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Temas: inteligencia artificial

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Large language models (LLMs) are transforming higher education, yet their learning benefits likely depend on students’ AI literacy and how they prompt and verify AI outputs. This mixed-methods study investigates how AI literacy relates to prompting proficiency and verification behavior, and how these behaviors operate through two mechanisms, trust calibration and extraneous cognitive load, to predict coursework performance across two discipline–institution contexts in Thailand. The author surveyed Design students (n = 221) and Business students (n = 222) and analyzed the data using multigroup structural equation modeling, complemented by thematic analysis of semi-structured interviews....

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With machine learning becoming more and more prevalent in society, it also becomes a topic that is relevant for general K12 education. At the same time, modern machine learning is relying heavily on mathematics, requiring novel approaches to teaching it in settings where a full coverage is not possible. In this context the study presented here investigates the conceptions that students have about the workings of a particular type of machine learning system before and after a short workshop. Students (N = 57) gave open ended answers that were analyzed qualitatively, following the approach of phenomenography. The resulting model was then validated with semi-structured interviews with...

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Student feedback on teaching at the end of the semester is an important source of information for instructors to gain insights into the effectiveness of their teaching. There are usually two forms of student feedback: quantitative scores and qualitative feedback. Quantitative scores can usually be easily summarized, while the analysis of qualitative feedback is usually effort-intensive as it deals with text. To help instructors glean insights from students' qualitative feedback, many previous studies used unsupervised approaches (i.e., topic modelling) for topic extraction in student feedback. Although topic modelling enables automated detection of previously unseen topics with minimal...

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Despite poetry’s important role in improving linguistic skills and creative thinking, students often find poetry writing to be difficult and boring. This study is an investigation of how the digital Poetry Machine influences students’ poetry writing by applying AI techniques. It uses qualitative and quantitative analysis of the log data of poems that the seventh graders wrote with the Poetry Machine. The results show that the draft poems functioned as affordances, which the students followed as models. The drafts encouraged students to experiment with several different poetic features. The data suggest an association between the number of edited versions and the quality of the final poem....

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Responding to the need for innovative educational environments in higher education, this study presents “Math City”, an immersive Metaverse platform designed to enhance inquiry-based mathematics learning and develop 21st-century skills. Traditional university-level mathematics courses often encounter difficulties in sustaining student engagement and fostering the application of abstract concepts, such as the properties of three-dimensional shapes. To address these challenges, Math City was designed using the Unity game engine, which incorporates the 5E instructional model (Engage, Explore, Explain, Elaborate, and Evaluate) alongside gamification elements such as quests, rewards, and badges...

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In recent years, distance learning using learning management and e-book systems has been actively conducted in higher education institutions and various other organizations. It is possible to collect and analyze learning logs even in classes with many learners, including clickstreams and quiz scores in detail for each individual. This research proposes using Moodle's learning logs to classify learning patterns and outliers in order to identify struggling learners. The proposed method uses the descriptive statistics between the learner's teaching material clickstream and the final test score accumulated in Moodle, and students can be classified into four learning patterns. The frequency of...

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Metodología: revisión bibliográfica

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This systematic review investigates learning task design for K-12 AI education, aiming to provide an overview of the status of AI education and identify trends, challenges, and opportunities. Through an analysis of 47 empirical studies, the review presents, synthesizes, and evaluates the educational theories underpinning the learning task design, the content, the pedagogies used for teaching, as well as the measurement and outcomes of the existing literature on AI education programs in K-12 settings. The principal findings reveal a diverse landscape of learning task design for teaching AI to K-12 students. Positive outcomes underscore the effectiveness of well-crafted hands-on tasks in...

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Predictive analytics in education holds promise for supporting student learning but poses risks of bias potentially disadvantaging certain groups based on demographic attributes such as gender. Prior work on fairness in educational prediction has largely focused on binary outcomes in higher education, leaving continuous predictions, such as standardized test scores, comparatively understudied, especially in early schooling where consequences can be long-lasting. Despite a few fairness metrics available for continuous outcomes, they often rely on mean differences, obscuring distributional disparities that may disadvantage specific students. Even fewer studies have attempted to address bias...

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Assessment feedback is important to student learning. Learning analytics (LA) powered by artificial intelligence exhibits profound potential in helping instructors with the laborious provision of feedback. Inspired by the recent advancements made by Generative Pre-trained Transformer (GPT) models, we conducted a study to examine the extent to which GPT models hold the potential to advance the existing knowledge of LA-supported feedback systems towards improving the efficiency of feedback provision. Therefore, our study explored the ability of two versions of GPT models – i.e., GPT-3.5 (ChatGPT) and GPT-4 – to generate assessment feedback on students' writing assessment tasks, common in...

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Large language models have recently been proposed as tools for automated essay scoring, but their agreement with human grading remains unclear. In this work, we evaluate how LLM-generated scores compare with human grades and analyze the grading behavior of several models from the GPT and Llama families in an out-of-the-box setting, without task-specific training. Our results show that agreement between LLM and human scores remains relatively weak and varies with essay characteristics. In particular, compared to human raters, LLMs tend to assign higher scores to short or underdeveloped essays, while assigning lower scores to longer essays that contain minor grammatical or spelling errors. We...

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