An Analysis of Response Times in Adaptive Practice of Geography Facts

ABSTRACT Online educational systems can easily measure both answers and response times. Student modeling, however, typically focuses only on correctness of answers. In this work we analyze response times from a widely used system for adaptive practice of geography facts. Our results show that response times have simple relationship with the probability of answering correctly the next question about the same item. We also analyze the overall speed of students and its relation to several aspects of students' behaviour within the system

What Predicts Student Comprehension in Language Learning? Augmenting Student Action with Elapsed Time in an Educational Data Mining Approach

abstract: Reading comprehension is a critical aspect of life in America, but many English language learners struggle with this skill. Enhanced Moved by Reading to Accelerate Comprehension in English (EMBRACE) is a tablet-based interactive learning environment is designed to improve reading comprehension. During use of EMBRACE, all interactions with the system are logged, including correct and incorrect behaviors and help requests. These interactions could potentially be used to predict the child’s reading comprehension, providing an online measure of understanding. In addition, time-related features have been used for predicting learning by educational data mining models in mathematics and science, and may be relevant in this context. This project investigated the predictive value of data mining models based on user actions for reading comprehension, with and without timing information. Contradictory results of the investigation were obtained. The KNN and SVM models indicated that elapsed time is an important feature, but the linear regression models indicated that elapsed time is not an important feature. Finally, a new statistical test was performed on the KNN algorithm which indicated that the feature selection process may have caused overfitting, where features were chosen due coincidental alignment with the participants’ performance. These results provide important insights which will aid in the development of a reading comprehension predictor that improves the EMBRACE system’s ability to better serve ELLs.Dissertation/ThesisMasters Thesis Computer Science 201