|
Proposta de tesi |
Investigadors/es |
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). |
Mail: jminguillona@uoc.edu
|
|
|
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) based on authentication and authorship trustability and the systems used. 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. This line of research proposes to analyse a virtual assessment approach and systems based on a continuous trust level evaluation between students and the institution by analysing too the current online certification processes. |
Mail: mrodriguezgo@uoc.edu Mail: dbaneres@uoc.edu |
|
|
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). |
Mail: scaballe@uoc.edu Mail: jconesac@uoc.edu |
SMARTLEARN |
|
Enhancing educational support through an adaptive virtual educational advisor Nowadays, many systems help students to learn. Some of them aid students in finding learning resources or recommending exercises. Others aim to help the student in the assessment phase by giving feedback. Furthermore, others monitor the student's progress during the instructional process to recommend the best learning path to succeed in the course. Depending on the objectives/competencies of the subject, some features are more suitable than others.
|
Mail: dbaneres@uoc.edu Mail: mrodriguezgo@uoc.edu Mail: iguitarth@uoc.edu Mail: mserravi@uoc.edu |
|
|
Interactive recommendation systems for higher education enrollment
Higher education students at open / distance universities enjoy from a high degree of flexibility during enrollment, which allows them to choose from a long list of subjects to complete their degree. Although this can be seen as a success of enrollment flexibility measures, it may be also the source of one of the most well-known problems in open / distance education: high dropout rates, partly caused by inadequate enrollment. In this research line we will analyze and adapt state-of-the-art recommendation systems to the particularities of the enrollment procedure, taking into account enrollment data and academic results from previous semesters but also students’ preferences and personal interests. Our goal is to design and evaluate interactive recommendation systems that provide students and their mentors with support during enrollment, following a user-centered design approach.
|
Mail: jminguillona@uoc.edu
|
LAIKA |
|
Cognitive-affective chatbots and automated feedback for online programming courses
In online learning environments, when students study science or technical subjects, they often get stuck trying to solve a mathematical problem or they are unable to identify the source of an error in the program that are developing. This affects the pace of their learning whereas they may feel isolated. Having the opportunity to interact with instructors or with other online students helps them to partially solve the lack of immediateness while helping them face loneliness and letting them feel part of an academic community. However, a lot has to be done yet in order to face individual problems and to provide personalized learning.
In that sense, we propose PhD proposals around the following topics:
• to improve automatic feedback in programming assignments to help students accomplish these assignments more effectively. The idea is to extend DSLab - a self-assessment tool for programming assignments developed by our research group - with several interactive features and to validate them via authentic online learning experiences from different subjects of different degrees and levels (undergraduate and master).
• to improve the pace of student's learning by means of a cognitive-affective chatbot integrated into a chat service. This chat service is integrated into DSLab tool and is used to promote communication between instructors and students and among students who are involved in the same assignment. The chatbot should automatically answer questions related to the assignment taking into account their emotional states.
|
Mail: adaradoumis@uoc.edu
Mail: jmarquesp@uoc.edu
|
DPCS-ICSO |
|
Boardgames for education During the last years the board game field have experimented a great expansion in the means of the quantity of boardgames available, if the variety of them, of the broad coverage of topics they address and the variety of mechanics they provide. They have great potential to become a great tool for learning, as many research studies show.
In this research line, we would like to address the latest innovations of using boardgames for learning and to explore the potential of using boardgames in the eLearning context and the mechanisms that appear in them.
|
Mail: jconesac@uoc.edu Mail: aperezn@uoc.edu |
|
|
Research on how to enhance novice programmers' experience Teaching and learning programming is big challenge. While learning to program, students are not only required to learn the fundamental concepts
of programming itself, but also to grasp the syntax and grammar of a programming language. This research area focuses on improving how students, especially novices, learn to program. In other words, how novice programmers can be most effectively supported in their learning. The present researh line proposes to work on the following topics (not limited to):
- What is the Introductory Programming (CS1) course like in the world? How are CS1 courses taught in the world?
- How students interact with tools (e.g. IDEs), error messages, lecture notes, resources, etc. How does they feel while learning to program?
- Which grading schema (rubrics, test-based assessment, etc.) is more suitable for programming courses? Could we suggest one?
- How to provide students with effective feedback on their code?
- Which learning resources are more effective for teaching to program?
- Educational opportunities and challenges of AI code generation. Can AI help us to teach students to program?
- How to teach code quality issues early?
As seen, the research on previous topics can be conducted from a perspective close to both social sciences (e.g. survey, ethnography, case studies, etc.) and technology (i.e. design, development and test of a tool).
|
Mail: dgarciaso@uoc.edu |
LAIKA |
|
Authorship of the code-based assignments
Although online education is full of opportunities, it also has some drawbacks. Such drawbacks are, at the same time, challenges. One of them is how to guarantee the authorship of the assignments submitted by students. This is especially relevant with programming assignments. This research line focuses on the following topics (not limited to):
- Can students take personalized exams based on their code?
- Which traits of the code can be analyzed in order to guarantee the authorship of the assignments?
- Can we capture some data (e.g. keystroke dynamics) from the students while coding their programs?
- What computer-based techniques can we use in order to avoid and/or detect plagiarism?
As seen, this research area proposes to design and develop tools that help teachers/institutions to credit for students' contributions.
|
Mail: dgarciaso@uoc.edu |
LAIKA |