Year: 2016
Type: dissertation
Abstract: With the wide usage of online tutoring systems, researchers become interested in mining data from logged files of these systems, so as to get better understanding of students. Varieties of aspects of students’ learning have become focus of studies, such as modeling students’ mastery status and affects. On the other hand, Randomized Controlled Trial (RCT), which is an unbiased method for getting insights of education, finds its way in Intelligent Tutoring System. Firstly, people are curious about what kind of settings would work better. Secondly, such a tutoring system, with lots of students and teachers using it, provides an opportunity for building a RCT infrastructure underlying the system. With the increasing interest in Data mining and RCTs, the thesis focuses on these two aspects. In the first part, we focus on analyzing and mining data from ASSISTments, an online tutoring system run by a team in Worcester Polytechnic Institute. Through the data, we try to answer several questions from different aspects of students learning. The first question we try to answer is what matters more to student modeling, skill information or student information. The second question is whether it is necessary to model students’ learning at different opportunity count. The third question is about the benefits of using partial credit, rather than binary credit as measurement of students’ learning in RCTs. The fourth question focuses on the amount that students spent Wheel Spinning in the tutoring system. The fifth questions studies the tradeoff between the mastery threshold and the time spent in the tutoring system. By answering the five questions, we both propose machine learning methodology that can be applied in educational data mining, and present findings from analyzing and mining the data. In the second part, we focused on RCTs within ASSISTments. Firstly, we looked at a pilot study of reassessment and relearning, which suggested a better system setting to improve students’ robust learning. Secondly, we proposed the idea to build an infrastructure of learning within ASSISTments, which provides the opportunities to improve the whole educational environment.
Source: Digital WPI
Author: Yan Wang
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
Awards: G112550528, Improving Formative Assessment Practices: Using Learning Trajectories to Develop Resources that Support Teacher Instructional Practice and Student Learning in CMP2, PIMSE: A GK-12 Partnership Implementing Mathematics and Science Education (PIMSE): Assisting Middle School Use of Tutoring Technology in the Classroom, Collaborative Research: The Downside of Perseverance--Investigating and Moving Students Beyond Unproductive Persistence, G5672457875, G5906920533, Empirical Research, Contextual Research: Taking advantage of Cognitive Science Principles: Adding to a Computer-Based Tutor an Automatic Reassessment and Relearning System, SI2-SSE: Adding Research Accounts to the ASSISTments' Platform: Helping Researchers Do Randomized Controlled Studies with Thousands of Students, Making Math Tutors More Engaging and Effective through Interaction Design Patterns and Educational Data Mining, Research: Predicting STEM Career Choice from Computational Indicators of Student Engagement within Middle School Mathematics Classes
OpenAlex ID: W2921042701