Human-Centered Digital Sustainability: Handling Enumerated Lists in Digital Texts

In advance to the present study, the authors introduced a method which makes it possible to calculate the entropy of natural language digital texts, focusing on word-processed texts, presentations, and webpages. This entropy reveals that the more underdeveloped documents are, the more demanding their content-related modification becomes. It was also found that the time and data required to complete a modification task in an erroneous document is several times more than in its correct counterpart. This finding leads to the end-user paradox: the less trained end-users are, the more errors they make, and the modification of their documents requires more resources. To resolve these discrepancies, the present study defines the sustainability rate of natural language digital texts which calculates the losses – the waste of human resources, time, workspace, computers, energy, frustration, working in bees, losing data – generated by negligent text management. Furthermore, we present examples of how manual and enumerated lists behave to modifications in a 213-page long document and conclude from our investigations that while the waste of human and machine resources occurs repeatedly in erroneous documents, the sustainability rate remains low. To prove the necessity of correction, we cleared the sample document, which took approximately 67 hours of two experts of our research group (
$2\times67$
hours). With this method, we found that the correction of errors can be extremely demanding, but uses resources only once, and further modifications in the now correct document need only the content-required amount of time, activities, entropy, and resources, in accordance with the expectations of the person intended to update the document. To correct documents, we present the Error Recognition Model, which is proved effective and efficient in digital education. All our findings indicate that both education and industry should adapt the presented approach (1) to develop students’ and end-users’ computational thinking skills, (2) to manage and take advantage of errors, (3) to recognize connections between the structure of the text and the complex word processing tools, (4) to pay attention to digital sustainability – beyond hardware and software development and recycling – with a focus on the human factor. Recently, the Error Recognition Model is a reactive problem-solving approach, whose effectiveness is justified. However, the near future is to run parallel the reactive and proactive uses of this approach, while if we look far into the future, the proactive use to digital born natural language texts should dominate.