Boštjan Šumak

Priročnik je namenjen podpori sistematičnemu vključevanju digitalnih kompetenc v pedagoške študijske programe in njihovi prilagoditvi slovenskemu visokošolskemu prostoru. Obravnava ključna področja digitalnih kompetenc, kot so informacijska in podatkovna pismenost, digitalna komunikacija in sodelovanje, ustvarjanje digitalnih vsebin, varna in odgovorna raba tehnologij, digitalno državljanstvo, uporaba umetne inteligence ter kritično vrednotenje digitalnih vsebin. Za posamezne kompetence vključuje opise, primere uporabe, tehnološka priporočila, povezave z DigCompEdu, kazalnike doseganja in priporočila za izobraževalce. Poseben poudarek namenja pedagoško utemeljeni, etični, vključujoči in reflektirani rabi digitalnih tehnologij ter digitalni dobrobiti. Priročnik spodbuja horizontalno vključevanje kompetenc v predmete in praktično usposabljanje pedagoških študijskih programov ter poudarja pomen kontinuiranega strokovnega razvoja visokošolskih učiteljev, ki na teh programih poučujejo.

Qualitative data analysis (QDA) tools are essential for extracting insights from complex datasets. This study investigates researchers’ perceptions of the usability, user experience (UX), mental workload, trust, task complexity, and emotional impact of three tools: Taguette 1.4.1 (a traditional QDA tool), ChatGPT (GPT-4, December 2023 version), and Gemini (formerly Google Bard, December 2023 version). Participants (N = 85), Master’s students from the Faculty of Electrical Engineering and Computer Science with prior experience in UX evaluations and familiarity with AI-based chatbots, performed sentiment analysis and data annotation tasks using these tools, enabling a comparative evaluation. The results show that AI tools were associated with lower cognitive effort and more positive emotional responses compared to Taguette, which caused higher frustration and workload, especially during cognitively demanding tasks. Among the tools, ChatGPT achieved the highest usability score (SUS = 79.03) and was rated positively for emotional engagement. Trust levels varied, with Taguette preferred for task accuracy and ChatGPT rated highest in user confidence. Despite these differences, all tools performed consistently in identifying qualitative patterns. These findings suggest that AI-driven tools can enhance researchers’ experiences in QDA while emphasizing the need to align tool selection with specific tasks and user preferences.

Web accessibility, crucial for ensuring equitable access to online information for users with diverse needs, is an essential aspect of website quality. This chapter provides empirical insight into evaluating web accessibility within healthcare websites. Using an automatic evaluation tool, this study assessed the official entry websites of 58 Health Centers. The results revealed that none of the investigated websites fully comply with all WCAG 2.1 requirements. The website with the highest number of violated success criteria had 367 accessibility issues, containing one critical, 264 serious, and 102 minor issues. In contrast, the website with the least violated success criterion presented 24 accessibility issues, all recognized as serious.

Software evolution is driven by changes made during software development and maintenance. While source control systems effectively manage these changes at the commit level, the intent behind them are often inadequately documented, making understanding their rationale challenging. Existing commit intent classification approaches, largely reliant on commit messages, only partially capture the underlying intent, predominantly due to the messages’ inadequate content and neglect of the semantic nuances in code changes. This paper presents a novel method for extracting semantic features from commits based on modifications in the source code, where each commit is represented by one or more fine-grained conjoint code changes, e.g., file-level or hunk-level changes. To address the unstructured nature of code, the method leverages a pre-trained transformer-based code model, further trained through task-adaptive pre-training and fine-tuning on the downstream task of intent classification. This fine-tuned task-adapted pre-trained code model is then utilized to embed fine-grained conjoint changes in a commit, which are aggregated into a unified commit-level vector representation. The proposed method was evaluated using two BERT-based code models, i.e., CodeBERT and GraphCodeBERT, and various aggregation techniques on data from open-source Java software projects. The results show that the proposed method can be used to effectively extract commit embeddings as features for commit intent classification and outperform current state-of-the-art methods of code commit representation for intent categorization in terms of software maintenance activities undertaken by commits.

This study aimed to observe the impact of eight explainable AI (XAI) explanation techniques on user trust and satisfaction in the context of XAI-enhanced learning analytics while comparing two groups of STEM college students based on their Bologna study level, using various established feature relevance techniques, certainty, and comparison explanations. Overall, the students reported the highest trust in local feature explanation in the form of a bar graph. Additionally, master’s students presented with global feature explanations also reported high trust in this form of explanation. The highest measured explanation satisfaction was observed with the local feature explanation technique in the group of bachelor’s and master’s students, with master’s students additionally expressing high satisfaction with the global feature importance explanation. A detailed overview shows that the two observed groups of students displayed consensus in favored explanation techniques when evaluating trust and explanation satisfaction. Certainty explanation techniques were perceived with lower trust and satisfaction than were local feature relevance explanation techniques. The correlation between itemized results was documented and measured with the Trust in Automation questionnaire and Explanation Satisfaction Scale questionnaire. Master’s-level students self-reported an overall higher understanding of the explanations and higher overall satisfaction with explanations and perceived the explanations as less harmful.

Despite efforts to promote digital inclusion for all, individuals with disabilities continue to experience a significant digital divide. Developing usable and accessible solutions also demands conducting user-based testing with real end users, particularly for users with disabilities, to detect and address real problems and barriers they experience and improve usability and accessibility based on their feedback through a user-centered approach. When including users with disabilities in user testing, additional attention must be paid to ensure that the testing is planned and carried out in such a way as to enable the successful and efficient integration of users with disabilities. In doing so, it is necessary to consider various restrictions related to users’ disabilities. By conducting a systematic literature review, we collected and analyzed the challenges of including users with disabilities in user-based testing and best practices that researchers can apply in future user-based testing with users with disabilities. In the existing literature, a positive trend toward publishing articles describing testing with users with disabilities is noticeable. There is also an apparent need for more reporting on some phases of the testing studies. The main result of this study is a list of challenges and best practices that are important in the different phases of user-based testing with users with disabilities.

During maintenance, software systems undergo continuous correction and enhancement activities due to emerging faults, changing environments, and evolving requirements, making this phase expensive and time-consuming, often exceeding the initial development costs. To understand and manage software under development and maintenance better, several maintainability measures have been proposed. The Maintainability Index is commonly used as a quantitative measure of the relative ease of software maintenance. There are several Index variants that differ in the factors affecting maintainability (e.g., code complexity, software size, documentation) and their given importance. To explore the variants and understand how they compare when evaluating software maintainability, an experiment was conducted with 45 Java-based object-oriented software systems. The results showed that the choice of the variant could influence the perception of maintainability. Although different variants presented different values when subjected to the same software, their values were strongly positively correlated and generally indicated similarly how maintainability evolved between releases and over the long term. Though, when focusing on fine-grained results posed by the Index, the variant selection had a larger impact. Based on their characteristics, behavior, and interrelationships, the variants were divided into two distinct clusters, i.e., variants that do not consider code comments in their calculation and those that do.