Jul 2026· Annual Conference on Innovation and Technology in Computer Science Education· 0 citations· 37 references
Computer Science
TL;DR
A systematic literature review of empirical studies on UML SDs in computing education, with a focus on notation, consistency, completeness, and model quality shows that research on comprehension represents the primary focus of literature, followed by studies addressing model quality, while relatively few studies focus on methods and tools.
Abstract
The Unified Modelling Language (UML) is a standard notation for object-oriented modelling and plays a central role in the design of software systems. Both static and dynamic UML models are essential for representing complex system behaviour; however, prior studies indicate that novice learners often struggle to understand the relationships between these models. Students frequently construct sequence diagrams (SDs) that are inconsistent with corresponding class diagrams. Although formative feedback and tool support are well established in programming education, comparable support for UML modelling remains limited. This paper presents a systematic literature review of empirical studies on UML SDs in computing education, with a focus on notation, consistency, completeness, and model quality. The review identified 26 relevant studies from five major electronic databases. These studies were classified into three main categories: comprehension, model quality, and methods and tools. The results show that research on comprehension represents the primary focus of literature, followed by studies addressing model quality, while relatively few studies focus on methods and tools. The review highlights gaps, including limited support for novices and a lack of immediate formative feedback, pointing to opportunities for tools that provide ongoing diagnoses and guidance to help novices create valid SDs, while supporting consistency, completeness, and qualitative aspects.
This survey provides the first systematic synthesis of LLM-based diagram modelling research, highlighting needs for standardised benchmarks, stronger evaluation protocols, broader diagram coverage, and techniques for improving semantic reliability and multi-view consistency.
Unified Modelling Language (UML) class diagrams are critical in computing education, yet students face significant cognitive challenges in interpreting these complex visual-technical representations. This study examines three dimensions: cognitive load (mental effort), order (the sequential analytical strategies students employ), and symbolic confusion (misinterpretation of UML notation). Building on prior semiotic analyses of UML class diagram cognition, this exploratory study, using thematic analysis, applies Cognitive Load Theory (CLT) to examine how 11 first-year computing students at a South African university manage mental load during the interpretation of UML class diagrams. Through the semi-structured interviews, student-created artefacts, and systematic behavioural observations across three activities of increasing complexity (simple element identification, relationship analysis, and complex diagram construction), we identified four primary cognitive strategies: analytical sequencing and prior knowledge; symbolic confusion and clarification; diagram orientation and redrawing; and cognitive load management through decomposition. Participants demonstrated metacognitive awareness of working memory limits, employing adaptive strategies like spatial rearrangement, symbolic error correction, and systematic breakdown of visual complexity. Two key sources of extraneous load emerged: symbolic confusion from ambiguous notation and spatial disorientation from unconventional layouts, prompting compensatory behaviours such as diagram redrawing and self-correction. This exploratory study provides preliminary evidence for the potential application of CLT to diagrammatic reasoning, suggesting that these participants acted as active cognitive agents. Given the exploratory nature of this study, pedagogical implications tentatively suggest the value of explicit training in systematic analysis, symbol fluency, standardised visual designs, and metacognitive strategies. These findings require validation through larger-scale studies before broader implementation recommendations can be made. These findings inform cognitive load management in technical education and suggest directions for broader validation.
Pakiso J. Khomokhoana, Rouxan C. Fouché· Discover Education· 0 citations
Mathematical communication skills are among the most important skills in mathematics education, helping students convey ideas, explain their thought processes, and represent concepts mathematically. The development of these skills remains a focus of various studies in mathematics education. The purpose of this study is to systematically analyze research trends regarding the application of the PBL model to students’ mathematical communication skills. This study employed a Systematic Literature Review (SLR) method with a qualitative descriptive approach and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The literature search was conducted via the Google Scholar database using the Publish or Perish version 8 application to identify publications from 2022 to 2026. From an initial search yielding 600 articles, 16 articles that met the inclusion and exclusion criteria were selected for analysis. The data were analyzed descriptively through a process of extraction, categorization, and synthesis based on educational level, instructional content, research methods, and research findings. The results indicate that the majority of studies on the application of the PBL model to mathematical communication skills were conducted at the junior high school level, accounting for 56% of the total. Regarding research methods, quantitative methods were the most prevalent, particularly quasi-experimental designs, which accounted for 62%. The variety of instructional materials used was also quite diverse. Overall, the research findings indicate that the implementation of the PBL model effectively improves students’ mathematical communication skills. Additionally, several studies also show that the use of instructional media in PBL can enhance student engagement and support the process of mathematical communication in the classroom. Thus, the PBL model can be considered one of the relevant instructional models to help develop students’ mathematical communication skills.
Melisha Dwi Pangesty Wijaya, M. Putra, Adi Ihsan Imami· JUPIKA: JURNAL PENDIDIKAN MA...· 0 citations
Computational thinking has become a fundamental competency in software engineering education due to its contribution to problem-solving, algorithm design, abstraction, and logical reasoning. At the same time, software frameworks have gained increasing relevance as technological environments capable of supporting both software development and educational processes. Despite the growing body of literature in these areas, limited evidence has been synthesized regarding the role of software frameworks in fostering computational thinking skills within software engineering education. The objective of this study was to analyze the role of software frameworks in the development of computational thinking through a systematic review of the scientific literature. The review followed the PRISMA 2020 guidelines and was conducted using publications retrieved from IEEE Xplore, ScienceDirect, SpringerLink, SciELO, Redalyc, and Dialnet. A total of 211 records were initially identified, of which 24 studies met the predefined eligibility criteria and were included in the final analysis. The findings revealed the predominance of widely adopted software frameworks such as Django and Spring, together with the increasing use of educational and adaptive learning frameworks. Among the dimensions of computational thinking, algorithmic thinking and abstraction were the most frequently reported. The reviewed studies suggest that software frameworks may contribute to the development of computational thinking by promoting modular problem decomposition, pattern recognition, abstraction, and structured algorithmic reasoning. In conclusion, software frameworks represent valuable educational resources that may support the development of computational thinking competencies within software engineering education. However, the available evidence remains methodologically heterogeneous, highlighting the need for further empirical and experimental research to better understand the impact of framework-based learning environments on computational thinking development.
E. Ruano-Lara· Frontiers in Education· 0 citations
Nowadays, large language models (LLMs) are increasingly used in software engineering education. However, evidence regarding their pedagogical value for improving Requirements Engineering (RE) education remains scant. Past research has largely relied on small-scale or tool-centric evaluations, which are limited in their ability to provide meaningful insights into students' perceptions and learning impact, necessary to guide the systematic integration of LLMs into curricula. This work investigates the impact of using LLMs as co-analysts on student learning outcomes, student perceptions, and pedagogical feasibility in undergraduate RE education. In a controlled experiment, we involved 238 undergraduate students studying Software Engineering. We assigned participants the manual and LLM-assisted RE tasks and had them work on three RE learning activities: stakeholder analysis, development of user stories, and requirement prioritization and conflict detection. The learning outcomes were assessed by structured rubrics addressing the quality of created artifacts, while student perceptions were measured through post-activity surveys and feedback forms. Additionally, emotion and thematic analyses were performed to identify events that triggered these emotions. Results suggest that LLM-supported co-analysis positively impacts student-created RE artifacts in terms of quality and consistency, particularly when addressing early-level problems or when used to convey structural information, while human insight remains critical for refinement and strategic-level cognitive processes. Emotion analysis shows a general acceptance with productive hesitancy, implying that LLMs are better off as human-guided co-analysts in RE education.
M. Karimi, Saurabh Tiwari, S. Rathore· SIGSOFT FSE Companion· 0 citations
It is demonstrated that models and code achieve comparable overall correctness, and thus models alone may be sufficient in model-centric scenarios where access to code is limited or unavailable, and a consistent structure-behavior comprehension gap is revealed.
Iris Reinhartz-Berger, Monique Snoeck· Journal of Software and Syst...· 0 citations