Predicting Self-Correction Attempts with FACT, an Automated Teaching Assistant for Algebra Classes

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Description

Machine learning is a rapidly growing field, with no doubt in part due to its countless applications to other fields, including pedagogy and the creation of computer-aided tutoring systems. To extend the functionality of FACT, an automated teaching assistant, we

Machine learning is a rapidly growing field, with no doubt in part due to its countless applications to other fields, including pedagogy and the creation of computer-aided tutoring systems. To extend the functionality of FACT, an automated teaching assistant, we want to predict, using metadata produced by student activity, whether a student is capable of fixing their own mistakes. Logs were collected from previous FACT trials with middle school math teachers and students. The data was converted to time series sequences for deep learning, and ordinary features were extracted for statistical machine learning. Ultimately, deep learning models attained an accuracy of 60%, while tree-based methods attained an accuracy of 65%, showing that some correlation, although small, exists between how a student fixes their mistakes and whether their correction is correct.

Date Created
2022-05
Agent

The Primer Project: Creating an Interactive Children's Storybook

Description
As part of a group project, myself and four teammates created an interactive children's storybook based off of the "Young Lady's Illustrated Primer" in Neal Stephenson's novel The Diamond Age. This electronic book is meant to be read aloud by

As part of a group project, myself and four teammates created an interactive children's storybook based off of the "Young Lady's Illustrated Primer" in Neal Stephenson's novel The Diamond Age. This electronic book is meant to be read aloud by a caregiver with their child, and is designed for reading over long distances through the use of real-time voice and video calling. While one part of the team focused on building the electronic book itself and writing the program, myself and two others wrote the story and I provided illustrations. Our Primer tells the story of a young princess named Charname (short for character name) who escapes from a tower and goes on a mission to save four companions to help her on her quest. The book is meant for reader-insertion, and teaches children problem-solving, teamwork, and critical thinking skills by presenting challenges for Princess Charname to solve. The Primer borrows techniques from modern video game design, focusing heavily on interactivity and feelings of agency through offering the child choices of how to proceed, similar to choose-your-own-adventure books. If brought to market, the medium lends itself well to expanded quests and storylines for the child to explore as they learn and grow. Additionally, resources are provided for the narrator to help create an engaging experience for the child, based off of research on parent-child cooperative reading and cooperative gameplay. The final version of the Primer included a website to run the program, a book-like computer to access the program online, and three complete story segments for the child and narrator to read together.
Date Created
2016-05
Agent

Using Learner Profiles to Create Teachable Tutor Systems

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Description
In the 2 Sigma Learning Lab we use Quinn as an embodied agent in the tangible activities for geometry (TAG) system. The TAG system is focused on teaching students basic geometry skills, mainly plotting points in a Cartesian Grid. The

In the 2 Sigma Learning Lab we use Quinn as an embodied agent in the tangible activities for geometry (TAG) system. The TAG system is focused on teaching students basic geometry skills, mainly plotting points in a Cartesian Grid. The TAG system's main tool is Quinn's reactive prompts which help students overcome various misconceptions and teach basic concepts about the Cartesian plane and points. By analyzing past studies using the TAG system my goal is to develop prototypical agent profiles based off of my findings in learner profiles that might facilitate focused tutoring that addresses important misconceptions. Furthermore, applied use of these prototypical agent profiles would allow for students to teach Quinn as an application of the Learning-by-Teaching paradigm.
Date Created
2016-05
Agent

LudoNarrare: A Model for Verb Based Interactive Storytelling

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Description
Instead of providing the illusion of agency to a reader via a tree or network of prewritten, branching paths, an interactive story should treat the reader as a player who has meaningful influence on the story. An interactive story can

Instead of providing the illusion of agency to a reader via a tree or network of prewritten, branching paths, an interactive story should treat the reader as a player who has meaningful influence on the story. An interactive story can accomplish this task by giving the player a large toolset for expression in the plot. LudoNarrare, an engine for interactive storytelling, puts "verbs" in this toolset. Verbs are contextual choices of action given to agents in a story that result in narrative events. This paper begins with an analysis and statement of the problem of creating interactive stories. From here, various attempts to solve this problem, ranging from commercial video games to academic research, are given a brief overview to give context to what paths have already been forged. With the background set, the model of interactive storytelling that the research behind LudoNarrare led to is exposed in detail. The section exploring this model contains explanations on what storyworlds are and how they are structured. It then discusses the way these storyworlds can be brought to life. The exposition on the LudoNarrare model finally wraps up by considering the way storyworlds created around this model can be designed. After the concepts of LudoNarrare are explored in the abstract, the story of the engine's research and development and the specifics of its software implementation are given. With LudoNarrare fully explained, the focus then turns to plans for evaluation of its quality in terms of entertainment value, robustness, and performance. To conclude, possible further paths of investigation for LudoNarrare and its model of interactive storytelling are proposed to inspire those who wish to continue in the spirit of the project.
Date Created
2015-12
Agent

Analyzing user participation across different answering ranges in an online learning community

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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

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.
Date Created
2015
Agent