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- All Subjects: Educational Data Mining
- Creators: Walker, Erin
Description
EMBRACE (Enhanced Moved By Reading to Accelerate Comprehension in English) is an IPad application that uses the Moved By Reading strategy to help improve the reading comprehension skills of bilingual (Spanish speaking) English Language Learners (ELLs). In EMBRACE, students read the text of a story and then move images corresponding to the text that they read. According to the embodied cognition theory, this grounds reading comprehension in physical experiences and thus is more engaging.
In this thesis, I used the log data from 20 students in grades 2-5 to design a skill model for a student using EMBRACE. A skill model is the set of knowledge components that a student needs to master in order to comprehend the text in EMBRACE. A good skill model will improve understanding of the mistakes students make and thus aid in the design of useful feedback for the student.. In this context, the skill model consists of vocabulary and syntax associated with the steps that students performed. I mapped each step in EMBRACE to one or more skills (vocabulary and syntax) from the model. After every step, the skill level is updated in the model. Thus, if a student answered the previous step incorrectly, the corresponding skills are decremented and if the student answered the previous question correctly, the corresponding skills are incremented, through the Bayesian Knowledge Tracing algorithm.
I then correlated the students’ predicted scores (computed from their skill levels) to their posttest scores. I evaluated the students’ predicted scores (computed from their skill levels) by comparing them to their posttest scores. The two sets of scores were not highly correlated, but the results gave insights into potential improvements that could be made to the system with respect to user interaction, posttest scores and modeling algorithm.
In this thesis, I used the log data from 20 students in grades 2-5 to design a skill model for a student using EMBRACE. A skill model is the set of knowledge components that a student needs to master in order to comprehend the text in EMBRACE. A good skill model will improve understanding of the mistakes students make and thus aid in the design of useful feedback for the student.. In this context, the skill model consists of vocabulary and syntax associated with the steps that students performed. I mapped each step in EMBRACE to one or more skills (vocabulary and syntax) from the model. After every step, the skill level is updated in the model. Thus, if a student answered the previous step incorrectly, the corresponding skills are decremented and if the student answered the previous question correctly, the corresponding skills are incremented, through the Bayesian Knowledge Tracing algorithm.
I then correlated the students’ predicted scores (computed from their skill levels) to their posttest scores. I evaluated the students’ predicted scores (computed from their skill levels) by comparing them to their posttest scores. The two sets of scores were not highly correlated, but the results gave insights into potential improvements that could be made to the system with respect to user interaction, posttest scores and modeling algorithm.
ContributorsFurtado, Nicolette Dolores (Author) / Walker, Erin (Thesis advisor) / Hsiao, Ihan (Committee member) / Restrepo, M. Adelaida (Committee member) / Arizona State University (Publisher)
Created2016
Description
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.
ContributorsDexheimer, Matthew Scott (Author) / Walker, Erin (Thesis advisor) / Glenberg, Arthur (Committee member) / VanLehn, Kurt (Committee member) / Arizona State University (Publisher)
Created2017