conclude these results in three pages:

5.4 Survey Results
Upon completion of the NASA TLX assessment, results from each of the evaluated dimensions—such as mental demand, physical demand, temporal demand, performance, effort, and frustration level—were aggregated to yield a comprehensive measure of task complexity. The scoring process begins with each dimension assigned a base value that falls between 0 and 100, reflecting the perceived workload or strain associated with that facet of the task. These initial values are then adjusted through multiplication by corresponding weights, which are determined by participants in the second part of the survey, emphasizing their subjective importance. The cumulative score thus derived serves as an index of overall task complexity; the lower the aggregated total, the simpler and more manageable the task is deemed to be. Consequently, this score is inversely related to the user’s playing experience: tasks with lower final scores, signifying reduced complexity, tend to foster greater ease of interaction and enhance player satisfaction. The scoring system does not define a rigid threshold for tedium, thus the final scores should be interpreted with consideration for the specific context and the nuances of user experience.
5.4.1 Health potions
The NASA TLX, a subjective workload assessment tool, intentionally lacks a predefined boundary that designates when a task shifts from being manageable to tedious. This absence of a concrete threshold offers flexibility, entrusting researchers with the duty of analyzing and interpreting the scores in relation to the specific context of the study. In this manner, the significance of the results can vary based on the nature of the task and the subjective experiences of individuals. But this feature allows for a comparative approach of two surveys, especially when both of them were filled in by the same individuals. In such a case, it will be possible to directly compare and determine the best mechanic.
Table No. 1 charts the survey findings, it contains weighted scores, drawing attention to the right side where it can be viewed the tabulated final scores. Inspecting the data at the base of the table reveals the arithmetic means for each of the dimensions. In the analysis, Performance and Effort are revealed as the most challenging dimensions of the task according to participants, suggesting that these facets had a significant impact on their workload experience. The dimensions of Mental Demand, Physical Demand, and Temporal Demand cluster more centrally, with scores that lie in close proximity to one another, indicating a relatively balanced impact on the overall workload rating by these factors. Notably, Frustration appeared to be the least imposing, evidenced by its placement at the lower end of the spectrum with a modest average value of 22, and with only a tiny number of respondents - three in total - assigning it a weight greater than zero, emphasizing its minimal role in the overall complexity assessment.
Aggregate data analysis is further detailed in Table No. 2, which outlines key statistical metrics derived from the collated results. The table provides an insightful summary that incorporates a robust set of statistical values: the arithmetic mean of all collected final scores stands at 699, demonstrating a tendency toward higher perceived task complexity among participants, while the standard deviation of 191.51 points to a substantial variability in the scores, which reflects differing perceptions of task workload among the participants. The minimum and maximum scores—410 and 1000, respectively—present the range of perceived workload within the group, with the lower end indicating less complexity and the upper end representing a more demanding task. The median score of 655 offers an additional reference point, representing a middle ground in perceptions of task complexity and suggesting a central tendency that leans towards a higher workload as perceived by the study participants. These statistical functions together shape a comprehensive picture of the overall workload experience as appraised through the lens of the NASA TLX, allowing for informed interpretations and conclusions.
Table No. 3 displays the weights assigned to each of the dimensions within the NASA TLX along with their corresponding mean scores and standard deviations (std. dev.). This information serves as a tool to identify which specific parameters were perceived by respondents as most taxing. It is evident that Performance is deemed the most challenging with an average score of 3.5 coupled with a standard deviation of 0.71. Trailing close behind is Effort, which has an average score of 3.3 and a standard deviation of 1.06. The consistency of these outcomes with the data provided in Table No. 1 lends credibility to the findings. This level of agreement extends to the remaining dimensions as well.
Last but not least is 4th Table. This contains the raw survey results, omitting the previously applied weights. The bottom portion of the table provides the overall arithmetic mean and std. dev. for the compiled dataset. The results in this section exhibit some differences when juxtaposed with earlier presented data. Here, Temporal Demand and Effort dominate with mean scores of 50 and 49, respectively. Mental, Physical, and Temporal Demand do exhibit comparable levels in terms of both the mean and the standard deviation. In essence, all the parameters listed are on a comparable footing concerning these statistical indicators. It is only Frustration that distinctly lags, registering a score of 22. Intriguingly, this figure mirrors the outcome obtained when leveraging a weighted mean, underscoring the potential significance of Frustration within the set of parameters regardless of the weighting scheme applied.
5.4.2 Automatic Healing
The survey highlighted fascinating differences between how users perceive the mechanics under analysis. In general, we observed a tendency for lower scores across the board compared to the previous survey. This pattern hints that possibly, the features in question were less complicated and more user-friendly for those who participated.
This phenomenon is further evidenced by the scores presented in 5th Table. Looking closely at the data, it becomes evident that the user experience cannot be boiled down to a single metric. The nuanced responses, calculated by applying weights obtained from the preferences expressed in the second survey section, add depth to our understanding of user engagement. It is important to note that the aggregate final scores, found in the first column on the right of the table, reflect a multi-dimensional assessment that transcends basic satisfaction levels.
The high scores is Temporal Demand, which refers to the time pressure users feel while utilizing the mechanic, indicates that certain interactive elements may be seen as fast-paced or potentially stressful. The close follow-up, Effort, suggests that users are also considering the mental or physical exertion required to engage with the mechanic. These factors standing out significantly from the rest implies that when it comes to user interaction, speed and ease of use are at the forefront of the user’s mind. The dominance of these two aspects over others (Mental and Physical Demand, Performance)―which scored in the range of 60 to 80 points, and Frustration, which is at the lower end with an average of only 28―reflects particular areas that are less troublesome for the user..
The contrasting lower scores for Frustration suggest a positive sign, indicating that while the mechanic may demand time and effort, it does not necessarily provoke irritation or annoyance. This is a crucial insight for developers aiming to create engaging yet challenging experiences without discouraging the user due to excessive difficulty or complexity.
Moving to the summary encapsulated in Table number 6, further patterns emerge. The stark difference in the average final score for Automatic Healing – 497, being 200 points lower than its counterpart, points towards an increased level of satisfaction or perhaps an indication that the mechanic is more straightforward and less demanding. The standard deviation confirms the tight clustering of responses around the central tendency, alluding to a more consistent user experience across the board with this mechanic.
Notably, the median score, which hovers at 455 points, gives us another way to look at the results. It helps us ignore the very high or very low numbers that could make the average seem different from what most people actually experienced. The range of scores from the lowest at 230 to the highest at 870 further informs us about the complete spectrum of user interactions, indicating that while many may find the mechanic unproblematic, there is a subset of users that may struggle significantly more or, conversely, find it extremely easy to use.
Next, Table number 7 provides insight into how much weight users gave to each aspect during the survey. Of particular interest are the final results, categorized by the arithmetic mean and the standard deviation. This analysis reveals that the most emphasized parameters by users were Effort, Temporal Demand, and Performance, each having significant mean scores. However, Performance turned out to be the dark horse, with not only high scores but also boasting the narrowest standard deviation, suggesting that users consistently rated it as an important factor. The other parameters, while scoring lower, also exhibited narrow standard deviations, ranging from 1.2 to 1.8, pointing to a reliable concurrence among responses.
In exploring the final piece of this study, as per Table number 8, a more raw yet equally telling set of data comes into view. Following through with analyzing raw survey scores, without weights, grants us an alternative perspective on user preferences. The Temporal Demand dimension, consistent with previous data sets, emerges with the top scores. In its wake, Effort takes a strong second place while the other dimensions assemble at roughly similar point levels. What is captured here is an echo of the prior tables; the standard deviation, neatly similar across all dimensions, reflects consistent and balanced feedback from the respondents, ensuring that no extreme values are distorting our view of the user experience.
Altogether, without drawing conclusions, the information presented through these survey results paints a nuanced picture of user interactions with the mechanics being studied. The data—both weighted and raw—provide a host of valuable clues as to user preferences, behaviors, and satisfaction levels. These clues are instrumental for those in the process of crafting and refining interactive experiences, serving as a roadmap for recognizing areas that resonate positively with users as well as those that might benefit from thoughtful redesign or simplification. As we delve into and decode these numbers, we continue to enrich our understanding of the complex and ever-evolving relationship between users and the mechanisms they engage with.