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- All Subjects: Educational Data Mining
- Creators: Walker, Erin
- Status: Published
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
Description
Online learning communities have changed the way users learn due to the technological affordances web 2.0 has offered. This shift has produced different kinds of learning communities like massive open online courses (MOOCs), learning management systems (LMS) and question and answer based learning communities. Question and answer based communities are an important part of social information seeking. Thousands of users participate in question and answer based communities on the web like Stack Overflow, Yahoo Answers and Wiki Answers. Research in user participation in different online communities identifies a universal phenomenon that a few users are responsible for answering a high percentage of questions and thus promoting the sustenance of a learning community. This principle implies two major categories of user participation, people who ask questions and those who answer questions. In this research, I try to look beyond this traditional view, identify multiple subtler user participation categories. Identification of multiple categories of users helps to provide specific support by treating each of these groups of users separately, in order to maintain the sustenance of the community.
In this thesis, participation behavior of users in an open and learning based question and answer community called OpenStudy has been analyzed. Initially, users were grouped into different categories based on the number of questions they have answered like non participators, sample participators, low, medium and high participators. In further steps, users were compared across several features which reflect temporal, content and question/thread specific dimensions of user participation including those suggestive of learning in OpenStudy.
The goal of this thesis is to analyze user participation in three steps:
a. Inter group participation analysis: compare pre assumed user groups across the participation features extracted from OpenStudy data.
b. Intra group participation analysis: Identify sub groups in each category and examine how participation differs within each group with help of unsupervised learning techniques.
c. With these grouping insights, suggest what interventions might support the categories of users for the benefit of users and community.
This thesis presents new insights into participation because of the broad range of
features extracted and their significance in understanding the behavior of users in this learning community.
In this thesis, participation behavior of users in an open and learning based question and answer community called OpenStudy has been analyzed. Initially, users were grouped into different categories based on the number of questions they have answered like non participators, sample participators, low, medium and high participators. In further steps, users were compared across several features which reflect temporal, content and question/thread specific dimensions of user participation including those suggestive of learning in OpenStudy.
The goal of this thesis is to analyze user participation in three steps:
a. Inter group participation analysis: compare pre assumed user groups across the participation features extracted from OpenStudy data.
b. Intra group participation analysis: Identify sub groups in each category and examine how participation differs within each group with help of unsupervised learning techniques.
c. With these grouping insights, suggest what interventions might support the categories of users for the benefit of users and community.
This thesis presents new insights into participation because of the broad range of
features extracted and their significance in understanding the behavior of users in this learning community.
ContributorsSamala, Ritesh Reddy (Author) / Walker, Erin (Thesis advisor) / VanLehn, Kurt (Committee member) / Hsieh, Gary (Committee member) / Wetzel, Jon (Committee member) / Arizona State University (Publisher)
Created2015