Instructor(s): David Joyner
Course Page: Link

This class was simultaneously an introductory course about educational technology and an advanced, project-oriented class on designing or researching technology’s intersection with education. The course provides student's with information about a large number of topics within educational technology, including pedagogical strategies, research methodologies, current tools, open problems, and broader issues.


CS-6460 was an extremely open-ended course, so you'll likely get as much out of this course as you put into it. You're given the option to either pursue one of three tracks:

1) Development - Work on a project/tool that will improve educational technology

2) Research - Conduct research on some field in educational technology, typically some form of study or survey of MOOCs

3) Content - Develop your own course material and/or MOOC

For myself, I chose to pursue a combination of both the development and research task, using this course as a structured format for myself to learn more about Natural Language Processing and it's application to educational technology

Course Gotchas

  • Start early especially if you're taking this course during the summer semester! Look ahead at the assignments and prepare ahead of time. There is a substatntial amount of writing in the first couple of weeks and you'll be reading ALOT of papers.
  • If you already have a topic or project you want to tackle, structure your research and preperation before the course starts. It'll make your life alot easier since you can spend more time on development/research
  • There is a course participation component. You should be able to get full marks through just completing regular peer feedback. Make sure you review how the points are calculated so you know at a minimum how much points you're currently at. The course instructor will provide you snapshots every month, but it'll also help to have a mental model of where you should be at any point in the semester

Research Topic: Multi-Document Summarization

For my research topic, I investigated the problem of multi-document summarization of medical research for literature reviews as part of a shared task for the workshop on Scholarly Document Processing 2022. The goal of the task was to build a machine learning model that could be applied to any set of medical research documents and generate a succinct summary that is understandable by a medical researcher. This task used two datasets of review summaries derived from the scientific literature [1][2]. Participating teams were then evaluated using automated and human evaluation metrics.

I wasn’t able to make any improvements on the dataset benchmark, but I was able to establish some evidence that current summarization metrics are insufficient in measuring summarization accuracy. I also built a small web tool to demonstrate the viability of summarization models for future investigators. Luckily enough my work was accepted into the workshop and presented at the workshop proceedings at COLING 2022!