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

Linking assessment and learning analytics to support learning processes in higher education

In higher education assessments are mostly used for summative purposes such as grading and certifying. Albeit, assessments are also considered to support learning processes by offering formative feedback to learners about their current performance and how to improve. Even though such feedback might enhance learners’ self-regulated learning processes, it is used infrequently due to resource constraints. In addition, the competences, skills, and knowledge that should be assessed are evermore complex. To derive valid inferences about learners’ current performance, ongoing assessments across contexts are desirable. With the advancing use of digital learning environments, learning analytics are also coming in for increasing discussion in higher education. However, learning analytics are still not sufficiently linked to learning theory and are lacking empirical evidence. Hence, the purpose of this paper is to propose how theory on assessment and related feedback can be linked to learning analytics with regard to supporting self-regulated learning. Therefore, relevant concepts of assessment, assessment design, and feedback plus current perspectives on learning analytics are introduced. Based on this theoretical foundation, a conceptual integrative framework and potential learning analytics features were derived. The framework and its implications plus further research needs are discussed and concluded