survey that consisted of 8 questions on their confidence in using, analyzing, and communicating data.Throughout the workshop, an increase in confidence is seen through all statements (Figure 6).Before the start of the workshop, participants had a lower confidence in all statement compared to the end of the workshop: Q1: "I feel confident when using data"    1).An increase is also seen in the Temporal demand, Effort, and Frustration categories.However, a drop is seen after the 6th task, where a decrease in all categories can be seen.The categories of Physical demand and Performance see a similar value throughout the workshop.The analysis of NASA-TLX shows that there are significant differences in Physical demand and Temporal demand over the 4 phases of the workshop.Afterwards, the significant categories (Physical demand and Temporal demand) were analyzed using PostHoc tests for pairwise differences.However, both categories were indicated as being not significant: Physical demand (F(3,76) = 0.115, p = 0.951,  2 = 0.005) and Temporal demand (F(3,76) = 1,223, p = 0.304,  2 = 0.046).

Interviews
Based on the reflexive thematic analysis 4 themes were set focusing on: (i) Intuitiveness of SensorBricks, (ii) Role of the toolkit: current and future design, (iii) Role to improve data literacy, and (iv) Combining data physicalization with digital interfaces.also take more and more freedom and you just learn to think more freely [when working with data]"., S3P2: "I liked that you can put it together physically and that you get a reaction from it.It was easy to understand" and S3P2: "It's nice that you can do so many different types of inputs and can also link it to different types of outputs." Participants indicated that they did not need a lot of information or prior knowledge to use the toolkit while having the freedom to easily explore different configurations: S3P3: "It is a very easy, user-friendly toolkit because you can explore a lot of different things because you can make different configurations", S4P4: "It's just very simple ... and just easy to understand that something is changing [referring to the data]."and S5P2: "I think it is good that you have autonomy over the system and it is also nice that it is tangible ... you can also put it away very easily, take it back later, and click things together" 5.4.2Role of the toolkit: current and future design.The input and output bricks in the SensorBricks toolkit facilitate individuals with the opportunity to explore and reflect on the use of data and data physicalization.Participants reflected on the aesthetics and future form of the bricks, the frame of reference for the data, the social role of the bricks, and the threshold for the data physicalization.
Aesthetics and future SensorBricks toolkit: Throughout the workshop, participants were asked to reflect on the SensorBricks toolkit, including its future form and input of the toolkit.Participants indicated that for a future iteration, they would have additional input bricks including a timer, heart rate monitor (to measure stress), and sitting behavior (to improve posture): S2P1: "Maybe a heart rate monitor, to see if I'm busy and to measure my stress.", S2P5: "I would also like a clock to measure the time, a TimerBrick.Both as input and output.", S3P5: "how long have you been sitting that you have to walk after a certain time . . .maybe something like a timer or something that if it falls below a certain value, it will start counting down".For the output bricks, participants reflected on bricks having additional possibilities including the possibility of adding sound or warmth as output modalities: S1P2: "You have a buzzer, but it really makes a monotonous sound ... It would be nice if you can make it possible that you just play audio fragments to make it personal." And S5P2: "I also find warmth always funny as output, so you can also feel the heat.That would be very good in combination with the temperature sensor." Participants also reflected on the aesthetics of the bricks, which could improve the usability of the toolkit: S3P3: "I think maybe something can be done with the colors [of the bricks] that you know more what input is and what output is." and S3P1: "Maybe a kind of sticker on the input that you know, a microphone or something for sound or a temperature meter for temperature ... " Threshold and frame of reference: The threshold for the output data physicalization bricks was predetermined by the researchers to create a homogenous system based on several data inputs.However, participants preferred to determine the threshold themselves: S2P3: "maybe I'll make an intermediate step from input to output.That you can sort of set when something is right or wrong.", S4P2: "that you can use this to make a distinction from above a certain value it becomes green and below it becomes red and that you can also determine it yourself." And S4P3: "the data must be different.You should indeed be able to indicate a level.Decide for yourself what you think is right or wrong".
To determine these thresholds participants would prefer a frame of reference for the data input: S2P1: "But that's also easy because you can set a threshold, but then I think with air quality I have no idea what it does or says, then I think it's fine if it's preset", S1P1: "a minimum recommended value on the left and the maximum recommended values on the right, then you already have a very nice frame of reference" and S1P3: "I would also like to know right away, this is the value, this is the frame of reference.I think I would also like to know the context about why something is good or bad, that I can judge or say whether I agree".Social role of SensorBricks: Participants reflected on the (potential) social role of the SensorBricks in an environment.This includes the role of placing the system in the environment, reflecting on the data of others, and building upon each other's sensor system.S2P1 "I'd like to have an extended display brick to see what it was linked to, in case someone else made it.Assuming other people would also use this within an environment.", and S5P2: "I think that if you do it personally, it will work.But if you put something together like this as a joint group, [that was not involved in the building] they don't really understand what you built." 5.4.3Data literacy.The SensorBricks toolkit aimed to make data more accessible to non-expert and novice users.The data literacy of individuals played a key role during the workshops with the SensorBricks.This was seen in the way the SensorBricks lowered the threshold and made it easier for individuals to interact with data: S4P1: "I do think it lowers the threshold.I don't understand data processing at all.In the beginning, I thought: oh, what exactly are we going to do and I felt a kind of resistance in myself.But once I started and it works, it gets more and more fun.",S1P3: "it's made simpler with the bricks then I can imagine people using it in an office for instance. . .this is somehow simpler than that with all kinds of statistics and graphs.", Additionally, participants' trust in working with data increased during the workshop when they became more familiar with the toolkit: S3P3: "at a certain point you know what those bricks do ... then it becomes easier to use the data that you see on the screen.Then you know at a high temperature, that the light goes on and you start to understand the data" and S5P2: "you kind of have to figure out when a change [in data] takes place, then I think you learn much better about how those sensors work and what influence you and the environment have on the sensor." Participants also indicated how systems such as the SensorBricks could help them to better understand the environment.Currently, a lot of actions in their (smart) environment are happening based on data.However, individuals don't have any insights into the data and therefore no understanding of why certain actions are happening: S1P1: "I never considered that there is a lot of data before a trigger happens.It is now more like "the window opens", but why the windows open is unknown and I would like to know why.", S5P1: "People who work in a smart building and they could be aware of what is being measured.Because a lot of people don't know what kind of data is collected from them.I think it might help to get a bit more of an understanding for data [that is measured in the environment].".Placing the SensorBricks in context could therefore help individuals to gain an understanding of why these actions are happening, providing users with insights into the data on which these data-based decisions are made.S5P3: "You can try out yourself [when talking about the Bricks] in a very accessible way so that you better understand what is happening in the building." SensorBricks provides users with an easy and understandable way of using data, removing processes such as the electronics, programming, and software development.However, did could limit the development of data literacy, since participants don't have the opportunities to learn about these processes: S4P1: "I was confused, because normally I am quite familiar with data.I now had a bit of a question: what is happening here?And why do the lights go on and what data is coming in." And S4P3: "It should have been a little less magic, because now you build something and it just works and you don't know why".Then I look at this [the output bricks] and if I really want to know more information then I will look at the digital in case I missed it."And S5P3: "The combination, if you want more detailed information then you can look at the dashboard and if you want awareness, you look at the bricks."

DISCUSSION
This study focused on the role of the SensorBricks toolkit to support the development for data literacy.In this discussion, we reflect on (i) the role of data literacy, (ii) role of data in the environment, (iii) collaborative sensemaking and (iv) combination of data physicalization and digital interfaces.

Data Literacy
The SensorBricks toolkit was designed to increase the data literacy of individuals, by creating tools and activities which are focused, guiding, inviting and expendable [10].Based on the response during the workshop, we observe that SensorBricks helps with lowering the 'fear' of using data.The toolkit evokes curiosity by introducing data in a fun, accessible and easy way.As such, creating a low threshold for users to interact with data, sensors and output modalities.The toolkit provides individuals with the option to explore data and corresponding environments, without having to do the programming, electronics, statistics, or data interpretation (e.g., numbers and graphs) [36].SensorBricks also provides individuals with the possibility to educate themselves about data by comparing different data sources and output modalities.By exploring this, individuals can create, change, or rebuild their own ambient sensor system based on their needs and preferences [48].
Data is presented to individuals via the SensorBricks in a nonnumerical and non-graphical way.Data is classified in an understandable way, making it accessible for novice users [17,36,102].However, individuals should be able to determine themselves the threshold for the data representation based on their comfort and preferences, showing the importance of user input within the data collection [17,110].Nonetheless, individuals should be guided on how to determine these thresholds.Individuals are more familiar with certain data types (e.g., temperature) than others (e.g., air quality).Therefore, experts need to interpret the data before users can make sense of these types of data, stressing the importance of a collaboration between users an experts [36,102].To enhance the development of data literacy of individuals, a balance should be found where users have the option to freely explore data in an understandable way, while potential obstacles are removed by experts on topics which is initially hard to grasp.

Role of Data in the Environment
SensorBricks provided individuals with the opportunity to reflect on the use of data and data outputs in several scenarios using the task cards.Participants indicated that they are currently experiencing several actions within their environment (e.g., window opening automatically or light turning off) without them having insights on why these actions are happening or having control over these events; thus, creating problems in terms of the intelligibility of data-based decision making.These sensor systems are a "black box" where individuals are seen as passive data subjects [17,110].There should be data transparency in which data is collected on how these effects the actions in the environment [8,102].Individuals should also have control over their environment, be able to make decisions based on data, and overrule the system if they think it will improve their well-being [91].The physicality of the toolkit creates transparency by making it visible where the data is collected, while the tangible interaction with the system creates the possibility to place the system at several locations, making users active data subjects.Being able to understand these actions and having control over them can help individuals to increase their data literacy.
Using a tangible toolkit allows individuals to explore data and output modalities in a real environment while creating the possibility for individuals to collaborate [109].In previous data literacy tools [10,32], data is often only accessible via a digital (online) dashboard, limiting the possibility of social and contextual situated information.The tangibility of the SensorBricks, which differed from commonly used apps or dashboards used for data literacy, extends the possibilities of displaying and visualizing data.Through its situatedness [107], the toolkit creates awareness to better understand the data in context [17] and the possibility for social awareness between individuals who can reflect on each other's experiences [109], while at the same time being part of the physical environment [45].Additionally, the tangibility of the SensorBricks could help with overcoming the overload through notifications [88], display blindness [69], and low recall in content [73], which is currently seen in purely digital-based tools.The use of tangibility in the field of data literacy is a promising area for the development of toolkits, creating awareness about the collection and use of data [17], and the possibility for social collaboration between individuals [109].

Collaborative Sensemaking
SensorBricks uses collaborative sensemaking in its design creating a structured way by collaboratively collecting and organizing data and information, creating new knowledge, insights, and understanding for individuals [78].The toolkit fits the characteristics of tangibility, commonality, visual representation, and ambiguity [98] to support collaborative sensemaking.The tangibility of the toolkit makes it possible for groups to grasp, build, and explore while learning about the data.The commonality is seen in the social coordination to reach the common goal of completion of the task [50].The visual representation of both the system itself and the output modalities embodies the representation of the participants' input, while the ambiguity allows openness to the interpretation and gives meaning to the situation or setting (e.g., the tasks).Collaborative sensemaking was observed throughout the workshop where participants mostly worked as a group either having 'Active discussion' (C1) between all participants or having a 'Single shared view' (C2) when building the sensor system.Collective sensemaking helped individuals to discuss the data and learn from each other's previous experiences when using the data, helping users to create a meaningful interpretation of simple sensor data and a shared understanding of their meaning [4].
Participants indicate a potential challenge with the social collaboration with the SensorBricks in the long term.The systems are built based on the preferences of a group of users.However, the reasoning behind the construction of the system is only known by that group, making it difficult to build upon each other's systems and gain knowledge.Collaborative sensemaking by visualizing sensemaking trajectories through timelines can help with sharing information found by different group members [72].Including features such as sensemaking, and trajectories can enhance the collaborative sensemaking in IoT toolkits such as SensorBricks.Therefore, future design iterations of SensorBricks or toolkits similar to it should explore including these types of sense-making trajectories, to explore whether the sensor systems can be extended to other groups of users who were not present during the phase of conception.Additionally, future scenarios are possible where individuals prefer to independently use the toolkit to explore data.Independently using the toolkit limits the possibility to learn from each other's experiences.To overcome this, the toolkit can provide users with the option to buffer and share experiences (e.g, via the tablet in the toolkit) with a collaborator may ease the transition between independent and joint work [44].

Combination of Data physicalization and Digital Interfaces
The SensorBricks toolkit presents the data in both a tangible way via the output data physicalization bricks and the digital dashboard on the iPad.When reflecting on the data representation, participants indicated a preference for combining data physicalization and digital output.Purely focusing on a data physicalization output resulted in assumption-based interpretation, due to a lack of detailed data, while the purely digital data was hard to interpret, due to a lack of knowledge of the meaning of the data [57,102].Therefore, a combination should be adopted where the two forms of data representation can strengthen each other [37,45,103].Data physicalization should be used to create awareness of when the data changes (e.g., reaches a certain threshold), while the digital output should give an in-detail overview of the data (e.g., data values or long-term data patterns).
The combination of these fields is already defined by the term "physecology" as defined by Sauve et al., [94] as: "the relations between the different design elements-physical and digital-of a physicalization, and their coupling to the audience and (physical) surroundings.".Artifacts such as Emerge [100] and Econundrum [92] use this concept, combining the physical representation of data via physicalization with a digital component to provide individuals with additional interactions and/or information.The combination of these components is a promising field for future work, where data physicalization and digital interfaces are used to create an understandable way of presenting data.

Future work and Limitations
The current version of the SensorBricks consists of 6 output bricks that physicalize the data.However, other modalities could be used to physicalize the data such as scent [19,67], taste [54], or heat.These outputs are not possible in the current state of SensorBricks due to power issues (heating elements), being time-consuming (3D printing food takes time), or impracticalities (smell takes time to disappear).Individuals also do not have the freedom to determine what the thresholds are for the output bricks.As a next step in introducing individuals to data, an initial step in the programming of the Bricks could be given.Software such as pipe-based programming are easy-to-use interfaces for the configuration of IoT devices and have been proven to help non-expert users with an introduction to programming [48].Integrating this kind of easy-to-use programming and new forms of data physicalizations can be explored for the development of future IoT-toolkits to increase the data literacy of individuals.
The SensorBricks toolkit was used in a workshop setting.The current toolkit focused on data literacy for individuals in general, with the participants building systems based on tasks.However, the toolkit can also be used in specific settings (e.g., home, office) or target groups (e.g., educational settings).After having an understanding of the use of the toolkit, the SensorBricks can be used in different settings, to explore the use of data.To enhance the replicability of the toolkit, all stl-files and codes for the development of the Bricks are provided 5 .Additionally, individuals could not use the toolkit in-situ, for a longer period.Future work will focus on placing the toolkit in a setting where individuals can explore, build, and reconfigure their system based on their needs and curiosity.With an improved system, individuals can learn about the long-term effect and role of data in their environment, while getting more familiar with the concepts of data collection and visualization [24,57].This artifact should be evaluated in a longer, more extensive research to gain an understanding of influencing the long-term effect of the SensorBricks on the data literacy of individuals.

CONCLUSION
Data is often a "black box" for non-expert users, leading to a lack of data transparency and speculation in the understanding and interpretation of data, not providing personal value to individuals.The SensorBricks toolkit introduced data to individuals in a fun, accessible and easy way, creating a low boundary for users to interact with data, sensors and output modalities.The toolkit helped individuals to reflect on the role of data in their environment.The collaborative aspect gave individuals the option to discuss the data and learn from each other's experiences helping users to create a meaningful interpretation and shared understanding of sensor data.The combined digital and data physicalization data representation gives individuals both awareness and in-detail information, creating an understandable and informative way of presenting data.
With our research, we contribute to the field of Human-Computer Interaction (HCI) by creating an understanding of the use and development of IoT toolkits to support the development of data literacy.The produced knowledge is relevant for both the development of design interventions aiming to introduce individuals to data in a creative, fun and iterative way.

Figure 1 :
Figure 1: Left: the SensorBricks toolkit.Right: Four pictures showcasing the steps of building a sensor system.

-
C3 Disjoint and Shared View -Disjoint and shared view denoted the focus of 1-3 members on SensorBricks, whilst others focused on their own bricks.-C4 Disjoint and Distributed View -Denoted 1-2 group members focusing on the SensorBricks, whilst the other two were engaged in active discussion (not using their own devices).-C5 Distributed View -Denoted that participants were focused on the SensorBricks with little to no discussion between each other.Complete focus on the task at hand.-C6 Distributed View with Discussion -Denoted that each participant was focused on their own bricks, whilst continuing the conversation with the others in the group.

Figure 4 :
Figure 4: Sensor systems built during the workshop.In total 30 systems were developed consisting of 22 single sensor systems and 8 dual sensor systems.The red squares highlight the sensor systems, which utilize output bricks as input for input bricks and data collection.

Figure 5 :
Figure 5: Overview of the collaboration styles, brick selection and duration of the workshops.The width of the bar corresponds to the distribution of the collaboration style over the task time.The thicker the bar, the more time was spent on the collaboration style.

Figure 6 :
Figure 6: Overview of the data literacy of participants during the different phases of the workshop.

5. 3
.2 NASA TLX.The data from the NASA TLX was analyzed using Repeated measures ANOVA, based on the 4 phases of the workshop.The initial observation of the analysis of the NASA TLX, shows an increase in Mental demand throughout the workshop (Table

5. 4 . 1
Intuitiveness of SensorBricks.The SensorBricks were experienced as an informative and easy-to-use toolkit that can help individuals to get more familiar with data: S1P1: "As you just go through the tasks, it becomes clear what you have to do.You can

Table 1 :
Data analysis of the NASA TLX, including the mean score of the questions after each phase of the workshop and Mauchly's test.

5. 4 . 4
Combining Data Physicalization with Digital Interfaces.The data is presented in the SensorBricks toolkits in both a digital, numerical way (via an Ipad) and via the data physicalization on the output bricks.Participants reflected on the role of the data representation during the workshop.Participants indicated a preference for combining both a digital and tangible/physicalized data representation when presenting the data: S1P2: "The combination of the digital where you can indeed check the factual information, but then one of the output modules to get the awareness.",S4P2: "I really like the combination, because then I also know exactly how much it is wrong [the data value].", S5P1 "I really see it as a backup [the digital data].