Proposta de tesi


Grup de recerca

Visual learning analytics for virtual learning environments

Virtual learning environments generate huge amounts of interaction data that can be analysed and visualized in order to better understand both the teaching/learning process and users' behaviour. This analysis can be done at different levels of detail, combining data from multiple sources (services, learners' profiles, etc.) coming from one or more educational scenarios (a virtual classroom, blog, repository, etc.). Research on this topic is meant to build robust models that can be used to help learners, teachers and managers to fulfil their goals, and to detect and resolve bottlenecks in virtual learning environments, as well as identifying and explaining the most relevant reasons for these, by means of visual learning analytics (both methodologies and tools).

Dr. Julià Minguillón


Algorithms and methods for educational data mining and learning analytics

Analysing what is going on in a virtual learning environment means dealing with huge data sets, which are gathered with different levels of detail. Traditional statistical analysis and data mining techniques need to be adapted in order to cope with partial records, categorical data, evolving models and, in general, large amounts of noisy data. Research on this topic includes the extension of state-of-the-art machine learning algorithms and techniques, in order for them to be used for building robust and interpretable models.

Dr. Julià Minguillón LAIKA

ICT education through formative assessment, learning analytics and gamification

ICT degrees include very practical skills, which can only be acquired by means of experience, performing exercises, designs, projects, etc. In addition to the challenge of motivating students to solve activities, lecturers face the problem of assessing and providing suitable feedback for each submission. Receiving immediate and continuous feedback can facilitate the acquisition of the skills, although this requires support in the form of automatic tools. The automation of the assessment process may be simple in some activities (eg practical activities on programming) but it may be complex in activities about design or modelling. Monitoring the use of these tools can reveal very valuable information for the tracking, management and continuous improvement of the course by the teaching team. However, in order to leverage all its potential, this information should be complemented with data from other sources (eg the student's academic file) and historical information from previous editions of the course.

The main goal of this line of research is to design and build a set of e-learning tools and services to provide support to the learning process in university degrees in the field of ICT (Information and Communication Technologies). The expected benefits will have a repercussion on the students (improvement of the educational experience, greater participation and performance, lower drop-out rate) and on the lecturers, managers and academic coordinators (resources for monitoring a course, making decisions and predictions).

Taking into account these elements, the contributions will focus on three axes: - Tools for formative assessment, which can provide immediate feedback by means of automatic assessment. In particular, the research activity will focus on knowledge areas with high cognitive or modelling levels, such as the design or modelling of software and hardware. - Learning analytics that monitor the activity and the progress of the student regarding the use of the aforementioned tools and allow for analysis of the learning results, identifying the critical points and defining improvement actions. These analytics will also incorporate other sources of academic and historical information to facilitate the course tracking and decision making processes for the teaching team. - Gamification, as an incentive scheme in order to motivate students to perform new activities and increase their engagement without sacrificing the academic rigor.

A relevant aspect to be considered by e-learning tools developed in this line of research is the modularity and independence from technologies or particular virtual campuses, with the aim of facilitating its application to different courses and contexts. To this end, the functionalities of these tools will be offered as a set of services, using appropriate standards. The tools will be evaluated in courses on mathematics, computing engineering and telecommunication and it is expected that their use will become feasible as part of both self-taught education (life-long learning) and traditional formal education as well as massive on-line learning courses (MOOCs).

The research conducted here will be supported by the Spanish research project ICT-FLAG: Enhancing ICT education through Formative assessment, Learning Analytics and Gamification (Ref: TIN2013-45303-P).

Dr. Robert Clarisó

Dr. Santi Caballé

SOM Research Lab



Multi-modal emotion awareness e-learning tools

Emotions and affective factors, such as confusion, frustration, shame and pride, are acknowledged as major influences in education in LMSs (Learning management systems). However, despite major advancements in fields such as artificial intelligence, human-computer interaction, and sensorial technologies, e-learning environments are still struggling with incorporating emotion awareness tools. The limited to null adoption of emotional analysis tools and affective feedback prevents both learners and teachers from reaping the benefits of emotion awareness LMSs.

This line of research aims at enhancing existing e-learning platforms by developing tools and services that support the detection and representation of learners’ emotions, as well as emotion-based learning adaptation and affective feedback. To this end, the research will apply novel emotion detection models to rich multimodal data collected using state of the art channels, advanced sensors and novel adaptive interfaces. Moreover, via multiple small-scale pilots in formal, informal and workplace learning environments, the research will intend to demonstrate a positive impact of emotion-aware e-learning on decreasing learner drop-out rates, increasing satisfaction and improving learning performance, thus making learning as a whole a better experience.

The ultimate goal of the research conducted here is to understand the underlying mechanisms of socio-affective processes as well as how best to build multi-modal emotion awareness e-learning tools that are adaptive not only to learners’ cognitive performances but also to their affective states and social interactions with peers and teachers. This goal is thus two-fold:

  • To embed non-intrusive, module-based emotion awareness tools into LMSs that allow for socio-affective learning and assessment of individuals and groups in different environments: formal (university, primary/secondary school, and special education), informal (open education e-learning for adults), and the workplace.
  • To validate and measure improvements in knowledge gain, drop-out rate, learning analytics capacity, and affective profiling as measured by changes in socio-cognitive performance, motivation, collaborative and social interactions, together with the cost-effectiveness of the platform, including the rate of adoption of these technologies for the modernization of education and training, and also validating gender differences.

Dr. Santi Caballé

Dr. Anastasi Daradoumis


ICSO Research Group

Cloud, cluster and distributed computing for e-learning

This research line will leverage intensive computational capabilities of Cloud, Cluster and Distributed computing for eLearning in order to integrate adaptive and personalised approaches capable of identifying learners’ requirements (using Artificial Intelligence and data mining techniques), building users models based on navigation patterns in virtual campus, intelligently monitoring progress to purposeful and meaningful advice both learners and teachers, among others. In particular:

Cloud computing technologies are more and more popular in eLearning, most computing platforms and standalone eLearning applications are being deployed in Cloud platforms and offered as a service (SaaS) with many benefits. For instance, by porting eLearning applications to Cloud, it is possible to offer on-line learning as a Cloud service, which would alleviate the final user from the burden of installing and configuring at local computer or local networking infrastructure. Moreover, porting to Cloud allows for tackling mining of very large data sets, i.e. Big Data for eLearning.

User modeling in eLerning implies a constant processing and analysis of user interaction data during long-term learning activities, which produces huge amounts of valuable data stored typically in server log files. Due to the large or very large size of log files generated daily in Virtual Campuses, the massive processing is a foremost step in extracting useful information. Cluster computing is commonly used for this purpose using different distributed frameworks, such as Hadoop Map Reduce.

Non-functional requirements in eLearning systems, such as maintenance cost, scalability and fault-tolerance are important aspects to consider. Distributed technologies, such as P2P are an important alternative to develop decentralized online learning systems in which students can be more than mere clients and can use their own computational resources for task accomplishment during online learning process.

This research line will implement and evaluate the eLearning approaches using the above computing paradigms in order to explore the real complexities and challenges, such as time performance of massive processing of daily log files implemented following the master-slave paradigm and the actual time efficiency of porting some Data Miming frameworks to the Cloud for mining Big Data for eLearning.

Dr. Santi Caballé SMARTLEARN

Computer-mediated collaboration and learning within an adaptive, interactive, personalized, emotion and context-aware environment

The increasing use of social networking sites (SNS) introduces new problems including SNS addiction and cyber-bullying that interfere with school and learning. Such social network problems are based on failure to address the development of socio-cognitive and socio-emotional skills in formal school curricula. Moreover, while in the recent past, personalization has been mostly explored through learner's profiles, context-aware can enhance such system considerably by capturing not only learner's preferences but also the learner's context, group context as well as learning spaces and objects context. The aim is to provide learners with advanced and enriched information on the context where learning and interaction takes place and provide learners with a situational/context awareness.

In fact, a new pedagogy is needed based on self-regulated, experiential learning in groups where learners are supported to achieve a deeper understanding of self in relation to others. In order to contribute to the goal of building cognitive- and emotion-centered learning programs, research should focus on the investigation of how group awareness tools can be adapted to support the social regulation of cognition and emotions in learning contexts.
Self-awareness, control of impulsivity, working cooperatively, and caring about oneself and others are key factors that can lead to effective self-regulated learning and motivation regulation in distance learning environments. One needs to identify, encourage and reinforce the social, cognitive and emotional skills needed for a successful engagement in computer-mediated collaboration (CMC) and learning within an adaptive, interactive, personalized, emotion and context-aware environment (
The use of group awareness technologies is becoming necessary to circumvent the bottlenecks of CMC. Such technologies aim at analyzing users' characteristics and behavior and feeding that information back to the group. In CSCL contexts, group awareness tools should be designed not only to improve and expand social and cognitive processes during collaborative learning (Buder, 2011), by making explicit and visible what is not directly observable like e.g., the group members' prior knowledge (Sangin et al., 2011) or their participation level during online discussions (Janssen et al., 2011), but also to provide collaborators with information about their partner's affective states during online collaboration. Ultimately, we need to investigate the degree of positive impact of socio-cognitive coupled with socio-emotional awareness tools on collaborative processes and outcomes, as well as the way to provide effective and timely cognitive and emotional feedback that can help in monitoring and assessing learners' behavior, performance and individual progress.

Dr. Atanasi Daradoumis ICSO Research Group

Information models for enhancing security in e-learning

This research line aims at incorporating information security properties and services into on-line e-Learning. The main goal is to design innovative security solutions, based on methodical approaches, to provide e-Learning designers and managers with guidelines for incorporating security into on-line learning. These guidelines include all processes involved in e-Learning design and management such as security analysis, learning activities design, detection of anomalous actions, trustworthiness data processing, and so on.

This research is to be conducted by multidisciplinary perspective, the most significant are e-Learning and on-line collaborative learning, information security, learning management systems, and trustworthiness assessment and prediction models. In this scope, the problem of ensure collaborative on-line learning activities will be tackled by a hybrid model based on functional and technological solutions, such as, trustworthiness modeling and information security technologies.

Dr. Santi Caballé SMARTLEARN

Authorship and authentication through activities in e-assessment

Even though online education is a key factor for lifelong learning, institutions are still reluctant to wager for a fully online educational model. At the end, they keep relying on on-site assessment systems, mainly because fully virtual alternatives do not have the deserved social recognition or credibility. Thus, the design of virtual assessment systems that are able to provide effective proof of student authenticity, authentication and authorship and the integrity of the activities in a scalable and cost efficient manner would be very helpful.

This research line proposes to analyse how online assessment in distance learning environments is performed (using tools and resources through continuous e-assessment activities). The activities and their evaluation are core pieces to obtaining evidences about user authentication and authorship. Then the e-assessment approach will be based on a continuous trust level between students and the institution across curricula.

Dra. Anna Guerrero

Dr. David Bañeres


SOM Research Group

Ontologies in support for affective and emotional collaborative learning systems

Human-computer interaction (HCI) applied to ITS (intelligent tutoring system) can be used to develop and design methodologies that are pedagogically guided and which would handle the emotional/affective systems of learning and provide the e-learning system the ability to offer more intelligent adaptive and collaborative services.

To this end, this research line focuses on ontological frameworks that include emotional information about the sentiment and opinion of students when collaborating. The use of automatic opinion mining and sentiment analysis techniques is fostered to study the opinion that a learning document expresses and determine certain sentiments felt by a student when writing an opinion in a post text, in terms of subjectivity, polarity, strength and so on.

Dr. Jordi Conesa SMARTLEARN

Technology-enhanced assessment, analytics and feedback

Technology can support nearly every aspect of assessment in one way or another, from the administration of individual tests and assignments to the management of assessment across a faculty or institution; from automatically marked on-screen tests to tools to support human marking and feedback. This research line is related to technology-enhanced assessment, and focuses on the wide range of technologies and ways in which technology can be used to support assessment, feedback and its analytics. Research topics include, but are not limited to: Design, development and evaluation of e-assessment systems, Technologies and specifications for e-assessment, Technology-enhanced assessment design, validity and reliability, Feedback generation, support and automation, Human-Computer Interaction in e-assessment and feedback, Learning and Assessment Analytics, Collection, analysis and visualization of data for e-assessment and feedback.

Dra. M. Antonia Huertas

Dr. Enric Mor


Video-games and gamification in higher education learning environments

Since the dawn of time, games have been used as an effective learning method, not just for humans, but for many living beings. Even though gaming as a learning tool tends to be associated to early development stages (childhood), and thus, labelled as a frivolous activity, this perspective has slowly shifted.

In the computers era, video-games have dethroned all other types of media, becoming an activity shared by groups of people with very different interests and ages. This research line focuses on the study of how video-games or/and game-like activities (gamification) can be embedded into the learning process in university-grade studies to improve students experience and performance.

Dr. Joan Arnedo

Dr. Daniel Riera

KISON Research Group

ICSO Research Group

Conversational Agents and Learning Analytics for MOOCs

Higher Education Massive Open Online Courses (MOOCs) introduce a way of transcending formal higher education by realizing technology-enhanced formats of learning and instruction and by granting access to an audience way beyond students enrolled in any one Higher Education Institution. However, although MOOCs have been reported as an efficient and important educational tool, there is a number of issues and problems related to the educational aspect. More specifically, there is an important number of drop outs during a course, little participation, and lack of students’ motivation and engagement overall. This may be due to one-size-fits-all instructional approaches and very limited commitment to student-student and teacher-student collaboration.

This thesis aims to enhance the MOOCs experience by integrating:

• Collaborative settings based on Conversational Agents (CA) both in synchronous and asynchronous collaboration conditions

• Screening methods based on Learning Analytics (LA) to support both students and teachers during a MOOC course

CA guide and support student dialogue using natural language both in individual and collaborative settings. Moreover, LA techniques can support teachers’ orchestration and students’ learning during MOOCs  by evaluating students' interaction and participation. Integrating CA and LA into MOOCs can both trigger peer interaction in discussion groups and considerably increase the engagement and the commitment of online students (and, consequently, reduce MOOCs dropout rate).

Dr. Jordi Conesa

Dr. Santi Caballé


Verification of authorship of learning activities

Virtual environments have many advantages, but the student is not physically present in the classroom. This fact complicates the verification of the student and the authorship of the work. There are techniques that allow the non-invasive biometric user identification that can be applied to identification in virtual learning environments. Particularly, we propose the research in the use of voice analysis and its integration with other non-invasive behavioural techniques.

Dr. Jose Antonio Morán

Dra. Eugènia Santamaría


Intelligent tutoring systems for learning digital systems

The synthesis of digital circuits is a basic skill in all the bachelors around the ICT area of knowledge, such as Computer Science, Telecommunication Engineering or Electrical Engineering. An important hindrance in a virtual learning environment is that the student does not have the face-to-face support of the instructor during their learning process.

This research deals with the design of a unified automated framework to provide a set of self-assessment services to learn digital systems. In addition to design tools where the personalized feedback is crucial, the research also focuses on the instructor point of view giving specific information related to the analysis of the learning progress of the students.

Dr. David Bañeres

Dr. Robert Clarisó

SOM Research Lab

Learning analytics for automated feedback generation and self-regulated learning when assessing programming assignments

Feedback gives information to students about how their learning achievements and performance relate to the expected goals. Self-regulated learning help students set goals for their learning, monitor, direct, and regulate those actions that can best lead toward the accomplishment of these goals. Moreover, self-regulation can show differences in how we see ourselves compared to our peers, which can spur changes in awareness, motivation and behaviour. Learning analytics is the discovery, interpretation and identification of patterns in data that helps improving performance and behaviour.

The aim of this PhD proposal is to apply learning analytics to data generated by a tool that is used for automatic assessment of programming assignments and which aims at providing automated formative feedback to students so that they improve self-regulation and performance.

More specifically, we take DSLab ( as a base, a tool that is currently used to evaluate distributed systems assignments in a realistic environment. This tool allows students to upload the classes that implement the distributed algorithm or protocol to evaluate, run it in a realistic distributed environment, and finally, get a grade for the practical assignment.

The thesis will take an interdisciplinary approach and will, among other things, identify patterns of behaviour that result on good or bad results, propose models and mechanisms to improve self-regulation, identify feedback opportunities that will help learners to achieve their learning goals and will also help instructors have a clearer idea of how their students progress.

Dr. Atanasi Daradoumis

Dr. Joan Manuel Marquès

ICSO Research Group