Many first-year university students do not complete the study plan and drop out. By investigating how students earn ECTS credits we create a model that makes it possible to predict students who are at risk of failure and drop out of the university. Weekly analysis of student data allows us to identify patterns important for prediction. Early predictions inform students about the potential danger of failure and also allow tutors to intervene. On the other hand, from the data of successful students, it is possible to derive study trajectories leading to the successful completion of the academic year and offer these trajectories to students. The described techniques for student support are demonstrated by examples.

At present, universities collect study-related data about their students. This information can be used to support students at risk of failing their studies. At the Faculty of Mechanical Engineering (FME), Czech Technical University in Prague (CTU), the group of the first-year students is the most vulnerable. The most critical part of the first year is the winter exam period when students usually divide into those who will pass and fail. One of the most important abilities, students need to learn, is exam planning, and our research aims at the exploration of the exam strategies of successful students. These strategies can be used for improving first-year students retention. The outgoing research on the analysis of exam strategies of the first-year students in the academic year 2017/2018 is reported. From a total of 361 first-year students, successful students have been selected. The successful student is the one who finished all three mandatory exams before the end of the first exam period. From the exam sequences of 153 selected students, a "layered" Markov chain probabilistic model has been constructed. It uncovered the most common exam strategies taken by those students.

In this paper, we describe a large-scale study about the use of predictive learning analytics data with 240 teachers in 10 modules at a distance learning higher education institution. The aim of the study was to illuminate teachers' uses and practices of predictive data, in particular identify how predictive data was used to support students at risk of not completing or failing a module. Data were collected from statistical analysis of 17,033 students' performance by the end of the intervention, teacher usage statistics, and five individual semi-structured interviews with teachers. Findings revealed that teachers endorse the use of predictive data to support their practice yet in diverse ways and raised the need for devising appropriate intervention strategies to support students at risk.

This paper focuses on the problem of identifying students, who are at risk of failing their course. The presented method proposes a solution in the absence of data from previous courses, which are usually used for training machine learning models. This situation typically occurs in new courses. We present the concept of a "self-learner" that builds the machine learning models from the data generated during the current course. The approach utilises information about already submitted assessments, which introduces the problem of imbalanced data for training and testing the classification models.

This paper introduces two measures for the recommendation of study materials based on students' past study activity. We use records from the Virtual Learning Environment (VLE) and analyse the activity of previous students. We assume that the activity of past students represents patterns, which can be used as a basis for recommendations to current students.

This workshop explores how data literacy impacts on learning analytics both for practitioners and for end users. The term data literacy is used to broadly describe the set of abilities around the use of data as part of everyday thinking and reasoning for solving real-world problems. It is a skill required both by learning analytics practitioners to derive actionable insights from data and by the intended end users, such that it affects their ability to accurately interpret and critique presented analysis of data. The latter is particularly important, since learning analytics outcomes can be targeted at a wide range of end users, some of whom will be young students and many of whom are not data specialists.

Learning analytics (LA) as a field remains in its infancy. Many of the techniques now prominent from practitioners have been drawn from various fields, including HCI, statistics, computer science, and learning sciences. In order for LA to grow and advance as a discipline, two significant challenges must be met: 1) development of analytics methods and techniques that are native to the LA discipline, and 2) practitioners in LA to develop algorithms and models that reflect the social and computational dimensions of analytics. This workshop introduces researchers in learning analytics to machine learning (ML) and the opportunities that ML can provide in building next generation analysis models.

One of the key interests for learning analytics is how it can be used to improve retention. This paper focuses on work conducted at the Open University (OU) into predicting students who are at risk of failing their module. The Open University is one of the worlds largest distance learning institutions. Since tutors do not interact face to face with students, it can be difficult for tutors to identify and respond to students who are struggling in time to try to resolve the difficulty. Predictive models have been developed and tested using historic Virtual Learning Environment (VLE) activity data combined with other data sources, for three OU modules. This has revealed that it is possible to predict student failure by looking for changes in user's activity in the VLE, when compared against their own previous behaviour, or that of students who can be categorised as having similar learning behaviour. More focused analysis of these modules applying the GUHA (General Unary Hypothesis Automaton) method of data analysis has also yielded some early promising results for creating accurate hypothesis about students who fail.

Inquiry learning involves the learner acquiring new concepts and skills by means of carrying out an investigation. Some previous studies have looked into how these learning activities can be carried out on source materials, such as web documents, and the provision of appropriate scaffolding to guide the learning process. Here we consider how intelligent support can be provided to guide the learner in analysing source materials and building knowledge from their interpretation. An important feature of our case study is that the source materials are images, potentially having greater variation in how they are interpreted and therefore increasing the need for intelligent support. Intelligent support provided by our system can identify patterns and inconsistencies in the learner's interpretation and knowledge building, and offer recommendations based on comparison with a reference model. The recommendations derived from a reference model aim to guide the learner in reviewing and revising their interpretation of the images and the implications of these for their inferences, rather than directly providing a “right” or “wrong” evaluation of their answer.

In this paper, we present an ontology developed to support annotating and reasoning about philosophical knowledge. By this term, we refer to both the factual domain of philosophers, concerning their lives and publications, and the theoretical domain, concerning the ideas they have produced and their relationships. The ontology provides a novel model, which brings together a number of existing formalizations and a series of new ones. In this paper we describe the design of the ontology, in particular focusing on the solutions we have devised to address the modelling problems and natural language ambiguities, which occur when capturing knowledge in a domain as complex as that of philosophy. This work is being carried out in the context of developing a tool, PhiloSURFical, which aims to support ontology-driven exploration of philosophical resources. Specifically, the ontology makes it possible to navigate the philosophical domain, according to a number of narratives, which include theoretical, historical, argumentational, geographic, and others.

In this paper, we propose a generic library of problem-solving methods for scheduling applications. Although some attempts have been made in the past at developing the libraries of scheduling problem-solvers, these only provide limited coverage. Many lack generality, as they subscribe to a particular scheduling domain. Others simply implement a particular problem-solving technique, which may be applicable only to a subset of the space of scheduling problems. In addition, most of these libraries fail to provide the required degree of depth and precision. In our approach, we subscribe to the task-method-domain-application knowledge modeling framework which provides a structured organization for the different components of the library. At the task level, we construct a generic scheduling task ontology to formalize the space of scheduling problems. At the method level, we construct a generic problem-solving model of scheduling that generalizes from the variety of approaches to scheduling problem-solving, which can be found in the literature. The generic nature of this model is demonstrated by constructing seven methods for scheduling as an alternative specialization of the model. Finally, we validated our library on a number of applications to demonstrate its generic nature and effective support for developing scheduling applications

Scene-Driver is a software toolkit for the reuse of broadcast animation content to provide new engaging experiences for children. It has been developed and tested using content from the children's television series "Tiny Planets". Scene-Driver can be used to produce variations on a domino-like game. When playing, the child selects from a set of tiles that depict, for example, characters from the series. The child manipulates the direction of a story in the Tiny Planet world by their choice of tile. The successful selection of a tile will result in a scene from the show being played. A scene is defined as a section from an episode which has certain self-contained narrative elements such as conflict introduction, conflict resolution or comedic event. A scene-supervisor uses these descriptions to ensure that as well as having all the properties prescribed by the child's choice of tile, the scenes are presented in a coherent order according to certain plot and directorial principles. Inter-scene continuity is provided in the form of transition scenes which depict the departure and arrival of relevant characters between one scene and the next. Preliminary evaluations have demonstrated the potential of Scene-Driver to produce engaging and usable games based on broadcast content for young children.

Increasingly heritage institutions are making digital artifacts available to the general public and research groups to promote the active exploration of heritage and encourage visits to heritage sites. Stories, such as folklore and first person accounts form a useful and engaging heritage resource for this purpose. Story Fountain provides intelligent support for the exploration of digital stories. The suite of functions provided in Story Fountain together support the investigation of questions and topics that require the accumulation, association or induction of information across the story archive. Story Fountain provides specific support toward this end such as for comparing and contrasting story concepts, the presentation of story paths between concepts, and mapping stories and events according to properties such as who met whom and who lived where.

In this paper we describe a generic library of problem-solving methods (PSMs) for scheduling applications. Although, some attempts have been made in the past at developing libraries of scheduling methods, these only provide limited coverage: in some cases they are specific to a particular scheduling domain; in other cases they simply implement a particular scheduling technique; in other cases they fail to provide the required degree of depth and precision. Our library is based on a structured approach, whereby we first develop a scheduling task ontology, and then construct a task-specific but domain independent model of scheduling problem-solving, which generalises from specific approaches to scheduling problem-solving. Different PSMs are then constructed uniformly by specialising the generic model of scheduling problem-solving. Our library has been evaluated on a number of real-life and benchmark applications to demonstrate its generic and comprehensive nature.

Integrating working and learning is seen as a desirable alternative to traditional training regimes. An integrated approach to working and learning is more gradual, contextual, problem oriented and cost effective. Knowledge technology aims to catalyze workplace learning, but requires the right organisational culture and brings additional costs regarding the articulation, representation and transmission of knowledge. Our approach reduces these costs by making articulation a natural part of collaborative working, designing for both informal and formal knowledge, and facilitating the transition of socially situated knowledge through enriched documents. To be successful, our approach has certain prequesites concerning organisational culture, and the nature of shared documents, organisational knowledge and work activities.

Engineering design is a complex activity, relying heavily on know-how gained from personal experience. Competitive pressures and new technology are making further demands on the skills and experience of designers, as effective knowledge reuse in design is seen as increasingly vital, and the work of design teams is often a collaborative and distributed activity. University students with a thorough knowledge of the engineering domain can be ill prepared for professional practice, with its increasing reliance on skills and know-how as well as knowledge of theory. Our approach aims to better prepare students for professional practice, through hands-on experience of design reuse, participation in distributed collaboration, and the development of presentation and documentation skills. Our case-study in the domain of modelling engineering systems, in which the course materials themselves are evolving and distributed, has ramifications for the publication model of educational materials, and the way students should be prepared for working life.

One way of enhancing the problem-solving power of a domain-specialised automatic problem solver is the introduction of domain-important concepts, defined as patterns, for streamlining the problem-solving process. The number of patterns needed for certain kinds of tasks, or domains, like chess, can be so high that the success of this approach to the development of powerful problem-solvers critically depends on two questions: <(a) How much programming effort is needed to implement such patterns in a programming language? (b) How efficiently, in terms of execution time, can these patterns be evaluated? In this paper several fundamentally different approaches to the implementation of problem-domain meaningful patterns, using a complex chess problem as an example, are compared. In particular, an approach based on cellular array processing operations is investigated.