This research WIP paper describes the case of a first-year engineering course which, contrary to modern trends in education, reduced the presence of blended learning in its methodology, transitioning from the flipped classroom and intensive continuous assessment models back to traditional instruction. Typically, blended learning methodologies like the flipped classroom are regarded to have the positive effects of improving engagement and fostering a student-centered learning approach. However, there are also concerns regarding the potentially increased workload that this system imposes on students. This study analyzes course resource usage trends by students, comparing the final year under the blended learning method with the first year after the switch to traditional instruction. More specifically, the volume of student activity, its timing throughout the semester, and the preferred types of resources are compared between both methodologies. The study found that, in terms of time spent interacting with resources, there was not a significant decrease between both iterations of the course, although the types of resources favored by students do vary between methodologies. Additionally, the activity distribution throughout the semester is observed to be heavily influenced by the timing of exams in both scenarios.

This research WIP paper describes the DACEM project, aimed at enhancing course catalogues to support the objectives of the Erasmus+ program. Within Erasmus+, course catalogues serve as essential tools for students in their quest for suitable academic opportunities and for institutions in negotiating bilateral agreements on shared programs. Despite numerous initiatives and years of experience, European higher education institutions still face significant challenges in providing appropriate course catalogues, which directly impacts student mobility. This paper conducts an analysis of the current course catalogues situation in 7 European countries (Estonia, France, Greece, Hungary, Portugal, Slovenia, and Spain), examining general trends, the availability of solutions, and key difficulties. Additionally, it evaluates the needs and use cases of end users and stakeholders, highlighting the essential technical features that course catalogue software solutions should encompass. By compiling key requirements, this paper contributes towards the development of appropriate and sustainable course catalogues for Higher Education Institutions.

Among the different types of applications within the realm of learning analytics, predictors and early warning systems are particularly interesting thanks to their ability to identify potentially struggling students at an early stage of a course. This paper presents a performance comparison of different classification algorithms that aim to predict whether students will pass or fail a course, using only LMS activity data from its first few weeks. Two different time frames are considered: first, using exclusively data generated before any assessment activities are performed in the course; and second, allowing the use of data originating within the time period up to, but not including, the second partial exam of the course. The study targets a first-year engineering course at the University of Vigo. In the first scenario, the tested classifiers struggled to correctly identify passing students, an issue that is mitigated in the second scenario. The main hindrance to the classifiers' performance, particularly in the first analyzed scenario, is working with a set of unbalanced data in which the number of students who pass the course is extremely low compared to those who fail.

This Research Work-in-Progress focuses on how students make use of supporting videos provided in a flipped classroom context. More specifically, this study analyzes the academic implications of the timing of video accesses: late (during the few days preceding an exam) or early. The experiment was performed in a first-year engineering course. The course is divided into 6 splits, ranging from 2 to 4 weeks each. Every split introduces new videos and ends with an exam. Data from 90 first-year students were collected. Two main analysis tasks were undertaken: assessing the correlation between the number of videos accessed early or late and exam scores, and student clustering depending on the general timing of video accesses. Significant correlation between early accesses and exam scores was only observed on the first split. Also in this first split, 3 student groups were identified via clustering: low activity, mostly late and mostly early. Interestingly, there were significant differences in long-term performance between these groups. This is a valuable result towards the development of an early warning system, as it helps identify students at risk at a early stage.

In learning analytics applications, some of the most important data sources often include the activity of students in online platforms such as learning management systems. While the utility of logs extracted from these kinds of systems is undeniable, it is important to be aware that there may be contextual factors that can change how these data is interpreted. The study showcased in this paper analyzes the direct influence between the activity volume of students in a course, as given by Moodle LMS logs, and academic performance, in the form of the final grade obtained. Moreover, we perform separate observations of this influence depending on whether the student is enrolled in the course for the first time, or if they are retaking it after failing to pass in previous academic years. The results show that a correlation exists between raw volume of interactions with the LMS and final grade, but only for first-time students. This correlation is nonexistent when considering retaking students.

This Research-to-Practice Work in Progress addresses the need for predictors in educational environments to make reliable predictions of student performance as early as possible. This is the main foundation of early warning systems, tools that aim to detect students at risk of failing or dropping out of a course, and do so at a point in time at which it is still possible to execute effective interventions to help these students. This study, carried out in the context of a first-year university course, presents a classifier of students depending on whether they are expecting to pass or fail the course, using exclusively data available before any assessment activities are performed. This classifier is based on the Random Forest algorithm. The obtained results, while limited by the scarcity of data, show promise that it is indeed possible to detect students at risk of failing with acceptable reliability in the studied educational context.

Colonoscopy, or lower gastrointestinal endoscopy, is a medical procedure whereby displacing a flexible endoscope along the large intestine, a health care professional can perform examinations and even perform some interventions. This paper presents a Work-in-Progress towards a virtual training environment for colonoscopy, LoGIViT, based on the use of immersive technologies. Employing Unity engine and virtual reality headsets, LoGIViT aims to provide an affordable simulator reproducing the physics involved in the procedures. LoGIViT is prepared to enable the programmatic generation of large intestine scenarios with varied features and conditions dynamically generated that can be configured by the user and generated dynamically.

This paper details the most recent developments that were performed in the online platform BeA (Blended e-Assessment) in order to increase its usefulness and utility within the new prominent learning contexts that appeared in the year 2020 as a consequence of the COVID-19 pandemic. Due to safety recommendations such as social distancing, in-person exams need to be performed under strict restrictions, and remote exams have increased in popularity as a way to evaluate students without them leaving their home. The new BeA features allow students to directly upload pictures of their answers to the platform, circumventing the need for the exams to be scanned by the teacher. This both minimizes physical contact during in-person exams and allows students to submit their answers from home in remote exams. These new features were tested during remote exams in the 2019/2020 academic year, garnering an overall positive reception by students.

The continuous effort to provide novel solutions to the educational field makes necessary to engage those technologies to improve the experiences gained from education. This review presents the current state of Personal Teaching Environments (PTEs) that help teachers in fulfilling their educational tasks. We performed a study to investigate how PTEs have been applied in educational settings. This review focus on presenting the concepts of PTEs and applications, systems, and techniques that help teachers in their educational tasks from planning to assessing (including feedback). The information extracted from the study gave us a useful overview of PTEs, such as new methods, possible advantages, and drawbacks when using PTEs. This review considered literature from the period of 2009 till 2019 as a ten years timeline. Finally, an attempt has been made to identify future research paths.

This paper presents a new module for the BeA (Blended e-Assessment) platform, aimed to be used in a higher education subject that combines the flipped classroom system and gamification features. Currently, this subject implements the flipped classroom methodology as well as intensive continuous assessment. Under this system, students are asked to watch videos that explain course contents, doing so as homework on a weekly basis. Then, students undertake an exam in the classroom covering the corresponding week's contents, counting towards the final grade of the subject. In a new approach, we decided to have students take exams every two weeks instead. However, this creates the need of maintaining a high level of student engagement, in order for them to keep watching the videos every single week. That is why we are developing a tool that allows us to apply gamification on days when students are not assessed. This tool consists of an online question system (as a multi-choice test) accessible through a web browser, which students can answer while being involved in a game.

The participation of students in the form of questions to be answered in the classroom is a key activity of the flipped classroom methodology. Those questions arise when the students watch educational videos out of the classroom (another key part of the aforementioned methodology). However, low student participation is a common problem within a flipped classroom setting. As a solution to this problem, and in order to encourage the active participation of students making questions, we are developing a system with which students can anonymously ask questions about the content of the videos, which will be later answered in class by the teacher.

This Research to Practice Work in Progress presents the first steps and ideas of a framework to address the problem of suggesting the most suitable recommendations for instructors (for teaching and assessing), by designing an intelligent multi-agent recommender system that uses data analysis methods. The paper addresses the whole framework, specifically focusing on the assessment part. The framework proposed takes into consideration the heterogeneous personalities and teaching/assessing styles of different instructors to personalize and customize their experience. It provides immediate and customize instructions and feedback to help instructors improve their educational tasks. The dataset contains data collected from the engagement of the instructor with the agents, to predict their teaching/assessing style from their behavior. The agent system has the ability to recommend methods and tools against a topic. It tries to build different instructors' profiles, to generalize the most common practices toward an activity for future use. The agent system is like a personal assistant that helps teachers with finding information, and it gives them recommendations.

This Research to Practice Work in Progress presents an analytics add-on for QMSFC (Question Management System for Flipped Classroom), an online tool with the purpose of increasing student engagement and participation in a flipped classroom setting. This is done by providing an interface that students can use to post questions about educational videos, which would then be answered by the teacher in the classroom. The data generated by QMSFC could be collected and used to expand upon the functionalities of the platform, such as alerting the teacher about particularly tough topics for students. Additionally, these data could be analyzed together with student grades in order to find out the correlation between the volume of activity on the platform and exam performance. Work remaining on this project involves using the fully developed tool in a real learning situation and analyzing the results and feedback obtained.

The year 2012, the IEEE Education Society Spanish Chapter published a study about the introduction of the Bologna Process in engineering degrees in several Universities in Spain. The study tried to know the professors' thoughts about the core aspects of this process and its deployment in their institutions. Five years later, the IEEE Education Society Spanish Chapter is carrying out a similar study taking into account that the EHEA has been active at least for seven years. The study has two parts. The paper presents the structure of the study, and the results of the first part, comparing (when possible) with the results of the 2012 study.

Self-regulated learning involves students taking the responsibility of their own learning. Self-regulated learning students usually adopt a variety of learning strategies and behaviors, such as the performance of forethought-performance-reflection cycles or the regular and sequenced work over time, that eventually enable them to achieve a more significant and long-lasting learning. In this paper, we explore if these particular behaviors and strategies can be analyzed through the application of process mining techniques taking as data the events registered during the performance of learning activities. The discovery of the underlying processes followed by students can open new approaches to study the real self-regulated strategies used by students. The paper reviews the techniques and tools available to perform the process mining of events related to self-regulated learning and describes some initial works in this area. Furthermore, as an initial empirical study, we analyze the process followed by students regarding the visualization of videos provided in a first-year engineering subject. The obtained results are studied taking into account the grades obtained by the students. The results show that the students that obtained the best grades follow more varied routes than the students that obtained the worst grades. In addition, the best ones are more regular over time regarding weekly video visualization, mainly at the beginning of the term, while the worst ones visualize the videos mainly at the second part of the term.

This Research Full Paper shows and analyzes the results of a study carried out by the IEEE-Education Society Spanish Chapter about several key aspects of the implementation in engineering degrees along Spain of the Bologna process, also known as the European Higher Education Area (EHEA). The study was divided into two surveys. The first survey consisted in an updated version of a previous one performed six years ago for comparison purposes. The main results were used to identify some key topics about the EHEA implementation along these years. The second survey involved new questions focused on the key topics identified in the first one. The main contribution of this paper is to offer a clear vision of the development of the EHEA in Engineering in Spain. It gathers the opinion of engineering professors related to which objectives of the EHEA have been met and which have not, and if it has really been worthwhile and our engineers come out better prepared than with the previous system. The findings of this study, as a main conclusion, show a general agreement about the lack of resources and support and the many different views about what Bologna indicates. Therefore, the need arises for a debate, not only in Spain but also in the EHEA, about the advantages and drawbacks of the Bologna process, and its possible improvements.

This Research to Practice Work in Progress presents a chatterbot used as a frequently asked questions assistant for students that follow a Computers Architecture course in the degree of Telecommunication Engineering Technology at the University of Vigo (Spain). The chatterbot is a conversational agent that interacts in natural language with the students. This agent will be in charge of doing the tasks, usually carried out by the teachers, such as answering the students' questions and solving the common doubts that arise over and over again in every academic year. The bot is being developed in AIML (Artificial Intelligence Markup Language).

This paper presents a walkthrough about developing educational audiovisual contents to produce OER (Open Educational Resources) and the use of these resources in a blended flipped classroom. All along the paper we detail the process of developing this educational material and the later use of it. Several operating systems (such as Windows, Android, and iOS) and (free and commercial) applications were involved in the developing of the resources. The combined use of these tools has allowed us to produce a long list of educational videos, which use several formats to show the contents to the students, avoiding the monotony that may show up during their watching. Specifically, the content of the videos is related to educational material used in a first course of telecommunication engineering, in the matter Computer's Architecture. These videos are used to carry out a blended flipped classroom educational experience over two classes in these course (each with approximately 30 students). We think that the work presented in this paper can be used as a guide by people who want to make videos for their own flipped classrooms (even with the support of our OER videos).

This paper presents a system that works as an assistant for students in their learning process. The assistant system has two main parts: an Android application and a server platform. The Android application is a chatterbot (i.e., an agent intended to conduct a conversation in natural language with a human being) based on AIML, one of the more successful languages for developing conversational agents. The chatterbot acts as an intermediation agent between a student and the server platform. The server platform contains four repositories and a recommender (which are part of a bigger professor assessment system). The final objective of the assistant system is to make a student able to carry out several actions related to his/her learning and assessment processes, such as: to consult exam questions from a repository, to receive recommendations about learning material, to ask questions about a course, and to check his/her own assessed exams. These actions are carried out through and Android application using a natural language interface (by voice or typing). The purpose of this development is to facilitate the access to this information through a friendly interface.

BeA (Blended e-Assessment) is an e-assessment solution that supports both teachers evaluating and students accessing and reviewing their assessments on line, anytime and anywhere. Students perform their tests, exams or exercises in paper, but the assessment made by teachers and reporting of results to students is performed on-line using scanned copies of the papers. BeA includes functionalities allowing teachers to classify and manage common errors, guaranteeing coherence and consistence along the assessment process. This paper introduces a work in progress using BeA to support Self-Regulated Learning and Formative Assessment. This involves promoting a reflective dialogue among teacher(s) and students around the assessment results. BeA statistical tools enable teachers to identify the more frequent mistakes and to group students based on such mistakes. Teachers can send general or specific feedback to learners and use predefined prompts in order to initiate student self-reflection and self-awareness of their mistakes and misconceptions.

The Open Educational Resource concept was proposed several years ago as a way to develop the Open Movement principles in the educational domain. Despite the open and participative view underlying this proposal, there has not been a significant adoption of this concept by users. Many problems and barriers have been considered and analyzed. Nevertheless, we consider that a main issue is the contradiction among the definitions proposed and the real support provided by platforms. In addition, there exist also different approaches to the concept by users and organizations. In this paper we call for a real open approach to the Open Educational Resource concept and introduce a platform where specific functionalities have been introduced to support its development.