This article introduces a process for computational text classification that can be used in a variety of qualitative research and evaluation settings. The process leverages supervised machine learning based on an implementation of a multinomial Bayesian classifier. Applied to a community of inquiry framework, the algorithm was used to identify…

A multi-label variant of email classification named ML-EC[superscript 2] (multi-label email classification using clustering) has been proposed in this work. ML-EC[superscript 2] is a hybrid algorithm based on text clustering, text classification, frequent-term calculation (based on latent dirichlet allocation), and taxonomic term-mapping…

Objective: Identify the most performant automated text classification method (eg, algorithm) for differentiating empirical studies from nonempirical works in order to facilitate systematic mixed studies reviews. Methods: The algorithms were trained and validated with 8050 database records, which had previously been manually categorized as…

This research is part of the Artificial Intelligence Virtual Trainer (AI-VT) project which aims to create a system that can identify the user's skills from a text by means of machine learning. AI-VT is a case-based reasoning learning support system can generate customized exercise lists that are specially adapted to user needs. To attain this…

Cognitive learning complexity identification of assessment questions is an essential task in the domain of education, as it helps both the teacher and the learner to discover the thinking process required to answer a given question. Bloom's Taxonomy cognitive levels are considered as a benchmark standard for the classification of cognitive…

This study examined the factors of learning behaviors in digital-learning-material reader that predict university students' productive participation identified by social presence indicators in online collaborative learning. Data were collected from 76 first-year university students, including learning behaviors in digital learning-material…

This study focuses on how teachers' pedagogical content knowledge (PCK) of mathematics may differ depending on teacher interactions in an online teacher community of practice (CoP). The study utilizes data from 26,857 posts collected from the South Korean self-generated online teacher CoP, 'Indischool'. This data was then analyzed using natural…

An adequate level of linguistic complexity in learning materials is believed to be of crucial importance for learning. The implication for school textbooks is that reading complexity should differ systematically between grade levels and between higher and lower tracks in line with what can be called the systematic complexification assumption.…

Artificial Intelligence is widely used across contexts and for different purposes, including the field of education. However, a review of the literature showcases that while there exist various review studies on the use of AI in education, missing remains a review focusing on science education. To address this gap, we carried out a systematic…

To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K-12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created,…

We propose a model that employs convolutional neural networks (CNN) to evaluate sociomoral reasoning maturity, a key social ability, necessary for adaptive social functioning. Our model is used in a serious game to evaluate learners. It uses pre-annotated textual data (verbatims) and a coding scheme (SoMoral) applied by experts in psychology.…

This paper demonstrated the use of the supervised Machine Learning (ML) for text classification to predict students' final course grades in a hybrid Advanced Statistics course and exhibited the potential of using ML classified messages to identify students at risk of course failure. We built three classification models with training data of 76,936…

The automated classification of examination questions based on Bloom's Taxonomy (BT) aims to assist the question setters so that high-quality question papers are produced. Most studies to automate this process adopted the machine learning approach, and only a few utilised the deep learning approach. The pre-trained contextual and non-contextual…

Automating the classification of instructional strategies from a large-scale online tutorial dialogue corpus is indispensable to the design of dialogue-based intelligent tutoring systems. Despite many existing studies employing supervised machine learning (ML) models to automate the classification process, they concluded that building a…

Background: Deep learning methods, where models do not use explicit features and instead rely on implicit features estimated during model training, suffer from an explainability problem. In text classification, saliency maps that reflect the importance of words in prediction are one approach toward explainability. However, little is known about…