A Spectrum of Applications of Automated Reasoning

The likelihood of an automated reasoning program being of substantial assistance for a wide spectrum of applications rests with the nature of the options and parameters it offers on which to base needed strategies and methodologies. This article focuses on such a spectrum, featuring W. McCune's program OTTER, discussing widely varied successes in answering open questions, and touching on some of the strategies and methodologies that played a key role. The applications include finding a first proof, discovering single axioms, locating improved axiom systems, and simplifying existing proofs. The last application is directly pertinent to the recently found (by R. Thiele) Hilbert's twenty-fourth problem--which is extremely amenable to attack with the appropriate automated reasoning program--a problem concerned with proof simplification. The methodologies include those for seeking shorter proofs and for finding proofs that avoid unwanted lemmas or classes of term, a specific option for seeking proofs with smaller equational or formula complexity, and a different option to address the variable richness of a proof. The type of proof one obtains with the use of OTTER is Hilbert-style axiomatic, including details that permit one sometimes to gain new insights. We include questions still open and challenges that merit consideration.Comment: 13 page

The BG News December 16, 2013

The BGSU campus student newspaper, December 16, 2013 Volume 93 - Issue 48https://scholarworks.bgsu.edu/bg-news/9694/thumbnail.jp

Placing trust: The political ecology of chicken meat in Japan

Ph.D.Ph.D. Thesis. University of Hawaiʻi at Mānoa 201

A self study of a higher education tutor: How can I improve my practice?

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The role of temporal patterns in students’ behavior for predicting course performance: A comparison of two blended learning courses

In higher education, many studies have tried to establish which student activities predict achievement in blended courses, with the aim of optimizing course design. In this paper, we examine whether taking into account temporal patterns of student activity and instructional conditions of a course help to explain course performance. A course with a flipped classroom model (FCM) and a course with an enhanced hybrid model (EHM) were compared. The results show that in both cases, a regular pattern of activity is more effective than low activity. In the FCM, initial low activity is detrimental, whereas in the EHM the strategy of cramming later on in the course can still lead to higher course performance. In the FCM, a combination of face‐to‐face and online activity led to sufficient course performance, whereas in the EHM, face‐to‐face or online activity on its own could lead to sufficient course performance. This study offers a methodological and empirical contribution to exploring the role of patterns of activity and instructional conditions for course performance. Teaching and Teacher Learning (ICLON

The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses

In higher education, many studies have tried to establish which student activities predict achievement in blended courses, with the aim of optimizing course design. In this paper, we examine whether taking into account temporal patterns of student activity and instructional conditions of a course help to explain course performance. A course with a flipped classroom model (FCM) and a course with an enhanced hybrid model (EHM) were compared. The results show that in both cases, a regular pattern of activity is more effective than low activity. In the FCM, initial low activity is detrimental, whereas in the EHM the strategy of cramming later on in the course can still lead to higher course performance. In the FCM, a combination of face-to-face and online activity led to sufficient course performance, whereas in the EHM, face-to-face or online activity on its own could lead to sufficient course performance. This study offers a methodological and empirical contribution to exploring the role of patterns of activity and instructional conditions for course performance

The role of temporal patterns in students' behavior for predicting course performance : A comparison of two blended learning courses

In higher education, many studies have tried to establish which student activities predict achievement in blended courses, with the aim of optimizing course design. In this paper, we examine whether taking into account temporal patterns of student activity and instructional conditions of a course help to explain course performance. A course with a flipped classroom model (FCM) and a course with an enhanced hybrid model (EHM) were compared. The results show that in both cases, a regular pattern of activity is more effective than low activity. In the FCM, initial low activity is detrimental, whereas in the EHM the strategy of cramming later on in the course can still lead to higher course performance. In the FCM, a combination of face-to-face and online activity led to sufficient course performance, whereas in the EHM, face-to-face or online activity on its own could lead to sufficient course performance. This study offers a methodological and empirical contribution to exploring the role of patterns of activity and instructional conditions for course performance