In this paper, we present an approach that enables a broad re-use of English-authored smart learning content by translating it into other languages. To make it possible, we integrated translation functionalities directly into a smart content authoring system and engaging human-AI collaboration. The approach has been used to translate a large volume of worked examples and completion problems in Python from English to Spanish. The translated content has been piloted in several universities in a Spanish-speaking country.

In hybrid learning environments, designing effective assessment strategies is particularly challenging due to the need to engage students across synchronous and asynchronous modalities while promoting self-regulated learning. This study examines the effect of two sequencing strategies on short assessments, utilizing an educational tool designed to support both in-class and at-home assessments. The intervention was implemented in a 16-week undergraduate web development course at the Costa Rica Institute of Technology. Two in-class scenarios were compared: end-of-class (Scenario 1) versus start-of-class (Scenario 2). A total of 14 students participated in the study. The research employed a mixed-methods approach, collecting log data from the interactions between students and the tool, as well as students’ preferences and learning behaviors, through a focus group and individual interviews. Results indicate that students perceived in-class and at-home assessments as complementary and expressed distinct preferences for assessment timing based on cognitive and motivational factors. In particular, the majority favored start-of-class short assessments due to increased focus and the opportunity to review prior material. Findings suggest that the sequencing of formative assessments can influence student engagement and metacognitive behaviors. They also highlight the importance of carefully designing the timing of such assessments, as these moments can have meaningful effects on students’ learning experiences.

Various studies have studied the impact of Generative AI on Computing Education. However, they have focused on the implications for novice programmers. In this experience report, we analyze the use of GenAI as a support tool for learning, creativity, and productivity in a web development course for undergraduate students with extensive programming experience. We collected diverse data (assignments, reflections, logs, and a survey) and found that students used GenAI on different tasks (code generation, idea generation, etc.) with a reported increase in learning and productivity. However, they are concerned about over-reliance and incorrect solutions and want more training in prompting strategies.

Abstraction is a fundamental skill and concept in computer science and it is also a difficult skill to teach. The purpose of the working group is to analyse different perspectives of abstraction's conceptualisation and ways of teaching the skill. Therefore as a result of the working group we will be first identifying how abstraction is discussed and defined in key literature. As a team we will agree on the perspectives and models we will like to explore in teaching context. Finally we will work with computing educators and computing education researchers to design a pedagogical framework that will enable the development of the abstraction skills.

A typical index at the end of a textbook contains a manually-provided vocabulary of terms related to the content of the textbook. In this paper, we extend our previous work on extraction of knowledge models from digital textbooks. We are taking a more critical look at the content of a textbook index and present a mechanism for classifying index terms according to their domain specificity: a core domain concept, an in-domain concept, a concept from a related domain, and a concept from a foreign domain. We link the extracted models to DBpedia and leverage the aggregated linguistic and structural information from textbooks and DBpedia to construct and prune the domain-specific knowledge graphs. The evaluation experiments demonstrate (1) the ability of the approach to identify (with high accuracy) different levels of domain specificity for automatically extracted concepts, (2) its cross-domain robustness, and (3) the added value of the domain specificity information. These results clearly indicate the improved quality of the refined knowledge graphs and widen their potential applicability.

Textbooks are educational documents created, structured and formatted by domain experts with the main purpose to explain the knowledge in the domain to a novice. Authors use their understanding of the domain when structuring and formatting the content of a textbook to facilitate this explanation. As a result, the formatting and structural elements of textbooks carry the elements of domain knowledge implicitly encoded by their authors. Our paper presents an extendable approach towards automated extraction of this knowledge from textbooks taking into account their formatting rules and internal structure. We focus on PDF as the most common textbook representation format; however, the overall method is applicable to other formats as well. The evaluation experiments examine the accuracy of the approach, as well as the pragmatic quality of the obtained knowledge models using one of their possible applications -- semantic linking of textbooks in the same domain. The results indicate high accuracy of model construction on symbolic, syntactic and structural levels across textbooks and domains, and demonstrate the added value of the extracted models on the semantic level.

Quality of learning objects metadata, in many respects, defines the quality of an adaptive learning environment presenting these learning objects to a student. Metadata inconsistencies and gaps may be the cause of various problems: from a system malfunction to ineffective learning experiences. In this paper, we propose an intelligent and rigorous mechanism for detecting metadata gaps in collections of learning content. The mechanism converts learning objects metadata into an OWL2 ontology, detects logical conflicts using Semantic Web reasoning techniques and generates human-readable explanations for an author to resolve the gaps. The evaluation of the developed semantic gap detection tool with real learning content collections demonstrates its effectiveness.