1,720,959 research outputs found
Assessment as learning. Bridging research and practice between schools and Universities
No full textThe need to innovate teaching-learning practices to enhance students’ learning outcomes and promote nowadays transversal skills often clashes with the reiteration of standardized and outdated teaching and assessment methods. Recent developments in the assessment field have highlighted the need to shift the focus of assessment from the product (or the outcome) to the learning process itself, moving from an assessment of learning and for learning to an assessment as learning, in which the student actively participates in the process. This perspective moves toward learning-oriented assessment practices and involves the integration of three key elements: tasks appropriate to the approach, development of assessment competence, and student involvement in feedback processes. These practices thus support self-regulated learning by leading students to take an active role, monitoring their progress through self-assessment, reflecting on the effectiveness of their learning approaches, and considering their mistakes as an opportunity to learn and improve. The purpose of the present study is to investigate whether the change in assessment modes affects students’ ability to self-regulate their own learning path
Facilitating feedback at university using AI-based techniques
No full textMany recent studies highlighted the importance of feedback on the quality of learning. It empowers students to take ownership of their learning, guides institutions in making informed decisions, ensures continuous improvement, fosters engagement and motivation, facilitates open communication, and enables personalized learning experiences. However, despite its relevance, the use of feedback processes in everyday teaching often becomes unsustainable, due to the number of students and the timing of the courses. On the other hand, the expansion of ubiquitous learning in digital environments has led to an exponential growth of significant data for tracking learning. Although the use of these data can be beneficial, tools and technologies are needed for automated data collection and analysis. In this direction, significant support can be provided by technologies incorporating Artificial Intelligence (AI), which include a wide collection of different technologies and algorithms. Notably, Learning Analytics (LA) and Educational Data Mining (EDM) can be useful in developing a student-focused strategy. The systematic use of AI techniques and algorithms could enable new scenarios for educators, profiling and predicting learning outcomes and supporting the creation of sustainable patterns of assessment. However, even though several studies aimed at integrating EDM and LA techniques in online learning environments, only few of them focused on applying them to real-world physical learning environments to support teachers in providing timely and quality feedback based on minimally invasive measurements. The present paper presents an approach aimed at addressing the feedback problem in real university classes, laying the groundwork for the development of an intelligent system that can inform and support the university teacher in delivering personalized feedback to a large group of students
Personalized Feedback in University Contexts: Exploring the Potential of AI-BasedTechniques
No full textMany recent studies highlighted the importance of feedback on the quality of learning. It empowers students to take ownership of their learning, fosters engagement and motivation, and enables personalized learning experi- ences. However, the use of feedback processes in real-world everyday teaching often becomes unsustainable, due to the number of students and the timing of the courses, especially in university contexts. The present study aimed to address this challenge in real university classes, laying the groundwork for the future devel- opment of an automated intelligent system that can support university teachers in delivering personalized feedback to a large group of students in real-world envi- ronments. Specifically, it focused on the prospect of gathering quality data from an actual academic course, intending to appropriately fuel AI-based techniques. These techniques could enhance the comprehension of students’ learning evidence and offer valuable insights to teachers. This paper presents an experimental work, carried out in the academic year 2020/21, which involved 220 students attending the first year of the Master’s Degree course in Primary Education. The compo- nents of teachers’ professional vision were explored using a rubric developed by the research team. Preliminary results suggested that the rubric can be effective in capturing sequential information about students’ development of professional vision. Thus, it can be further exploited to collect data from the process and feed AI-based algorithms. Further developments include exploring other machine learning techniques to reduce observers’ bias and support teachers in providing more effective personalized feedback
Personalized Feedback in University Contexts: Exploring the Potential of AI-Based Techniques
No full textMany recent studies highlighted the importance of feedback on the quality of learning. It empowers students to take ownership of their learning, fosters engagement and motivation, and enables personalized learning experiences. However, the use of feedback processes in real-world everyday teaching often becomes unsustainable, due to the number of students and the timing of the courses, especially in university contexts. The present study aimed to address this challenge in real university classes, laying the groundwork for the future development of an automated intelligent system that can support university teachers in delivering personalized feedback to a large group of students in real-world environments. Specifically, it focused on the prospect of gathering quality data from an actual academic course, intending to appropriately fuel AI-based techniques. These techniques could enhance the comprehension of students’ learning evidence and offer valuable insights to teachers. This paper presents an experimental work, carried out in the academic year 2020/21, which involved 220 students attending the first year of the Master’s Degree course in Primary Education. The components of teachers’ professional vision were explored using a rubric developed by the research team. Preliminary results suggested that the rubric can be effective in capturing sequential information about students’ development of professional vision. Thus, it can be further exploited to collect data from the process and feed AI-based algorithms. Further developments include exploring other machine learning techniques to reduce observers’ bias and support teachers in providing more effective personalized feedback
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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