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Santos, R. M. C., & Henriques, R. (2023). Predicting student performance from Moodle logs in higher education: A course-agnostic approach. In M. Carmo (Ed.), Education and New Developments 2023 (Vol. 2, pp. 77-81). Science Press. https://end-educationconference.org/wp-content/uploads/2023/06/Education-and-New-Developments_2023_Vol_II.pdf The institutional adoption of learning management systems (LMS) aims to improve educational outcomes and reduce churn through student engagement with educational content. Modern LMS record all student interactions and store them as activity logs that encode patterns of learning behaviour. Previous research has shown that insights derived from log data can detect students at risk of failing in a single or a few courses, but comprehensive institution-wide surveys are few and far between. The work presented herein uses machine learning to create predictive models to identify students at risk or excellent students using the Moodle logs generated by a sample of 9296 course enrollments at a Portuguese information management school. 31 candidate features were extracted to create and train different predictive models. Model performance was evaluated through 30 repetitions of Stratified K-Fold Cross-Validation, using the area under the receiver operating characteristic (ROC) curve (AUC) and the F1-score. All experiments were repeated with the addition of the average of the intermediate grades obtained by the student in the course as a 32nd candidate feature. The results suggest that features extracted from Moodle logs are good predictors of students at risk, as indicated by the 0.752 AUC score achieved by Random Forest. The addition of intermediate grades significantly improves the predictive performance, leading to an AUC score of 0.922 and F1-Score of 0.693 for the best classifier, Gradient Boosting. However, the performance for identifying excelling students was comparatively lower, with an AUC score of 0.781 and F1-Score of 0.567 for Gradient Boosting. Future work should focus on exploring the implementation of an early warning system that can assist educators in identifying students in need while there is still time to provide feedback and develop corrective measures. publishersversion published

Elbawab, M., & Henriques, R. (2023). Machine Learning applied to student attentiveness detection: Using emotional and non-emotional measures. Education and Information Technologies, 1-21. https://doi.org/10.1007/s10639-023-11814-5 --- Open access funding provided by FCT|FCCN (b-on). This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project—UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. Fundação para a Ciência e a Tecnologia,UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS, Roberto Henriques. Electronic learning (e-learning) is considered the new norm of learning. One of the significant drawbacks of e-learning in comparison to the traditional classroom is that teachers cannot monitor the students' attentiveness. Previous literature used physical facial features or emotional states in detecting attentiveness. Other studies proposed combining physical and emotional facial features; however, a mixed model that only used a webcam was not tested. The study objective is to develop a machine learning (ML) model that automatically estimates students' attentiveness during e-learning classes using only a webcam. The model would help in evaluating teaching methods for e-learning. This study collected videos from seven students. The webcam of personal computers is used to obtain a video, from which we build a feature set that characterizes a student's physical and emotional state based on their face. This characterization includes eye aspect ratio (EAR), Yawn aspect ratio (YAR), head pose, and emotional states. A total of eleven variables are used in the training and validation of the model. ML algorithms are used to estimate individual students' attention levels. The ML models tested are decision trees, random forests, support vector machines (SVM), and extreme gradient boosting (XGBoost). Human observers' estimation of attention level is used as a reference. Our best attention classifier is the XGBoost, which achieved an average accuracy of 80.52%, with an AUROC OVR of 92.12%. The results indicate that a combination of emotional and non-emotional measures can generate a classifier with an accuracy comparable to other attentiveness studies. The study would also help assess the e-learning lectures through students' attentiveness. Hence will assist in developing the e-learning lectures by generating an attentiveness report for the tested lecture. publishersversion epub_ahead_of_print

Victorino, G., Coelho, P. S., & Henriques, R. (2023). The Value of Design Thinking for PhD Students: A Retrospective Longitudinal Study. Emerging Science Journal, 7(Special Issue: Trends, and New Ideas in Education), 16-31. https://doi.org/10.28991/ESJ-2023-SIED2-02 Doctoral studies are changing worldwide, with growing concerns about doctoral graduates' employability and ability to develop relevant links with industrial challenges. The present study aims examine the impact of Design Thinking skills on PhD students on their future academic and professional performance. Drawing on 7 years of pedagogical experimentation, we conducted a mixed methods longitudinal study to investigate the perceptions of students who attended a two-day Design Thinking workshop. Two questionnaires, with a total of 40 items measuring the quality and course impact dimensions, were given to 415 and 41 students, respectively. Finally, 12 students were chosen for in-depth interviews to learn more about how they applied their newly acquired design thinking skills in their research and work. Our findings show that developing Design Thinking skills impacts the professional lives of students of all fields of knowledge, ages, and stages of their PhD. The primary outcomes mentioned are associated with increased creative confidence and collaboration abilities. This study focuses on relevant dimensions for designing and delivering Design Thinking skills within doctoral programmes, as well as the impact of design thinking on the quality of PhD education and student employability opportunities. publishersversion published

Funding Information: This work was supported by ‘Fundação para a Ciência e a Tecnologia’—FCT to AP (PD/BD/136887/2018), MPM (PTDC/MEC‐MET/29314/2017 and PTDC/BIM‐MET/2115/2014), iNOVA4Health (UIDB/Multi/04462/2020); by the European Commission Marie Skłodowska‐Curie Action H2020 (grant agreement n. 734719); by FEDER and Lisboa2020 and Alentejo2020; and by the Sociedade Portuguesa de Diabetologia. Publisher Copyright: © 2022 The Authors. European Journal of Clinical Investigation published by John Wiley & Sons Ltd on behalf of Stichting European Society for Clinical Investigation Journal Foundation. Background: Type 2 Diabetes (T2D) diagnosis is based solely on glycaemia, even though it is an endpoint of numerous dysmetabolic pathways. Type 2 Diabetes complexity is challenging in a real-world scenario; thus, dissecting T2D heterogeneity is a priority. Cluster analysis, which identifies natural clusters within multidimensional data based on similarity measures, poses a promising tool to unravel Diabetes complexity. Methods: In this review, we scrutinize and integrate the results obtained in most of the works up to date on cluster analysis and T2D. Results: To correctly stratify subjects and to differentiate and individualize a preventive or therapeutic approach to Diabetes management, cluster analysis should be informed with more parameters than the traditional ones, such as etiological factors, pathophysiological mechanisms, other dysmetabolic co-morbidities, and biochemical factors, that is the millieu. Ultimately, the above-mentioned factors may impact on Diabetes and its complications. Lastly, we propose another theoretical model, which we named the Integrative Model. We differentiate three types of components: etiological factors, mechanisms and millieu. Each component encompasses several factors to be projected in separate 2D planes allowing an holistic interpretation of the individual pathology. Conclusion: Fully profiling the individuals, considering genomic and environmental factors, and exposure time, will allow the drive to precision medicine and prevention of complications. publishersversion published

Victorino, G., Bandeira, R., Painho, M., Henriques, R., & Coelho, P. S. (2022). Rethinking the Campus Experience in a Post-COVID World: A Multi-Stakeholder Design Thinking Experiment. Sustainability (Switzerland), 14(13), 1-13. [7655]. https://doi.org/10.3390/su14137655 In a post-COVID world, rethinking the campus experience is critical for defining new pedagogical strategies. As higher education moves toward more student-centred action learning models, university leaders should engage in democratic design methods that empower students and professors. Design thinking (DT) is a user-centred design approach that can aid in the creation of future learning environments. While DT has been used in innovative space design, we know little about how students, professors, and other community members can act as codesign partners. To understand their role in codesign and how their experiences are incorporated into new building design decisions, we need a conceptual model. To develop this model, we examined a case study of the evolutionary co-design process of a new building for a leading information management school in Europe. Using the concept of three phases of design thinking defined by Brown (2009): Inspire, Ideate, and Implement, we collaborated with a group of 50 design thinking students and more than 500 members of the community representing different stakeholders, to create new spaces and rethink the learning experience. Our discussion will centre on the creation of a participatory design thinking model that positions students as design partners alongside university decision makers. The findings conclude that, by applying design thinking methods, it was possible to unveil new dimensions of the success of future campuses that go beyond the building design. Creating meaningful learning spaces that inspire creativity and critical thinking requires an alignment between human centred design, organizational change management and new pedagogical strategies. publishersversion published