This paper presents insights from a series of interviews with academics at a public university in Ecuador, exploring their experiences in transitioning to synchronous hybrid teaching during the COVID-19 pandemic. This study reveals the challenges faced by academics in navigating the cultural, infrastructural, and technological seams present in the delivery of hybrid lessons in a country in the Global South. The findings provide empirical evidence of the invisible work undertaken by academics to address these challenges, the importance of providing adequate supports for academics when adopting hybrid learning, and the role of student agency in these settings. Finally, we reflect on the implications of deploying hybrid learning for academics' pedagogical practice. By applying the analytical language of seams and patchwork, the study sheds light on the complexities of hybrid learning implementation in a context marked by socio-economic and technological constraints.

Recent advancements in machine learning (ML) have paved the way for new research paths across various disciplines. Nevertheless, students in the global south often have limited exposure to these breakthroughs and may not actively pursue opportunities in these emerging fields. This paper presents a case study of a training workshop that aimed to involve students from diverse undergraduate programs to actively engage in research opportunities utilizing ML skills in other application domains. To do so, we present empirical quantitative findings based on the participation of 24 undergraduate students from 10 distinct programs in workshops designed to introduce them to ML concepts and applications, as well as the research process and methodologies. We hypothesized that this kind of workshop would lead to a higher intention to do research, along with positive improvements in their assessment of self-efficacy, focusing on perceived behavioral control (PBC), behavioral beliefs (BB), and subjective norms (SN). The workshop led to an improvement in students' PBC in self-efficacy beliefs over their perceived ability, especially in the development of research and ML skills. These findings provide valuable insights and set a clear course for future initiatives, with the aim of further encouraging students' involvement and confidence in computing research initiatives.

This research full paper presents a literature review that seeks to answer: What is the role of Service-Learning Internships (SLI) in developing professional skills in students as future engineers? This is because SLI has been pervasively included in the formation of engineering students. More specifically, this review aims to answer: What is the theoretical base of service-learning (SL) experiences? How are internships organized within the engineering programs? What kind of activities are performed? What are the more common learning outcomes and how are they assessed? Where are the SLI activities performed? and; Which are stakeholders for SLI? The articles reviewed in this study were selected from the Web of Science Database. The PRISMA methodology was used to identify, screen, evaluate eligibility, and include the relevant articles for the review. A total of 2969 journal articles and proceeding papers, in English and Spanish, from 2000 to 2023, were screened with the inclusion and exclusion criteria, resulting in 18 articles that were analyzed. The studies analyzed reveal that the main role of SLI is to train students to address and solve real-world problems, applying their technical knowledge, and as a result, students tend to improve their critical thinking and analytical skills, their social responsibility, and citizen engagement, among other benefits. This is considered important since it relates to their readiness for competent professional practice in a constantly changing labor market. The studies connect the improvement of skills to Robert Bringle's SL experiences, Kolb's experiential learning and John Dewey's experimentalism. The characteristics of the practices and the activities performed differ according to the specific areas of the engineering programs. SL experiences are usually integrated into the programs' curricula, but not always as internships. Regarding location, SLIs are conducted in diverse settings, including university campuses, schools, online platforms, and mostly in community-based environments. The learning outcomes differ from knowledge areas and half of the experiences and the assessment methods vary, with self-assessment via questionnaires being the most prevalent, followed by content assessment and instructor evaluation through written reports. The stakeholders include schools and high schools, non-government organizations, and different spaces from society such as farms, museums or recreation parks. This review contributes to recognizing the role of SLI in fostering professional skills. It provides valuable insights into the perception of students, lecturers and community stakeholders regarding the university's involvement in addressing real-world challenges. The results establish the necessity of refining assessment methods for professional skills since most of them rely on self-assessment and activity reports, and also offer the opportunity to explore the correlation between instructorevaluated skill development and the practical application of technical knowledge in real-world settings.

This research-to-practice full paper describes an experimental study investigating the benefits of note-taking to enhance pre-class content learning in a flipped classroom (FC) environment applied to an Engineering Physics course. In an FC, fundamental content learning occurs before the class (targeting low cognitive levels on Bloom's taxonomy), allowing in-class time to reinforce and apply concepts (addressing high cognitive levels on Bloom's taxonomy). However, there is a lack of empirical and controlled research studies investigating optimal strategies for obtaining high-value pre-class content learning. This study aims to contribute to this matter. Four groups are considered, each comprising an average of 40 students, following the FC instructional methodology. Pre-class activities precede the class, including reading prepared documents and watching prepared videos. In-class assessments consist of a brief multiple-choice test (maximum of 5 questions) related to the pre-class activities, aiming to evaluate low cognitive levels on Bloom's taxonomy. To enhance note-taking practices, students are encouraged to take notes, and at the beginning of the course, a video showcasing five note-taking strategies is provided. The experiment carried out along one of the chapters revised in the Engineering Physics course includes one control group and one experimental group. In the control group, students are encouraged to take notes without additional guidance, whereas in the experimental group, students receive a fill-in-the-blank style note- taking guide. The results indicate that students who engage in note-taking, irrespective of the strategy used, outperform those who do not take notes. It is well-documented that note-taking produces an improvement in the in-class learning process. Here, we show how this benefit can be translated to activities before class, enhancing self-regulation learning and reducing the cognitive load during in-class note-taking. Regarding the note-taking guide, there is no significant evidence to support the improvement of student performance. This lack of progress may be attributed to the nature of the guide, using a linear note-taking strategy that ends with non-generative notes. This study shows the benefits of note-taking in enhancing pre-class content learning in an FC environment applied to an Engineering Physics course and invites us to rethink how the note-taking guide structure could encourage the production of generative notes.

Academic advising brings numerous benefits to the mission of Higher Education Institutions. One central and challenging duty of advisors is course recommendation for term planning. This task requires both knowledge of the study programs as well as a thorough analysis of the students’ unique circumstances. Limited time and a large student population make this task overwhelming. As a result, an important body of research has sought to expedite term planning via data-oriented decision-support tools. The impact of such tools on students has been extensively studied. However, the advisors’ perspective remains largely unexplored. We contribute to redressing this gap by studying how a grade prediction tool shapes academic advisors’ approach to course recommendation. We found that while the advisors’ usual strategies tend to prevail, their recommendations largely depend on the advisee’s historical performance. That said, advisors also acknowledge the limitations of grades as a measure of academic success.

Collaborative Learning Analytics (CLA) tools have recently emerged as a potential solution to address the onerous process of monitoring and providing timely feedback on collaboration skills in higher education students. However, prior studies on the efficacy of such tools have mainly been carried out in small, controlled settings. This study aims to measure the impact of a specific CLA tool that can be easily deployed on a larger scale with minimal instructor effort in real-world online group work activities. Additionally, this research examines the potential influence that the characteristics of the collaborative activity may have on the tool's effectiveness. The CLA tool under investigation displays speaking participation time and peer evaluation scores from students engaged in online collaborative activities as part of their regular courses. The tool was evaluated with five instructors and 156 students over the course of one semester. The effects of the tool on students' speaking participation and peer evaluation scores were quantitatively measured and tested. A qualitative analysis of reflections from both students and instructors provided supplementary information on the quantitative results. The main finding of this study indicates that the tool has an overall small positive impact. The effectiveness of the CLA tool is primarily modulated by the synchronous or asynchronous presence of the instructor, as students tend to interact more naturally and feel less scrutinized in the absence of instructor evaluation. Based on the discussion of the findings, this research suggests design insights to enhance future CLA tools at scale for the purpose of supporting the development of online collaboration skills.

Providing formative feedback to foster collaboration and improve students’ practice has been an emerging topic in CSCL and LA research communities. However, this pedagogical practice could be unrealistic in authentic classrooms, as observing and annotating improvements for every student and group exceeds the teacher’s capabilities. In the research area of group work and collaborative learning, current learning analytics solutions have reported accurate computational models to understand collaboration processes, yet evaluating formative collaboration feedback, where the final user is the student, is an under-explored research area. This paper reports an exploratory evaluation to understand the effects a collaboration feedback report through an authentic study conducted in regular classes. Fifty students from a Computer Science undergraduate program participated in the study. We followed an user-centered design approach to define six collaboration aspects that are relevant to students. These aspects were part of initial prototypes for the feedback report. From the exploratory intervention, we did not find effects between students who received the feedback (experimental condition) report and those who did not (control condition). Finally, this paper discusses design implications for further feedback report designs and interventions.

Course selection is a crucial activity for students as it directly impacts their workload and performance. It is also time-consuming, prone to subjectivity, and often carried out based on incomplete information. This task can, nevertheless, be assisted with computational tools, for instance, by predicting performance based on historical data. We investigate the effects of showing grade predictions to students through an interactive visualization tool. A qualitative study suggests that in the presence of predictions, students may focus too much on maximizing their performance, to the detriment of other factors such as the workload. A follow-up quantitative study explored whether these effects are mitigated by changing how predictions are conveyed. Our observations suggest the presence of a framing effect that induces students to put more effort into course selection when faced with more specific predictions. We discuss these and other findings and outline considerations for designing better data-driven course selection tools.

Data science is now impacting the education sector, with a growing number of commercial products and research prototypes providing learning dashboards. From a human-centred computing perspective, the end-user's interpretation of these visualisations is a critical challenge to design for, with empirical evidence already showing that `usable' visualisations are not necessarily effective from a learning perspective. Since an educator's interpretation of visualised data is essentially the construction of a narrative about student progress, we draw on the growing body of work on Data Storytelling (DS) as the inspiration for a set of enhancements that could be applied to data visualisations to improve their communicative power. We present a pilot study that explores the effectiveness of these DS elements based on educators' responses to paper prototypes. The dual purpose is understanding the contribution of each visual element for data storytelling, and the effectiveness of the enhancements when combined.

The traditional recording of student interaction in classrooms has raised privacy concerns in both students and academics. However, the same students are happy to share their daily lives through social media. Perception of data ownership is the key factor in this paradox. This article proposes the design of a personal Multimodal Recording Device (MRD) that could capture the actions of its owner during lectures. The MRD would be able to capture close-range video, audio, writing, and other environmental signals. Differently from traditional centralized recording systems, students would have control over their own recorded data. They could decide to share their information in exchange of access to the recordings of the instructor, notes form their classmates, and analysis of, for example, their attention performance. By sharing their data, students participate in the co-creation of enhanced and synchronized course notes that will benefit all the participating students. This work presents details about how such a device could be build from available components. This work also discusses and evaluates the design of such device, including its foreseeable costs, scalability, flexibility, intrusiveness and recording quality.

Multimodality is an integral part of teaching and learning. Over the past few decades researchers have been designing, creating and analyzing novel environments that enable students to experience and demonstrate learning through a variety of modalities. The recent availability of low cost multimodal sensors, advances in artificial intelligence and improved techniques for large scale data analysis have enabled researchers and practitioners to push the boundaries on multimodal learning and multimodal learning analytics. In an effort to continue these developments, the 2015 Multimodal Learning and Analytics Grand Challenge includes a combined focus on new techniques to capture multimodal learning data, as well as the development of rich, multimodal learning applications.

This paper summarizes the third Multimodal Learning Analytics Workshop and Grand Challenges (MLA'14). This subfield of Learning Analytics focuses on the interpretation of the multimodal interactions that occurs in learning environments, both digital and physical. This is a hybrid event that includes presentations about methods and techniques to analyze and merge the different signals captured from these environments (workshop session) and more concrete results from the application of Multimodal Learning Analytics techniques to predict the performance of students while solving math problems or presenting in the classroom (challenges sessions). A total of eight articles will be presented in this event. The main conclusion from this event is that Multimodal Learning Analytics is a desirable research endeavour that could produce results that can be currently applied to improve the learning process.

One of the key promises of Learning Analytics research is to create tools that could help educational institutions to gain a better insight of the inner workings of their programs, in order to tune or correct them. This work presents a set of simple techniques that applied to readily available historical academic data could provide such insights. The techniques described are real course difficulty estimation, dependance estimation, curriculum coherence, dropout paths and load/performance graph. The description of these techniques is accompanied by its application to real academic data from a Computer Science program. The results of the analysis are used to obtain recommendations for curriculum re-design.

Multimodal Learning Analytics is a field that studies how to process learning data from dissimilar sources in order to automatically find useful information to give feedback to the learning process. This work processes video, audio and pen strokes information included in the Math Data Corpus, a set of multimodal resources provided to the participants of the Second International Workshop on Multimodal Learning Analytics. The result of this processing is a set of simple features that could discriminate between experts and non-experts in groups of students solving mathematical problems. The main finding is that several of those simple features, namely the percentage of time that the students use the calculator, the speed at which the student writes or draws and the percentage of time that the student mentions numbers or mathematical terms, are good discriminators be- tween experts and non-experts students. Precision levels of 63% are obtained for individual problems and up to 80% when full sessions (aggregation of 16 problems) are analyzed. While the results are specific for the recorded settings, the methodology used to obtain and analyze the features could be used to create discriminations models for other contexts.