TY - CONF AU - Tóth, Róbert AU - Hoffmann, Miklós AU - Zichar, Marianna TI - viSkillz - a project dealing with Mental Cutting Test exercises T2 - 12th International Conference on Applied Informatics (ICAI 2023) PB - Eszterházy Károly Katolikus Egyetem C1 - Eger PY - 2023 SP - 1 EP - 2 PG - 2 UR - https://m2.mtmt.hu/api/publication/34095521 ID - 34095521 LA - English DB - MTMT ER - TY - JOUR AU - Tóth, Róbert AU - Tóth, Bálint AU - Hoffmann, Miklós AU - Zichar, Marianna TI - viskillz-blender—A Python package to generate assets of Mental Cutting Test exercises using Blender JF - SOFTWAREX J2 - SOFTWAREX VL - 22 PY - 2023 PG - 6 SN - 2352-7110 DO - 10.1016/j.softx.2023.101328 UR - https://m2.mtmt.hu/api/publication/33639889 ID - 33639889 N1 - Cited By :2 Export Date: 18 February 2024 Correspondence Address: Tóth, R.; Faculty of Informatics, Hungary; email: toth.robert@inf.unideb.hu Funding details: Nemzeti Kutatási, Fejlesztési és Innovaciós Alap, NKFIA Funding details: Innovációs és Technológiai Minisztérium Funding text 1: Supported by the ÚNKP-22-3 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funders. AB - Several different methods are used to test the spatial abilities or visual skills of people. One of them is the Mental Cutting Test (MCT), the exercises of which offer a 2D projection of a 3D shape and a 2D plane, and testees should determine the shape of their intersection. MCT exercises need various 2D and 3D assets that should be developed before publishing a test. In recent decades, very few exercises have been available to instructors and researchers. In 2019, we published our first solution that could be used to calculate the intersections of MCT scenarios, then render or export their assets using Blender. This paper proposes an extended, open-source package for Blender that generates assets of MCT exercises by permuting predefined shapes, cutting planes, rotation, and scale operators. The additional wrapper script helps users use the package for various purposes, such as developing exercise offering methods, designing exercises, practicing, or organizing exams and measurements. LA - English DB - MTMT ER - TY - JOUR AU - Tóth, Róbert AU - Hoffmann, Miklós AU - Zichar, Marianna TI - Lossless Encoding of Mental Cutting Test Scenarios for Efficient Development of Spatial Skills JF - EDUCATION SCIENCES J2 - EDUC SCI VL - 13 PY - 2023 IS - 2 PG - 21 SN - 2227-7102 DO - 10.3390/educsci13020101 UR - https://m2.mtmt.hu/api/publication/33573320 ID - 33573320 N1 - Cited By :1 Export Date: 18 February 2024 Correspondence Address: Hoffmann, M.; Faculty of Informatics, Kassai 26, Hungary; email: hoffmann.miklos@inf.unideb.hu Funding details: Nemzeti Kutatási, Fejlesztési és Innovaciós Alap, NKFIA Funding text 1: Supported by the ÚNKP-22-3 New National Excellence Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation Fund. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funders. AB - 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. LA - English DB - MTMT ER - TY - CHAP AU - Tóth, Róbert AU - Tóth, Bálint AU - Zichar, Marianna AU - Fazekas, Attila AU - Hoffmann, Miklós ED - Cheng, Liang Yee TI - Educational Applications to Support the Teaching and Learning of Mental Cutting Test Exercises T2 - Proceedings of the 20th International Conference on Geometry and Graphics, ICGG 2022 PB - Springer Netherlands CY - Cham SN - 9783031135873 T3 - Lecture Notes on Data Engineering and Communications Technologies, ISSN 2367-4512 ; 146. PY - 2023 SP - 928 EP - 938 PG - 11 DO - 10.1007/978-3-031-13588-0_81 UR - https://m2.mtmt.hu/api/publication/33066496 ID - 33066496 N1 - Faculty of Informatics, University of Debrecen, Debrecen, Hungary Faculty of Economics, University of Debrecen, Debrecen, Hungary Faculty of Informatics, Eszterházy Károly University, Eger, Hungary Doctoral School of Informatics, University of Debrecen, Debrecen, Hungary Cited By :1 Export Date: 24 March 2023 Correspondence Address: Tóth, R.; Faculty of Informatics, Hungary; email: toth.robert@inf.unideb.hu AB - Developing or measuring the spatial skills of people is still an interesting research topic nowadays. Well-known assignments such as the exercises of Mental Cutting Test are still popular tools of performing measurements or validating the knowledge of our students. However, we found that researchers, instructors and students are not entirely supported with tools that can be used to enhance their processes. In this paper, we present a resource browser and a quiz application, that might be useful for people who have to deal with Mental Cutting Test exercises. LA - English DB - MTMT ER - TY - CHAP AU - Tóth, Róbert AU - Tóth, Bálint AU - Zichar, Marianna AU - Fazekas, Attila AU - Hoffmann, Miklós ED - Cheng, Liang Yee TI - Detecting and Correcting Errors in Mental Cutting Test Intersections Computed with Blender T2 - Proceedings of the 20th International Conference on Geometry and Graphics, ICGG 2022 PB - Springer Netherlands CY - Cham SN - 9783031135873 T3 - Lecture Notes on Data Engineering and Communications Technologies, ISSN 2367-4512 ; 146. PY - 2023 SP - 904 EP - 916 PG - 13 DO - 10.1007/978-3-031-13588-0_79 UR - https://m2.mtmt.hu/api/publication/33066494 ID - 33066494 N1 - Faculty of Informatics, University of Debrecen, Debrecen, Hungary Faculty of Economics, University of Debrecen, Debrecen, Hungary Faculty of Informatics, Eszterházy Károly University, Eger, Hungary Doctoral School of Informatics, University of Debrecen, Debrecen, Hungary Cited By :2 Export Date: 18 February 2024 Correspondence Address: Tóth, R.; Faculty of Informatics, Hungary; email: toth.robert@inf.unideb.hu Funding text 1: Acknowledgement. Supported by the ÚNKP-21-3 new national excellence program AB - Mental Cutting Test is a widely used format to develop or measure the spatial skills of people in various situations, having different purposes. An exercise consists of a 2D projection of a 3D shape and an intersection plane, denoted by a frame. The task is to choose the shape of their intersection from the set of five shapes. In this paper, we investigate a great number of different shapes that are rendered with Blender, containing various errors that change the basic morphological features of the shapes. After a human-based validation process, we developed a post-processing Python script which detects and corrects the issues of the automatically generated intersections. LA - English DB - MTMT ER - TY - CHAP AU - Balla, Dániel AU - Zichar, Marianna AU - Tóth, Róbert AU - Kiss, Emőke AU - Karancsi, Gergő AU - Lázár, Vilmos AU - Baranyi, Imre AU - Mester, Tamás ED - Lukács, Gábor ED - Szanati, Angéla TI - Vízminőségi indexek vizualizációs lehetőségei KML leíró nyelv alkalmazásával. Visualization of Water Quality Index using Keyhole Markup Language TS - Visualization of Water Quality Index using Keyhole Markup Language T2 - LXII. Georgikon Napok konferenciakötet PB - Szent István Egyetem, Georgikon Campus CY - Keszthely SN - 9789632699424 PY - 2021 SP - 1 EP - 6 PG - 6 UR - https://m2.mtmt.hu/api/publication/32371973 ID - 32371973 LA - Hungarian DB - MTMT ER - TY - JOUR AU - Tóth, Róbert TI - Script-aided generation of Mental Cutting Test exercises using Blender JF - ANNALES MATHEMATICAE ET INFORMATICAE J2 - ANN MATH INFORM VL - 54 PY - 2021 SP - 147 EP - 161 PG - 15 SN - 1787-5021 DO - 10.33039/ami.2021.03.011 UR - https://m2.mtmt.hu/api/publication/31938849 ID - 31938849 LA - English DB - MTMT ER - TY - CHAP AU - Tóth, Róbert AU - Zichar, Marianna AU - Hoffmann, Miklós ED - Cheng, Liang-Yee TI - Improving and Measuring Spatial Skills with Augmented Reality and Gamification T2 - ICGG 2020 - Proceedings of the 19th International Conference on Geometry and Graphics PB - Springer Netherlands CY - Berlin SN - 9783030634025 T3 - Advances in Intelligent Systems and Computing, ISSN 2194-5357 ; 1296. PY - 2021 SP - 755 EP - 764 PG - 10 DO - 10.1007/978-3-030-63403-2_68 UR - https://m2.mtmt.hu/api/publication/31687556 ID - 31687556 N1 - Conference code: 252709 Cited By :4 Export Date: 6 December 2022 Correspondence Address: Tóth, R.; Faculty of Informatics, Hungary; email: toth.robert@inf.unideb.hu Funding details: European Commission, EC Funding details: European Social Fund, ESF Funding text 1: Acknowledgement. This work was supported by the construction EFOP-3.6.3-VEKOP-16-2017-00002. The project was supported by the European Union, co-financed by the European Social Fund. To provide our charts and diagrams we use the AnyChart Android Chart data visualization library with educational license. LA - English DB - MTMT ER - TY - CHAP AU - Balla, Dániel AU - Zichar, Marianna AU - Tóth, Róbert AU - Kiss, Emőke AU - Gergő, Karancsi AU - Vilmos, Lázár AU - Imre, Baranyi AU - Mester, Tamás ED - Lukács, Gábor ED - Kormos, Éva TI - Visualization of Water Quality Index using Keyhole Markup Language T2 - LXII. Georgikon Napok Nemzetközi Tudományos Konferencia PB - Szent István Egyetem, Georgikon Campus CY - Keszthely SN - 9789632699417 PY - 2020 SP - 12 UR - https://m2.mtmt.hu/api/publication/31757768 ID - 31757768 LA - English DB - MTMT ER - TY - CONF AU - Tóth, Róbert AU - Hoffmann, Miklós AU - Kósa, Márk Szabolcs AU - Zichar, Marianna ED - Kovásznai, Gergely ED - Fazekas, István ED - Tómács, Tibor TI - Motivating students’ study with ICT T2 - Proceedings of the 11th International Conference on Applied Informatics (ICAI 2020) PB - CEUR Workshop Proceedings C1 - Eger T3 - CEUR Workshop Proceedings, ISSN 1613-0073 ; 2650. PY - 2020 UR - https://m2.mtmt.hu/api/publication/31634271 ID - 31634271 LA - English DB - MTMT ER -