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Designing the Learning Analytics Capability Model

Educational institutions in higher education encounter different thresholds when scaling up to institution-wide learning analytics. This doctoral research focuses on designing a model of capabilities that institutions need to develop in order to remove these barriers and thus maximise the benefits of learning analytics.

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Designing the Learning Analytics Capability Model

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Physical performance in daily life and sports

Physical activity is crucial in human life, whether in everyday activities or elite sports. It is important to maintain or improve physical performance, which depends on various factors such as the amount of physical activity, the capability, and the capacity of the individual. In daily life, it is significant to be physically active to maintain good health, intense exercise is not necessary, as simple daily activities contribute enough. In sports, it is essential to balance capacity, workload, and recovery to prevent performance decline or injury.With the introduction of wearable technology, it has become easier to monitor and analyse physical activity and performance data in daily life and sports. However, extracting personalised insights and predictions from the vast and complex data available is still a challenge.The study identified four main problems in data analytics related to physical activity and performance: limited personalised prediction due to data constraints, vast data complexity, need for sensitive performance measures, overly simplified models, and missing influential variables. We proposed end investigated potential solutions for each issue. These solutions involve leveraging personalised data from wearables, combining sensitive performance measures with various machine learning algorithms, incorporating causal modelling, and addressing the absence of influential variables in the data.Personalised data, machine learning, sensitive performance measures, advanced statistics, and causal modelling can help bridge the data analytics gap in understanding physical activity and performance. The research findings pave the way for more informed interventions and provide a foundation for future studies to further reduce this gap.

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Through the clouds

Data has been collected since mankind, but in the recent years the technical innovations enable us to collect exponentially growing amounts of data through the use of sensors, smart devices and other sources. In her lecture Nanda will explore the role of Big Data in urban environments. She will give an introduction to the world of Big Data and Smart Cities, and an assessment of the role that data analytics plays in the current state of the digital transformation in our cities. Examples are given in the field of energy and mobility.

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Heterogeneity in trajectories of cybercriminals

Longitudinal criminological studies greatly improved our understanding of the longitudinal patterns of criminality. These studies, however, focused almost exclusively on traditional types of offending and it is therefore unclear whether results are generalizable to online types of offending. This study attempted to identify the developmental trajectories of active hackers who perform web defacements. The data for this study consisted of 2,745,311 attacks performed by 66,553 hackers and reported to Zone-H between January 2010 and March 2017. Semi-parametric group-based trajectory models were used to distinguish six different groups of hackers based on the timing and frequency of their defacements. The results demonstrated some common relationships to traditional types of crime, as a small population of defacers accounted for the majority of defacements against websites. Additionally, the methods and targeting practices of defacers differed based on the frequency with which they performed defacements generally.

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Heterogeneity in trajectories of cybercriminals

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Werken in Wijk en Web

In hoofdstuk 1 wordt ingegaan op de aanleiding voor de literatuurstudie, de keuze voor OWO en doel, onderzoeksvragen en verantwoording van de literatuurstudie. Hoofdstuk 2 schetst een beeld van gebiedsgebonden politiewerk en gaat in op de rol en samenstelling van basisteams (onderzoeksvragen 1 en 2). Hoofdstuk 3 gaat in op het begrip digitalisering, beschrijft de digitalisering van gebiedsgebonden politiewerk en inventariseert knelpunten (onderzoeksvraag 3). In hoofdstuk 4 wordt het begrip ‘digitale handelingsbekwaamheid’ verkent, de betekenis van digitale handelingsbekwaamheid voor het GGP wordt in kaart gebracht en in verband gebracht met leerstijlen binnen organisaties. Ook worden de knelpunten ten aanzien van digitale handelingsbekwaamheid geïnventariseerd (onderzoeksvraag 4). In hoofdstuk 5 worden relevante theorieën over het de gebruik en de acceptatie van digitale technologieën, veranderingsbereid en leertheorieën besproken (onderzoeksvraag 5). In hoofdstuk 6 ten slotte wordt het conceptueel model gepresenteerd en verantwoord. Het conceptueel model dient als basis voor het op te stellen Programma van Eisen.

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The role of time, weather and Google Trends in understanding and predicting web survey response

In the literature about web survey methodology, significant eorts have been made to understand the role of time-invariant factors (e.g. gender, education and marital status) in (non-)response mechanisms. Time-invariant factors alone, however, cannot account for most variations in (non-)responses, especially fluctuations of response rates over time. This observation inspires us to investigate the counterpart of time-invariant factors, namely time-varying factors and the potential role they play in web survey (non-)response. Specifically, we study the effects of time, weather and societal trends (derived from Google Trends data) on the daily (non-)response patterns of the 2016 and 2017 Dutch Health Surveys. Using discrete-time survival analysis, we find, among others, that weekends, holidays, pleasant weather, disease outbreaks and terrorism salience are associated with fewer responses. Furthermore, we show that using these variables alone achieves satisfactory prediction accuracy of both daily and cumulative response rates when the trained model is applied to future unseen data. This approach has the further benefit of requiring only non-personal contextual information and thus involving no privacy issues. We discuss the implications of the study for survey research and data collection.

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The role of time, weather and Google Trends in understanding and predicting web survey response

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Automatic Measurement of Corporate Reputation for Retail Companies from Online Public Data on the Web

Retail industry consists of the establishment of selling consumer goods (i.e. technology, pharmaceuticals, food and beverages, apparels and accessories, home improvement etc.) and services (i.e. specialty and movies) to customers through multiple channels of distribution including both the traditional brickand-mortar and online retailing. Managing corporate reputation of retail companies is crucial as it has many advantages, for instance, it has been proven to impact generated revenues (Wang et al., 2016). But, in order to be able to manage corporate reputation, one has to be able to measure it, or, nowadays even better, listen to relevant social signals that are out there on the public web. One of the most extensive and widely used frameworks for measuring corporate reputation is through conducting elaborated surveys with respective stakeholders (Fombrun et al., 2015). This approach is valuable but deemed to be laborious and resource-heavy and will not allow to generate automatic alerts and quick and live insights that are extremely needed in this era of internet. For these purposes a social listening approach is needed that can be tailored to online data such as consumer reviews as the main data source. Online review datasets are a form of electronic Word-of-Mouth (WOM) that, when a data source is picked that is relevant to retail, commonly contain relevant information about customers’ perceptions regarding products (Pookulangara, 2011) and that are massively available. The algorithm that we have built in our application provides retailers with reputation scores for all variables that are deemed to be relevant to retail in the model of Fombrun et al. (2015). Examples of such variables for products and services are high quality, good value, stands behind, and meets customer needs. We propose a new set of subvariables with which these variables can be operationalized for retail in particular. Scores are being calculated using proportions of positive opinion pairs such as <fast, delivery> or <rude, staff> that have been designed per variable. With these important insights extracted, companies can act accordingly and proceed to improve their corporate reputation. It is important to emphasize that, once the design is complete and implemented, all processing can be performed completely automatic and unsupervised. The application makes use of a state of the art aspect-based sentiment analysis (ABSA) framework because of ABSA’s ability to generate sentiment scores for all relevant variables and aspects. Since most online data is in open form and we deliberately want to avoid labelling any data by human experts, the unsupervised aspectator algorithm has been picked. It employs a lexicon to calculate sentiment scores and uses syntactic dependency paths to discover candidate aspects (Bancken et al., 2014). We have applied our approach to a large number of online review datasets that we sampled from a list of 50 top global retailers according to National Retail Federation (2020), including both offline and online operation, and that we scraped from trustpilot, a public website that is well-known to retailers. The algorithm has carefully been evaluated by manually annotating a randomly sampled subset of the datasets for validation purposes by two independent annotators. The Kappa’s score on this subset was 80%.

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Automatic Measurement of Corporate Reputation for Retail Companies from Online Public Data on the Web

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HR Analyticscultuur

Op basis waarvan worden in je organisatie HR-beslissingen genomen? Op basis van eigen cijfers of van best practices bij andere organisaties? Op basis van gefundeerde argumenten, of eenvoudigweg door hoger management, waarbij je je eigen organisatie-expertise aan de kant zet? Beslisculturen in organisaties kunnen heel fact free of meer evidence-based zijn. In een fact-freecultuur heeft HR Analytics als zevende zintuig geen schijn van kans. Cijfers kunnen als moeilijkdoenerij worden gezien of zelfs een bedreiging vormen voor stakeholders die cijfers enkel beschouwen als een middel om af te rekenen met het bestaande beleid. Of cijfers zijn een ritueel geworden: niemand vraagt zich meer af wat ze eigenlijk betekenen. In een evidence-basedcultuur worden belangrijke beslismomenten serieus genomen. Op die momenten is er de kans om de kwaliteit van de beslissing te verrijken met analytische inzichten – naast informatie die andere zintuigen kunnen aanleveren. HR Analytics kan hier haar meerwaarde doen gelden. Maar de HR Analyticspraktijk is weerbarstig. Zelfs als het belang en de uitvoering van HR Analytics goed ingebed zijn, betekent dat niet automatisch dat analytische inzichten de HR-beleidsvoering altijd bereiken en invloed uitoefenen.

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Artikel Therapeutic Alliance in Web-based Treatment for Eating Disorders

Background: In face-to-face therapy for eating disorders, therapeutic alliance (TA) is an important predictor of symptomreduction and treatment completion. To date, however, little is known about TA during web-based cognitive behavioral therapy(web-CBT) and its association with symptom reduction, treatment completion, and the perspectives of patients versus therapists.Objective: This study aimed to investigate TA ratings measured at interim and after treatment, separately for patients andtherapists; the degree of agreement between therapists and patients (treatment completers and noncompleters) for TA ratings;and associations between patient and therapist TA ratings and both eating disorder pathology and treatment completion.Methods: A secondary analysis was performed on randomized controlled trial data of a web-CBT intervention for eatingdisorders. Participants were 170 females with bulimia nervosa (n=33), binge eating disorder (n=68), or eating disorder nototherwise specified (n=69); the mean age was 39.6 (SD 11.5) years. TA was operationalized using the Helping AllianceQuestionnaire (HAQ). Paired t tests were conducted to assess the change in TA from interim to after treatment. Intraclasscorrelations were calculated to determine cross-informant agreement with regard to HAQ scores between patients and therapists.A total of 2 stepwise regressive procedures (at interim and after treatment) were used to examine which HAQ scores predictedeating disorder pathology and therapy completion.Results: For treatment completers (128/170, 75.3%), the HAQ-total scores and HAQ-Helpfulness scores for both patients andtherapists improved significantly from interim to post treatment. For noncompleters (42/170, 24.7%), all HAQ scores decreasedsignificantly. For all HAQ scales, the agreement between patients and therapists was poor. However, the agreement was slightlybetter after treatment than at interim. Higher patient scores on the helpfulness subscale of the HAQ at interim and after treatmentwere associated with less eating disorder psychopathology. A positive association was found between the HAQ-total patientscores at interim and treatment completion. Finally, posttreatment HAQ-total patient scores and posttreatment HAQ-Helpfulnessscores of therapists were positively associated with treatment completion.Conclusions: Our study showed that TA in web-CBT is predictive of eating disorder pathology and treatment completion. Ofparticular importance is patients’ confidence in their abilities as measured with the HAQ-Helpfulness subscale when predictingposttreatment eating disorder pathology and treatment completion.

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Designing the Learning Analytics Capability Model