The widespread adoption of Generative AI (GenAI) tools by students across different educational levels highlights the need for them to develop robust GenAI literacy, including a working understanding of these systems' fundamental concepts, their limitations, and implications for responsible use. However, misconceptions about GenAI, such as perceiving these systems as mere search engines or database lookup systems, are commonly observed among students, while the availability of teaching resources remains fragmented, and learning objectives lack alignment. This Working Group aims to design pedagogical resources for computing science instructors, enabling them to develop students' GenAI literacy. To achieve this, the Working Group will first identify a concise set of GenAI literacy learning objectives informed by instructor experience, research literature, and community input, and subsequently design pedagogical resources aligned with these objectives.

Database education is a cornerstone under many of the more popular topics in computer science such as machine learning and visualization. Although, in recent years, more fundamental research into database education has come out, there are many more ways in which it can be extended. Research on the practice of teaching databases, namely on the educational materials and explanations of teachers, can help us create new building blocks for fundamental research. This working group aims to collect and present notional machines of different types, for a wide range of database subtopics. These materials offer and updated context for database educators to design their courses from, as well as open up pathways of further research into database education.

ChatGPT potential value in education is broadly recognized and many studies report experiments of its use inside or outside the classroom by students and teachers. On the other hand, the use of ChatGPT rises lots of concerns about well-known problems such as hallucination, plagiarism, overreliance, or misinformation. It is of primary importance to teach students a correct and constructive use of ChatGPT and a critical approach to its returned outputs. The paper presents a classroom experience where students were asked to interact with ChatGPT in the context of a database course. The declared challenge for the students was, given a set of predefined relational database schemata, to invent questions for ChatGPT and try to force wrong SQL solutions. Students had to record the question, the ChatGPT solution, their solution, and the comments about the eventual ChatGPT syntactical and/or semantical errors. This gamification approach was meant to enhance students' motivation, but the main teachers' goal was to make them reflect critically (i) on ChatGPT output, experiencing that it does make mistakes, (ii) on the interpretation of ChatGPT errors, and (iii) on the possible strategies for forcing ChatGPT errors. The experiment involved 166 B.S. students in Engineering and the collected data have been analyzed under different points of view to get an insight into the approach and the critical attitude of the students. The paper reports the results of this analysis and discusses the impact of the activity on learning by analyzing the correlation between students' participation and exam performance.

The use of Large Language Models (LLMs) such as OpenAI ChatGPT to enhance teachers' and learners' experience has become established. The impressive capabilities of ChatGPT in solving Text2SQL problems prompts their use in database courses to solve SQL exercises. In this paper, we dig deep into ChatGPT abilities applied to SQL exercises. We quantitatively and qualitatively evaluate the performance of a ChatGPT-as-a-SQL-assistant on benchmark data, with particular attention paid to its ability to correctly detect syntactic and semantic errors, provide insightful judgment explanations, and assign grades comparable to those of human teachers. Furthermore, we also analyze the benefits of leveraging few-shot learning to adapt LLM responses to the expectation.

Learning environments foster the exchange of large amounts of data among learners and teachers. Summarization techniques leverage information retrieval and machine learning techniques to condense the key information hidden in large data collections into actionable summaries. Their integration into existing education technology systems is particularly appealing as it enables smart, automated solutions to challenging learning tasks such as content curation, accessibility, and personalization. This paper presents a general-purpose summarization-based methodology to learn. It aims at extending the current ed-ucational learning systems by envisaging the integration of summarization methods at different learning stages. Specifically, it tailors the output summaries to different end-users (either teachers or learners), content types (e.g., text, audio, video), and learning goals. With the goal of making the devised methodology actionable, the paper also examines the current role of summa-rization in the learning process and highlights the open directions and perspectives.

What is serious? What is funny? Who is a player? Who is a student? But, most importantly, who is the murderer? Serious games are gaining an ever increasing interest in education and training, and recent studies have used board games as inspirational. This study introduces SQL Murder Mystery, a serious game inspired by the popular board game Cluedo. This game has been designed to assess students' SQL skills and has been tested in a university database management system course during a lab session in which students played in teams. Query logs were examined to explore the behavioural patterns of the teams, by distinguishing different categories of queries: exploratory, focused, review queries, and to relate behaviour with specific SQL learning goals. The analysis revealed that success in the game and fulfilment of SQL learning goals are correlated. In addition, the game helped the instructors to identify the major knowledge gaps of the students, to allow on-time recovery.

Due to the rapid growth of multimedia data and the diffusion of remote and mixed learning, teaching sessions are becoming more and more multi-modal. To deepen the knowledge of specific topics, learners can be interested in retrieving educational videos that complement the textual content of teaching books. However, retrieving educational videos can be particularly challenging when there is a lack of metadata information. To tackle the aforesaid issue, this paper explores the joint use of Deep Learning and Natural Language Processing techniques to retrieve cross-media educational resources (i.e., from text snippets to videos and vice versa). It applies NLP techniques to both the audio transcript of the videos and to the text snippets in the books in order to quantify the semantic relationships between pairs of educational resources of different media types. Then, it trains a Deep Learning model on top of the NLP-based features. The probabilities returned by the Deep Learning model are used to rank the candidate resources based on their relevance to a given query. The results achieved on a real collection of educational multimodal data show that the proposed approach performs better than state-of-the-art solutions. Furthermore, a preliminary attempt to apply the same approach to address a similar retrieval task (i.e., from text to image and vice versa) has shown promising results.

The project aims to test the effectiveness of applying the principles of experiential learning within a university course. In particular, the objective of the paper is to investigate the educational effectiveness of the Alternate Reality Games (ARGs) and of their characterizing elements: the immersive storytelling, which blends reality and fiction, and the collaborative approach, which activates collective intelligence dynamics. The project combines the concepts of a Database course with the transmedial interaction techniques of a Transmedia course. The idea was to stimulate the interest of Databases course’s students in this subject and help them learn and consolidate SQL. The result was the creation of a playful experience that is classified as Alternate Reality Game, a realistic and highly immersive interactive storytelling, set in a likely fictional universe where the basic rule is "This is not a game". The ARG was designed to complement the laboratory practice in the context of a Databases university course. In this way, students can practice, review and consolidate the skills acquired during the course. Furthermore, the playful component is accompanied by on-demand educational content, which players have the opportunity to request when they experience difficulties in solving puzzles that require querying the database.

Learning Analytics aims at supporting the understanding of learning mechanisms and their effects by means of data-driven strategies. LA approaches commonly face two big challenges: first, due to privacy reasons, most of the analyzed data are not in the public domain. Secondly, the open data collections, which come from diverse learning contexts, are quite heterogeneous. Therefore, the research findings are not easily reproducible and the publicly available datasets are often too small to enable further data analytics. To overcome these issues, there is an increasing need for integrating open learning data into unified models. This paper proposes UNIFORM, an open relational database integrating various learning data sources. It presents also a machine learning supported approach to automatically extending the integrated dataset as soon as new data sources become available. The proposed approach exploits a classifier to predict attribute alignments based on the correlations among the corresponding textual attribute descriptions. The integration phase has reached a promising quality level on most of the analyzed bechmark datasets. Furthermore, the usability of the UNIFORM data model has been demonstrated in a real case study, where the integrated data have been exploited to support learners' outcome prediction. The F1-score achieved on the integrated data is approximately 30% higher that those obtained on the original data.

Many universities adopt educational systems where the teacher lecture is video recorded and the video lecture is made available to students with minimum post-processing effort. These cost-effective solutions suffer from the limited amount of annotations associated with the video content, which strongly limits the usability of the service when students need to retrieve specific portions of video, e.g., to revise unclear aspects covered in the past lectures. This paper presents, as a real case study, the system developed and implemented in our university for video lecture annotation and indexing. The original video recordings, which last around 1.5 hour, are first partitioned into smaller segments and then annotated by mapping their content with the entities in a multilingual knowledge base. To this purpose, the proposed approach analyzes both the transcription of the teacher's speech and the text appearing in the video (e.g., the slide content, the note written on the whiteboard) by means of an ad hoc Named Entity Recognition and Disambiguation (NERD) step. NERD relies on a supervised classification approach tailored to the domain under analysis. More specifically, to identify the most salient entities of the knowledge base matching the video content it considers not only text similarity measures but also the semantic pertinence of the candidate entities to the main subject of the video lectures. The performance of the proposed system was validated on a ground truth against the techniques available in the general entity annotation system GERBIL. The preliminary results demonstrate the effectiveness of the proposed approach.

Most engineering courses include fundamental practice activities to be performed by students in computer labs. During lab sessions, students work on solving exercises with the help of teaching assistants, who often have a hard time for guaranteeing a timely, optimized, and "democratic" support to everybody. This paper presents a learning environment to improve the experience of the lab sessions participants, both the students and the teaching assistants. In particular, the environment was designed, implemented, and experimented in the context of a database course. The application designed to support the learning environment stores all the events occurring during a SQL practice lab, i.e., task progression, query submissions, error feedback, assistance requests and interventions, and it provides information useful both for use on-the-fly and for later analysis. Thanks to the analysis of these data, the application dynamically provides teaching assistants with a graphical interface highlighting where assistance is most needed, by considering different factors such as the progression rate, the percentage of correct solutions, and the difficulties in solving the current exercise. Furthermore, the stored data allow teachers later on to analyze and to interpret the behavior of the students during the lab, and to have insights on their main mistakes and misconceptions. After describing the environment, the interfaces, and the approaches used to identify the students' teams that need timely assistance, the paper presents the results of different analyses performed using the collected data, to help the teacher better understand students' educational needs.

New information and communication technologies offer today many opportunities to improve the quality of educational services in universities and in particular they allow to design and implement innovative learning models. This paper describes and validates our university blended learning model, and specifically the massive educational video service that we offer to our students since 2010. In these years, we have gathered a huge amount of detailed data about the students' access to the service, and the paper describes a number of analyses that we carried out with these data. The common goal was to find out experimentally whether the main objectives of the educational video service we had in our mind when we designed it, namely appreciation, effectiveness and flexibility, were reflected by the users' behavior. We analyzed how many students used the service, for how many courses, and how many videos they accessed within a course (appreciation of the service). We analyzed the correlation between the use of the service and the performance of the students in terms of successful examination rate and average mark (effectiveness of the service). Finally, by using data mining techniques we profiled users according to their behavior while accessing the educational video service. We found out six different patterns that reflect different uses of the services matching different learning goals (flexibility of the service). The results of these analyses show the quality of the proposed blended learning model and the coherency of its implementation with respect to the design goals.

The diffusion of learning technologies has fostered the use of mobile and Web-based applications to assess the knowledge level of learners. In parallel, an increasing research interest has been devoted to studying new learning analytics tools able to summarize the content of large sets of learning documents. To bridge the gap between formative assessment tools and document summarization systems, this paper addresses the problem of recommending short summaries of large sets of learning documents based on the outcomes of multiple-choice tests. Specifically, it presents a new methodology for integrating formative assessment through mobile applications and summarization of learning documents in textual form. The content of the multiple-choice tests is exploited to drive the generation of document summaries tailored to specific topics. Furthermore, the outcomes of the tests are used to automatically recommend the generated summaries to learners based on their actual needs. As a case study, we performed an evaluation experience of students' progresses, which was conducted in the context of a university-level course. The achieved results show the applicability of the proposed methodology.

Our university has offered a massive educational video service since 2010, as part of a blended learning model that allows students to balance active participation in the classroom with remote access to video-recorded lectures. In these years, we have collected a huge amount of very detailed data about the students' access to the service. Together with additional information that characterize a university system (e.g. students' performance or course population), these data represent a precious ground set to assess the educational model. The paper describes an experimental set to profile the use of the educational video service, whose results will contribute to improve the model. Specifically the paper analyzes the students' service use relatively to different transversal course characteristics, such as level, main topic, population, success rate. As a result, it outlines the profile of the “ideal” courses for which students highly appreciate the service. This information will help educational designers to select the future courses to be included in the service, but it will also give directions on the sectors where improvements are necessary. Finally, the paper experimentally demonstrates a positive impact of the educational video service on students' performance, and specifically on the exam success rate.

The Social Web is quickly becoming a way of life: millions of people, everywhere, use social network sites to stay connected with their friends, discover new people and activities, and share user-created contents. Moreover, the Social Web phenomenon experiments an astoundingly rapid growth that is not likely to slow down in the near future. At the same time, the borderline between social networks and social media is more and more blurred. This complex and evolving scenario requires a new generation of computer scientists and engineers that understand how to properly design software for supporting and fostering social interactions. This paper describes a university-level experience started four academic years ago with a Social Web course. The course uses a multidisciplinary and active learning approach by requesting the students to design and prototype a Social Web application, and the teachers provide an active support and follow-up along the semester. The paper presents the adopted teaching strategies and analyzes the attained learning outcomes, both from the qualitative and quantitative point of views.

E-learning systems commonly rely on advanced ICT technologies to enable users to access and browse electronic resources. Document summarization is an established text mining technique which focuses on extracting succinct summaries of potentially long textual documents. The application of summarization algorithms in the e-learning context is particularly appealing, because readers may want to pinpoint the key concepts by reading short summaries instead of the whole document content. This paper investigates the application of a state-of-the-art summarization algorithm to English-written academic teaching material. The summarizer produces an ordered sequence of key phrases extracted from learning material organized in different sections. The generated summaries are provided to students as additional material for study and revision. A crowd-sourcing experience of evaluation of the generated summaries was conducted by involving the students of a B.S. Course given by a technical university. The results show that the automatically generated summaries reflect, to a large extent, the student's expectations and therefore they can be useful for supporting individual and collective learning activities.

Faceted classification is a technique originated and refined in the library science field, that recently gained a lot of attention for creating efficient search interfaces for web databases. Faceted search requires the definition of a formal representation model, a search algorithm and a responsive user interface. This paper proposes FaSet, a representation model and search algorithm supporting the implementation of faceted search engines. FaSet relies on set theory, and strikes a good balance between expressive power and ease of implementation on web architectures. The paper presents the formal definition of the model, search and ranking algorithms, and a relational mapping of data structures and algorithms that enables its efficient implementation.

Searching the Web proved to be a critical task in which both user satisfaction and speed requirements must be satisfied. Nowadays search engines provide amazing capabilities of searching resources on the Web though they are still based on text indexing and seldom exploit semantics of resource content. Therefore they sometimes fail on the identification of query context, providing results that are not relevant with respect to user needs; this is especially true for nontrained users. The semantic Web specifically addresses such an issue by providing means to define resources and query semantics using ontologies and semantic annotations. We propose a concept-based search paradigm for document retrieval. We define a new representation of the involved information space, introducing the notion of conceptual spectrum to identify the global topic landscape of Web resources and queries. We exploit such a representation to design and implement a prototypical version of a concept-based search engine. We compare the proposed approach to a traditional keyword-based engine, on a specific domain. Results are promising and show the approach feasibility.

This paper presents the results of transmission of video data on 2-class DiffServ IP networks using perceptual packet classification and slicing. An analysis-by-synthesis technique to identify perceptually important video regions, to create optimal video slices and to assign the resulting packets to the appropriate DiffServ classes is described. The proposed technique was implemented using the ISO/IEC MPEG-2 video coding standard. Several transmission scenarios, including homogeneous video traffic and interfering FTP traffic, were simulated using network simulator (NS). The proposed perception-based video transmission approach outperformed classical data partitioning in all tested network usage and potential to match time-varying channels. Substantially higher PSNR values than the regular best-effort case were also obtained assigning to the high-QoS class as little as 10% of the traffic.

The paper proposes a distributed open semantic elaboration platform based on a modular multilingual enabled architecture, which includes ontology, annotations, lexical entities and search functions. The platform is implemented as a distributed set of services including: semantic annotations for document substructures (e.g. chapters, sections, paragraphs), an external annotation repository (based on XPath and XPointer technologies) that is automatically populated starting from a known ontology and a lexical representation of concept classes, and a semantic search engine used to extract and recombine relevant document fragments. Annotated resources may be XML or XHTML static or dynamic documents, and need not be stored nor modified. Preliminary experimental results are presented to show the feasibility and the advantages of the proposed approach.

We present results on the performance of Distributed Speech Recognition operating over simulated IP networks. ETSI AURORA front-end running at client nodes extracts the speech parameters, packetizes and sends them as real-time IP traffic to a remote recognizer based on Continuous Density Hidden Markov Models. The experimental framework is the ETSI STQ-AURORA Project Database 2.0. The impact of transmission over IP networks is modeled by (1) random losses, (2) losses generated by a Gilbert model and (3) network simulations. Results show that random losses and moderately bursty losses do not significantly affect the recognition performance. Strongly bursty packet losses, as those generated by real-time and Web traffic competing over a network bottleneck, instead, can have a very negative impact on recognition performance, indicating that DSR over the Internet, to be successful, requires high levels of Quality of Service.

The problem of assisting people with special needs is assuming a central role in our society, and information and communication technologies are asked to have a key role in aiding people with both physical and cognitive disabilities. This paper describes an eye tracking system, whose strong points are the simplicity and the consequent affordability of costs, designed and implemented to allow people with severe motor disabilities to use gaze as an input device for selecting areas on a computer screen. The motivation for this kind of input device, together with the communication impairments that it may help to solve are reported in the paper, that then describes the adopted technical solution, compared to existing approaches, and reports the results obtained by its experimentation.

We present a distortion-based packet marking technique for transmission of motion-compensated video over Differentiated Services networks. For each macroblock of an MPEG2 video sequence, the distortion that would be caused at the receiver by its loss is computed. High distortion macroblocks are grouped into perceptually important slices that can be transmitted as premium packets, while lower distortion slices are sent as less expensive, best-effort traffic. Firstly, computation of the distortion introduced in the current frame only is compared to exhaustive computation of the distortion introduced in the entire group of pictures (GOP) due to the error propagation. Secondly, allocation of the premium traffic on a frame-by-frame basis is compared to GOP-wide allocation. Results show that GOP-wide allocation of premium traffic is key in using premium bandwidth efficiently, with strong PSNR gains with respect to the other approaches. We also propose a model-based distortion computation technique, which, combined with GOP-level premium traffic allocation, delivers nearly the same performance of the exhaustive approach at a fraction of its complexity.

Both simulation for design verification and fault simulation in conjunction with automatic test pattern generation (ATPG) would benefit from forward and backward shifting of the simulation time. Except in some particular cases, however this has so far been only allowed through explicit save/restore commands issued by the user. The paper presents a general technique that makes a "run for T" command possible, where T can be any positive or negative time value. A major feature is that the user can set she maximum allowable overhead. Its generality allows its implementation in simulators for design verification and fault simulators, for both synchronous and asynchronous circuits, with either zero-delay or accurate delay models.

Both simulation for design verification and fault simulation in conjunction with automatic test pattern generation could take advantage of the possibility of moving the simulation time forward and backward. Up until now, rollback was allowed only by explicit backup/restore commands issued by the user. The paper presents a technique that makes possibles a "run for T" command, where T can be any time value, either positive or negative. It is based on an adaptive mechanism that automatically controls the parameters of an advanced network status recording system. A major feature is that the user may decide the maximum allowable overhead. Experiments show that the resulting rollback time is on the average very short, and therefore this technique can be efficiently used to improve sequential ATPG algorithms.>