Learning analytics is the analysis of student data with the purpose of improving learning. However, the process of data cleaning remains underexposed within learning analytics literature. In this paper, we elaborate on choices made in the cleaning process of student data and their consequences. We illustrate this with a case where data was gathered during six courses taught via Moodle. In this data set, only 21% of the logged activities were linked to a specific course. We illustrate possible choices in dealing with missing data by applying the cleaning process twelve times with different choices on copies of the raw data. Consequently, the analysis of the data shows varying outcomes. As the purpose of learning analytics is to intervene based on analysis and visualizations, it is of utmost importance to be aware of choices made during data cleaning. This paper's main goal is to make stakeholders of (learning) analytics activities aware of the fact that choices are made during data cleaning have consequences on the outcomes. We believe that there should be transparency to the users of these outcomes and give them a detailed report of the decisions made.
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Traditionally, most cleaning activities take place in the evening or during nighttime.In the Netherlands, day-time cleaning is becoming increasingly popular. It is however unknown how day-time cleaning affects perceptions and satisfaction of end-users. An experimental field study was conducted on trains of Netherlands Railways (NS) to determine how the presence of cleaning staff affects perceptions and satisfaction of train passengers.
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In sexual assault cases, the retrieved DNA quantity and sampling location from the victim’s underwear may provide valuable information for activity level evaluative reporting. DNA can transfer from site to site on an exhibit, or be lost within packaging, complicating interpretation. Experiments are needed to investigate these factors. This preliminary study compared two cleaning methods to prepare undergarments for such experimentation: hand-washing with warm water and washing with bleach before rinsing. Results show a significantly lower quantity of DNA on washed underwear using both methods. Warm-water hand-washing, the more straightforward method, was selected for further experimentation.
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Cleanliness is one of the key determinants of overall customer satisfaction in train stations. Customers’ perception of cleanliness is not limited to cleaning only but depends on multiple predictors. A better understanding of these predictors may contribute to the optimisation of perceived cleanliness in train stations. The current study was designed to examine how objective predictors (measures of cleaning quality), subjective predictors (e.g., customers’ perception of lighting, scent, staff), and demographic variables relate to perceived cleanliness in train stations. Data on cleaning quality were gathered by trained cleaning inspectors and data on subjective predictors of cleanliness were obtained through surveys collected at 25 train stations in the Netherlands (N = 19.206). Data were examined using correlation and regression analysis. Positive and significant correlates of perceived cleanliness in train stations were found, including: perception of scent, lighting, colour, and staff. In regression analysis, customers’ perception of scent and lighting appeared to be powerful predictors of perceived cleanliness. These findings underline that customers’ perception of cleanliness is not only influenced by cleaning quality, but also by other predictors, such as scent, lighting, colour, and staff behaviour.
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Purpose This paper aims to identify antecedents that influence perceived cleanliness by consulting experts and end-users in the field of facilities management (facility service providers, clients of facility service providers and consultants). Business models were evaluated to understand why some antecedents are adopted by practitioners and others are not. Design/methodology/approach A qualitative study, with end-users (n = 7) and experts (n = 24) in the field of facilities management, was carried out to identify antecedents of perceived cleanliness. Following the Delphi approach, different research methods including interviews, group discussions and surveys were applied. Findings Actual cleanliness, cleaning staff behaviour and the appearance of the environment were identified as the three main antecedents of perceived cleanliness. Client organisations tend to have a stronger focus on antecedents that are not related to the cleaning process compared to facility service providers. Practical implications More (visible) cleaning, maintenance, toilets, scent, architecture and use of materials offer interesting starting points for practitioners to positively influence perceived cleanliness. These antecedents may also be used for the development of a standard for perceived cleanliness. Originality/value A basis was created for the development of an instrument that measures perceived cleanliness and includes antecedents that are typically not included in most of the current standards of actual cleanliness (e.g. NEN 2075, ISSA).
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