Tools

Arithmetic Word Problems (AWPs) require students to combine mathematical skills with adequate language comprehension, and reading ability has been shown to be a key factor in solving them successfully. However, studying how children read these problems is not easy: techniques such as eye tracking require specialised equipment that most schools do not have. AWPSolver is a web-based tool designed to analyse the relationship between reading speed and the probability of success in AWPs among primary school students. It runs on any device (computer, tablet or smartphone) and needs no installation, so it can be used in any educational centre.

The tool offers one interface for teachers and researchers, who can create customised experiments from sets of problems, and another for students, who solve them one by one. To capture reading behaviour, statements are initially masked and students unmask each proposition by clicking on it, so every interaction is logged with millisecond precision. From these traces, AWPSolver computes the reading time per proposition, total reading time, reading speed, number of clicks, revisits, selected answers and correct responses, helping to pinpoint which parts of a problem are hardest to understand. Optional aids such as a calculator and a blank sheet of paper can be enabled depending on the aim of the experiment.

The Peer Review system is crucial for ensuring the quality and rigor of scientific publications, thereby maintaining the integrity of science. The credibility of the scientific process depends heavily on the excellence of review texts. High-quality reviews ensure the publication of good science, which in turn supports societal advancement. Therefore, it is vital to supervise and strive for excellence in these reviews.

This new tool aims to analyze the quality of scientific article reviews, providing recognition to reviewers and allowing them to evaluate the completeness and alignment of their reviews. The application assesses reviews based on ten quality dimensions and presents the results visually and dynamically through a web interface and REST API requests, enabling integration with other platforms for comprehensive quality analysis.

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Read&Learn is a software developed over 15 years by the Psicotext group at the University of Valencia, designed for conducting experiments on comprehension and learning from texts. It aims to study how students read different text segments while performing tasks, such as answering questions, and how they learn from various text genres and typologies. This web-based tool does not require installation, making it accessible and versatile for participating institutions. It integrates text reading with question completion, offering templates for displaying tasks on either one or two screens and providing different feedback modalities for multiple-choice questions.

In addition to its versatile design options, Read&Learn features a masking option and detailed online recording of readers’ actions, which are transformed into process variables like reading times and decisions. These variables are crucial for understanding comprehension and learning strategies. The software allows researchers to monitor reader activity in real-time and download the collected data immediately after the experiment. This capability is essential for comprehension and learning research, offering valuable insights into students’ reading and learning processes.

Natural Language Processing involves reshaping and refining data sets into data that can be used for analysis, ensuring that the data is well formatted. The efficiency gap of data scientists spending most of their time preparing data is an opportunity for the technology sector to work on solutions to the problem. For this reason, a web tool has been developed that is capable of, on the one hand, speeding up the text cleaning process and, on the other hand, facilitating the extraction of metrics by analyzing and processing the texts through customized dictionaries in LIWC format, uploaded by the users themselves, and through sentiment analysis. All this, from a single interface that allows the user to customize the whole pipeline offering different modules for pre-processing and metrics extraction in order to be a solution to facilitate, streamline and automate the whole process.