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Abstract: The use of large language models with chatbots like ChatGPT has become increasingly popular among students, especially in Computer Science education. However, significant debates exist in the education community on the role of ChatGPT in learning. Therefore, it is critical to understand the potential impact of ChatGPT on the learning, engagement, and overall success of students in classrooms. In this empirical study, we report on a controlled experiment with 182 participants in a first-year undergraduate course on object-oriented programming. Our differential study divided students into two groups, one using ChatGPT and the other not using it for practical programming assignments. The study results showed that the students’ performance is not influenced by ChatGPT usage (no statistical significance between groups with a p-value of 0.730), nor are the grading results of practical assignments (p-value 0.760) and midterm exams (p-value 0.856). Our findings from the controlled experiment suggest that it is safe for novice programmers to use ChatGPT if specific measures and adjustments are adopted in the education process.
Keywords: large language models, ChatGPT, artificial intelligence, controlled experiment, object-oriented programming, software engineering education
Published in DKUM: 12.08.2024; Views: 59; Downloads: 6
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Abstract: Traveling Salesman Problems (TSPs) have been a long-lasting interesting challenge to researchers in different areas. The difficulty of such problems scales up further when multiple objectives are considered concurrently. Plenty of work in evolutionary algorithms has been introduced to solve multi-objective TSPs with promising results, and the work in deep learning and reinforcement learning has been surging. This paper introduces a multi-objective deep graph pointer network-based reinforcement learning (MODGRL) algorithm for multi-objective TSPs. The MODGRL improves an earlier multi-objective deep reinforcement learning algorithm, called DRL-MOA, by utilizing a graph pointer network to learn the graphical structures of TSPs. Such improvements allow MODGRL to be trained on a small-scale TSP, but can find optimal solutions for large scale TSPs. NSGA-II, MOEA/D and SPEA2 are selected to compare with MODGRL and DRL-MOA. Hypervolume, spread and coverage over Pareto front (CPF) quality indicators were selected to assess the algorithms’ performance. In terms of the hypervolume indicator that represents the convergence and diversity of Pareto-frontiers, MODGRL outperformed all the competitors on the three well-known benchmark problems. Such findings proved that MODGRL, with the improved graph pointer network, indeed performed better, measured by the hypervolume indicator, than DRL-MOA and the three other evolutionary algorithms. MODGRL and DRL-MOA were comparable in the leading group, measured by the spread indicator. Although MODGRL performed better than DRL-MOA, both of them were just average regarding the evenness and diversity measured by the CPF indicator. Such findings remind that different performance indicators measure Pareto-frontiers from different perspectives. Choosing a well-accepted and suitable performance indicator to one’s experimental design is very critical, and may affect the conclusions. Three evolutionary algorithms were also experimented on with extra iterations, to validate whether extra iterations affected the performance. The results show that NSGA-II and SPEA2 were greatly improved measured by the Spread and CPF indicators. Such findings raise fairness concerns on algorithm comparisons using different fixed stopping criteria for different algorithms, which appeared in the DRL-MOA work and many others. Through these lessons, we concluded that MODGRL indeed performed better than DRL-MOA in terms of hypervolumne, and we also urge researchers on fair experimental designs and comparisons, in order to derive scientifically sound conclusions.
Keywords: multi-objective optimization, traveling salesman problems, deep reinforcement learning
Published in DKUM: 28.03.2024; Views: 184; Downloads: 28
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Abstract: V diplomskem delu sta primerjani dve metodi razvijanja spletnih aplikacij v knjižnici React. Primerjana sta načina izdelovanja razrednih komponent in način izdelovanja komponent s funkcionalnostmi React hooks. Primerjave so izvedene s kriteriji hitrosti razvijanja, enostavnosti razvijanja in hitrosti izvajanja same komponente. Praktičen del vsebuje dve implementaciji enake spletne aplikacije – eno, narejeno s klasičnimi razrednimi komponentami, in drugo, narejeno s funkcionalnimi komponentami React hooks.
Keywords: ReactJS, React hooks, spletne tehnologije
Published in DKUM: 25.10.2022; Views: 481; Downloads: 57
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Abstract: Tekom diplomske naloge smo izdelali aplikacijo za izobraževalne namene, ki simulira in vizualizira naravne evolucijske procese s pomočjo evolucijskih algoritmov. S spletnim orodjem Piskel smo ustvarili statične in dinamične grafične gradnike za vizualizacijo celotnega postopka simulacije. Nato smo implementirali genetski algoritem in osnovne mehanike simulatorja v programskem jeziku C# ter integrirali celotno rešitev v okolje Unity.
Keywords: vizualizacija, simulator, namizna aplikacija, evolucijski algoritmi, Unity
Published in DKUM: 18.10.2021; Views: 838; Downloads: 48
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Abstract: V diplomskem delu je prikazana implementacija nadzornega sistema Icinga v organizacijo. Predstavili smo postopek namestitve in konfiguracije sistema, njegovih komponent, dodatkov in modulov, ki pripomorejo k boljšemu nadzoru. Prav tako smo prikazali delovanje vtičnikov, razvili lastne, ki jih podjetje potrebuje za nadzor programske opreme in storitev ter opravili konfiguracijo obveščanja tako, da smo sistemski skrbniki nemudoma obveščeni o morebitnih napakah sistema v podjetju.
Keywords: nadzorni sistem, namestitev, konfiguracija, razvoj vtičnikov
Published in DKUM: 03.11.2020; Views: 1031; Downloads: 176
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