Postdoctoral project: Emotional and motivational states and traits in social learning interactions - an interdisciplinary approach
Part of research project: From understanding learners’ adaptive motivation and emotion to designing social learning companions
Description of the research project
We aim to develop an approach for integrated game- and agent-based Intelligent Tutoring Systems (ITS) and computational models that help to optimize scaffolding in social learning situations, and even go one step further and create a robotic learning companion based on these results. Researchers from analytic (educational research: Rebecca Lazarides) and synthetic disciplines (adaptive systems / robotics: Verena Hafner, educational technology / computer science: Niels Pinkwart) collaborate on this project. Collaborations with other projects that focus on social learning are intended.
The three objectives of our project are:
i) Examine how novel user modeling approaches and feedback strategies in ITSs incorporating virtual agents can enhance positive emotions and motivation (self-regulation, goal orientations) and reduce negative emotions in social learning situations and can thereby be used to impede inequalities in education.
ii) Explore the (moderating and mediating) processes that underlie the relations between pedagogical agents’ ‘behaviors’ and learners’ performance by investigating psychological factors that strengthen or reduce the effects of ITS on learners’ motivation and emotion.
iii) Create a robotic learning companion that keeps an updated model/simulation of the learner and their current knowledge, motivational and emotional state and acts accordingly.
Description of the postdoctoral project
The Postdoctoral student will examine the state- and trait emotions and motivations of learners in different experimental learning situations – we plan a 2×2 design using the groups “Agent gives task/Human teacher gives task” and “Task selected by ITS / Task selected by human teacher”. To assess state and trait motivation and emotions using experience sampling methods, the Postdoc will develop smartphone-based questionnaires. The aim of the project is to gain knowledge about the development of motivation and emotion during the different experimental conditions as well as the identification of possible moderating and mediating variables that strengthen, reduce and explain the effects of ITS and robotic learning companions on the states and traits of emotions and motivation. Against this backdrop, this project further focuses on how ITS and robotic learning can help to overcome educational inequalities (i.e. in relation to gender or ability) in motivation and emotion by fostering individualized learning processes that provide materials and tasks to learners that match their interest, level of emotion and motivation as well as their level of pre-knowledge and achievement.
The Postdoc will scientifically coordinate the research process of the planned project.
Project start date: October 1, 2019
Applicants must hold a Doctoral degree in Psychology or Educational Science or related sciences and should have proven skills/background in following topics:
- Complex statistical analyses including experience sampling approaches
- Skills in using statistical software (e.g. R, Mplus)
- Experiences in experimental studies in the context of psychological/educational research
- excellent English skills both written and spoken
- interest in intelligence research within an interdisciplinary and highly collaborative research team
- Classroom research experience and knowledge of data collection with students