Further testing the performance of ExposeHK in
CommandMaps-like interfaces and a semi-realistic task
Évaluations supplémentaires d'ExposeHK dans le contexte d'une
interface de type CommandMaps et d'une tâche semi-réaliste

Joel Harrison
joelharrisonnz@gmail.com
University of Canterbury
Christchurch, New Zealand

Sylvain Malacria
Univ. Lille, Inria, CNRS, Centrale Lille,
UMR 9189 CRISTAL
Lille, France
sylvain.malacria@inria.fr

Andy Cockburn
andrew.cockburn@canterbury.ac.nz
University of Canterbury
Christchurch, New Zealand

<figure><img src="image_1.png" /><figcaption>Figure 1: Left: illustration of the ExposeHK technique enabled in the macOS Safari software. When a modifier key is pressed, typically the cmd key, it displays all the available keyboard shortcuts over their corresponding toolbar buttons; Right: an example of a CommandMaps interface, showing a substantial fraction of the Microsoft Word's commands at once by concatenating all first-level tabs of the Ribbon interface</figcaption></figure>

Abstract

ExposeHK is an interaction technique that displays available key-
board shortcuts over a Graphical User Interface (GUI), making it
possible to activate keyboard shortcuts without having to memorize
them beforehand. We conducted two studies to further assess the po-
tential of ExposeHK as an efficient command selection mechanism
in a GUI displaying a large number of commands (CommandMaps),
and in a text formatting task. Our results suggest that ExposeHK is
26% faster than pointer for selecting commands, that users sponta-
neously use more keyboard shortcuts when ExposeHK is available,
and also hint that users might incidentally memorize some of them.

Résumé

ExposeHK est une technique d'interaction qui affiche les raccour-
cis clavier disponibles directement sur une interface graphique
 GUI), permettant ainsi de les activer sans avoir à les mémoriser

au préalable. Nous avons mené deux études complémentaires afin
d’évaluer davantage le potentiel d’ExposeHK en tant que méca-
nisme efficace de sélection de commandes dans une interface gra-
phique affichant un grand nombre de commandes (Command Maps)
et dans une tâche de mise en forme de texte. Nos résultats suggèrent
qu’ExposeHK permet de sélectionner des commandes 26 % plus
rapidement qu’avec le pointeur souris, que les utilisateurs utilisent
spontanément plus de raccourcis clavier lorsque ExposeHK est dis-
ponible, et indiquent également qu’ils pourraient en mémoriser
certains de manière incidente.

CCS Concepts

* Human-centered computing → Human computer interaction (HCI); Graphical user interfaces; Keyboards; Interaction techniques; Empirical studies in HCI.

Keywords

Keyboard Shortcuts, Menus, Command Selection, Toolbar

IHM '25, Toulouse, France
2025. ISBN 979-8-4007-1522-8/25/11
https://doi.org/10.1145/3765712.3765715
# Mots clés

Raccourcis clavier, Barre de Menu, Selection de Commande, Barre d'Outils

## Reference Format:

Joel Harrison, Sylvain Malacria, and Andy Cockburn. 2025. Further testing the performance of ExposeHK in CommandMaps-like interfaces and a semi-realistic task. In *IHM '25 : Articles Scientifiques de la 36ᵉ conférence Francophone sur l'Interaction Humain-Machine, Novembre 4-7, 2025, Toulouse, France.*, 10 pages. https://doi.org/10.1145/3765712.3765715

# 1 Introduction and Background

Keyboard shortcuts are a keyboard-based alternative to pointer-based command selection, consisting of pressing a combination of keys simultaneously to activate a command [1] Their efficiency comes from three mechanical advantages over pointing: first, in tasks like word processing, hands remain on the keyboard, avoiding the need to reach for a pointing device and return; second, they eliminate the back-and-forth pointer movement between the workspace and control widgets; and third, they enable quick command selection with a single key combination, bypassing menu or tab navigation. Their efficiency in terms of execution has been both characterized in various theoretical models [3, 4] and demonstrated in user studies [12, 24].

The efficiency and overall success of keyboard shortcuts has motivated a large amount of research adapting them, or designing similar interaction techniques, for various interactive contexts ranging from touch-based devices like smartphones [6], tablets [7, 13], smartwatches [17] or even tabletops [9, 10]. They have also been explored in extended reality contexts [23], or combining different input modalities [15]. Another branch of research has explored how the expressiveness of keyboard shortcuts could be augmented, for instance by discriminating which finger was used to activate them [29]. All these work demonstrate that keyboard shortcuts have, without a doubt, been highly influential in terms of interaction design.

Yet, in spite of all their advantages, it is also well-known that users underuse keyboard shortcuts, even in situations where using them might be beneficial [2, 18, 25] Several possible reasons might explain this. First, users have to discover that performing keyboard shortcuts is possible [20], and once done, be aware that a keyboard shortcut exists for a given command. Second, they need to memorize the keyboard shortcut beforehand, for later retrieval.

The latter problem then directly illustrates a phenomenon called the paradox of the active user [5]: users who would benefit the most from these shortcuts might be too busy with their own tasks to make the effort to memorize them. Research has shown that augmented feedback [12, 16] or reflective interfaces can assist users in learning and using shortcuts [22]. However, it is still needed to memorize the corresponding key combination beforehand.

Another solution is therefore to simply remove the need to have to memorize the key combination beforehand, which was proposed in ExposeHK [21]. The interaction principle is rather simple: when a modifier key is pressed, typically the cmd or Ctrl key, the interface reveals all available keyboard shortcuts of the GUI (e.g. toolbar, menubar, ribbon tab). Then, the user can simply perform

the corresponding keyboard shortcut to activate the target command. Because the key combination is displayed on screen, it is no longer necessary to memorize it.

The seminal ExposeHK paper [21] described the results of three studies. The first two studies, conducted with toolbar (Figure 2) and menubar (Figure 3-top) interfaces, contrasted keyboard shortcut usage and performance when using ExposeHK over the state of the art augmented feedback mechanism. Their results suggest that ExposeHK significantly increased keyboard shortcut usage, while no significant difference was found in terms of command selection time. The third study compared the performance of ExposeHK, Alt keytips (or hotkeys) [1], and pointer selection in a Microsoft Ribbon-like interface (Figure 3-bottom) and revealed that users were significantly faster to select commands using ExposeHK. Altogether, these experiments confirmed that ExposeHK efficiently increases keyboard shortcut usage and improve over alternative selection methods in tabbed interfaces like the Microsoft Ribbon.

Since then, several research projects have investigated systems that could assist users into using keyboard shortcuts without having to memorize them. Typically, IconHK investigated whether toolbar icons could be modified to permanently display the keyboard shortcut, thereby removing the need to rely on a mode triggered by pressing the modifier key [8], employing three different strategies. Results of a memorization experiment revealed that users can *retrieve* keyboard shortcuts from the icons using two of these strategies, but did not provide any evidence of actual *memorization*. Another project of this type can be found in Lewis et al.'s KeyMap work [19] in which instead of overlaying keyboard shortcuts over GUI components of the interface, they display an onscreen keyboard that informs which commands can be activated by typing them. Their user study suggests that KeyMap interfaces results in a higher implicit memorization of keyboard shortcuts compared to ExposeHK, even though it may seem contradictory to test memorization on a technique whose primary goal is to eliminate the need for it.

In this paper, we report on the results of two other studies conducted to further understand interaction with ExposeHK<fnref n="1" />, investigating the two following research questions. First, by displaying all of the available menu commands at once when used with menubars, ExposeHK effectively turns the command-access interface into a CommandMap [27, 28], a highly efficient pointer-based interface. Yet, it is unclear how ExposeHK would compare to CommandMaps in terms of performance, that is, whether *selecting commands using keyboard shortcuts remain faster than pointer based selection in large and flat menu interfaces*. Second, ExposeHK has been systematically evaluated in abstract tasks. However, it is unclear whether ExposeHK *would still increase keyboard shortcut usage when tested in a more realistic task*. In the following section, we first describe the experiment conducted with CommandMaps-like interface. We then report on the results of a pilot experiment, conducted using a text-formatting task and some realistic command-to-keyboard shortcut mappings. Altogether, the results of these studies further highlight the benefits of the ExposeHK techniques, confirming that

<footnote n="1">¹These studies were originally conducted a few weeks after the submission of the seminal ExposeHK paper (September 2012)</footnote>
<figure><img src="image_2.png" /><figcaption>Figure 2: Toolbar used for evaluating the performance of ExposeHK in the seminal paper [21], for selecting commands in a toolbar interface. By default, the toolbar displays the command icons and label (top). When the user presses the Ctrl modifier key, all buttons replace their icons with their corresponding keyboard shortcuts (bottom)</figcaption></figure>

it is possible to design highly efficient input methods that do not require users to memorize key combinations beforehand.

## 2 Study Maps

Our first experiment investigated how a keyboard-based command selection technique like ExposeHK compares to pointer-based selection in a large and flat command GUI like CommandMaps [27] (Figure 1-right). Indeed, similar to the implementation of ExposeHK for menu bars [21] (Figure 3-top), CommandMaps display a large set of commands at once when a modifier key is pressed. From this point, users can either click on the item or directly invoke the command using its keyboard shortcut, raising the question of which technique offers the best performance.

### 2.1 Experimental method

2.1.1 *Task and stimulus.* The task consisted of selecting a target using a specific command-selection technique. To start a selection, the participant had to move the cursor to a 2.4×2.4 cm square in the center of the screen and then press the spacebar. Once the spacebar was pressed, text was displayed showing the name of the item to be selected, and the timer for the selection started. Upon making a correct selection, a sound was played, and text was displayed instructing the user to move back to the center square and press the spacebar. Once the spacebar was pressed, the timer for that item selection stopped. Requiring the user to press the spacebar to start and end a selection simulated a round-trip command selection, where initially, while the user is working, their hands are on the keyboard, and after making the selection, they return to working on the keyboard.

Incorrect command selections resulted in a 3-seconds lockout from the interface. This was intended to discourage participants from trying to guess keyboard shortcuts when they were not completely certain. A sound was also played for an incorrect selection, which was different from the correct selection confirmation sound.

2.1.2 *Procedure.* Participants were welcomed to the experiment and invited to sit in front of the experimental computer. They were then given an explanation of the experimental task, which consisted of selecting items as quickly and accurately as possible using both experimental *Techniques*: ExposeHK or CommandMaps. Before each technique, participants completed 8 selections on a practice interface. They then had to perform 8 *blocks* of selections with the *Experimental Interface*, which displayed all tabs of a ribbon-like interface at once (Figure 4), consisting of 7 tabs with 18 items each.

Each item was 1×1 cm in size and would highlight a border when the mouse was moved over it.

A block of selections consisted of 2 sets of 6 *target* items. The targets were chosen based on their distance from the center of the screen. The three distances used were close (6.8 – 8.5 cm), medium (13.6 – 15.3 cm), and far (20.4 – 22.1 cm), with 2 targets selected from each distance. All other items in the interface acted as distractors and were never to be selected. The order of target presentation was randomly generated at the start of each block while ensuring that consecutive targets were not the same. Targets remained consistent across the 8 blocks, allowing users to gain experience in making those selections.

For both selection techniques, the *Experimental Interface* consisted of a CommandMap-like interface that was displayed when the participant pressed Ctrl and hidden when the participant released it (see Figure 4). In the CommandMaps (*pointer*<sub>CM</sub>) condition, item selection was performed using the mouse, whereas in the ExposeHK (*keyboard*<sub>CM</sub>) condition, selection was done by executing the corresponding keyboard shortcut. The keyboard shortcuts for the *keyboard*<sub>CM</sub> technique consisted of the Ctrl key plus a letter key. The letter key was chosen randomly from the 12 leftmost letter keys on the keyboard, ensuring that it did not match the first or last letter of the related item. The time taken, item selected, key presses, and errors were recorded for each selection. Upon completing each technique, participants completed a NASA-TLX questionnaire before moving on to the next technique. At the end of the experiment, they completed a questionnaire indicating their preferred technique for three questions.

2.1.3 *Design.* The experiment used a 2×8×3 within subject-design with *Technique* (*pointer*<sub>CM</sub>, *keyboard*<sub>CM</sub>), *Block* (1–8) and *Distance* (close, medium, far) as factors. With 12 target selection per block, we therefore collected 2 × 8 × 12 = 192 selection per participant. The order of the factor technique was counter balanced across the participants.

2.1.4 *Participants and apparatus.* 18 participants (ages 21–45, 1 female) were recruited from the computer science department and received a 10 NZ$ voucher for their time. The experimental system was developed in C# and was run on a Windows 7 computer. The screen used was 1280 x 1024, 22" display along with an external QWERTY keyboard and optical mouse.
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /><figcaption>Figure 3: Interfaces used for evaluating the performance of ExposeHK in the seminal paper [21]. Top: for selecting commands in a menubar interface. When the user presses the Ctrl modifier key, it reveals all first-level menus of the menubar at once, next to each other; Bottom: for selecting commands in a Microsoft Ribbon-like interface (tabbed toolbar). When the user presses the Ctrl modifier key, it reveals keyboard shortcuts for the button of the current Ribbon tab. Users can switch between tabs by using arrow keys or the mousewheel.</figcaption></figure>

## 2.2 Results

Our dependent measures were command selection *Times* and *Error* rates. In addition, we were also interested in the participants' subjective preferences for the different techniques.

### 2.2.1 Data processing and analysis.
For time and error analysis, we report mean values and conducted repeated measures (RM) ANOVAs, with Greenhouse-Geisser corrections applied to the degrees of freedom when sphericity was violated. For pairwise comparisons, we used a Bonferroni correction in which measured p-values are multiplied by the number of comparisons, keeping 0.05 as significance threshold. Note that due to data loss, the data analysis was conducted using log files that had already aggregated measurements over *DISTANCE*. As a result, each participant's data contained three measurements per *TECHNIQUE* per *BLOCK* — one for each *DISTANCE*. For subjective preferences, we analyzed NASA-TLX results by first converting each answer to its equivalent on a (1–5) scale, with a precision of half a point.

### 2.2.2 Error rate.
RM-ANOVA revealed an effect of *TECHNIQUE* ($F_{1,17} = 9.12, p = 0.007, \eta_g^2 = 0.035$), with *pointerCM* resulting in significantly fewer errors (0.3%) than *keyboardCM* (2.8%). No other significant difference was found.

### 2.2.3 Command Selection time.
First two blocks were significantly slower than others, as can be observed on Figure 5. Indeed, as we are mostly interested in user performance after familiarization, we first ran a RM-ANOVA on *Time* independently for each *TECHNIQUE* with *BLOCK* as factor. They revealed a significant effect of *BLOCK* for both *keyboardCM* ($F_{5,14,87,49} = 85.84, p < 0.001, \eta_g^2 = 0.73$) and *pointerCM* ($F_{5,65,100,97} = 100.97, p < 0.001, \eta_g^2 = 0.80$). Post-hoc tests revealed significant differences for both *TECHNIQUE* between the first block and all others, as well as between the second block and all others except block three<fnref n="2" />. We therefore remove the first two blocks from all subsequent analysis.

A RM-ANOVA on *Time* with *TECHNIQUE* and *DISTANCE* as factors revealed a significant effect of *TECHNIQUE* ($F_{1,17} = 44.98, p < 0.001$,

<footnote n="2">²Only exceptional other differences between other blocks was observed.</footnote>
<figure><img src="image_5.png" /><figcaption>Figure 4: Experimental interface without (top) and with (bottom) ExposeHK activated. Note that the screencapture is cropped and two more tabs, containing 18 commands each, were displayed below the "Animals" tab.</figcaption></figure>

$\eta_g^2 = 0.42$), with participants selecting commands in significantly 26% less time on average using *keyboard$_{CM}$* (1.87s) than *pointer$_{CM}$* (2.52s). No other effect were observed.

We also found a significant effect of **TECHNIQUE** in terms of *Performance* ($\chi^2(1, n = 18) = 9.3, p = 0.2$) with *keyboard$_{CM}$* (4.2, SD=1.05) being perceived as more performing than *pointer$_{CM}$* (3.39, SD=1.04). No other significant difference was found.

### 2.2.4 Subjective results.

**NASA-TLX.** The results reported regarding the NASA-TLX questionnaire are summarized in Figure 6. We found a significant effect of **TECHNIQUE** in terms of *Physical Demand* ($\chi^2(1, n = 18) = 11.26, p = 0.08$) with *keyboard$_{CM}$* (1.78, SD=0.8) being perceived as significantly less physically demanding than *pointer$_{CM}$* (2.94, SD=0.94).

*Preferences.* Regarding other subjective preferences, in general participants strongly preferred *keyboard$_{CM}$* over *pointer$_{CM}$* (17 participants Vs. 1), also finding it easier (14 Vs. 4) and faster (17 Vs.1) to use.
<figure><img src="image_6.png" /><figcaption>Figure 5: Average command selection time in seconds, per technique and per block. Error bars reflect 95% confident intervals. Data to the left of the vertical dashed lines were discarded for statistical analysis</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Figure 6: NASA-TLX reported scores</figcaption></figure>

## 2.3 Discussion

The main goal of this study was to compare the performance of a CommandMaps-like interface operated with ExposeHK via keyboard input ($keyboard_{CM}$) to that of a standard pointer-based selection method ($pointer_{CM}$). Our results suggest that selecting commands with $keyboard_{CM}$ took significantly less time (26% difference) than with $pointer_{CM}$. This performance increase was also reported by participants, that also found $keyboard_{CM}$ less physically demanding according to NASA-TLX reports. $Keyboard_{CM}$ was also strongly appreciated and considered being faster, easier and simply preferred overall over $pointer_{CM}$. Overall, this study suggests that $keyboard_{CM}$ was not only faster, but also strongly appreciated.

Our procedure required participants to press the space bar at the start and end of each trial to simulate a round-trip command selection. However, in the $pointer_{CM}$ condition, participants often placed one hand on the space bar and the other on the pointing device, meaning they never had both hands on the keyboard. This hand placement may not accurately reflect typical usage in text-based applications such as Microsoft Word. To better simulate real-world scenarios, we could have explicitly required both hands to

be on the keyboard at the start and end of each trial, introducing a homing time for pointer-based selection [26]. If this adjustment had been made, the observed difference would likely have been even larger.

In the end, the performance benefits of $keyboard_{CM}$ can be attributed to a technique that is easy to use, since it does not require memorizing keyboard shortcut associations, and yet fast, as performing a key chord is quick. The visual search that may be required to retrieve the keyboard shortcut did not seem to impede performance in comparison to the pointer based CommandMap in which the user's ultimate performance is limited by their mouse movement speed.

## 3 Study memory

The previous evaluation, similarly to previously published studies [21], suggests that ExposeHK can reduce command selection time compared to conventional interfaces such as the Microsoft Ribbon, toolbars, menubars, and even CommandMaps [27]. However, all of these studies relied on simple and abstract tasks in which participants were instructed to select a specific target command, and often constrained to a certain input method. As a result, we cannot determine how users would adopt and interact with ExposeHK in more realistic scenarios. To address this, we conducted a pilot study examining the short-term effects of ExposeHK in a semi-realistic task, a text-formatting task that required selecting multiple commands that users could activate using the modality they want, in their preferred order.

<figure><img src="image_8.png" /><figcaption>Figure 7: The experimental interface with a text editor on the left-hand side consisting of a text area and a toolbar of commands. The desired final format is displayed in the right-hand side of the text editor</figcaption></figure>

### 3.1 Experimental method

3.1.1 Task. The experimental semi-realistic task involved using a simple text editor interface to format raw text to match a specified target format (see Figure 7). The interface was set to a fixed size of 1310×700 pixels<fnref n="3" />. The top part of the interface was a toolbar displaying various formatting commands. All commands could also be activated using their corresponding keyboard shortcuts, which

<footnote n="3">³Reporting physical dimensions was not possible because display pixel density information was unavailable</footnote>
were consistent with those used in Microsoft Word (e.g., Bold was Ctrl+B, Italics was Ctrl+I) when existing. This allowed participants to use their pre-existing knowledge of keyboard shortcuts and ensured they would not be confused when learning new ones. Participants could expose keyboard shortcuts through tooltips which would appear when the user hovered the mouse over a command button for 500ms. The rightmost part of the interface displayed a non-editable pre-formatted text, the *target* text. The leftmost part displayed an editable text field containing the same text content than *target*, but *raw* (unformatted).

Participants were required to format the *raw* text to resemble the *target* version, applying only text formatting commands (e.g., alignment, bold, etc.). The task did not involve adding or modifying the content of the *raw* text. Participants were asked to simply inform the experimenter when they were done with text formatting. Since the task was used only as a mean to require participants to select various commands, no formal formatting verification was conducted.

### 3.1.2 Procedure.
Participants were invited to seat in front of the computer and were instructed they would have to format text using a certain interface, by selecting commands either by clicking on the button of the toolbar or by using the corresponding keyboard shortcut. They were told not to try and go as fast as possible but to just complete the task as they normally would with a text editor.

They first performed a rapid practice text formatting session using a GUI with ExposeHK available, that is when the participant pressed and held the Ctrl key, the keyboard shortcut of each button would be overlaid on the toolbar (see Figure 8). Then, each participant had to repeat the same text formatting tasks three times (three STAGES).

The first STAGE (*EHK*<sub>pre</sub>) was conducted using a normal GUI where participants could only reveal keyboard shortcuts via tooltips. The second STAGE (*EHK*) was conducted using a GUI where ExposeHK was available (keyboard shortcuts were overlaid on the toolbar when the Ctrl key was pressed). The third STAGE (*EHK*<sub>post</sub>) was conducted with ExposeHK disable (same interface as *EHK*<sub>pre</sub>, only possible to reveal keyboard shortcuts via tooltips).

For each interface, key presses, mouse movements, command selections, tooltip displays were logged to a log file.

<figure><img src="image_9.png" /><figcaption>**Figure 8: Toolbar of the text-formatting interface with ExposeHK enabled**</figcaption></figure>

### 3.1.3 Design, participants and apparatus.
The experiment used a within-subjects design with STAGE (*EHK*<sub>pre</sub>, *EHK*, *EHK*<sub>post</sub>) as a factor, always presented in that order. 12 participants (ages 21-30, 2 female) were recruited. The experimental system was developed in

C# and was run on Windows 7 computer along with an external QWERTY keyboard and optical mouse.

## 3.2 Results

### 3.2.1 Keyboard shortcut usage per STAGE.
We observed a large difference for each stage in terms of mean keyboard shortcut usage with 9.79% (s.d. 19.67), 86.20% (s.d. 12.87) and 73.09% (s.d. 21.15) for the *EHK*<sub>pre</sub>, *EHK* and *EHK*<sub>post</sub> stages, respectively. However, with that granularity, it is difficult to assess whether this evolution results from a simple learning effect or is affected by the possibility to use ExposeHK in the *EHK* stage. To better understand this, we split each STAGE into 10 blocks over time (see Figure 9). This shows that the keyboard shortcut usage did not drastically increase overtime during the *EHK*<sub>pre</sub> stage, suggesting that users are not trying to learn new keyboard shortcuts but are just continuing with the ones that they currently know. For the *EHK* stage, the figure suggests that keyboard shortcut usage is instantly very high and is consistent through the stage. Finally, we see that it remains relatively high in the *EHK*<sub>post</sub> stage, even though users cannot expose the keyboard shortcuts anymore and have to remember them, suggesting a certain memorization of the recently used keyboard shortcuts.

<figure><img src="image_10.png" /><figcaption>**Figure 9: Proportion (in %) of selections performed using keyboard shortcuts during the different stages of the experiment.**</figcaption></figure>

### 3.2.2 Keyboard shortcut usage per command.
The keyboard shortcut usage for each command is summarized in Figure 10⁴. All the keyboard shortcuts used in *EHK*<sub>pre</sub> stage correspond to those available in text editors such as Microsoft Word. Commands that did not have keyboard shortcuts in Microsoft Word at the time of the experiment<fnref n="5" /> were not used, suggesting that users were not attempting to learn new shortcuts but only relying on the ones they already knew. In the *EHK* stage, keyboard shortcut usage is high for most commands, with the lowest usage still reaching 56%. Finally, for the *EHK*<sub>post</sub> stage, keyboard shortcut usage varies. Familiar Microsoft Word commands, such as Bold (Ctrl + B) and Italics (Ctrl + I), show high usage. However, for less familiar commands and lacking an

<footnote n="4">⁴Note that we focus this discussion for commands that were required for the formatting task. Other commands selected occasionally, such as undo and redo, are not discussed.</footnote>

<footnote n="5">⁵This experiment was conducted in August 2012</footnote>
<figure><img src="image_11.png" /><figcaption>Figure 10: For each command, proportion (in %) of selections performed using keyboard shortcuts during the different stages of the experiment.</figcaption></figure>

intuitive shortcut, such as text color blue (Ctrl + H), there is a mod-
erately sustained usage with a small noticeable drop compared to
the *EHK* stage.

3.2.3 Tooltip exposure. For EHK<sub>pre</sub>, the total number of tooltip exposures across all participants was 749 (mean 62.42, s.d. 14.74), with only two keyboard shortcuts being activated after a tooltip exposure. This result indicates that although tooltips were frequently displayed, users paid little attention to them, activating a keyboard shortcut immediately after exposure only twice. This further suggests that in EHK<sub>pre</sub>, users were not learning new keyboard shortcuts but instead relying on their prior knowledge for command selection. For EHK<sub>post</sub>, there were a total of 210 tooltip exposures (mean 17.5, s.d. 11.37), with 15 keyboard shortcuts being activated after an exposure. The decrease in tooltip exposures can be attributed to the increase in keyboard shortcut usage.

3.3 Discussion

This study was a preliminary investigation of whether keyboard
 shortcut usage would increase on short term when using ExposeHK
in a realistic task. Our results suggest that with ExposeHK enabled
in the *EHK* stage, users instantly had an extremely high proportion
of keyboard shortcut usage. Interestingly, it also revealed that par-
ticipants retained some short-term keyboard shortcut knowledge,
even with this brief exposition, as suggested by the relatively high
proportion of hotkey usage in the *EHK*<sub>post</sub> stage. That being said,
we must note that, contrary to the previous experiment, we did
not avoid using mnemonics such as the first letter of the command
for some commands (e.g., Ctrl+G for Green), thereby facilitating
memorization.

Another interesting observation is the low shortcut usage (below 10%) in *EHK*<sub>pre</sub> stage. This result suggests that tooltips may be ineffective for discovering and learning new keyboard shortcuts, as users tend to ignore them. High tooltip exposure but minimal shortcut activation further supports this. Since tooltips primarily provide item information rather than shortcut prompts, users do not associate them with learning new shortcuts. This highlights the need for dedicated shortcut discovery methods.

That being said, this data collection was conducted on a very
short time period, with a small sample of participants. We therefore
avoid making strong claims, as broader generalization would re-
quire a more comprehensive evaluation. In addition, while our task
simulated document editing, real-world usage may differ, possibly
requiring a study where users create and format documents from
scratch. We considered using such a task in our experiment, but
we observed significant inter-subject differences in the time spent
writing text, which made the task excessively long for some partic-
ipants. An alternative evaluation approach would be to integrate
ExposeHK in real world applications to log usage patterns. How-
ever, integrating solutions like ExposeHK, that requires to access
different components of the interface (input devices, GUI compo-
nents, relationship between components such as buttons and menu
items in order to retrieve corresponding keyboard shortcuts, etc.)
is far from trivial. We have made numerous attempts to integrate
ExposeHK into real-world applications, but frequent software up-
dates and increased system integrity protections have repeatedly
undone our work.

4 Conclusion and Future Work

Keyboard shortcuts are an efficient command selection mechanism
that has motivated a large amount of research to facilitate their
usage. Among them, ExposeHK is a system that assists user in ac-
tivating commands using their keyboard shortcut without having
to memorize the corresponding key combination beforehand. We
conducted two additional studies to better understand the perfor-
mance and potential of ExposeHK in CommandMaps-like interface
and in a semi-realistic text formatting task. Altogether, our results
confirm the potential of ExposeHK as an efficient command selec-
tion technique that does not require users to rely on recall, even
when a large number of commands is displayed. They also suggest
that participants spontaneously use more keyboard shortcuts, and
hints that users might incidentally memorize some of them.

One important point to note, however, is that participants in both studies had a strong background in computer science. While this population serves as a good proxy for everyday productivity app users, who would ultimately benefit from any GUI improvement, ExposeHK has the distinctive advantage of not requiring users to memorize key combinations. This makes it potentially well-suited for users who are less experienced with computers. Although our first experiment, which used abstract commands and shortcuts, simulates the familiarization phase that might occur during initial use, it does not negate the influence of our participants' technical background. Therefore, testing ExposeHK with less computer-experienced users would provide valuable insights into its potential to support command selection via keyboard shortcuts for non-expert users.

Finally, the software landscape has evolved significantly since
2012, when ExposeHK was designed and the original studies were
conducted, and now in 2025. Today, an increasing number of ap-
plications are web-based and are frequently accessed through web
browsers that come with their own built-in menubars, toolbars,
and keyboard shortcuts. ExposeHK, however, overlays shortcuts
directly onto visible GUI components and would function without
issue for permanently displayed elements such as buttons in tool-
bars. For transient components like menubars, a rolling mechanism,
such as the one proposed in the discussion section of the original
ExposeHK paper for switching between menubar and toolbar [21],
could similarly be used to toggle between the menubars of the web
application and those of the browser. Regardless, native desktop
apps still prevail, as illustrated by the fact that, as of 2024, 68% of
office workers reportedly prefer using desktop applications for com-
plex tasks such as data analysis, content creation, and multitasking
[11], and 58% of developers continue to prioritize desktop app de-
velopment, valuing the reliability, performance, and efficiency that
native applications offer for complex operations [14]. Nevertheless,
future work could further investigate how systems like ExposeHK
perform when applied to web applications accessed through web
browsers.

References

[1] Gilles Bailly and Sylvain Malacria. 2020. *Command Selection*. Springer International Publishing, Cham, 1-35. doi:10.1007/978-3-319-27648-9_19-1

[2] Suresh K Bhavnani and Bonnie E John. 2000. The strategic use of complex computer systems. *Human-computer interaction* 15, 2 (2000), 107–137.

[3] Stuart K. Card, Thomas P. Moran, and Allen Newell. 1980. The keystroke-level model for user performance time with interactive systems. *Commun. ACM* 23, 7 (July 1980), 396–410. doi:10.1145/358886.358895

[4] Stuart K. Card, Allen Newell, and Thomas P. Moran. 1983. *The Psychology of Human-Computer Interaction*. L. Erlbaum Associates Inc., USA.

[5] John M. Carroll and Mary Beth Rosson. 1987. *Paradox of the active user*. MIT Press, Cambridge, MA, USA, 80–111.

[6] Katherine Fennedy, Sylvain Malacria, Hyowon Lee, and Simon Tangi Perrault. 2020. Investigating Performance and Usage of Input Methods for Soft Keyboard Hotkeys. In *22nd International Conference on Human-Computer Interaction with Mobile Devices and Services (Oldenburg, Germany) (MobileHCI '20)*. Association for Computing Machinery, New York, NY, USA, Article 29, 12 pages. doi:10.1145/3379503.3403552

[7] Katherine Fennedy, Angad Srivastava, Sylvain Malacria, and Simon T. Perrault. 2022. Towards a Unified and Efficient Command Selection Mechanism for Touch-Based Devices Using Soft Keyboard Hotkeys. *ACM Trans. Comput.-Hum. Interact.* 29, 1, Article 4 (Jan. 2022), 39 pages. doi:10.1145/3476510

[8] Emmanueloul Giannisakis, Gilles Bailly, Sylvain Malacria, and Fanny Chevalier. 2017. IconHK: Using Toolbar button Icons to Communicate Keyboard Shortcuts. In *Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI '17)*. Association for Computing Machinery, New York, NY, USA, 4715–4726. doi:10.1145/3025453.3025595

[9] Alix Goguey, Géry Casiez, Thomas Pietrzak, Daniel Vogel, and Nicolas Roussel. 2014. Adoiraccourcic: multi-touch command selection using finger identification. In *Proceedings of the 26th Conference on L'Interaction Homme-Machine (Villeneuve d'Ascq, France) (IHM '14)*. Association for Computing Machinery, New York, NY, USA, 28–37. doi:10.1145/2670444.2670446

[10] Alix Goguey, Daniel Vogel, Fanny Chevalier, Thomas Pietrzak, Nicolas Roussel, and Géry Casiez. 2017. Leveraging finger identification to integrate multi-touch command selection and parameter manipulation. *International Journal of Human-Computer Studies* 99 (2017), 21–36. doi:10.1016/j.ijhcs.2016.11.002

[11] Graphon. 2024. Desktop Applications Are Still Essential in 2024. https://www graphon.com/blog/desktop-application Accessed: 2025-06-22

[12] Tovi Grossman, George Fitzmaurice, and Ramtin Attar. 2009. A survey of software learnability: metrics, methodologies and guidelines. In *Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Boston, MA, USA) (CHI '09)*. Association for Computing Machinery, New York, NY, USA, 649–658. doi:10.1145/1518701.1518803

[13] Carl Gutwin, Andy Cockburn, Joey Scarr, Sylvain Malacria, and Scott C. Olson. 2014. Faster command selection on tablets with FastTap. In *Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Toronto, Ontario, Canada) (CHI '14)*. Association for Computing Machinery, New York, NY, USA, 2617–2626. doi:10.1145/2556288.2557136

[14] JhavTech Studios. 2024. Desktop App Development: A Complete Guide for 2025. https://jhavtech medium com / desktop-app-development-a-complete-guide-for-2025-931d4fe354e4 Accessed: 2025-06-22.

[15] Elio Keddisseh, Marcos Serrano, and Emmanuel Dubois. 2021. KeyTch: Combining the Keyboard with a Touchscreen for Rapid Command Selection on Toolbars. In *Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI '21)*. Association for Computing Machinery, New York, NY, USA, Article 191, 13 pages. doi:10.1145/3411764.3445288

[16] Brian Krisler and Richard Alterman. 2008. Training towards mastery: overcoming the active user paradox. In *Proceedings of the 5th Nordic Conference on Human-Computer Interaction: Building Bridges* (Lund, Sweden) (NordiCHI '08). Association for Computing Machinery, New York, NY, USA, 239–248. doi:10.1145/1463160.1463186

[17] Benjamin Lafreniere, Carl Gutwin, Andy Cockburn, and Tovi Grossman. 2016. Faster Command Selection on Touchscreen Watches. In *Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems* (San Jose, California, USA) (CHI '16). Association for Computing Machinery, New York, NY, USA, 4663–4674. doi:10.1145/2858036.2858166

[18] David M Lane, H Albert Napier, S Camille Peres, and Aniko Sandor. 2005. Hidden Costs of Graphical User Interfaces: Failure to Make the Transition from Menus and Icon Toolbars to Keyboard Shortcuts. *International Journal of Human-Computer Interaction* 18, 2 (may 2005), 133–144. doi:10.1207/s15327590ijhc1802_1

[19] Blaine Lewis, Greg d'Eon, Andy Cockburn, and Daniel Vogel. 2020. KeyMap: Improving Keyboard Shortcut Vocabulary Using Norman's Mapping. In *Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI '20)*. Association for Computing Machinery, New York, NY, USA, 1–10. doi:10.1145/3313831.3376483

[20] Eva Mackamul, Géry Casiez, and Sylvain Malacria. 2020. Clarifying and differentiating discoverability. *Human-Computer Interaction* 0, 0 (2024), 1–26. doi:10.1080/0737002.2024.2364606 arXiv:https://doi.org/10.1080/0737002.2024.2364606

[21] Sylvain Malacria, Gilles Bailly, Joel Harrison, Andy Cockburn, and Carl Gutwin. 2013. Promoting Hotkey use through rehearsal with ExposeHK. In *Proceedings of the SIGCHI Conference on Human Factors in Computing Systems* (Paris, France) (CHI '13). Association for Computing Machinery, New York, NY, USA, 573–582. doi:10.1145/2470654.2470735

[22] Sylvain Malacria, Joey Scarr, Andy Cockburn, Carl Gutwin, and Tovi Grossman. 2013. Skillometers: reflective widgets that motivate and help users to improve performance. In *Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology* (St. Andrews, Scotland, United Kingdom) (UIST '13). Association for Computing Machinery, New York, NY, USA, 321–330. doi:10.
1145/2501988.2501996

[23] Mark McGill, Stephen Brewster, Daniel Pires De Sa Medeiros, Sidney Bovet, Mario Gutierrez, and Aidan Kehoe. 2022. Creating and Augmenting Keyboards for Extended Reality with the Keyboard Augmentation Toolkit. *ACM Trans. Comput.-Hum. Interact.* 29, 2, Article 15 (Jan. 2022), 39 pages. doi:10.1145/3490495

[24] Daniel L. Odell, Richard C. Davis, Andrew Smith, and Paul K. Wright. 2004. Toolglasses, marking menus, and hotkeys: a comparison of one and two-handed command selection techniques. In *Proceedings of Graphics Interface 2004* (London, Ontario, Canada) (GI '04). Canadian Human-Computer Communications Society, Waterloo, CAN, 17-24.

[25] S Camille Peres, Franklin P Tamborello, Michael D Fleetwood, Phillip Chung, and Danielle L Paige-Smith. 2004. Keyboard shortcut usage: The roles of social factors and computer experience. In *Proceedings of the Human Factors and Ergonomics Society Annual Meeting*, Vol. 48. SAGE Publications Sage CA: Los Angeles, CA, 803-807.

[26] Thomas Pietrzak, Sylvain Malacria, and Gilles Bailly. 2014. Ctr Mouse et TouchCtrl: duplicating mode delimiters on the mouse. In *Proceedings of the 26th Conference on I'Interaction Homme-Machine* (Villeneuve d'Ascq, France)

(IHM '14). Association for Computing Machinery, New York, NY, USA, 38-47. doi:10.1145/2670444.2670447

[27] Joey Scarr, Andy Cockburn, Carl Gutwin, and Andrea Bunt. 2012. Improving command selection with CommandMaps. In *Proceedings of the SIGCHI Conference on Human Factors in Computing Systems* (Austin, Texas, USA) (CHI '12). Association for Computing Machinery, New York, NY, USA, 257-266. doi:10.1145/2207676.2207713

[28] Joey Scarr, Andy Cockburn, Carl Gutwin, Andrea Bunt, and Jared E. Cechanowicz. 2014. The usability of CommandMaps in realistic tasks. In *Proceedings of the SIGCHI Conference on Human Factors in Computing Systems* (Toronto, Ontario, Canada) (CHI '14). Association for Computing Machinery, New York, NY, USA, 2241-2250. doi:10.1145/2556288.2556976

[29] Jingjie Zheng, Blaine Lewis, Jeff Avery, and Daniel Vogel. 2018. FingerArc and FingerChord: Supporting Novice to Expert Transitions with Guided Finger-Aware Shortcuts. In *Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology* (Berlin, Germany) (UIST '18). Association for Computing Machinery, New York, NY, USA, 347-363. doi:10.1145/3242587.3242589