In the modern, information driven society managing and handling data is unavoidable. The most common form of data handling is to organize data into tables and complete operations on them in spreadsheets. Sprego (Spreadsheet Lego) is a programmingoriented methodology focusing on schemata construction and authentic problemsolving working with only a limited number of general-purpose functions. In our current study the goal is to present Sprego as an alternative method for spreadsheeting, and to measure its effectiveness in education compared to the traditional surface approach methods. We also aim to highlight the advantages of teaching datamanagement, spreadsheeting, and introduction to programming by applying an algorithmic and schemata centric method in a user-friendly interface. The teaching and testing were carried out in three classes of a local middle and high school with two experimental and one control groups. Based on our results, it is found that the Sprego methodology is significantly more effective than the traditional surface approach methods. Furthermore, it is also proved, in accordance with similar studies in sciences, that students, who had studied traditional spreadsheet management in advance to this experience, have difficulties switching to Sprego. Although these students alternate between the two approaches, our measurements clearly prove that the traditional approach is pushed into the background, as students prefer to solve problems using Sprego. Our findings also imply that traditional methods do not develop long-lasting knowledge which students could rely on, and have a negative effect on their development, while Sprego seems much more reliable.

In Hungary, K-12 informatics/computer science education focuses on mostly surfacebased methods. This approach can be observed in the teaching of several topics in the subject, of which we focus on spreadsheet management. This is further emphasized by regulatory documentsthe Hungarian National Core Curriculum and Hungarian Curriculum Frameworks-, where handling algorithms, calling schemata, and problem-solving in general are only assigned to the programming topic. In the process of fulfilling the requirements of the school curricula and the various tool-centered exams, students become familiar with the software interfaces and how to navigate them, instead of developing computational thinking skills and learning how to approach and solve real-world problems. Our educational system is based on a spiral teaching approach; therefore, spreadsheet management is taught throughout several grades in a small number of lessons. Prior research shows that students learning spreadsheet management with surface-approach methods do not build up a reliable knowledge structure. These students cannot solve problems in contexts differing to the ones in which they learned the topic and cannot use their surface navigation abilities in different software environments. Our research group focuses on spreadsheeting with an algorithm-building and problemsolving method at the center of the teaching-learning process. For this purpose, we have developed and introduced the Sprego (Spreadsheet Lego) methodology. Sprego is based on Pólya's four-step concept-based problem-solving approach, and its efficiency has already been proved compared to traditional low-mathability surface-approach methods. In the comparison of the low-and high-mathability approaches, several further questions arise, and amongst them one crucial aspect is how the different methods support the schemaconstruction and knowledge built up in long-term memory. In this paper we discuss this question using a delayed post-test that was carried out one year after the treatment period. We focused on the students' achievement both in the experimental (Sprego) and control (traditional surface-approaches) groups based on the methods used one year prior to the administration of the delayed post-test. The results show that students who learned the spreadsheet management topic with Sprego achieved significantly better scores on the delayed tests than those students who used low-mathability approaches.

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