Influence of selected factors on CPA licensure examination results

The researcher looked into the factors that affected the performance of Rizal Technological University CPA board examinees on the 2012 licensure examinations to determine the intervention necessary to improve the university’s review programs. Descriptive research design was used wherein twenty-four Bachelor of Science in Accountancy graduates who took the May and October 2012 board examinations were surveyed using a validated researcher-made questionnaire. Documentary analysis was used for evaluating qualitative data. Results revealed that: student factors (interest in the accounting program, time spent for studying lessons and availing of library/internet resources in research activities) and home/family factors (family financial support, parental involvement in studies, motivation and encouragement) highly influence performance; school factors (adequacy of relevant library books and materials, adequacy of technology hardware and software, good accounting program and good accounting faculty) were perceived to have average influence; and scholarships, grade weighted averages and attendance to review courses handled by prestigious review schools were found to be positive factors in passing the board examinations. Based on the findings, the researcher recommended that the regular assessment through qualifying examinations be conducted and that the pre-board/mock examinations in the review classes be strengthened

Structure from noise: Mental errors yield abstract representations of events

Humans are adept at uncovering abstract associations in the world around them, yet the underlying mechanisms remain poorly understood. Intuitively, learning the higher-order structure of statistical relationships should involve complex mental processes. Here we propose an alternative perspective: that higher-order associations instead arise from natural errors in learning and memory. Combining ideas from information theory and reinforcement learning, we derive a maximum entropy (or minimum complexity) model of people's internal representations of the transitions between stimuli. Importantly, our model (i) affords a concise analytic form, (ii) qualitatively explains the effects of transition network structure on human expectations, and (iii) quantitatively predicts human reaction times in probabilistic sequential motor tasks. Together, these results suggest that mental errors influence our abstract representations of the world in significant and predictable ways, with direct implications for the study and design of optimally learnable information sources.Comment: 62 pages, 7 figures, 10 table

Proceedings of the Joint Automated Reasoning Workshop and Deduktionstreffen: As part of the Vienna Summer of Logic – IJCAR 23-24 July 2014

Preface For many years the British and the German automated reasoning communities have successfully run independent series of workshops for anybody working in the area of automated reasoning. Although open to the general public they addressed in the past primarily the British and the German communities, respectively. At the occasion of the Vienna Summer of Logic the two series have a joint event in Vienna as an IJCAR workshop. In the spirit of the two series there will be only informal proceedings with abstracts of the works presented. These are collected in this document. We have tried to maintain the informal open atmosphere of the two series and have welcomed in particular research students to present their work. We have solicited for all work related to automated reasoning and its applications with a particular interest in work-in-progress and the presentation of half-baked ideas. As in the previous years, we have aimed to bring together researchers from all areas of automated reasoning in order to foster links among researchers from various disciplines; among theoreticians, implementers and users alike, and among international communities, this year not just the British and German communities