Conference Paper From the article: Abstract Learning analytics is the analysis and visualization of student data with the purpose of improving education. Literature reporting on measures of the effects of data-driven pedagogical interventions on learning and the environment in which this takes place, allows us to assess in what way learning analytics actually improves learning. We conducted a systematic literature review aimed at identifying such measures of data-driven improvement. A review of 1034 papers yielded 38 key studies, which were thoroughly analyzed on aspects like objective, affected learning and their operationalization (measures). Based on prevalent learning theories, we synthesized a classification scheme comprised of four categories: learning process, student performance, learning environment, and departmental performance. Most of the analyzed studies relate to either student performance or learning process. Based on the results, we recommend to make deliberate decisions on the (multiple) aspects of learning one tries to improve by the application of learning analytics. Our classification scheme with examples of measures may help both academics and practitioners doing so, as it allows for structured positioning of learning analytics benefits.
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Op uitnodiging van het Ministerie van Onderwijs, Cultuur en Wetenschap deelname aan internationale EU-conferentie in Wenen. Outline: Measures improving completion in Dutch higher education: 1.Performance agreements 2.Social lending system 3.Each student in right place (Quality in Diversity) 4.Matching 5.Enrolment date: large effect 6.Matching approaches and first results 7.Other types of measures to improve completion
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Background: Effective telemonitoring is possible through repetitive collection of electronic patient-reported outcome measures (ePROMs) in patients with chronic diseases. Low adherence to telemonitoring may have a negative impact on the effectiveness, but it is unknown which factors are associated with adherence to telemonitoring by ePROMs. The objective was to identify factors associated with adherence to telemonitoring by ePROMs in patients with chronic diseases. Methods: A systematic literature search was conducted in PubMed, Embase, PsycINFO and the Cochrane Library up to 8 June 2021. Eligibility criteria were: (1) interventional and cohort studies, (2) patients with a chronic disease, (3) repetitive ePROMs being used for telemonitoring, and (4) the study quantitatively investigating factors associated with adherence to telemonitoring by ePROMs. The Cochrane risk of bias tool and the risk of bias in nonrandomized studies of interventions were used to assess the risk of bias. An evidence synthesis was performed assigning to the results a strong, moderate, weak, inconclusive or an inconsistent level of evidence. Results: Five studies were included, one randomized controlled trial, two prospective uncontrolled studies and two retrospective cohort studies. A total of 15 factors potentially associated with adherence to telemonitoring by ePROMs were identified in the predominate studies of low quality. We found moderate-level evidence that sex is not associated with adherence. Some studies showed associations of the remaining factors with adherence, but the overall results were inconsistent or inconclusive. Conclusions: None of the 15 studied factors had conclusive evidence to be associated with adherence. Sex was, with moderate strength, not associated with adherence. The results were conflicting or indecisive, mainly due to the low number and low quality of studies. To optimize adherence to telemonitoring with ePROMs, mixed-method studies are needed.
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Background: Over the years, a plethora of frailty assessment tools has been developed. These instruments can be basically grouped into two types of conceptualizations – unidimensional, based on the physical–biological dimension – and multidimensional, based on the connections among the physical, psychological, and social domains. At present, studies on the comparison between uni- and multidimensional frailty measures are limited. Objective: The aims of this paper were: 1) to compare the prevalence of frailty obtained using a uni- and a multidimensional measure; 2) to analyze differences in the functional status among individuals captured as frail or robust by the two measures; and 3) to investigate relations between the two frailty measures and disability.
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Research Findings: A growing number of stakeholders in early childhood education (ECE) rely on self-assessment to assess and improve the quality of ECE. In this systematic review, we investigated the reliability and validity of self-assessment in ECE, summarizing findings from 27 publications. We meta-analytically synthesized findings from 25 publications for 1,882 groups and 79,163 children aged 0–72 months in center-based childcare. Most studies reported high internal consistency, but one study reported a lower consistency. Inter-rater reliability was generally high. A three-level meta-analysis (k = 13, ES = 45) revealed a positive association between self-assessment ratings and ratings with validated measures of ECE quality (r = .38), indicating a moderate convergent validity. Studies with lower methodological quality and published “peer reviewed” studies reported somewhat higher correlations between self-assessment ratings and ratings with validated measures. The meta-analytic correlation remained significant after removal of studies with lower methodological quality (r = .33) or studies from the “grey” literature (r = .44). A second meta-analysis (k = 16, ES = 71) with a focus on the predictive validity of self-assessment ratings showed a small significant association between self-assessed ECE quality and child outcomes (r = .09); there were no significant moderators. Practice or Policy: Despite empirical evidence for the validity of self-assessment, further studies are needed to investigate potential bias in self-assessment. Future studies should further explore the validity and reliability of self-assessment measures in ECE, including countries outside the United States.
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Illicit data markets have emerged on Telegram, a popular online instant messaging application, bringing together thousands of users worldwide in an unregulated exchange of sensitive data. These markets operate through vendors who offer enormous quantities of such data, from personally identifiable information to financial data, while potential customers bid for these valuable assets. This study describes how Telegram data markets operate and discusses what interventions could be used to disrupt them. Using crime script analysis, we observed 16 Telegram meeting places encompassing public and private channels and groups. We obtained information about how the different meeting places function, what are their inside rules, and what tactics are employed by users to advertise and trade data. Based on the crime script, we suggest four feasible situational crime prevention measures to help disrupt these markets. These include taking down the marketplaces, reporting them, spamming and flooding techniques, and using warning banners. This is a post-peer-review, pre-copyedit version of an article published in Trends in organized crime . The final authenticated version is available online at https://doi.org/10.1007/s12117-024-09532-6
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BACKGROUND: Mobility is a key determinant and outcome of healthy ageing but its definition, conceptual framework and underlying constructs within the physical domain may need clarification for data comparison and sharing in ageing research. This study aimed to (1) review definitions and conceptual frameworks of mobility, (2) explore agreement on the definition of mobility, conceptual frameworks, constructs and measures of mobility, and (3) define, classify and identify constructs.METHODS: A three-step approach was adopted: a literature review and two rounds of expert questionnaires (n = 64, n = 31, respectively). Agreement on statements was assessed using a five-point Likert scale; the answer options 'strongly agree' or 'agree' were combined. The percentage of respondents was subsequently used to classify agreements for each statement as: strong (≥ 80%), moderate (≥ 70% and < 80%) and low (< 70%).RESULTS: A variety of definitions of mobility, conceptual frameworks and constructs were found in the literature and among respondents. Strong agreement was found on defining mobility as the ability to move, including the use of assistive devices. Multiple constructs and measures were identified, but low agreements and variability were found on definitions, classifications and identification of constructs. Strong agreements were found on defining physical capacity (what a person is maximally capable of, 'can do') and performance (what a person actually does in their daily life, 'do') as key constructs of mobility.CONCLUSION: Agreements on definitions of mobility, physical capacity and performance were found, but constructs of mobility need to be further identified, defined and classified appropriately. Clear terminology and definitions are essential to facilitate communication and interpretation in operationalising the physical domain of mobility as a prerequisite for standardisation of mobility measures.
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