Work characteristics and determinants of job satisfaction in four age groups: university employees’ point of view

Contains fulltext : 79843.pdf (publisher's version ) (Closed access)PURPOSE: To investigate (a) differences in work characteristics and (b) determinants of job satisfaction among employees in different age groups. METHODS: A cross-sectional questionnaire was filled in by 1,112 university employees, classified into four age groups. (a) Work characteristics were analysed with ANOVA while adjusting for sex and job classification. (b) Job satisfaction was regressed against job demands and job resources adapted from the Job Demands-Resources model. Results : Statistically significant differences concerning work characteristics between age groups are present, but rather small. Regression analyses revealed that negative association of the job demands workload and conflicts at work with job satisfaction faded by adding job resources. Job resources were most correlated with more job satisfaction, especially more skill discretion and more relations with colleagues. CONCLUSIONS: Skill discretion and relations with colleagues are major determinants of job satisfaction. However, attention should also be given to conflicts at work, support from supervisor and opportunities for further education, because the mean scores of these work characteristics were disappointing in almost all age groups. The latter two characteristics were found to be associated significantly to job satisfaction in older workers

Awareness Is Not Enough: Pitfalls of Learning Analytics Dashboards in the Educational Practice

Presentation at 12th European Conference on Technology enhanced learning (EC-TEL 2017

Current approaches to gene regulatory network modelling

Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model

Opportunities and Challenges in Using Learning Analytics in Learning Design

Educational institutions are designing, creating and evaluating courses to optimize learning outcomes for highly diverse student populations. Yet, most of the delivery is still monitored retrospectively with summative evaluation forms. Therefore, improvements to the course design are only implemented at the very end of a course, thus missing to benefit the current cohort. Teachers find it difficult to interpret and plan interventions just-in-time. In this context, Learning Analytics (LA) data streams gathered from ‘authentic’ student learning activities, may provide new opportunities to receive valuable information on the students' learning behaviors and could be utilised to adjust the learning design already "on the fly" during runtime. We presume that Learning Analytics applied within Learning Design (LD) and presented in a learning dashboard provide opportunities that can lead to more personalized learning experiences, if implemented thoughtfully. In this paper, we describe opportunities and challenges for using LA in LD. We identify three key opportunities for using LA in LD: (O1) using on demand indicators for evidence based decisions on learning design; (O2) intervening during the run-time of a course; and, (O3) increasing student learning outcomes and satisfaction. In order to benefit from these opportunities, several challenges have to be overcome. We mapped the identified opportunities and challenges in a conceptual model that considers the interaction of LA in LD.SURF Foundation & NRO under the REFLECTOR project grant