Bibliografía: Rafael Ferreira Mello
Nº de publicaciones: 5Detalles Cerrar โ
Tipo: artículo
Metodología: estudio empírico
Temas: inteligencia artificial
Resumen:
Short open-ended questions represent a central resource in formative and summative assessments both face-to-face and online settings, ranging from elementary to higher education. However, grading these questions remains challenging for instructors, raising attention to the field of Automatic Short Answer Grading (ASAG). While ASAG has yielded valuable contributions to learning analytics, it often faces generalizability issues. Accordingly, the rapid advancement in Large Language Models (LLMs) has motivated their adoption to empower ASAG systems. Despite that, previous research has not investigated whether LLMs are fair graders in the context of ASAG. Therefore, this paper presents an...
Detalles Cerrar โ
Tipo: artículo
Metodología: revisión bibliográfica
Temas: inteligencia artificial
Resumen:
Feedback is an essential component of scaffolding for learning. Feedback provides insights into the assistance of learners in terms of achieving learning goals and improving self-regulated skills. In online courses, feedback becomes even more critical since instructors and students are separated geographically and physically. In this context, feedback allows the instructor to customize learning content according to the students' needs. However, giving feedback is a challenging task for instructors, especially in contexts of large cohorts. As a result, several automatic feedback systems have been proposed to reduce the workload on the part of the instructor. Although these systems have...
Detalles Cerrar โ
Tipo: artículo
Metodología: estudio empírico
Temas: inteligencia artificial
Resumen:
Feedback is a crucial element of a student's learning process. It enables students to identify weaknesses and improve self-regulation. However, studies show this to be an area of great dissatisfaction in higher education. With ever-growing course participation numbers, delivering effective feedback is becoming an increasingly challenging task. The efficacy of feedback will depend on four levels of feedback; namely, feedback about the self, task, process or self-regulation. Hence, this paper explores the use of automated content analysis to examine feedback provided by instructors for feedback practices measured on self, task, process, and self-regulation levels. For this purpose, four...
Detalles Cerrar โ
Tipo: artículo
Metodología: estudio empírico
Temas: inteligencia artificial
Resumen:
Open-ended assessments play a pivotal role in enabling instructors to evaluate student knowledge acquisition and provide constructive feedback. Integrating large language models (LLMs) such as GPT-4 in educational settings presents a transformative opportunity for assessment methodologies. However, existing literature on LLMs addressing open-ended questions lacks breadth, relying on limited data or overlooking question difficulty levels. This study evaluates GPT-4's proficiency in responding to open-ended questions spanning diverse topics and cognitive complexities in comparison to human responses. To facilitate this assessment, we generated a dataset of 738 open-ended questions across...
Detalles Cerrar โ
Tipo: artículo
Metodología: estudio empírico
Temas: inteligencia artificial
Resumen:
In higher education, delivering effective feedback is pivotal for enhancing student learning but remains challenging due to the scale and diversity of student populations. Learner-centered feedback, a robust approach to effective feedback that tailors to individual student needs, encompasses three key dimensions—Future Impact, Sensemaking, and Agency, which collectively include eight specific components, thereby enhancing its relevance and impact in the learning process. However, providing consistent and effective learner-centered feedback at scale is challenging for educators. This study addresses this challenge by automating the analysis of feedback content to promote effective learner-...
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