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.

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.