Research Areas

The PLATO program focuses on three areas:

1) Development of fundamental research and applied research for (higher) education and technological developments (e.g. digital learning tools)

2) The systematic building and development of the newly established young researchers community (e.g., M-GRK DIAPASON)

3) Research transfer into practice and policy


Research Topics

When investigating negative (NL) and positive learning (PL) outcomes PLATO is focusing particularly on a theoretical framework related to (i) cognitive psychology and didactic research on domain-specific misconceptions and epistemological beliefs and theories about conceptual change, (ii) the theory of negative knowledge and learning from mistakes, as well as (iii) inferential teaching-learning theories and negative induction.

The focus within the first funding phase is on intraindividual learning in Internet-based environments, that is, autonomous learning activities. Extending the first phase, the second research phase will analyze interindividual learning, including social communication and interaction via digital media and the Internet.

The research questions are based on three major research areas, and should be addressed using a combination of research methods from different fields:

  1. The Information Landscape (IL) refers to the online learning space comprising all locatable online information resources for a given domain or topic. The information landscape can be analyzed using data and text mining technologies as well as expert interviews, in particular, to identify biased information that may introduce misconceptions related to given domain-specific concepts.
  2. Online learning is the very core of the projects and can further be differentiated into:
    • Learning processes representing cognitive, meta-cognitive, motivational, and affective aspects of information processing and knowledge acquisition and behavioral (‘state’) characteristics. These learning processes refer to observable learner activities in online environments (e.g. search for information, navigation on and between websites, evaluation of information) that can be recorded by logging online activities and by means of observational techniques like eye tracking. The recorded small and big data can be analyzed using quantitative and qualitative methods such as network analyses and cognitive interviews.
    • Learner (‘trait’) characteristics (e.g. cognitive abilities, socio-economic background) that can be measured using tests and questionnaires and that can also be derived from stable behavior patterns in log files. The collected big data will be also analyzed using machine learning approaches.
    • Learning outcomes (e.g. acquired domain-specific concepts, i.e., positive or negative learning), primarily serving as dependent variables, can be assessed using various types of achievement tests, including automated analyses of texts written by students. To assess NL, specific misconceptions and related convictions (e.g. confidence in incorrect answer) need to be targeted in addition to agreed-upon correct knowledge, for instance, by using cog labs.