Recommending Tasks in Online Judges using Autoencoder Neural Networks

Programming contests such as International Olympiads in Informatics (IOI) and ACM International Collegiate Programming Contest (ICPC) are becoming increasingly popular in recent years. To train for these contests, there are several Online Judges available, in which users can test their skills against a usually large set of programming tasks. In the literature, so far few papers have addressed the problem of recommending tasks in online judges. Most notably, as opposed with traditional Recommender Systems, since the learners improve their skills as they solve more problems, there is an intrinsic dynamic dimension that has to be considered: when recommending movies or books, it is likely that the preferences of the users are more or less stable, whilst in recommending tasks this does not hold true. In order to help the learners, it is crucial to recommend them tasks that are challenging but not unsolvable compared with their current set of skills. In this paper we present a Recommender System (RS) for Online Judges based on an Autoencoder (Artificial) Neural Network (ANN). We also discuss the results of an experimental evaluation of our approach in both the scenarios in which we consider, or not, the intrinsic dynamic dimension of the problem. The ANNs are trained with the dataset of all the submissions in the Italian National Online Judge, used to train students for the Italian Olympiads in Informatics

Preference Learning

This report documents the program and the outcomes of Dagstuhl Seminar 14101 “Preference Learning”. Preferences have recently received considerable attention in disciplines such as machine learning, knowledge discovery, information retrieval, statistics, social choice theory, multiple criteria decision making, decision under risk and uncertainty, operations research, and others. The motivation for this seminar was to showcase recent progress in these different areas with the goal of working towards a common basis of understanding, which should help to facilitate future synergies

Use of aggregation functions in decision making

A key component of many decision making processes is the aggregation step, whereby a set of numbers is summarised with a single representative value. This research showed that aggregation functions can provide a mathematical formalism to deal with issues like vagueness and uncertainty, which arise naturally in various decision contexts

A Self-Regulated Learning Approach to Educational Recommender Design

Recommender systems, or recommenders, are information filtering systems prevalent today in many fields. One type of recommender found in the field of education, the educational recommender, is a key component of adaptive learning solutions as these systems avoid “one-size-fits-all” approaches by tailoring the learning process to the needs of individual learners. To function, these systems utilize learning analytics in a student-facing manner. While existing research has shown promise and explores a variety of types of educational recommenders, there is currently a lack of research that ties educational theory to the design and implementation of these systems. The theory considered here, self-regulated learning, is underexplored in educational recommender research. Self-regulated learning advocates a cyclical feedback loop that focuses on putting students in control of their learning with consideration for activities such as goal setting, selection of learning strategies, and monitoring of one’s performance. The goal of this research is to explore how best to build a self-regulated learning guided educational recommender and discover its influence on academic success. This research applies a design science methodology in the creation of a novel educational recommender framework with a theoretical base in self-regulated learning. Guided by existing research, it advocates for a hybrid recommender approach consisting of knowledge-based and collaborative filtering, made possible by supporting ontologies that represent the learner, learning objects, and learner actions. This research also incorporates existing Information Systems (IS) theory in the evaluation, drawing further connections between these systems and the field of IS. The self-regulated learning-based recommender framework is evaluated in a higher education environment via a web-based demonstration in several case study instances using mixed-method analysis to determine this approach’s fit and perceived impact on academic success. Results indicate that the self-regulated learning-based approach demonstrated a technology fit that was positively related to student academic performance while student comments illuminated many advantages to this approach, such as its ability to focus and support various studying efforts. In addition to contributing to the field of IS research by delivering an innovative framework and demonstration, this research also results in self-regulated learning-based educational recommender design principles that serve to guide both future researchers and practitioners in IS and education

ResuMatcher: A Personalized Resume-Job Matching System

Today, online recruiting web sites such as Monster and Indeed.com have become one of the main channels for people to find jobs. These web platforms have provided their services for more than ten years, and have saved a lot of time and money for both job seekers and organizations who want to hire people. However, traditional information retrieval techniques may not be appropriate for users. The reason is because the number of results returned to a job seeker may be huge, so job seekers are required to spend a significant amount of time reading and reviewing their options. One popular approach to resolve this difficulty for users are recommender systems, which is a technology that has been studied for a long time. In this thesis we have made an effort to propose a personalized job-résumé matching system, which could help job seekers to find appropriate jobs more easily. We create a finite state transducer based information extraction library to extract models from résumés and job descriptions. We devised a new statistical-based ontology similarity measure to compare the résumé models and the job models. Since the most appropriate jobs will be returned first, the users of the system may get a better result than current job finding web sites. To evaluate the system, we computed Normalized Discounted Cumulative Gain (NDCG) and precision@k of our system, and compared to three other existing models as well as the live result from Indeed.com

Atas das Oitavas Jornadas de Informática da Universidade de Évora

Atas das Oitavas Jornadas de Informática da Universidade de Évora realizadas em Março de 2018

Decoding learning: the proof, promise and potential of digital education

With hundreds of millions of pounds spent on digital technology for education every year – from interactive whiteboards to the rise of one–to–one tablet computers – every new technology seems to offer unlimited promise to learning. many sectors have benefitted immensely from harnessing innovative uses of technology. cloud computing, mobile communications and internet applications have changed the way manufacturing, finance, business services, the media and retailers operate. But key questions remain in education: has the range of technologies helped improve learners’ experiences and the standards they achieve? or is this investment just languishing as kit in the cupboard? and what more can decision makers, schools, teachers, parents and the technology industry do to ensure the full potential of innovative technology is exploited? There is no doubt that digital technologies have had a profound impact upon the management of learning. institutions can now recruit, register, monitor, and report on students with a new economy, efficiency, and (sometimes) creativity. yet, evidence of digital technologies producing real transformation in learning and teaching remains elusive. The education sector has invested heavily in digital technology; but this investment has not yet resulted in the radical improvements to learning experiences and educational attainment. in 2011, the Review of Education Capital found that maintained schools spent £487 million on icT equipment and services in 2009-2010. 1 since then, the education system has entered a state of flux with changes to the curriculum, shifts in funding, and increasing school autonomy. While ring-fenced funding for icT equipment and services has since ceased, a survey of 1,317 schools in July 2012 by the british educational suppliers association found they were assigning an increasing amount of their budget to technology. With greater freedom and enthusiasm towards technology in education, schools and teachers have become more discerning and are beginning to demand more evidence to justify their spending and strategies. This is both a challenge and an opportunity as it puts schools in greater charge of their spending and use of technolog

Fair team recommendations for multidisciplinary projects

With the ever increasing amount of data in the world, it becomes harder to find useful and desired information. Recommender systems, which offer a way to analyze that data and suggest relevant information, are already common nowadays and a important part of several systems and services. While recommender systems are often used for suggesting items for users, there are not many studies about using them for problems such as team formation. This thesis focus on exploring a variation of that problem, in which teams have multidisciplinary requirements and members' selection is based on the match of their skills and the requirements. In addition, when assembling multiple teams there is a challenge of allocating the best members in a fair way between the teams. With the studied concepts from the literature, this thesis suggests a brute force and a faster heuristic method as solutions to create team recommendations to multidisciplinary projects. Furthermore, to increase the fairness between the recommended teams, the K-rounds and Pairs-rounds methods are proposed as variations of the heuristic approach. Several different test scenarios are executed to analyze and compare the efficiency and efficacy of these methods, and it is found that the heuristic-based methods are able to provide the same levels of quality with immensely greater performance than the brute force approach. The K-rounds method is able to generate substantially more fair team recommendations, while keeping the same levels of quality and performance as other methods. The Pairs-rounds method presents slightly better recommendations quality-wise than the K-rounds method, but its recommendations are less fair to a small degree. The proposed methods perform well enough for use in real scenarios

Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

Peer reviewe