Teaching algorithmic thinking enables students to use their knowledge in various contexts to reuse existing solutions to algorithmic problems. The aim of this study is to examine how students recognize which algorithmic concepts can be used in a new situation. We developed a card sorting task and investigated the ways in which secondary school students arranged algorithmic problems (Bebras tasks) into groups using algorithm as a criterion. Furthermore, we examined the students’ explanations for their groupings. The results of this qualitative study indicate that students may recognize underlying algorithmic concepts directly or by identifying similarities with a previously solved problem; however, the direct recognition was more successful. Our findings also include the factors that play a role in students’ recognition of algorithmic concepts, such as the degree of similarity to problems discussed during lessons. Our study highlights the significance of teaching students how to recognize the structure of algorithmic problems.
Open Educational Resources have emerged as important elements of education in the contemporary society, promoting life-long and personalized learning that transcends social, economic and geographical barriers. To achieve the potential of OERs and bring impact on education, it is necessary to increase their development and supply. However, one of the current challenges is how to produce quality and relevant OERs to be reused and adapted to different contexts and learning situations. In this paper we proposed an agile method for the development of OERs - AM-OER, grounded on agile practices from Software Engineering. Learning Design practices from the OULDI project (UK Open University) are also embedded into the AM-OER aiming at improving quality and facilitating reuse and adaptation of OERs. In order to validate AM-OER, an experiment was conducted by applying it in the development of an OER on software testing. The results showed preliminary evidences on the applicability, effectiveness and efficiency of the method in the development of OERs.
The purpose and the main aim of the pedagogic experiment were to practically verify the success of Multimedia Teaching Aid (MTA) in conditions of primary schools. We assumed that the use of our multimedia teaching aid in teaching technical education on the 2nd level of primary schools would significantly affect the level of knowledge of pupils from the issue of Road Safety Education (RSE), particularly in terms of performing, remembering, understanding, specific transfer and active learning of pupils.
The Lithuanian Informatics Olympiads (LitIO) is a problem solving programming contest for students in secondary education. The work of the student to be evaluated is an algorithm designed by the student and implemented as a working program. The current evaluation process involves both automated (for correctness and performance of programs with the given input data) and manual (for programming style, written motivation of an algorithm) grading. However, it is based on tradition and has not been scientifically discussed and motivated. To create an improved and motivated evaluation model, we put together a questionnaire and asked a group of foreign and Lithuanian experts having experience in various informatics contests to respond. We identified two basic directions in the suggested evaluation models and made a choice based on the goals of LitIO. While designing the model in the paper, we reflected on the suggestions and opinions of the experts as much as possible, even if they were not included into the proposed model. The paper presents the final outcome of this work, the proposed evaluation model for the Lithuanian Informatics Olympiads.
Interaction and feedback are key factors supporting the learning process. Therefore many automatic assessment and feedback systems have been developed for computer science courses during the past decade. In this paper we present a new framework, TRAKLA2, for building interactive algorithm simulation exercises. Exercises constructed in TRAKLA2 are viewed as learning objects in which students manipulate conceptual visualizations of data structures in order to simulate the working of given algorithms. The framework supports randomized input values for the assignments, as well as automatic feedback and grading of students' simulation sequences. Moreover, it supports automatic generation of model solutions as algorithm animations and the logging of statistical data about the interaction process resulting as students solve exercises. The system has been used in two universities in Finland for several courses involving over 1000 students. Student response has been very positive.