Sirvi Autor "Jaanuska, Ljubov, juhendaja" järgi

Nüüd näidatakse 1 - 11 11

Bee-Bot kui vahend algoritmilise mõtlemise õpetamiseks: lähenemised ja praktikad Midrimaa lasteaia eelkoolirühma laste näitel
(Tartu Ülikool, 2025) Meister, Joonas; Jaanuska, Ljubov, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
This bachelor's thesis explores the use of the Bee-Bot floor robot as a tool for developing algorithmic thinking of preschool children at Luunja Kindergarten Midrimaa. The theoretical part of the thesis provides an overview of the role of robotics in early childhood education and analyzes the learning materials developed for the Bee-Bot floor robot, highlighting their strengths and limitations. In the practical part, a study was conducted in which children individually solved algorithmic tasks of varying difficulty using the Bee-Bot robot. The aim of the study was to identify the problem-solving strategies used by the children, as well as the challenges and difficulties that arose during task completion. In addition to observation, semi-structured interviews were conducted to better understand children perceptions of the Bee-Bot and thoughts when solving the problems. As a result of the research, a methodological brochure was created for teachers, compiling various practical strategies for using the Bee-Bot robot to support children's algorithmic thinking.
Changelog API – veebipõhine muudatuste logimise teenus
(Tartu Ülikool, 2025) Räni, Elisabeth; Räni, Erik, juhendaja; Jaanuska, Ljubov, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Over time, developers release new versions of software projects that include one or more changes. Information regarding these changes is typically documented in text files, web pages, distribution platform introduction pages, or dedicated sections within the software itself. Users and stakeholders rely on these sources to assess version updates and determine the most suitable release for their needs. This bachelor’s thesis aims to develop an open-source, web-based service that centralizes changelog management and establishes standardized guidelines for its content.
Informaatikaviktoriini Kobras esimese vooru ülesannete kogu
(Tartu Ülikool, 2020) Lehes, Klaarika; Jaanuska, Ljubov, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
The goal of this thesis is to prepare tasks of the computer science quiz Kobras for the publication in Estonian. The tasks have been taken from the first round of the quiz that took place in 2018/2019. In addition, the results of the participants will be analysed to determine the level of difficulty of the tasks.
KaiOS Application For Listening Podcasts
(Tartu Ülikool, 2020) Arro, Fredi; Jaanuska, Ljubov, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
The aim of the present thesis was to develop an application for the KaiOS devices for listening podcasts. No convenient alternative application had been created before. In this thesis, the waterfall model was used for the development process. The requirements, design and structure were implemented using HTML, CSS, and jQuery. iTunes Search API was used for searching new podcast. IndexedDB database stored the data about the subscribed podcasts. In the future, it is planned to upload the application into KaiStore and add new features such as push notifications and download episodes.
Laste kaasatus ja lahendusviisid digitaalsetes ja traditsioonilistes õppemängudes
(Tartu Ülikool, 2025) Koobas, Linda; Jaanuska, Ljubov, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
The use of digital tools in early childhood education has grown rapidly in recent years, highlighting the importance of examining the impact of different learning methods on young children. This thesis aimed to compare the effects of digital and traditional educational games on 6–7-year-old children, with a focus on their problem-solving strategies and body language. Data were collected through observations and interviews conducted in a preschool group at Luunja Kindergarten Midrimaa, involving 20 participants. The analysis revealed that tasks were generally completed faster and more consistently in the digital format, whereas the traditional (paper-based) format showed more variation and required greater planning and concentration. Furthermore, the digital format encouraged independent experimentation and offered immediate feedback, while the traditional format supported deeper concentration, communication, and hands-on activities. Thus, a balanced integration of both formats may help support children's development in digital competence and broader learning skills.
Machine Learning Methods for Crack Identification in Euler-Bernoulli Beams
(Tartu Ülikool, 2025) Radsin, Kristjan; Jaanuska, Ljubov, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
The objective of this bachelor’s thesis is to evaluate the suitability of various machine learning methods for crack identification in Euler-Bernoulli beams placed on a Pasternak foundation. Methods available in Python libraries are used for this purpose. The machine learning methods of interest are linear regression, kernel ridge, Gaussian process regression, K-nearest neighbours, random forest, gradient boosting, and artificial neural network. The models are trained on three datasets: the one is based on the beam’s natural frequencies, the second one is based on the Haar wavelet coefficients obtained from the mode shape, and the third one is based on the Fourier transformed frequencies. In addition, the models are tested on a dataset containing white noise in the 5% range. The results show that the best results for predicting crack location can be achieved with models trained on the Haar wavelet coefficients, and Gaussian process regression turns out to be the best method for this case. Fourier transformed frequencies give the best results in predicting crack depth, and random forests are the most successful method in this case.
Masinõppe meetodite võrdlus vardas esinevate pragude iseloomustamiseks
(Tartu Ülikool, 2025) Tamuri, Kaarel; Jaanuska, Ljubov, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Rods are common components in engineering, where the presence of cracks can significantly reduce load-bearing capacity and pose safety risks. This bachelor’s thesis evaluates the suitability of different machine learning methods for crack identification in rods. Three machine learning models, linear regression, random forest and XGBoost, are compared in their ability to predict crack depth and location using input features from three different domains: natural frequencies, 16 Haar wavelet coefficients and 32 Haar wavelet coefficients. The models are assessed using standard regression metrics and noise is introduced to simulate real-world measurement uncertainty. The results show that the Haar wavelet coefficients outperform natural frequencies, especially in predicting crack location. Among the models, XGBoost consistently delivers the highest accuracy, achieving R2 up to 0.896 in the combined prediction task using the dataset of 32 Haar wavelet coefficients. Random forest also performs well, while linear regression provides fast but less accurate results. The study concludes that XGBoost trained on 32 Haar wavelet coefficients is the most effective approach for crack identification in rods.
Meiobentose hulka kuuluvate organismide automaatne tuvastamine masinõppe meetoditega
(Tartu Ülikool, 2025) Lokko, Külli; Jaanuska, Ljubov, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
The objective of the bachelor's thesis was to automate the detection and identification of meiobenthos in micrographs using machine learning methods. Meiobenthos consists of many different groups of microscopic organisms living in the aquatic sediments. In aquatic environments, meiobenthos plays an important role in energy flow and nutrient cycles. Counting and identifying these organisms manually is very labor-intensive and time-consuming, and requires extensive training. Therefore, there is a clear need for automation, which would greatly facilitate abundance and biomass estimation, taxonomic composition assessment, and the study of the ecological relationships and nutrient cycles. First, an annotated database of micrographs was created as part of the thesis. To ensure high-quality training data, each meiobenthic organism in the images was manually annotated. Prior to this work, no comparable dataset was publicly available. Next, models were trained using two object detection models (Faster R-CNN and YOLO11) to automatically detect all meiobenthic organisms in the images and to identify their taxonomic group. The detection performance of the best models was comparable to that of existing models trained on plankton datasets, achieving an error rate of only 3.7% in abundance estimation on the test set. The models performed slightly less well in identifying the correct organism group (taxon). The best Faster R-CNN model correctly identified both the location and taxonomic group of organisms in 86.3% of cases while the YOLO11 model identified both correctly in 80.7% of cases.
Õppematerjalide koostamine ingliskeelsele kursusele „Objektorienteeritud programmeerimine“
(Tartu Ülikool, 2025) Pehter, Liisa-Lotte; Jaanuska, Ljubov, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
The aim of this bachelor’s thesis was to develop study materials for two learning modules in the course “Object-Oriented Programming” in English. Each module includes reading materials, learning videos, self-assessment tests, homework assignments and practical exercises. The course is intended for foreign-language speaking students who wish to learn the fundamentals of object-oriented programming. The development of the study materials was based on the ADDIE model, which provides a structured approach to instructional design. Existing programming courses were reviewed and recommendations from previous studies were taken into account. The appropriateness of the created materials was evaluated by lab instructors and the course was further modified based on their feedback.
Veebisaidi loomine ettevõttele HeiVäl OÜ
(Tartu Ülikool, 2020) Allik, Aima; Jaanuska, Ljubov, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
The aim of the bachelor’s thesis is to create a new website for the company HeiVäl OÜ. The thesis contains an analysis of the previous website, setting of the functional and non-functional requirements for the new website, drawing an overview of the technologies used in the new website and the development of the new website.
Veebisaidi loomine ettevõttele Niitvälja Tallid OÜ
(Tartu Ülikool, 2020) Pilvet, Magnar; Jaanuska, Ljubov, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Käesoleva bakalaureusetöö raames valmis uus koduleht ettevõttele Niitvälja Tallid OÜ. Töö sisaldab vana veebisaidi analüüsi, ülevaadet uuele veebisaidile seatud funktsionaalsetest nõuetest ja kasutatud tehnoloogiatest ning valminud kodulehe analüüsi ja loomise protsessi kirjeldust.