Proceedings ArticleDOI
Crowd-scale interactive formal reasoning and analytics
Ethan Fast,Colleen Lee,Alex Aiken,Michael S. Bernstein,Daphne Koller,Eric Smith +5 more
- pp 363-372
TLDR
DeduceIt is presented, a system for creating, grading, and analyzing derivation assignments in any formal domain, and suggests that automated reasoning can extend online assignments and large-scale education to many new domains.Abstract:
Large online courses often assign problems that are easy to grade because they have a fixed set of solutions (such as multiple choice), but grading and guiding students is more difficult in problem domains that have an unbounded number of correct answers One such domain is derivations: sequences of logical steps commonly used in assignments for technical, mathematical and scientific subjects We present DeduceIt, a system for creating, grading, and analyzing derivation assignments in any formal domain DeduceIt supports assignments in any logical formalism, provides students with incremental feedback, and aggregates student paths through each proof to produce instructor analytics DeduceIt benefits from checking thousands of derivations on the web: it introduces a proof cache, a novel data structure which leverages a crowd of students to decrease the cost of checking derivations and providing real-time, constructive feedback We evaluate DeduceIt with 990 students in an online compilers course, finding students take advantage of its incremental feedback and instructors benefit from its structured insights into course topics Our work suggests that automated reasoning can extend online assignments and large-scale education to many new domainsread more
Citations
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PeerStudio: Rapid Peer Feedback Emphasizes Revision and Improves Performance
TL;DR: PeerStudio is introduced, an assessment platform that leverages the large number of students' peers in online classes to enable rapid feedback on in-progress work and demonstrates how large classes can leverage their scale to encourage mastery through rapid feedback and revision.
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Codewebs: scalable homework search for massive open online programming courses
TL;DR: A method for decomposing online homework submissions into a vocabulary of "code phrases", and based on this vocabulary, a queryable index that allows for fast searches into the massive dataset of student homework submissions is designed.
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Scaling short-answer grading by combining peer assessment with algorithmic scoring
TL;DR: This paper integrates peer and machine grading to preserve the robustness of peer assessment and lower grading burden and provides an example of how peer work and machine learning can combine to improve the learning experience.
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How Can Automatic Feedback Help Students Construct Automata
TL;DR: This work focuses on providing feedback for constructing a deterministic finite automaton that accepts strings that match a described pattern and finds that providing either counterexamples or hints is judged as helpful, increases student perseverance, and can improve problem completion time.
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Example-based learning in computer-aided STEM education
TL;DR: Example-based reasoning techniques developed for programming languages also help automate repetitive tasks in education and improve the quality of teaching and learning.
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On Distinguishing Epistemic from Pragmatic Action
David Kirsh,Paul P. Maglio +1 more
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