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001-es BibID:BIBFORM098493
035-os BibID:(WoS)000721995100001 (Scopus)85120484045
Első szerző:Csernoch Mária (informatika tanár)
Cím:Developing computational thinking skills with algorithm-driven spreadsheeting / Csernoch Mária, Biró Piroska, Máth János
Dátum:2021
ISSN:2169-3536
Megjegyzések:The paper presents the details of a four-year project to test the effectiveness of teaching spreadsheeting with spreadsheet programming, instead of the traditional, widely accepted surface approach methods. The novel method applied in the project, entitled Sprego (Spreadsheet Lego), is a concept-based problem-solving approach adapted from the didactics of other sciences and computer programming. In the experimental group contextualized, real-world programming problems are presented in a spreadsheet environment. A semi-unplugged data-driven analysis is carried out based on each problem, which is followed by the building of a feasible algorithm, expressed by natural language expressions. The coding is completed in the following step by applying a limited number of spreadsheet (Sprego) functions, multilevel, and array formulas. The final steps of the process are discussion and debugging. On the other hand, classical, tool-centered approaches are applied in the control groups. Our research reveals that the traditional surface approach methods for teaching spreadsheeting do not provide long lasting, reliable knowledge which would provide students and end-users with effective problem-solving strategies, while Sprego does. Beyond this finding, the project proves that Sprego supports schema construction and extended abstraction, which is one of the major hiatus points of traditional surface navigation methods. The project also reveals that developing computational thinking skills should not be downgraded, and the misconceptions of self-taught end-users and user-friendly applications should be reconsidered, especially their application in educational environments. Gaining effective computer problem-solving skills and knowledge-transfer abilities is not magic, but a time-consuming process which requires consciously developed and effective methods, and teachers who accept the incremental nature of the sciences.
Tárgyszavak:Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
algorithm-driven spreadsheeting
long lasting knowledge
schema construction
cognitive load
end-user computing
computational thinking
Megjelenés:IEEE Access. - 9 (2021), p. 153943-153959. -
További szerzők:Biró Piroska (1983-) (informatikus, matematikus) Máth János (1959-) (matematikus)
Pályázati támogatás:EFOP-3.6.3-VEKOP-16-2017-00002
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001-es BibID:BIBFORM082861
035-os BibID:(WoS)000524745100014 (Scopus)85077292469
Első szerző:Ghori, Khawaja Moyeezullah (informatikus)
Cím:Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection / Khawaja Moyeezullah Ghori, Rabeeh Ayaz Abbasi, Muhammad Awais, Muhammad Imran, Ata Ullah, Laszlo Szathmary
Dátum:2020
ISSN:2169-3536
Megjegyzések:With the ever-growing demand of electric power, it is quite challenging to detect and prevent Non-Technical Loss (NTL) in power industries. NTL is committed by meter bypassing, hooking from the main lines, reversing and tampering the meters. Manual on-site checking and reporting of NTL remains an unattractive strategy due to the required manpower and associated cost. The use of machine learning classifiers has been an attractive option for NTL detection. It enhances data-oriented analysis and high hit ratio along with less cost and manpower requirements. However, there is still a need to explore the results across multiple types of classifiers on a real-world dataset. This paper considers a real dataset from a power supply company in Pakistan to identify NTL. We have evaluated 15 existing machine learning classifiers across 9 types which also include the recently developed CatBoost, LGBoost and XGBoost classifiers. Our work is validated using extensive simulations. Results elucidate that ensemble methods and Artificial Neural Network (ANN) outperform the other types of classifiers for NTL detection in our real dataset. Moreover, we have also derived a procedure to identify the top-14 features out of a total of 71 features, which are contributing 77% in predicting NTL. We conclude that including more features beyond this threshold does not improve performance and thus limiting to the selected feature set reduces the computation time required by the classifiers. Last but not least, the paper also analyzes the results of the classifiers with respect to their types, which has opened a new area of research in NTL detection.
Tárgyszavak:Természettudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:IEEE Access. - 8 (2020), p. 16033-16048. -
További szerzők:Abbasi, Rabeeh Ayaz Awais, Muhammad Imran, Muhammad (1981-) (informatikus) Ullah, Ata Szathmáry László (1977-) (programtervező-informatikus)
Pályázati támogatás:EFOP-3.6.3-VEKOP-16-2017-00002
EFOP
Internet cím:Intézményi repozitóriumban (DEA) tárolt változat
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