Abstract
Background: Many progranunmg environments include automated feedback in the form of hints to help novices learn autonomously. Some experimental studies investigated the impact of automated liints in the immediate, performance and learning retention in that context. Automated feedback is also becoming a popular research topic in the context of formal specification languages, but so far no experimental studies have been conducted to assess its impact while learning such languages. Objective: We aim to investigate the impact of different types of automated hints while learning a formal specification language, not only in terms of immediate performance and learning retention, but also in the emotional response of the students. Method: We conducted a simple one-factor randomised experiment in 2 sessions involving 85 BSc students majoring in CSE. In the 1st session students were divided in 1 control group and 3 experimental groups, each receiving a different type of hint while learning to specify simple, requirements with the Alloy formal specification language. To assess the impact of hints on learning retention, in the 2nd session, 1 week later, students had no hints while formalising requirements. Before and after each session the students answered a standard self-reporting emotional survey to assess their emotional response to the experiment. Results: Of the 3 types of hints considered, only those pointing to the precise location of an error had a positive impact on the immediate performance and none had significant impact in learning retention. Hint availability also causes a significant impact on the emotional response, but no significant emotional :impact exists once hints are no longer available (i.e. no deprivation effects were detected). Conclusion: Although none of the evaluated hints had an impact on learning retention, learning a formal specification language with an environment that provides hints with precise error locations seems to contribute to a better overall experience without apparent drawbacks. Further studies are needed to investigate if other kind of feedback, namely hints combined with some sort of self explanation prompts, can have a positive impact in learning retention.

Abstract
This panel commemorates the 50th anniversary of the IFIP TC2 Working Conference on Command Languages (CCL) and the 30th anniversary of the workshop series on Design Specification and Verification of Interactive Systems (DSV-IS), and uses that opportunity to position EICS within the HCI community. The discussion traces the origins of the EICS conference, from the union of seminal conferences to its current status and looks forward into its (possible) future. Reflecting on its contributions to the evolution of HCI methodologies, tools, and practices, the panel highlights the conference's role and impact on shaping the engineering of interactive systems.

Abstract
The use of model checking tools allows for the formal verification of properties over models of systems, improving their robustness. However, these tools are challenging to use, and their results require much work of interpretation to communicate to stakeholders. To address this issue, the IVY Workbench offers a plethora of options to make the process of creating and understanding the models, properties and results of the verification process more accessible, with a particular focus on interactive computing systems. Despite this, there is still a significant requirement of expertise to use the tool. To solve this, an approach to provide structured natural language explanations for the results of model checking-based tools is being developed, to be later incorporated into the IVY Workbench. This paper presents the current state of the approach's development, stating its objective and what results can already be achieved.

Abstract

Abstract
Model checkers can automatically verify a system’s behavior against temporal logic properties. However, analyzing the counterexamples produced in case of failure is still a manual process that requires both technical and domain knowledge. However, this step is crucial to understand the flaws of the system being verified. This paper presents a language created to support the generation of natural language explanations of counterexamples produced by a model checker. The language supports querying the properties and counterexamples to generate the explanations. The paper explains the language components and how they can be used to produce explanations. © Ezequiel José Veloso Ferreira Moreira and José Creissac Campos.