Gaze data are still uncommon in statistics education despite their promise. Gaze data provide teachers and researchers with a new window into complex cognitive processes. This article discusses how gaze data can inform and be used by teachers both for their own teaching practice and with students. With our own eye-tracking research as an example, background information on eye-tracking and possible applications of eye-tracking in statistics education is provided. Teachers indicated that our eye-tracking research created awareness of the difficulties students have when interpreting histograms. Gaze data showed details of students' strategies that neither teachers nor students were aware of. With this discussion paper, we hope to contribute to the future usage and implementation of gaze data in statistics education by teachers, researchers, educational and textbook designers, and students.
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Purpose - Peer instruction has been widely adopted as an instructional method in higher education. However, due to students' different preconceptions, the authors argued that peer instruction is not a panacea in international business education when students' prior knowledge extensively varies. The paper aims to discuss these issues. Design/methodology/approach - In this experimental study, the authors focused on three conditions of an introductory statistics course: individual problem solving, peer instruction, and peer instruction with hints. Findings - The authors have found students in peer instruction with hints class did not only outperform in the final exam, but also achieved the highest frequency of successful conceptual changes in comparison with their counterparts in the other two classes. Practical implications - Providing instructional hints to improve the effectiveness of peer instruction may shed light on classroom instruction in higher education. Originality/value - As for international business education, this was a unique exploration to capture students' conceptual changes using clickers. The authors believed this research paper will help the education practitioners to know their business students better.
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Frequent claims are made for the importance of the hospitality industry, and wider tourism sector, as potential and actual creators of employment. Many of these claims emanate from industry representative and advocacy organizations, often as part of their legitimate efforts to lobby governments for favourable treatment of their sectors. Good quality universal statistical data on employment in hospitality are noticeable by their absence, although information collected by bodies such as the International Labour Organization is extensive. This paper reviews the current state of data availability on global hospitality employment (with a primary focus on commercial hospitality operations) and seeks to employ these secondary sources in investigating the question as to whether we can in fact make plausible statements about the extent of such employment. This exercise is important both to contextualizing claims made for the employment generating capacity of the hospitality industry and to shedding light on the degree of seriousness with which data might be treated in wider policy contexts. The paper concludes, with cautious optimism, that commercial hospitality is a significant global employer and that the claims made for this employment by representative and advocacy organizations are plausible if treated with circumspection.
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Challenges that surveys are facing are increasing data collection costs and declining budgets. During the past years, many surveys at Statistics Netherlands were redesigned to reduce costs and to increase or maintain response rates. From 2018 onwards, adaptive survey design has been applied in several social surveys to produce more accurate statistics within the same budget. In previous years, research has been done into the effect on quality and costs of reducing the use of interviewers in mixed-mode surveys starting with internet observation, followed by telephone or face-to-face observation of internet nonrespondents. Reducing follow-ups can be done in different ways. By using stratified selection of people eligible for follow-up, nonresponse bias may be reduced. The main decisions to be made are how to divide the population into strata and how to compute the allocation probabilities for face-to-face and telephone observation in the different strata. Currently, adaptive survey design is an option in redesigns of social surveys at Statistics Netherlands. In 2018 it has been implemented in the Health Survey and the Public Opinion Survey, in 2019 in the Life Style Monitor and the Leisure Omnibus, in 2021 in the Labour Force Survey, and in 2022 it is planned for the Social Coherence Survey. This paper elaborates on the development of the adaptive survey design for the Labour Force Survey. Attention is paid to the survey design, in particular the sampling design, the data collection constraints, the choice of the strata for the adaptive design, the calculation of follow-up fractions by mode of observation and stratum, the practical implementation of the adaptive design, and the six-month parallel design with corresponding response results.
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This quasi-experimental study examines the effect of short instruction videos on students’ business statistics learning. Two hundred and thirty-one Dutch students attended 6-week online seminars on Business Statistics. One hundred and nineteen students were in an experimental group, and 112 in a control group. Students in the experimental group watched short instructional videos and studied online quizzes at their own pace. In the control group, students followed teachers’ instructions throughout the seminars. It was found students watching short videos significantly outperformed those following teachers’ virtual instruction. Short videos were especially useful for those who were good at math. The research sheds light on the design of hybrid learning, particularly for business statistics education at the university level.
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On this website I blog about Quantified Self, health, statistics, wearables, self-tracking. Relevant experiments, issue's, thoughts, and idea's are discussed.
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On April 16 and 17, 2020, the third edition of the Sensor data challenge was held by The Hague University of Applied Sciences, Statistics Netherlands, Utrecht University and the National Institute for Public Health and the Environment. The Sensor data challenge provides hardware (various sensors, raspberryPI) and software to teams with a mix of expertise in electronics, mechatronics, data science, user experience and industrial design. Teams need to design a tool and demonstrate its feasibility and relevance for one of the presented challenges. The third challenge had sensor measurements for living and working as the central theme. Winning solutions of the two previous editions have been the starting point for large-scale ongoing research projects. We like to present a brief summary of the solutions presented by the participating teams at the third challenge and offer the winning team the opportunity to share and discuss their ideas at the BigSurv20 with a larger audience.
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