Early-warning prediction of student performance and engagement in open book assessment by reading behavior analysis

Digitized learning materials are a core part of modern education, and analysis of the use can offer insight into the learning behavior of high and low performing students. The topic of predicting student characteristics has gained a lot of attention in recent years, with applications ranging from affect to performance and at-risk student prediction. In this paper, we examine students reading behavior using a digital textbook system while taking an open-book test from the perspective of engagement and performance to identify the strategies that are used. We create models to predict the performance and engagement of learners before the start of the assessment and extract reading behavior characteristics employed before and after the start of the assessment in a higher education setting. It was found that strategies, such as: revising and previewing are indicators of how a learner will perform in an open ebook assessment. Low performing students take advantage of the open ebook policy of the assessment and employ a strategy of searching for information during the assessment. Also compared to performance, the prediction of overall engagement has a higher accuracy, and therefore could be more appropriate for identifying intervention candidates as an early-warning intervention system

Análisis de errores en redacciones de un grupo de alumnos de un colegio bilingüe

A través del estudio de redacciones escritas en inglés de un grupo de alumnos de 3º, 4º ,5º y 6º de Educación Primaria, y 2º de Educación Secundaria Obligatoria de un colegio bilingüe de Huesca, se desarrolla el análisis de errores

Classification of English language learner writing errors using a parallel corpus with SVM

In order to overcome mistakes, learners need feedback to prompt reflection on their errors. This is a particularly important issue in education systems as the system effectiveness in finding errors or mistakes could have an impact on learning. Finding errors is essential to providing appropriate guidance in order for learners to overcome their flaws. Traditionally the task of finding errors in writing takes time and effort. The authors of this paper have a long-term research goal of creating tools for learners, especially autonomous learners, to enable them to be more aware of their errors and provide a way to reflect on the errors. As a part of this research, we propose the use of a classifier to automatically analyse and determine the errors in foreign language writing. For the experiment in this paper, we collected random sentences from the Lang-8 website that had been written by foreign language learners. Using predefined error categories, we manually classified the sentences to use as machine learning training data. This was then used to train a classifier by applying SVM machine learning to the training data. As the manual classification of training data takes time, it is intended that the classifier would be used to accelerate the process used for generating further training data

Classification of English language learner writing errors using a parallel corpus with SVM

In order to overcome mistakes, learners need feedback to prompt reflection on their errors. This is a particularly important issue in education systems as the system effectiveness in finding errors or mistakes could have an impact on learning. Finding errors is essential to providing appropriate guidance in order for learners to overcome their flaws. Traditionally the task of finding errors in writing takes time and effort. The authors of this paper have a long-term research goal of creating tools for learners, especially autonomous learners, to enable them to be more aware of their errors and provide a way to reflect on the errors. As a part of this research, we propose the use of a classifier to automatically analyse and determine the errors in foreign language writing. For the experiment in this paper, we collected random sentences from the Lang-8 website that had been written by foreign language learners. Using predefined error categories, we manually classified the sentences to use as machine learning training data. This was then used to train a classifier by applying SVM machine learning to the training data. As the manual classification of training data takes time, it is intended that the classifier would be used to accelerate the process used for generating further training data