Error Recognition Model: High-mathability End-user Text Management

Katalin, Sebestyén; Gábor, Csapó; Csernoch, Mária; Aradi, Bernadett

doi:10.12700/aph.19.1.2022.1.10

Cited by 5 publications

(13 citation statements)

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“…This orientation, however, can lead to a variety of errors and a lot of time spent recovering from errors. …" [31] This approach seems to be the forerunner of the Error Recognition Model [32] but disappeared without any traces and/or impacts.…”

Section: Positioning Digital Text Managementmentioning

confidence: 99%

“…The Error Recognition Model [32] is a complex system to recognize, correct, and eliminate errors in natural language digital texts. It focuses on word-processed texts, presentations, and webpages, but many other documents might be considered with a certain amount of text (e.g., spreadsheets, online quizzes, formative assessment tools, computer games).…”

Section: Error Recognition Model (Erm)mentioning

confidence: 99%

“…In a remote village, the news reached a young schoolteacher who, with his students, worked using ERM (Error Recognition Model) [32] [89] [113] [114]. One of their best companions were errors, because they learned that errors are not bad, but something to learn from [2] [117].…”

Section: H the King And The Digital Reportmentioning

confidence: 99%

“…One of their best companions were errors, because they learned that errors are not bad, but something to learn from [2] [117]. They learned how to recognize and categorize errors [32] [89], how to correct them, and how to handle digital texts efficiently without errors. Their favorite occupation was error hunting [32].…”

Section: H the King And The Digital Reportmentioning

confidence: 99%

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

Csernoch,

Nagy,

Nagy

et al. 2024

IEEE Access

Self Cite

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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 e 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 lossesthe waste of human resources, time, workspace, computers, energy, frustration, working in bees, losing datagenerated 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 × 67 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 sustainabilitybeyond hardware and software development and recyclingwith 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.

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Section: Positioning Digital Text Managementmentioning

confidence: 99%

Section: Error Recognition Model (Erm)mentioning

confidence: 99%

Section: H the King And The Digital Reportmentioning

confidence: 99%

Section: H the King And The Digital Reportmentioning

confidence: 99%

See 2 more Smart Citations

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

Csernoch,

Nagy,

Nagy

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…The question repeatedly arises, how can we convince people that their text-treatment practice, habits, concepts, and approaches do more harm than good? Even fully developed pieces of software lose their power by misuse (Panko, 1998(Panko, , 2013Csernoch and Dani, 2022;EuSpRIG Horror Stories, 2022;Sebestyén et al, 2022). On the one hand, scientific papers presented on this subject reach neither the target population (Ben-Ari and Yeshno, 2006;Wing, 2006;Csernoch, 2009Csernoch, , 2010Ben-Ari, 2015;EuSpRIG Horror Stories, 2022) nor the appropriate segment of education (Malmi et al, 2019(Malmi et al, , 2022.…”

Section: Introductionmentioning

confidence: 99%

Pre-testing erroneous text-based documents: Logging end-user activities

Nagy

Csernoch²

2023

Front. Educ.

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An extremely high number of erroneous text-based documents are in circulation, being multiplied, serving as samples both in the office world and in education. Creating, editing, and modifying these documents generates a serious financial loss when both human and machine resources are considered. Our research team developed an application and a testing method for building up an objective measurement system, in order to see the rate of loss in the handling of erroneous documents. In the pre-testing period, the error factor of a sample text was set up based on the logged activities of an expert researcher. It was found that first level modifications require about five times more human and machine resources in a short, one-paragraph text burdened with layout errors than in its properly formatted version. Further testing is required to find out how demanding longer texts and more serious modifications are, but it is already obvious that erroneous text-editing, and the lack of fundamental computational thinking skills involve unnecessary use of our resources.

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Tanulók szövegkezelés-ismereteinek és önértékelésének kapcsolata

Nagy¹,

Csernoch²

2023

InfTars

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A szövegkezelés és a szövegszerkesztés oktatása során az informatika kerettantervvel összhangban, felület alapú módszereket és azt támogató tankönyvek használatát figyelhetjük meg a közoktatásban. Ennek következtében a tanulók és a felhasználók egyre inkább a szoftveres környezetre és felületre fókuszálnak, az eszközhasználatot tekintik elsődleges célnak, figyelmen kívül hagyva a helyesen szerkesztett dokumentumokkal szemben támasztott követelményeket. A tanulási folyamatból hiányzik a valódi problémamegoldás, melynek következtében a tanulók, később végfelhasználók, a dokumentum helyességének ellenőrzését sem igénylik. E megközelítésekkel nemcsak sokkal időigényesebb egy dokumentum elkészítése, hanem az esetleges változtatások szükségtelen extra gépeléseket és formázásokat vonnak maguk után. Jelen tanulmányban bizonyítjuk, hogy a hagyományos felületalapú oktatási módszerekkel elsajátított tudás alacsony szintű, amelyhez már az általános iskolában túlzott magabiztosság társul.

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Error Recognition Model: High-mathability End-user Text Management

Cited by 5 publications

References 0 publications

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

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

Pre-testing erroneous text-based documents: Logging end-user activities

Tanulók szövegkezelés-ismereteinek és önértékelésének kapcsolata

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