ClassInSight

I'm currently working as a developer/manager/designer (in that order) with the Oh! Lab at Carnegie Mellon University to develop a web app that helps teachers improve their teaching skills. Using key metrics known to improve classroom outcomes, the project will ultimately use class sensors to track and collect data on teacher behavior. This data is then presented back to the teacher in meaningful ways that may help teachers notice, act, and reflect on their performance. We are currently wrapping up initial designs and I am developing our first viable product using the Django framework.

Together

Together was my capstone project in the METALS program (Master of Educational Technology and Applied Learning Science) at Carnegie Mellon University. My team partnered with Virginia Tech to improve the accessibility and effectiveness of undergraduate tutoring services in science and engineering calculus courses. 

Acting as research lead, I guided the team through user-centered research methods alongside an iterative design process and prototype development. We created the mobile app "Together" to enhance peer-learning and group tutoring experiences in calculus.

Intelligent Tutor: Picture Algebra

This project was part of as well as an extension of my forgetting study. Many of the findings of the link below can be found in the interactive document of the forgetting study. The study focused on a data set consisting of students using an intelligent tutor in a picture algebra unit.

The key take-away is that particular skills (or knowledge components) appear more prone to decay in predicted mastery over time than other skills. I redesigned the picture algebra tutor to provide targeted personalized practice on skills that may be prone to forgetting. Once a student's mastery is predicted to have dipped below some set threshold, they may receive practice targeting that specific skill as opposed to the entire problem that may consist of many mastered steps. The tutor mitigates forgetting and provides more efficient personalized learning. 

BluePrint

Blueprint is a peer to peer app that encourages kids to explore their interests and provide feedback to other students. Through careful scaffolding the app strives to move beyond “Good job!” feedback, and helps students engage at a more meaningful level. Feedback is an essential component to learning and developing a skill set, by harnessing crowd knowledge our team believes we can enable students to further develop their skill sets.

Intelligent Tutor: Statistical Validity

Students often struggle when reasoning about cause-and-effect relationships and interpreting research designs. A basic understanding of this topic helps all people act and behave as informed citizens. So, I created an e-learning module using design research and established scientific findings to help students learn and recognize causal claims and common threats to their validity. This project was my first major foray out of the lab and into educational applications created through design methodologies. 

First, the module was developed by setting the scope through definitions of learning goals. The goals were then narrowed down into a reasonable project. Next, I developed assessments and tested them on novices which in turn uncovers the concepts or skills that are particularly difficult. I then conducted a cognitive task analysis with two domain experts. Finally a number of iterative prototypes were developed into a final product.

Intelligent Tutor: Probability

I have long been interested in mitigating student forgetting through educational tools, and so in this project I tried to push the limits of what I could do with Adobe Captivate. I chose introductory probability as the learning domain as it is relatively simple. The goal was to add mastery learning and decay of mastery over time in a simple module. I settled with implementing standard Bayesian Knowledge Tracing using learner variables captured in JavaScript. The variables were then used to lock or unlock paths through the modules depending on whether the student was predicted to have the prerequisite knowledge.

 

While the project was largely successful, the main complications came from the way Captivate handles quizzes. The quiz system is too rigid and it's nearly impossible to generate quizzes of any length from a pool of questions or randomly generated questions. 

LookOut

In this project I worked with a small team challenged with finding a way for students, staff, parents, and teachers to easily and safely capture information and report school safety concerns to authorities. In response to this challenge, my team conducted user research and created an interface design for a mobile app to combat high school bullying. 

Lookout is a mobile app with an optional recording device that can be attached to the users clothes. The wearable hypothetically can recognize and react to incidents in the environment through a machine learning classifier. The wearable takes inputs but does not share information to any outside users. It's purpose is purely for detection and sending prompts to users. For example, if the user or someone nearby is using language reminiscent of bullying, the detector pushes a message to the student to inform them about their choice of language, or if necessary suggests actions to students to find help. Additionally, Lookout allows students to anonymously take and send photos or messages about school safety issues or bullying incidents to school administrators. 

Intelligent Tutor: Statistics

In this project I developed a example tracing tutor for a statistics module using CTAT authoring tools and Flash. The primary purpose of the tutor was to present students with a problems represented in words, graphs, or formal mathematics and they had to correctly match the given representation with the appropriate alternate representation i.e. given this equation which of these graphs best represent it. 

 

The details of this project have sadly been lost in the great hard drive crash of 2016 and I therefore do not have any pictures or examples of the tutor.  

Translating Problems for Far Transfer

This goal of this project is to counter difficulties students have when trying to apply mathematical concepts across many contexts. It's common that learning materials focus on a few simplified contexts early in learning. For example, in probability we tend to see problems posed as coins and cards. The problem then is that students understand how to solve coin and card problems but cannot easily transfer this knowledge to new contexts.

 

I've for some time played around with creating a modeling language to help ground mathematical or statistical concepts in simple and standard graphical representations. The dream is that students will learn how to translate problems into a standard language and this may provide a means for more general far transfer of learning. This is similar to saying, if you understand probability with coins, it may be useful to learn the skill of translating problems in other contexts into coin problems to capitalize on your knowledge rather than hoping for spontaneous transfer. 

Please reload

Tools and Languages:

Django Framework
Python, SQL, HTML, CSS, Javascript

Capstone Project:

Spring & Summer 2017

Tools and Languages:

Axure, Invision, Figma, InDesign

Methods:

Contextual interviews, surveys, affinity diagramming, journey mapping, personas, rapid prototyping, user testing

Course:

Personalized Online Learning - Spring 2017

Tools and Languages:

Jess, CTAT Authoring Tools

Methods:

Cognitive task analysis, User testing

Course:

Learning Media Design - Fall 2016

Tools and Languages:

Axure, Invision, Figma, InDesign

Methods:

Contextual interviews, affinity diagramming, journey mapping, personas, rapid prototyping

Course:

E-Learning Design Principles - Fall 2016

Tools and Languages:

Adobe Captivate

Methods:

Interviews, cognitive task analysis, surveys, prototyping, user testing

Course:

Tools for Online Learning - Fall 2016

Tools and Languages:

Adobe Captivate​, JavaScript

Methods:

Cognitive task analysis, prototyping, user testing

Course:

Interaction Design Overview - Fall 2016

Tools and Languages:

Invision, ​Figma, InDesign, Photoshop

Methods:

Contextual interviews, affinity diagramming, journey mapping, personas, storyboards, rapid prototyping

Tools and Languages:

Flash, CTAT