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Abstract
This workshop aims at identifying, examining, structuring and sharing educational resources and approaches to support the process of teaching/learning Human-Computer Interaction (HCI) Engineering. The broadening of the range of available interaction technologies and their applications, many times in safety and mission critical areas, to novel and less understood application domains, brings the question of how to address this ever-changing nature in university curricula usually static. Beyond, as these technologies are taught in diverse curricula (ranging from Human Factors and psychology to hardcore computer science), we are interested in what the best approaches and best practices are to integrate HCI Engineering topics in the curricula of programs in software engineering, computer science, human-computer interaction, psychology, design, etc. The workshop is proposed on behalf of the IFIP Working Groups 2.7/13.4 on User Interface Engineering and 13.1 on Education in HCI and HCI Curricula.

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Abstract
Objective The aim of the study was the development and evaluation of a Descriptive Cognitive Model (DCM) for the identification of three types of usability issues in a low-code development platform (LCDP). Background LCDPs raise the level of abstraction of software development by freeing end-users from implementation details. An effective LCDP requires an understanding of how its users conceptualize programming. It is necessary to identify the gap between the LCDP end-users' conceptualization of programming and the actions required by the platform. It is also relevant to evaluate how the conceptualization of the programming tasks varies according to the end-users' skills. Method DCMs are widely used in the description and analysis of the interaction between users and systems. We propose a DCM which we called PRECOG that combines task decomposition methods with knowledge-based descriptions and criticality analysis. This DCM was validated using empirical techniques to provide the best insight regarding the users' interaction performance. Twenty programmers (10 experts, 10 novices) were observed using an LCDP and their interactions were analyzed according to our DCM. Results The DCM correctly identified several problems felt by first-time platform users. The patterns of issues observed were qualitatively different between groups. Experts mainly faced interaction-related problems, while novices faced problems attributable to a lack of programming skills. Conclusion By applying the proposed DCM we were able to predict three types of interaction problems felt by first-time users of the LCDP. Application The method is applicable when it is relevant to identify possible interaction problems, resulting from the users' background knowledge being insufficient to guarantee a successful completion of the task at hand.

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Abstract