The Algorithms & Computing for Education (ACE) Lab brings together an interdisciplinary group of researchers from Computer Science, the iSchool, and the Graduate School of Education, working at the intersection of education and computing. Our projects involve novel educational software that helps educators educate better and students learn better, spanning traditional, online, and hybrid learning.
The ACE Lab’s physical location is the BiD Lab (Berkeley Institute of Design), room 360, Hearst Memorial Mining Building.
This is a second offering of the very successful Fall 2023 course. You can repeat it for credit.
In this special topics course, small teams (2-4) of graduate and undergraduate students will develop and rigorously evaluate rich, machine-gradable assessments that would address learning goals that might arise in typical EECS courses. The assessments will promote mastery learning (aka proficiency based learning)
by following the acronym STAR:
- Specific to a learning goal in a particular domain or topic. Examples from EECS could include timing diagrams, graph labeling, connect the circuit elements, etc, but not generic-format multiple-choice/numeric-answer/fill-in-the-blanks questions. Developing and evaluating novel question formats and types are the main goals.
- Tagged to specific learning outcomes, skills to be demonstrated (competencies), etc. within the context of a full or partial concept inventory for the course, so that evaluation can be focused on whether the learning outcome is in fact reinforced by the assessment
- Autogradable with instant or near-instant automatic feedback
- Randomized, so each exercise has a large or very large number of variants and can therefore be used for practice (formative assessment for mastery learning)
In addition to developing assessments, student teams will evaluate
them by using the methods of HCI and education research to run either informal or formal pilot studies. We will encourage students to make any resulting artifacts available as open educational resources, and if appropriate, submit results for publication.