procesamiento del lenguaje natural

Word sense awareness is a feature which has not yet been implemented in most Computer-Assisted Language Learning (CALL) environments or in computer-readable resources for pedagogical purposes such as graded word lists. The current study aims to contribute to filling this gap by presenting a word sense disambiguation (WSD) method which relies on a tailor-made sense inventory, exploits readily available large language models, and only requires a limited number of prototypical examples sentences as manually curated data. The methodology is evaluated on a set of 74 lexically ambiguous items, with a Spanish language for specific purposes course as the target setting. With weighted F1 scores up to...

Opportunities of natural language processing for comparative judgment assessment of essays

Michiel De Vrindt, Anaïs Tack, Wim Van den Noortgate (2025)

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Tipo: artículo

Metodología: trabajo teórico

Temas: inteligencia artificial

Resumen:

Comparative judgment (CJ) is an assessment method commonly used for assessing essay quality, where assessors compare pairs of essays and judge which essays are superior in quality. A psychometric model is used to convert judgments into quality scores. Although CJ yields reliable and valid scores, its widespread implementation in educational practice is hindered by its inefficiency and limited feedback capabilities. This conceptual study explores how Natural Language Processing (NLP) can address these limitations, drawing upon existing NLP techniques and the very limited research on their integration within CJ. More specifically, we argue that, at the start of the assessment, initial essay...

Are deeper reflectors better goal-setters? AI-empowered analytics of reflective writing in pharmaceutical education

Yuheng Li, Mladen Raković, Wei Dai (2023)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Reflection and goal-setting are interrelated processes in well-established educational theories to promote in-depth self-reflection and self-regulated learning. Prior studies have considered reflection to be an important antecedent for meaningful goal-setting. Yet, there lacks empirical evidence to shed light on how students' abilities to reflect inform their abilities to set goals. Hence, in the present study, we aimed to quantify the connection between students' retrospective reflection and their subsequent goal-setting, and derive more in-depth insights to benefit educators in their teaching to promote deeper reflection, more specific goal-setting and better self-regulation. To this end...

Perceived MOOC satisfaction: A review mining approach using machine learning and fine-tuned BERTs

Xieling Chen, Haoran Xie, Di Zou (2025)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

This study investigates the application of machine learning and BERT models to identify topic categories in helpful online course reviews and uncover factors that influence the overall satisfaction of learners in massive open online courses (MOOCs). The research has three main objectives: (1) to assess the effectiveness of machine learning models in classifying review helpfulness, (2) to evaluate the performance of fine-tuned BERT models in identifying review topics, and (3) to explore the factors that influence learner satisfaction across various disciplines. The study uses a MOOC corpus containing 102,184 course reviews from 401 courses across 13 disciplines. The methodology involves...

Artificial intelligence in history education. Linguistic content and complexity analyses of student writings in the CAHisT project (Computational assessment of historical thinking)

Christiane Bertram, Zarah Weiss, Lisa Zachrich (2021)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

The use of standardized test formats in the assessment of historical competencies has recently come under severe criticism, especially in the United States, where standardized tests are particularly common. History researchers have argued that open-ended items are more appropriate for assessment. However, providing largescale evaluations of open-ended answers is time consuming and poses challenges regarding the objectivity, validity, and replicability of ratings. To address this issue, we investigated the extent to which computer-based evaluation methods are suitable for evaluating student answers by combining qualitative methods from history education research with quantitative, computer-...

Comparative analysis of NLP-driven MCQ generators from text sources

Asmae Azzi, Ferenc Erdős, Richárd Németh (2025)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

The application of learning sciences with technology has been shown to boost learner interactions, yet the potential of advanced tool, particularly those that leverage Natural Language Processing (NLP), still very much untapped in learning contexts. This paper speaks to this age-old problem of generating quality Multiple-Choice questions (MCQs) – a prevalent but time-consuming mode of assessment – via the suggested comprehensive comparison study of template-based AI solutions. The study contrasts general-purpose Large Language Models (LLMs) with specialized MCQ-focused AI programs. The scientific approach employed was quite stringent, where each of the software applications was benchmarked...

English grammar multiple-choice question generation using Text-to-Text Transfer Transformer

Peerawat Chomphooyod, Atiwong Suchato, Nuengwong Tuaycharoen (2023)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

English grammar multiple-choice questions (MCQs) can be automatically generated to reduce preparation time. Previous studies have focused on semiautomated methods based on the transformation of human-made sentences/articles into MCQs, owing to which the number of generated questions is dependent on the size of a given text corpus. This study proposes an artificial intelligence-assisted MCQ generation system that increases the number of generable questions using controllable text generation techniques. In this system, the questions for MCQs are generated using a text generation model trained using the Text-to-Text Transfer Transformer (T5) architecture, a powerful deep learning model for...

Towards the implementation of automated scoring in international large-scale assessments: Scalability and quality control

Ji Yoon Jung, Lillian Tyack, Matthias von Davier (2025)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

Even before the age of artificial intelligence, automated scoring received considerable attention in educational measurement. However, its application to constructed response (CR) items in international large-scale assessments (ILSAs) has remained a challenge, primarily due to the difficulty of handling multilingual responses spanning many languages. This study addresses this challenge by investigating two machine learning approaches — supervised and unsupervised learning — for scoring multilingual responses. We explored various scoring methods to assess three science CR items from TIMSS 2023 across all participating countries and 42 languages. The results showed that the supervised...

Towards automated content analysis of educational feedback: A multi-language study

Ikenna Osakwe, Guanliang Chen, Alex Whitelock-Wainwright (2022)

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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...

AI-based teaching evaluations: How well do they reflect student perceptions?

Yossi Ben Zion, Shir Yakov, Einat Abramovitch (2025)

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Tipo: artículo

Metodología: estudio empírico

Temas: inteligencia artificial

Resumen:

This study presents an innovative solution for evaluating university-level teaching quality using artificial intelligence (AI), focusing on key aspects such as clarity of explanation and lecture structure. Traditional student surveys, while valuable, are often subject to biases and lack the necessary granularity, creating a need for objective, scalable solutions that provide consistent results. We propose an automated framework utilizing advanced natural language processing (NLP) models to assess teaching quality based on lecture transcripts. The methodology combines AI-driven transcription, machine learning-based assessments, and correlation with institutional student evaluations to...