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001-es BibID:	BIBFORM098067
035-os BibID:	(cikkazonosító)590 (WoS)000713096100001 (Scopus)85116346579
Első szerző:	Nagy Tímea Katalin (informatika-matematika tanár)
Cím:	The Comparison of Students' Self-Assessment, Gender, and Programming-Oriented Spreadsheet Skills / Nagy Tímea, Csernoch Mária, Biró Piroska
Dátum:	2021
ISSN:	2227-7102
Megjegyzések:	Previous research proved that teaching spreadsheeting from a programming perspective is much more effective than the widely accepted tool-centered surface approach methods. Spreadsheeting as an introductory programming approach allows students to build up schemata leading to contextualized, concept-based problem-solving. Furthermore, it provides tools for real-world problem-solving in other disciplines, and supports knowledge-transfer to database management and "serious" programming. The present study provides the details of a nationwide testing of Grades 7?10 students on how they evaluate their spreadsheet knowledge, which classroom activities form their self-assessment values, and the results of three spreadsheet tasks evaluated by the SOLO categories of understanding. The comparison reveals that most students' spreadsheet knowledge is pre-structural. On the other hand, they assess themselves much higher, which is primarily based on the number of activities carried out in classes. Traces of conscious problem-solving and knowledge-transfer within the scope of spreadsheeting are hardly detectable, while knowledge brought from mathematics is recognizable. In general, we found proof that the pieces of knowledge remain unconnected, not allowing students to reach the relational level of understanding and build up long-lasting knowledge.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk spreadsheet self-assessment knowledge-transfer computer problem-solving programming
Megjelenés:	Education Sciences. - 11 : 10 (2021), p. 1-29. -
További szerzők:	Csernoch Mária (1963-) (informatika tanár) Biró Piroska (1983-) (informatikus, matematikus)
Pályázati támogatás:	EFOP-3.6.3-VEKOP-16-2017-00002 EFOP
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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001-es BibID:	BIBFORM106845
035-os BibID:	(cikkazonosító)101 (WoS)000938554700001 (Scopus)85148767987
Első szerző:	Tóth Róbert (informatikus)
Cím:	Lossless Encoding of Mental Cutting Test Scenarios for Efficient Development of Spatial Skills / Róbert Tóth, Miklós Hoffmann, Marianna Zichar
Dátum:	2023
ISSN:	2227-7102
Megjegyzések:	In the last decade, various mobile applications have been developed to improve and measure spatial abilities using different spatial tests and tasks through augmented reality (AR), Virtual Reality (VR), or embedded 3D viewers. The Mental Cutting Test (MCT) is one of the most well-known and popular tests for this purpose, but it needs a vast number of tasks (scenarios) for effective practice and measurement. We have recently developed a script-aided method that automatically generates and permutes Mental Cutting Test scenarios and exports them to an appropriate file format (to GLB (glTF 2.0) assets) representing the scenarios. However, the significant number of permutations results in more than 1,000,000 assets, requiring more than 6 GB of storage space. This paper introduces an encoding scheme consisting of four stages to handle this issue through significantly reducing the storage space, making the app suitable for everyday individual use, even on a mobile phone. The proposed method encodes a subset of assets from which it can decode the whole dataset with 3% time complexity compared to classical Blender's computations, exceeding the compression ratio of 10,000 and storage space saving 99.99%. This paper explains the features of the original assets, introduces the encoding and decoding functions with the format of documents, and then measures the solution's efficiency based on our dataset of MCT scenarios.
Tárgyszavak:	Műszaki tudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban folyóiratcikk Mental Cutting Test augmented reality GLB assets encoding compression
Megjelenés:	Education Sciences. - 13 : 2 (2023), p. 1-21. -
További szerzők:	Hoffmann Miklós (1966-) (matematikus, informatikus) Bodroginé Zichar Marianna (1971-) (informatikus, matematikus)
Pályázati támogatás:	ÚNKP-22-3 Egyéb
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