Christopher PagaczUX/UI designer
Selected projects

Interaction design · Prototyping · UX research

Beyond Minimalism.

MSc study comparing touchscreen and physical vehicle controls.

Context
MSc dissertation · Submitted 15 October 2026 · Result pending
My focus
Study design, interface specification & prototype integration
Tools & methods
HTML/CSS/JS · Python · Hardware integration · Mixed methods
Deliverable
Study prototypes, research findings & proposed framework
Side view of the Goldsmiths cockpit simulator with a dashboard display, steering wheel and physical controls, surrounded by a projected driving scene.
The study setup in the existing Goldsmiths simulator. I specified and integrated the touchscreen and physical-control conditions, with fabrication and electronics support from Mirko Febbo. Photograph: Christopher Pagacz; simulator projection: BeamNG.drive.View full size

Study overview

licensed drivers
22
everyday control tasks
4
conditions per participant
2

The four tasks

  • Increase temperature
  • Change fan speed
  • Skip to the next track
  • Activate the front demister

01 · Research aims

Research aims and my role

My dissertation compared touchscreen and physical controls for four automotive tasks: adjusting temperature, changing fan speed, skipping a track and activating the front demister. I investigated task performance, workload and reported usability, and used interviews to understand participants’ experiences.

I designed and ran the simulator study with 22 licensed drivers, specified the interfaces, led prototype development and integration, and analysed the findings. I then proposed an exploratory framework for deciding which parts of a task could use physical, touchscreen or hybrid controls.

02 · Prototype development

Developing the study interfaces

I checked the touchscreen layout from the driver’s seat on the simulator’s 1920 × 364 display. Reaching some controls required stretching, so I used cockpit photographs and specific width and position constraints to direct a more compact layout with a separate instrument area.

I specified the climate and media behaviour, selected the physical components and tested the software integration. I also found buttons that worked locally but failed in the hosted version, then directed revisions and retested them. These checks informed the prototype layout and behaviour; I did not measure an ergonomic improvement.

The wide dashboard display showing the touchscreen panel to the left of the instruments and steering wheel.
The installed display shows the touchscreen panel beside the instruments and steering wheel. I used this setup to check placement and reach. Photograph: Christopher Pagacz; existing cockpit: Goldsmiths; simulator projection: BeamNG.drive.View full size
See the physical controls and system architecture (2)
Physical radio and climate-control panels mounted below the simulator display.
The physical-control condition. I sourced components and specified the arrangement; Mirko Febbo supported fabrication, mounting, wiring and radio-to-MQTT electronics. Photograph: Christopher Pagacz.View full size
Architecture diagram separating live radio and vehicle-telemetry routes from video-based task-outcome analysis.
Final dissertation Figure 3.4. Radio/MQTT/Python/Supabase and vehicle telemetry supported live apparatus state; task outcomes came from video review. Diagram created with ChatGPT image generation under my direction.View full size

03 · Study method

Study procedure and data collection

Each driver used both conditions. Eleven started with touchscreens and eleven with physical controls, using alternating condition order. Temperature, fan speed, next track and front demister followed the same fixed task sequence.

I conducted the sessions, recorded task completion, time and errors, and collected perceived-workload and usability ratings alongside comparative interviews. I checked task outcomes against the recordings and documented exclusions. An attempt to automate pilot video timing produced inaccurate results, so I rejected it and completed the timing manually.

Annotated photographs comparing the cockpit overview, physical-condition display, touchscreen climate panel and physical-control panels.
Final dissertation Figure 3.3 shows the two implementations in context. The study-specific controls were installed within the existing simulator, rather than a cockpit built from scratch.View full size
See the session procedure (1)
Session procedure showing 22 drivers divided between the two condition orders, four tasks in each condition, ratings and comparative interviews.
Supplementary procedure diagram, checked against the submitted methods. Condition order was balanced, not randomly assigned; task order remained fixed.View full size

The simulator supplied driving context; the controls did not operate real heating, airflow or demisting. Fixed task order and unrecorded self-paced familiarisation limit interpretation.

04 · Task results

More fan-control errors in the touchscreen condition

Across 21 valid pairs, the tested touchscreen fan control produced an average of 2.05 more total errors than the physical implementation (Holm-adjusted p = .010). This was the only primary task difference that remained statistically significant after its relevant adjustment.

Other primary comparisons remained uncertain. Access depth, feedback, placement and display style differed alongside control modality, so this finding does not establish that touchscreen fan controls are always worse.

Paired total-error differences for the four tasks. The fan task averaged 2.05 more touchscreen errors across 21 valid pairs, with an unadjusted 95 percent interval of approximately 0.86 to 3.43.
Positive values indicate more touchscreen errors. Intervals are unadjusted 95% paired-bootstrap intervals; the fan comparison’s reported p-value is Holm-adjusted. Chart from the submitted dissertation.View full size
Compare the completion-time results (1)
Successful-completion-time differences for the four tasks, with unadjusted 95 percent confidence intervals.
Completion time among successful paired trials. No primary time comparison survived its relevant Holm adjustment; the positive fan-time interval alone should not be read as a significant adjusted result.View full size

05 · Interview findings

Interview findings and design implications

I analysed the interviews to identify explanations and countercases for the numerical results. Participants described task needs, access, how easily controls could be distinguished, first-use clarity, remembered locations and feedback. The experiment did not independently isolate these possible explanations.

Input and value display
Making an approximate adjustment and checking an exact value are different needs. The input and the display can be considered separately.
Media information and track controls
A screen can present media information, while a frequent next-track action can use a separate control.
Action, state and outcome feedback
Acknowledging a press, displaying the selected state and confirming what the system has done are different forms of feedback.
Four-theme synthesis connecting task characteristics, access and differentiation, familiarity and first-use clarity, and feedback.
Final dissertation Figure 4.5: a reporting structure for the qualitative findings, not a causal model. Created with ChatGPT image generation from my analytical structure and drafts.View full size

Visual demand was reported by participants. The study did not measure eye movements, lane keeping or road-safety outcomes.

06 · Design contribution

Proposed framework for allocating controls

I combined the quantitative and interview findings into a proposed task-first framework. It starts with what the driver needs to do and know, then considers input, access, display and feedback separately before selecting physical, touchscreen or hybrid controls.

Proposed framework: characterise task demand, allocate components, check cockpit fit, then validate the system.
Final dissertation Figure 6.1. The four-stage framework is an exploratory design contribution; the study did not validate a new hybrid interface.View full size
  1. Characterise task demand

    Identify frequency, urgency, precision and the information the driver needs.

  2. Allocate components

    Consider input, access, display, feedback and persistence separately.

  3. Check cockpit fit

    Examine placement, reach, differentiation, reliability and learning in context.

  4. Validate the system

    Test the complete arrangement and revise the allocation against evidence.

07 · Contribution & next steps

My contribution, support and next steps

I was responsible for the research design, recruitment, participant sessions, interaction requirements, development direction, integration, final testing and analytical decisions. The physical-control setup and live vehicle-data route were contributed to Goldsmiths, with the telemetry application left for future students to use or adapt.

Goldsmiths supplied the simulator and laboratory. Mirko Febbo supported fabrication and electronics, alongside simulator and MQTT advice. Tony Russell-Rose supervised the project; Yoram Chisik provided guidance and laboratory access.

Software development, analysis support and documentation involved substantial AI assistance. I directed and checked that work, accepting, amending or rejecting outputs. Testing in the simulator also identified issues that software checks alone had not caught.

My next study would isolate the number of steps needed to reach the fan control and how clearly controls are distinguished, then compare alternative control arrangements. I would also rehearse complete sessions earlier, test failure conditions and archive the exact tested configuration. These are proposed next steps, not completed validation.

Submitted on 15 October 2026; academic result pending. Findings come from one simulator, two research implementations and small, outcome-specific samples.