Abstract
We report on using logic software in a novel course-format for an undergraduate logic course for students in computer science or artificial intelligence. Although being designed as the students’ basic introduction to the field of logic, the course features a novel structure and it adds some modern content, such as SAT and SMT solving, to the traditional and established topics, such as propositional logic and first order predicate logic. The novel course design is characterized by, among others, the integration of existing logic software into the teaching of logic.
In this paper we focus on the module on first-order predicate logic and the use of the Theorema system as a proof-tutor for the students. We report on statistical evaluation of data collected over two consecutive years of teaching this course. On the one hand, we asked for feedback of students on how helpful they felt the software support was. On the other hand, we evaluated their results in the exams during the course and their development over the entire teaching period. The performance in exams is then correlated with students’ own perception of the helpfulness of software.
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Notes
- 1.
Note that, in the statistical evaluation presented later, we neglect the final lab exercise at the end of the module, because there is no item following the lab exercise, in which we could measure some influence of doing the lab exercise or not.
- 2.
Lecture recordings are almost mainstream nowadays, but we switched to flipped-classroom with videos already one year before the pandemic.
- 3.
Not able to do the proofs by hand but feel capable after using Theorema.
- 4.
Hard time doing the proofs by hand but feel improvement through using Theorema.
- 5.
No problems doing the proofs by hand but will do proofs differently after having used Theorema.
- 6.
No problems doing the proofs by hand but unable with Theorema although keen.
- 7.
Note that the statistical test gives much more confidence in different mean values than only comparing observed averages and taking into account the standard deviations or variances in the samples.
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Windsteiger, W. (2022). Learning to Reason Assisted by Automated Reasoning. In: Buzzard, K., Kutsia, T. (eds) Intelligent Computer Mathematics. CICM 2022. Lecture Notes in Computer Science(), vol 13467. Springer, Cham. https://doi.org/10.1007/978-3-031-16681-5_21
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