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In this study, the displacement of 687 murderers in Spain (2010–2012) is analysed. The Euclidean distance between the offender's residence and the furthest scene related to the event has been calculated. The analyses showed that the murderers registered a displacement of 0.5 km, with 255 cases of non‐traveller homicide (37.1%). The bivariate analysis found that men, young and foreign, with a criminal record and unrelated to the victim, registered a large distance, as well as multi‐offender homicides and were related to criminal activities. Traveller homicide registered a displacement of 4.3 km, with adults unrelated to the victim travelling the farthest. Multi‐offender and crime‐related homicides continued to show the largest distance. Multivariate analyses showed that if the offender is foreign, has no previous relationship with the victim and there are several offenders involved, the distance will be larger. When only traveller homicides were analysed, only the type of relationship showed significant differences. [ABSTRACT FROM AUTHOR]

Student engagement within the development of learning analytics services in Higher Education is an important challenge to address. Despite calls for greater inclusion of stakeholders, there still remains only a small number of investigations into students' beliefs and expectations towards learning analytics services. Therefore, this paper presents a descriptive instrument to measure student expectations (ideal and predicted) of learning analytics services. The scales used in the instrument are grounded in a theoretical framework of expectations, specifically ideal and predicted expectations. Items were then generated on the basis of four identified themes (Ethical and Privacy Expectations, Agency Expectations, Intervention Expectations, and Meaningfulness Expectations), which emerged after a review of the learning analytics literature. The results of an exploratory factor analysis and the results from both an exploratory structural equation model and confirmatory factor analysis supported a two‐factor structure best accounted for the data pertaining to ideal and predicted expectations. Factor one refers to Ethical and Privacy Expectations, whilst factor two covers Service Feature Expectations. The 12‐item Student Expectations of Learning Analytics Questionnaire (SELAQ) provides researchers and practitioners with a means of measuring of students' expectations of learning analytics services. Lay Description: What is already known about this topic: Understanding student expectations of learning analytics is an important challenge for higher education institutions to address.Research has measured student beliefs regarding the features and ethical procedures of a learning analytics service. What this paper adds: This study builds on prior work by developing an instrument to measure student expectations of learning analytics services.This study proposes that student expectations of learning analytics can be measured using two subscales: (a) ethical and privacy expectations and (b) service expectations. Implications for practice and/or policy: Higher education institutions should understand what students expect from learning analytics services before any implementations are actioned.Higher education institutions have a validated instrument to gauge and understand student expectations of learning analytics services.Results obtained from the instrument can be used to inform the development of specific learning analytics policies for each higher education institution. [ABSTRACT FROM AUTHOR]